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| United States Patent Application |
20050144664
|
| Kind Code
|
A1
|
|
Smith, Oscar S.
; et al.
|
June 30, 2005
|
Plant breeding method
Abstract
Methods for using genetic marker genotype (e.g., gene sequence diversity
information) to improve the process of developing plant varieties (e.g.,
single cross hybrids) with improved phenotypic performance are provided.
Methods for predicting the value of a phenotypic trait in a plant are
provided. The methods use genotypic, phenotypic, and optionally family
relationship information for a first plant population to identify an
association between at least one genetic marker and the phenotypic trait,
and then use the association to predict the value of the phenotypic trait
in one or more members of a second, target population of known marker
genotype. Methods for identifying new allelic variants affecting the
trait are also provided. Plants selected, provided, or produced by any of
the methods herein, transgenic plants created by any of the methods
herein, and digital systems for performing the methods herein are also
provided.
| Inventors: |
Smith, Oscar S.; (Johnston, IA)
; Cooper, Mark; (Johnston, IA)
; Tingey, Scott V.; (Wilmington, DE)
; Rafalski, J. Antoni; (Wilmington, DE)
; Luedtke, Roy; (Ankeny, IA)
; Niebur, William S.; (Des Moines, IA)
|
| Correspondence Address:
|
QUINE INTELLECTUAL PROPERTY LAW GROUP, P.C.
P O BOX 458
ALAMEDA
CA
94501
US
|
| Assignee: |
Pioneer Hi-Bred International, INC.
Johnston
IA
50131-1000
|
| Family ID:
|
33551489
|
| Appl. No.:
|
10/856113
|
| Filed:
|
May 27, 2004 |
Related U.S. Patent Documents
| | | | |
|---|
|
| Application Number | Filing Date | Patent Number |
|---|
| | 60474359 | May 28, 2003 | |
|
|
| Current U.S. Class: |
800/266 ; 800/267; 800/278; 800/298 |
| Current CPC Class: |
A01H 1/02 20130101; A01H 5/10 20130101; A01H 1/04 20130101 |
| Class at Publication: |
800/266 ; 800/267; 800/298; 800/278 |
| International Class: |
A01H 001/00; C12N 015/82; A01H 005/00 |
Claims
What is claimed is:
1. A method of predicting a value of a phenotypic trait in a target plant
population, the method comprising: (a) providing an association between
at least one genetic marker and the phenotypic trait; wherein the
association is evaluated in a first plant population, the first plant
population being an established breeding population or a portion thereof;
wherein the association is evaluated in the first plant population
according to a statistical model that incorporates a genotype of the
first plant population for a set of genetic markers and a value of the
phenotypic trait in the first plant population; and, (b) providing the
value of the phenotypic trait in at least one member of the target plant
population, wherein the providing comprises predicting the value from the
association of (a) and from a genotype of the at least one member for the
at least one genetic marker associated with the phenotypic trait.
2. The method of claim 1, wherein the first plant population comprises a
plurality of inbreds, single cross F1 hybrids, or a combination thereof.
3. The method of claim 2, wherein the first plant population consists of
inbreds, single cross F1 hybrids, or a combination thereof.
4. The method of claim 2, wherein the ancestry of each inbred and/or
single cross F1 hybrid is known, and wherein each inbred and/or single
cross F1 hybrid is a descendent of at least one of three or more
founders.
5. The method of claim 1, wherein the established breeding population
comprises at least three founders and descendents of the founders,
wherein the ancestry of the descendents is known.
6. The method of claim 5, wherein the established breeding population
comprises between about 100 and about 200 founders and descendents of the
founders, wherein the ancestry of the descendents is known.
7. The method of claim 1, wherein the members of the first plant
population span at least three breeding cycles.
8. The method of claim 7, wherein the members of the first plant
population span at least four breeding cycles.
9. The method of claim 7, wherein the members of the first plant
population span at least seven or at least nine breeding cycles.
10. The method of claim 1, wherein the phenotypic trait is a quantitative
phenotypic trait.
11. The method of claim 1, wherein the phenotypic trait is a qualitative
phenotypic trait.
12. The method of claim 1, further comprising selecting at least one of
the members of the target plant population having a desired predicted
value of the phenotypic trait.
13. The method of claim 12, further comprising breeding at least one
selected member of the target plant population with at least one other
plant.
14. The method of claim 1, wherein the first plant population comprises
between about 50 and about 5000 members.
15. The method of claim 1, wherein the first plant population comprises a
plurality of inbreds.
16. The method of claim 1, wherein the first plant population comprises a
plurality of single cross F1 hybrids.
17. The method of claim 1, wherein the first plant population comprises a
plurality of a combination of inbreds and single cross F1 hybrids.
18. The method of claim 1, wherein the value of the phenotypic trait in
the first plant population is obtained by evaluating the phenotypic trait
among the members of the first plant population in at least one topcross
combination with at least one tester parent.
19. The method of claim 1, wherein the phenotypic trait is selected from
the group consisting of: yield, grain moisture content, grain oil
content, root lodging resistance, stalk lodging resistance, plant height,
ear height, disease resistance, insect resistance, drought resistance,
grain protein content, test weight, and cob color.
20. The method of claim 1, wherein the set of genetic markers comprises
one or more of: a single nucleotide polymorphism (SNP), a multinucleotide
polymorphism, an insertion of at least one nucleotide, a deletion of at
least one nucleotide, a simple sequence repeat (SSR), a restriction
fragment length polymorphism (RFLP), a random amplified polymorphic DNA
(RAPD) marker, or an arbitrary fragment length polymorphism (AFLP).
21. The method of claim 1, wherein the set of genetic markers comprises
between one and ten markers.
22. The method of claim 1, wherein the set of genetic markers comprises
between 500 and 50,000 markers.
23. The method of claim 1, wherein the genotype of the first plant
population for the set of genetic markers is obtained by experimentally
determining the genotype of each inbred and predicting the genotype of
each single cross F1 hybrid present in the first plant population.
24. The method of claim 23, wherein experimentally determining the
genotype of each inbred comprises sequencing a set of DNA segments from
each inbred.
25. The method of claim 24, wherein the set of DNA segments comprises the
5'-untranslated regions and/or the 3'-untranslated regions of two or more
genes.
26. The method of claim 1, wherein providing the association between at
least one genetic marker and the phenotypic trait comprises providing an
association between a haplotype comprising two or more genetic markers
and the phenotypic trait.
27. The method of claim 1, wherein the statistical model incorporates
family relationships among the members of the first plant population.
28. The method of claim 1, wherein evaluating the association according to
the statistical model comprises performing Bayesian analysis using a
linear model, a mixed linear model, or a nonlinear model.
29. The method of claim 28, wherein the Bayesian analysis is implemented
via a reversible jump Markov chain Monte Carlo algorithm, a delta method,
or a profile likelihood algorithm.
30. The method of claim 1, wherein evaluating the association according to
the statistical model comprises performing Bayesian analysis using a
linear model, the Bayesian analysis being implemented via a reversible
jump Markov chain Monte Carlo algorithm.
31. The method of claim 1, wherein evaluating the association according to
the statistical model comprises performing a transmission disequilibrium
test.
32. The method of claim 1, wherein evaluating the association comprises
and/or permits determining identity by descent information for founder
alleles of the at least one genetic marker in one or more pedigrees of
related inbreds and/or single cross F1 hybrids, and permits tracking of
the at least one genetic marker throughout such pedigrees.
33. The method of claim 1, wherein the genotype of the at least one member
of the target plant population for the at least one genetic marker is
determined experimentally.
34. The method of claim 33, wherein the genotype is determined
experimentally by high throughput screening.
35. The method of claim 1, wherein the genotype of the at least one member
of the target plant population for the at least one genetic marker is
predicted.
36. The method of claim 1, wherein the target plant population comprises
inbred plants.
37. The method of claim 1, wherein the target plant population comprises
hybrid plants.
38. The method of claim 37, wherein the hybrid plants comprise F1 progeny
produced from single crosses between inbred lines.
39. The method of claim 38, wherein the F1 progeny are produced from
single crosses between inbreds comprising the first plant population, the
hybrid plants not comprising the first plant population.
40. The method of claim 1, wherein the target plant population comprises
an advanced generation produced from breeding crosses comprising at least
one of the members of the first plant population.
41. The method of claim 1, wherein predicting the value of the phenotypic
trait in the at least one member of the target plant population comprises
predicting the value using a best linear unbiased prediction method.
42. The method of claim 1, wherein predicting the value of the phenotypic
trait in the at least one member of the target plant population comprises
predicting the value using a multiple regression method, a selection
index technique, a ridge regression method, a linear optimization method,
or a non-linear optimization method.
43. The method of claim 1, wherein the first and target plant populations
consist of diploid plants.
44. The method of claim 1, wherein the first and target plant populations
are selected from the group consisting of: maize, soybean, sorghum,
wheat, sunflower, rice, canola, cotton, and millet.
45. The method of claim 44, wherein the first and target plant populations
comprise maize.
46. The method of claim 45, wherein the first and target plant populations
comprise Zea mays.
47. The method of claim 1, further comprising cloning a gene that is
linked to the at least one genetic marker associated with the phenotypic
trait, wherein expression of the gene affects the phenotypic trait.
48. The method of claim 47, further comprising constructing a transgenic
plant by expressing the cloned gene in a host plant.
49. A plant selected by the method of claim 12.
50. A plant produced by the breeding method of claim 13.
51. A transgenic plant created by the method of claim 48.
52. A method of selecting a plant, the method comprising: (a) providing an
association between at least one genetic marker and the phenotypic trait;
wherein the association is evaluated in a first plant population, the
first plant population being an established breeding population or a
portion thereof; wherein the association is evaluated in the first plant
population according to a statistical model that incorporates a genotype
of the first plant population for a set of genetic markers and a value of
the phenotypic trait in the first plant population; and, (b) providing
one or more plants from one or more non-adapted lines, wherein the
providing comprises selecting one or more plants for a selected genotype
comprising the at least one genetic marker associated with the phenotypic
trait.
53. The method of claim 52, wherein the first plant population comprises a
plurality of inbreds, single cross F1 hybrids, or a combination thereof.
54. The method of claim 53, wherein the first plant population consists of
inbreds, single cross F1 hybrids, or a combination thereof.
55. The method of claim 53, wherein the ancestry of each inbred and/or
single cross F1 hybrid is known, and wherein each inbred and/or single
cross F1 hybrid is a descendent of at least one of three or more
founders.
56. The method of claim 52, wherein the established breeding population
comprises at least three founders and descendents of the founders,
wherein the ancestry of the descendents is known.
57. The method of claim 56, wherein the established breeding population
comprises between about 100 and about 200 founders and descendents of the
founders, wherein the ancestry of the descendents is known.
58. The method of claim 52, wherein the members of the first plant
population span at least three breeding cycles.
59. The method of claim 58, wherein the members of the first plant
population span at least four breeding cycles.
60. The method of claim 58, wherein the members of the first plant
population span at least seven or at least nine breeding cycles.
61. The method of claim 52, wherein the phenotypic trait is a quantitative
phenotypic trait.
62. The method of claim 52, wherein the phenotypic trait is a qualitative
phenotypic trait.
63. The method of claim 52, further comprising evaluating the phenotypic
trait in the one or more plants having the selected genotype.
64. The method of claim 63, further comprising selecting at least one
plant having the selected genotype and a desirable value of the
phenotypic trait.
65. The method of claim 64, further comprising breeding the at least one
selected plant having the selected genotype and the desirable value of
the phenotypic trait with at least one other plant.
66. The method of claim 52, wherein the value of the phenotypic trait in
the first plant population is obtained by evaluating the phenotypic trait
among the members of the first plant population in at least one topcross
combination with at least one tester parent.
67. The method of claim 52, wherein the phenotypic trait is selected from
the group consisting of: yield, grain moisture content, grain oil
content, root lodging resistance, stalk lodging resistance, plant height,
ear height, disease resistance, insect resistance, drought resistance,
grain protein content, test weight, and cob color.
68. The method of claim 52, wherein the set of genetic markers comprises
one or more of: a single nucleotide polymorphism (SNP), a multinucleotide
polymorphism, an insertion of at least one nucleotide, a deletion of at
least one nucleotide, a simple sequence repeat (SSR), a restriction
fragment length polymorphism (RFLP), a random amplified polymorphic DNA
(RAPD) marker, or an arbitrary fragment length polymorphism (AFLP).
69. The method of claim 52, wherein the genotype of the first plant
population for the set of genetic markers is obtained by experimentally
determining the genotype of each inbred and predicting the genotype of
each single cross F1 hybrid present in the first plant population.
70. The method of claim 69, wherein experimentally determining the
genotype of each inbred comprises sequencing a set of DNA segments from
each inbred.
71. The method of claim 70, wherein the set of DNA segments comprises the
5'-untranslated regions and/or the 3'-untranslated regions of two or more
genes.
72. The method of claim 52, wherein providing the association between at
least one genetic marker and the phenotypic trait comprises providing an
association between a haplotype comprising two or more genetic markers
and the phenotypic trait.
73. The method of claim 52, wherein the statistical model incorporates
family relationships among the members of the first plant population.
74. The method of claim 52, wherein evaluating the association according
to the statistical model comprises performing Bayesian analysis using a
linear model, a mixed linear model, or nonlinear model.
75. The method of claim 74, wherein the Bayesian analysis is implemented
via a reversible jump Markov chain Monte Carlo algorithm, a delta method,
or a profile likelihood algorithm.
76. The method of claim 52, wherein evaluating the association according
to a statistical model comprises performing Bayesian analysis using a
linear model, the Bayesian analysis being implemented via a reversible
jump Markov chain Monte Carlo algorithm.
77. The method of claim 52, wherein evaluating the association according
to the statistical model comprises performing a transmission
disequilibrium test.
78. The method of claim 52, wherein the first plant population and the one
or more non-adapted lines consist of diploid plants.
79. The method of claim 52, wherein the first plant population and the one
or more non-adapted lines are selected from the group consisting of:
maize, soybean, sorghum, wheat, sunflower, rice, canola, cotton, and
millet.
80. The method of claim 79, wherein the first plant population and the one
or more non-adapted lines comprise maize.
81. The method of claim 80, wherein the first plant population and the one
or more non-adapted lines comprise Zea mays.
82. The method of claim 64, further comprising cloning a gene that is
linked to the at least one genetic marker associated with the phenotypic
trait from the at least one selected plant having the selected genotype
and the desirable value of the phenotypic trait, wherein expression of
the gene affects the phenotypic trait.
83. The method of claim 82, further comprising constructing a transgenic
plant by expressing the cloned gene in a host plant.
84. A plant provided by the method of claim 52.
85. A plant selected by the method of claim 64.
86. A plant produced by the breeding method of claim 65.
87. A transgenic plant created by the method of claim 83.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a non-provisional utility patent application
claiming priority to and benefit of the following prior provisional
patent application: U.S. Ser. No. 60/474,359, filed May 28, 2003,
entitled "Plant Breeding Method" by Smith et al., which is incorporated
herein by reference in its entirety for all purposes.
FIELD OF THE INVENTION
[0002] The present invention provides a process for predicting the value
of a phenotypic trait in a plant. The process uses genotypic, phenotypic,
and family relationship information for a first plant population to
identify an association between at least one genetic marker and the
phenotypic trait, and then uses the association to predict the value of
the phenotypic trait in members of a second, target population of known
marker genotype. The invention also relates to a process for identifying
new allelic variants affecting the phenotypic trait.
BACKGROUND OF THE INVENTION
[0003] Selective breeding has been employed for centuries to improve, or
attempt to improve, phenotypic traits of agronomic and economic interest
in plants (e.g., yield, percentage of grain oil, and the like). In its
most basic form, selective breeding involves selection of individuals as
parents of the next generation on the basis of one or more phenotypic
traits. However, such phenotypic selection is complicated by effects of
the environment (e.g., soil type, rainfall, temperature range, and the
like) on the expression of the phenotypic trait(s). Another problem with
such phenotypic selection is that most phenotypic traits of interest are
controlled by more than one genetic locus.
[0004] It has been estimated that 98% of the economically important
phenotypic traits in domesticated plants are quantitative traits (U.S.
Pat. No. 6,399,855 to Beavis, entitled "QTL mapping in plant breeding
populations"). These traits are classified as oligogenic or polygenic
based on the perceived numbers and magnitudes of segregating genetic
factors affecting the variability in expression of the phenotypic trait.
[0005] Historically, the term quantitative trait has been used to describe
variability in expression of a phenotypic trait that shows continuous
variability and is the net result of multiple genetic loci possibly
interacting with each other and/or with the environment. To describe a
broader phenomenon, the term "complex trait" has been used to describe
any trait that does not exhibit classic Mendelian inheritance
attributable to a single genetic locus (Lander & Schork, Science 265:
2037 (1994)). The two terms are often used synonymously herein.
[0006] The development of ubiquitous polymorphic genetic markers (e.g.,
RFLPs, SNPS, or the like) that span the genome has made it possible for
quantitative and molecular geneticists to investigate what Edwards, et
al., in Genetics 115: 113 (1987) referred to as quantitative trait loci
(QTL), as well as their numbers, magnitudes and distributions. QTL
include genes that control, to some degree, qualitative and quantitative
phenotypic traits that can be discrete or continuously distributed within
a family of individuals as well as within a population of families of
individuals.
[0007] Experimental paradigms have been developed to identify and analyze
QTL (see, e.g., U.S. Pat. No. 5,385,835 to Helentjaris et al. entitled
"Identification and localization and introgression into plants of desired
multigenic traits," U.S. Pat. No. 5,492,547 to Johnson entitled "Process
for predicting the phenotypic trait of yield in maize," and U.S. Pat. No.
5,981,832 to Johnson entitled "Process predicting the value of a
phenotypic trait in a plant breeding program"). One such paradigm
involves crossing two inbred lines to produce F1 single cross hybrid
progeny, selfing the F1 hybrid progeny to produce segregating F2 progeny,
genotyping multiple marker loci, and evaluating one to several
quantitative phenotypic traits among the segregating progeny. The QTL are
then identified on the basis of significant statistical associations
between the genotypic values and the phenotypic variability among the
segregating progeny. This experimental paradigm is ideal in that the
parental lines of the F.sub.1 generation have known linkage phases, all
of the segregating loci in the progeny are informative, and linkage
disequilibrium between the marker loci and the genetic loci affecting the
phenotypic traits is maximized.
[0008] However, considerable resources must be devoted to determining the
phenotypic performance of large numbers of hybrid and/or inbred progeny.
Because the progeny from only two parents are studied, the experiments
described above can only detect the trait loci (e.g., QTL) for which the
two parents are polymorphic. This set of trait loci may only represent a
fraction of the loci segregating in breeding populations of interest
(e.g., breeding populations of maize, sorghum, soybean, canola, or the
like, for example). In general, these progeny show variation for only one
or a small number of the phenotypic traits that are of interest in
applied breeding programs. This means that separate populations may need
to be developed, scored for marker loci, and grown in replicated field
experiments and scored for the phenotypic traits of interest.
Additionally, methods used to detect QTL produce biased estimates of the
QTL that are identified (see, e.g., Beavis (1994) "The power and deceit
of QTL experiments: Lessons from comparative QTL studies" in Wilkinson
(ed.) Proc. 49.sup.th Ann. Corn and Sorghum Res. Conf., American Seed
Trade Assoc, Chicago, Ill., pp 250-266). Additional imprecision is
introduced in extrapolating the identification of QTL to the progeny of
genetically different parents within a breeding population. Furthermore,
many if not all traits are affected by environmental factors, which can
also introduce imprecision.
[0009] The present invention overcomes the above noted difficulties, for
example, by identifying QTL-associated genetic markers through an
association analysis that can accommodate complex plant populations (in
which larger numbers of genetic loci affecting the phenotype for multiple
traits of interest are expected to be segregating, as compared to
bi-parental populations), take advantage of information generated by
existing breeding programs, and optionally account for environmental
effects, and by applying this information to predict phenotypes, e.g., of
hybrid progeny. A complete understanding of the invention will be
obtained upon review of the following.
SUMMARY OF THE INVENTION
[0010] The present invention provides a process for predicting the value
of a phenotypic trait in a plant. The process uses genotypic, phenotypic,
and family relationship information for a first plant population to
identify an association between at least one genetic marker and the
phenotypic trait, and then uses the association to predict the value of
the phenotypic trait in members of a second, target population of known
marker genotype. The invention also relates to a process for identifying
new allelic variants affecting the phenotypic trait.
[0011] Thus, a first general class of embodiments provides methods of
predicting a value of a phenotypic trait in a target plant population. In
the methods, an association between at least one genetic marker and the
phenotypic trait is provided. For example, an association between the
phenotypic trait and a haplotype comprising two or more genetic markers
can be provided. The association is evaluated in a first plant population
which is an established breeding population or a portion thereof. The
association is evaluated in the first plant population according to a
statistical model that incorporates a genotype of the first plant
population for a set of genetic markers and a value of the phenotypic
trait in the first plant population. The statistical model can also
incorporate family relationships among the members of the first plant
population. The value of the phenotypic trait in at least one member of
the target plant population is then provided. The value is predicted from
the association and from a genotype of the at least one member for the at
least one genetic marker associated with the phenotypic trait, e.g., by
using both pedigree and genetic marker information.
[0012] In one class of embodiments, the first plant population comprises a
plurality of inbreds, single cross F1 hybrids, or a combination thereof.
For example, the first plant population optionally consists of inbreds,
single cross F1 hybrids, or a combination thereof. Since the members of
the first plant population are members of an established breeding
population, the ancestry of each inbred and/or single cross F1 hybrid is
typically known, and each inbred and/or single cross F1 hybrid is
typically a descendent of at least one of three or more founders. Since
the members of the first plant population typically come from an
established breeding population with a multi-generation pedigree, the
members of the first plant population optionally span multiple breeding
cycles (e.g., at least three, at least four, at least five, at least
seven, or at least nine breeding cycles). The established breeding
population itself typically comprises at least three founders (e.g., at
least 10 founders, at least 50 founders, at least 100 founders, or at
least 200 founders, e.g., between about 100 and about 200 founders) and
descendents of the founders, wherein the ancestry of the descendents is
known. The first plant population can comprise essentially any number of
members, e.g., between about 50 and about 5000.
[0013] The phenotypic trait can be, e.g., a qualitative trait, a
quantitative trait, a single gene trait, a multigenic trait, and/or the
like. The value of the phenotypic trait in the first plant population is
obtained, e.g., by evaluating the phenotypic trait among the members of
the first plant population. The phenotype can be evaluated in the members
of first plant population (e.g., the inbreds and/or single cross F1
hybrids comprising the first plant population). Alternatively, the value
of the phenotypic trait in the first plant population can be obtained by
evaluating the phenotypic trait among the members of the first plant
population in at least one topcross combination with at least one tester
parent. Phenotypic traits include, but are not limited to, yield, grain
moisture content, grain oil content, root lodging resistance, stalk
lodging resistance, plant height, ear height, disease resistance, insect
resistance, drought resistance, grain protein content, test weight, and
cob color.
[0014] The set of genetic markers can comprise essentially any convenient
number and type of genetic markers. For example, the set of genetic
markers can comprise one or more of: a single nucleotide polymorphism
(SNP), a multinucleotide polymorphism, an insertion or a deletion of at
least one nucleotide (indel), a simple sequence repeat (SSR), a
restriction fragment length polymorphism (RFLP), a random amplified
polymorphic DNA (RAPD) marker, or an arbitrary fragment length
polymorphism (AFLP). The set of genetic markers can comprise, for
example, between 1 and 50,000 (or even more) genetic markers; e.g.,
between one and ten markers or between 500 and 50,000 markers. The
genotype of the first plant population for the set of genetic markers can
be experimentally determined and/or predicted. Similarly, the genotype of
the members of the target plant population for the set of genetic markers
can be experimentally determined and/or predicted.
[0015] In a preferred class of embodiments, the association between the at
least one genetic marker and the phenotypic trait is evaluated by
performing Bayesian analysis using a linear model, a mixed linear model,
or a nonlinear model. In one such preferred class of embodiments, the
association is evaluated by performing Bayesian analysis using a linear
model, the Bayesian analysis being implemented via a reversible jump
Markov chain Monte Carlo algorithm. Typically, the Bayesian analysis is
implemented via a computer program or system. In another preferred class
of embodiments, the association is evaluated by performing a transmission
disequilibrium test.
[0016] The target plant population can comprise inbred plants, hybrid
plants, or a combination thereof. In a preferred class of embodiments,
the target plant population comprises hybrid plants that comprise F1
progeny produced from single crosses between inbred lines. These F1
progeny can be produced, e.g., from single crosses between inbred progeny
comprising the first plant population and/or new inbreds. Similarly, the
target plant population can comprise an advanced generation produced from
breeding crosses involving at least one of the members of the first plant
population.
[0017] The value of the phenotypic trait in the at least one member of the
target plant population can be predicted by any of a variety of methods.
For example, for simple qualitative traits, the phenotype can be
predicted from the identity of the genetic marker allele(s) found in the
member(s) of the target plant population. As other examples, the value of
the phenotypic trait in the at least one member of the target plant
population can be predicted using a best linear unbiased prediction
method, a multiple regression method, a selection index technique, a
ridge regression method, a linear optimization method, or a non-linear
optimization method.
[0018] The first and target plant populations can comprise essentially any
type of plants. For example, in a preferred class of embodiments, the
first and target plant populations comprise (e.g., consist of) diploid
plants, including, but not limited to, hybrid crop plants, such as maize
(e.g., Zea mays), soybean, sorghum, wheat, sunflower, rice, canola,
cotton, and millet, for example.
[0019] The methods optionally include selecting at least one of the
members of the target plant population having a desired predicted value
of the phenotypic trait. The at least one selected member of the target
plant population can be bred with at least one other plant or selfed,
e.g., to create a new line or hybrid having a desired value of the
phenotypic trait. In another class of embodiments, the methods include
cloning a gene that is linked to the at least one genetic marker
associated with the phenotypic trait, wherein expression of the gene
affects the phenotypic trait, and optionally include constructing a
transgenic plant by expressing the cloned gene in a host plant.
[0020] Another general class of embodiments provides methods of selecting
a plant. In the methods, an association between at least one genetic
marker and the phenotypic trait is provided. The association is evaluated
in a first plant population which is an established breeding population
or a portion thereof. The association is evaluated in the first plant
population according to a statistical model that incorporates a genotype
of the first plant population for a set of genetic markers and a value of
the phenotypic trait in the first plant population. The statistical model
can also incorporate family relationships among the members of the first
plant population. One or more plants from one or more non-adapted lines
are then provided. The one or more plants are selected for a selected
genotype comprising the at least one genetic marker associated with the
phenotypic trait. The selected genotype optionally comprises at least one
allele of at least one of the genetic markers associated with the
phenotypic trait that is novel with respect to the genetic marker alleles
found in the first population.
[0021] A novel genetic marker genotype can indicate the presence of a
novel allele of a QTL associated with the genetic marker (and with the
phenotypic trait). To determine if this putative novel QTL allele is one
that favorably affects the phenotypic trait, the methods can include
evaluating the phenotypic trait in the one or more plants having the
selected genotype. At least one plant having the selected genotype and a
desirable value of the phenotypic trait can be selected. In addition, the
at least one selected plant having the selected genotype and the
desirable value of the phenotypic trait can be bred with at least one
other plant (e.g., to introduce the genetic marker allele and thus the
putative novel QTL allele into the adapted germplasm).
[0022] In a preferred class of embodiments, the association between the at
least one genetic marker and the phenotypic trait is evaluated by
performing Bayesian analysis using a linear model, a mixed linear model,
or a nonlinear model. In one such preferred class of embodiments, the
association is evaluated by performing Bayesian analysis using a linear
model, the Bayesian analysis being implemented via a reversible jump
Markov chain Monte Carlo algorithm. In another preferred class of
embodiments, the association is evaluated by performing a transmission
disequilibrium test.
[0023] All of the various optional configurations and features noted for
the embodiments above apply here as well, to the extent they are
relevant, e.g., for composition of the first plant population and/or the
established breeding population, types of phenotypic traits, types and
number of genetic markers, and the like.
[0024] Plants selected, provided, or produced by any of the methods herein
form another feature of the invention, as do transgenic plants created by
any of the methods herein. Digital systems for practicing the methods or
aspects thereof are also provided. Kits comprising system components,
plants selected by the methods, or both, along with appropriate
containers, packaging materials, instructions for practicing the methods,
or the like, are also a feature of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] FIG. 1 is a pedigree schematically illustrating the relationships
between various inbred lines and single cross hybrids in an example of a
portion of an established breeding population (or an example first plant
population).
[0026] FIG. 2 provides a schematic overview of a typical pedigree corn
breeding program.
[0027] FIG. 3 schematically illustrates a software implementation of a
Bayesian analysis.
[0028] FIG. 4 depicts a plot of the TDT likelihood ratio statistic for cob
color for 511 markers ordered by their position on chromosome 1.
Definitions
[0029] Unless defined otherwise, all technical and scientific terms used
herein have the same meaning as commonly understood by one of ordinary
skill in the art to which the invention pertains. The following
definitions supplement those in the art and are directed to the current
application and are not to be imputed to any related or unrelated case,
e.g., to any commonly owned patent or application. Although any methods
and materials similar or equivalent to those described herein can be used
in the practice for testing of the present invention, the preferred
materials and methods are described herein. Accordingly, the terminology
used herein is for the purpose of describing particular embodiments only,
and is not intended to be limiting.
[0030] As used in this specification and the appended claims, the singular
forms "a," "an" and "the" include plural referents unless the context
clearly dictates otherwise. Thus, for example, reference to "a protein"
includes two or more proteins; reference to "a cell" includes mixtures of
cells, and the like.
[0031] An "allele" or "allelic variant" is any of one or more alternative
forms of a gene or genetic marker. In a diploid cell or organism, the two
alleles of a given gene (or marker) typically occupy corresponding loci
on a pair of homologous chromosomes.
[0032] The term "association" or "associated with" in the context of this
invention refers to one or more genetic marker alleles and phenotypic
trait alleles that are in linkage disequilibrium, i.e., the marker
genotypes and trait phenotypes are found together in the progeny of a
plant or plants more often than if the marker genotypes and trait
phenotypes segregated independently.
[0033] A "breeding cycle" describes the separation between two inbred
parents and an inbred offspring of these parents. A breeding cycle can
include, for example, crossing two inbred lines to produce an F1 hybrid,
selfing the F1 hybrid, and selfing several more times to produce the
inbred offspring. A breeding cycle optionally includes one or more
backcrosses to one of the inbred parents. The separation between an
inbred and a single cross F1 hybrid or between two single cross F1
hybrids can also be described in terms of breeding cycles. To determine
the breeding cycle distance of a single cross F1 hybrid to an inbred, the
breeding cycle difference between the inbred and each inbred parent of
the hybrid is determined; the larger of these two numbers is the number
of breeding cycles separating the F1 single cross hybrid and the inbred.
To determine the breeding cycle distance of a first single cross F1
hybrid to a second single cross F1 hybrid, all possible combinations of
the first hybrid's inbred parents with the second hybrid's inbred parents
are compared to each other, and the breeding cycle distance between the
two hybrids equals the largest distance between any one of these
combinations of inbred parents.
[0034] A "diploid plant" is a plant that has two sets of chromosomes,
typically one from each of its two parents.
[0035] An "established breeding population" is a collection of plants
produced by and/or used as parents in a breeding program, e.g., a
commercial breeding program. The members of the established breeding
population have typically been well-characterized; for example, several
phenotypic traits of interest may have been evaluated, e.g., under
different environmental conditions, at multiple locations, and/or at
different times.
[0036] "F.sub.1" refers to the first filial generation, the progeny of a
mating between two individuals or between two inbred lines. "Advanced
generations" are the F.sub.2, F.sub.3, and later generations produced
from the F.sub.1 progeny by selfing or sexual crosses (e.g., with other
F.sub.1 progeny, with an inbred line, etc.).
[0037] A "founder" is an inbred or single cross F1 hybrid that contains
one or more alleles (e.g., genetic marker alleles) that can be tracked
through the founder's descendents in a pedigree of a population, e.g., a
breeding population. In an established breeding population, for example,
the founders are typically (but not necessarily) the earliest developed
lines.
[0038] The term "gene" is used broadly to refer to any nucleic acid
associated with a biological function. Genes typically include coding
sequences and/or regulatory sequences required for expression of such
coding sequences.
[0039] A "genetic marker" is a nucleotide or a polynucleotide sequence
that is present in a plant genome and that is polymorphic in a population
of interest, or the locus occupied by the polymorphism, depending on
context. Genetic markers include, for example, SNPs, indels, SSRs, RFLPs,
RAPDs, and AFLPs, among many other examples. Genetic markers can, e.g.,
be used to locate on a chromosome genetic loci containing alleles which
contribute to variability in expression of phenotypic traits. Genetic
markers also refer to polynucleotide sequences complementary to the
genomic sequences, such as sequences of nucleic acids used as probes.
[0040] "Genotype" refers to the genetic constitution of a cell or
organism. An individual's "genotype for a set of genetic markers"
consists of the specific alleles, for one or more genetic marker loci,
present in the individual.
[0041] "Germplasm" is the totality of the genotypes of a population or
other group of individuals (e.g., a species). Germplasm can also refer to
plant material, e.g., a group of plants that act as a repository for
various alleles. "Adapted germplasm" refers to plant materials of proven
genetic superiority, e.g., for a given environment or geographical area,
while "non-adapted germplasm," "raw germplasm," or "exotic germplasm"
refers to plant materials of unknown or unproven genetic value, e.g., for
a given environment or geographical area; as such, non-adapted germplasm
refers to plant materials that are not part of an established breeding
population and that do not have a known relationship to a member of the
established breeding population.
[0042] A "haplotype" is the set of alleles an individual inherited from
one parent. A diploid individual thus has two haplotypes. The term
haplotype is often used in a more limited sense to refer to physically
linked and/or unlinked genetic markers (e.g., sequence polymorphisms)
associated with a phenotypic trait. A "haplotype block" (sometimes also
referred to in the literature simply as a haplotype) is a group of two or
more genetic markers that are physically linked on a single chromosome
(or a portion thereof). Typically, each block has a few common
haplotypes, and a subset of the genetic markers (i.e., a "haplotype tag")
can be chosen that uniquely identifies each of these haplotypes.
[0043] The phrase "high throughput screening" refers to assays in which
the format allows large numbers of genetic markers (e.g., nucleic acid
sequences), large numbers of individual or pools of genotypes, or both,
to be screened. In the context of the instant invention, high throughput
screening is the screening of large numbers of genotypes as individuals
or pools for nucleic acid sequences of the plant genome to identify the
presence of genetic marker alleles.
[0044] A "hybrid," "hybrid plant," or "hybrid progeny" is an individual
produced from genetically different parents (e.g., a genetically
heterozygous or mostly heterozygous individual). Typically, the parents
of a hybrid differ in several important respects. Hybrids are often more
vigorous than either parent, but they cannot breed true.
[0045] If two individuals possess the same allele at a particular locus,
the alleles are "identical by descent" if the alleles were inherited from
one common ancestor (i.e., the alleles are copies of the same parental
allele). The alternative is that the alleles are "identical by state"
(i.e., the alleles appear the same but are derived from two different
copies of the allele). Identity by descent information is useful for
linkage studies; both identity by descent and identity by state
information can be used in association studies such as those described
herein, although identity by descent information can be particularly
useful.
[0046] An "inbred line" of plants is a genetically homozygous or nearly
homozygous population. An inbred line, for example, can be derived
through several cycles of selfing. Inbred lines breed true, e.g., for one
or more phenotypic traits of interest. An "inbred," "inbred plant," or
"inbred progeny" is a plant sampled from an inbred line.
[0047] "Linkage" refers to the tendency of alleles at different loci on
the same chromosome to segregate together more often than expected by
chance if their transmission were independent, as a consequence of their
physical proximity.
[0048] The phrase "linkage disequilibrium" (also called "allelic
association") refers to a phenomenon wherein particular alleles at two or
more loci tend to remain together in linkage groups when segregating from
parents to offspring with a greater frequency than expected from their
individual frequencies in a given population. For example, a genetic
marker allele and a QTL allele show linkage disequilibrium when they
occur together with frequencies greater than those predicted from the
individual allele frequencies. It is worth noting that linkage refers to
a relationship between loci, while linkage disequilibrium refers to a
relationship between alleles.
[0049] A "locus" is a position on a chromosome (e.g., of a gene, a genetic
marker, or the like).
[0050] The term "nucleic acid" encompasses any physical string of monomer
units that can be corresponded to a string of nucleotides, including a
polymer of nucleotides (e.g., a typical DNA or RNA polymer), PNAs,
modified oligonucleotides (e.g., oligonucleotides comprising bases that
are not typical to biological RNA or DNA, such as 2'-O-methylated
oligonucleotides), and the like. A nucleic acid can be e.g.,
single-stranded or double-stranded. Unless otherwise indicated, a
particular nucleic acid sequence of this invention optionally comprises
or encodes complementary sequences, in addition to any sequence
explicitly indicated.
[0051] A "pedigree" is a record of the ancestor lines, individuals, or
germplasm for an individual or a family of related individuals.
[0052] The phrase "phenotypic trait" refers to the appearance or other
detectable characteristic of a plant, resulting from the interaction of
its genome with the environment.
[0053] The term "plurality" refers to more than half of the whole. For
example, a plurality of a population is more than half the members of
that population.
[0054] A "polynucleotide sequence" or "nucleotide sequence" is a polymer
of nucleotides (an oligonucleotide, a DNA, a nucleic acid, etc.) or a
character string representing a nucleotide polymer, depending on context.
From any specified polynucleotide sequence, either the given nucleic acid
or the complementary polynucleotide sequence (e.g., the complementary
nucleic acid) can be determined.
[0055] A "plant population" is a collection of plants. The collection
includes at least two plants, and can include, for example, 10 or more,
50 or more, 100 or more, 500 or more, 1000 or more, or even 5000 or more
plants. The members of the population can be related and/or unrelated to
each other; for example, the plants can have known pedigree relationships
to each other.
[0056] The term "progeny" refers to the descendant(s) of a particular
plant (selfcross) or pair of plants (cross-pollinated). The descendant(s)
can be, for example, of the F1, the F.sub.2, or any subsequent
generation.
[0057] A "qualitative trait" is a phenotypic trait that is controlled by
one or a few genes that exhibit major phenotypic effects. Because of
this, qualitative traits are typically simply inherited. Examples
include, but are not limited to, flower color, cob color, and disease
resistance such as Northern corn leaf blight resistance.
[0058] A "quantitative trait" is a phenotypic trait that can be described
numerically (i.e., quantitated or quantified). A quantitative trait
typically exhibits continuous variation between individuals of a
population; that is, differences in the numerical value of the phenotypic
trait are slight and grade into each other. Frequently, the frequency
distribution in a plant population of a quantitative phenotypic trait
exhibits a bell-shaped curve. A quantitative trait is typically the
result of a genetic locus interacting with the environment or of multiple
genetic loci (QTL) interacting with each other and/or with the
environment. Examples of quantitative traits include plant height and
yield.
[0059] The term "quantitative trait locus" ("QTL") or the term "marker
trait association" refers to an association between a genetic marker and
a chromosomal region and/or gene that affects the phenotype of a trait of
interest. Typically, this is determined statistically, e.g., based on one
or more methods published in the literature. A QTL can be a chromosomal
region and/or a genetic locus with at least two alleles that
differentially affect the expression of a phenotypic trait (either a
quantitative trait or a qualitative trait).
[0060] The phrase "sexually crossed" or "sexual reproduction" in the
context of this invention refers to the fusion of gametes to produce seed
by pollination. A "sexual cross" or "cross-pollination" is pollination of
one plant by another. "Selfing" is the production of seed by
self-pollinization, i.e., pollen and ovule are from the same plant.
[0061] A "single cross F1 hybrid" is an F.sub.1 hybrid produced from a
cross between two inbred lines.
[0062] A "tester" is a line or individual plant with a standard genotype,
known characteristics, and established performance. A "tester parent" is
a plant from a tester line that is used as a parent in a sexual cross.
Typically, the tester parent is unrelated to and genetically different
from the plant(s) to which it is crossed. A tester is typically used to
generate F1 progeny when crossed to individuals or inbred lines for
phenotypic evaluation.
[0063] The phrase "topcross combination" refers to the process of crossing
a single tester line to multiple lines. The purpose of producing such
crosses is to determine phenotypic performance of hybrid progeny; that
is, to evaluate the ability of each of the multiple lines to produce
desirable phenotypes in hybrid progeny derived from the line by the
tester cross.
[0064] A "transgenic plant" is a plant into which one or more exogenous
polynucleotides have been introduced by any means other than sexual cross
or selfing. Examples of means by which this can be accomplished are
described below, and include Agrobacterium-mediated transformation,
biolistic methods, electroporation, in planta techniques, and the like.
Transgenic plants may also arise from sexual cross or by selfing of
transgenic plants into which exogenous polynucleotides have been
introduced.
[0065] A "variety" is a subdivision of a species for taxonomic
classification. "Variety" is used interchangeably with the term
"cultivar" to denote a group of individuals that are genetically distinct
from other groups of individuals in a species. An agricultural variety is
a group of similar plants that can be identified from other varieties
within the same species by structural features and/or performance.
[0066] A variety of additional terms are defined or otherwise
characterized herein.
DETAILED DESCRIPTION
[0067] Association studies provide an alternative approach to identifying
chromosomal regions and/or genes affecting phenotypes of interest using
genetic linkage. In brief, while linkage studies attempt to identify QTL
that co-segregate with a phenotypic trait within one or more families,
association studies typically attempt to identify QTL by identifying
particular allelic variants that are associated with the phenotypic trait
in a population (not necessarily a bi-parental family). An allelic
variant identified as being associated with the trait can be, e.g., an
allelic variant of a genetic marker that is in linkage disequilibrium
with a functional variant (an allele of a gene that affects the
phenotypic trait), or the genetic marker and the functional variant can
be synonymous (e.g., a SNP in a coding region that results in an altered
activity of the encoded protein).
[0068] Linkage disequilibrium is a phenomenon observed in populations in
which particular alleles at two (or more) loci occur together at a
frequency greater than the product of the two (or more) allele
frequencies. For example, assume that a mutation at locus A occurs to
produce new allele A.sub.m on a chromosome bearing allele B.sub.n at
locus B. If no recombination occurs between loci A and B, the haplotype
A.sub.mB.sub.n is preserved. If recombination between the loci occurs,
the haplotype is not preserved. Eventually, as recombination occurs
through multiple generations, the new allele A.sub.m would occur with the
other alleles of B in proportion to their relative frequency (that is,
eventually linkage equilibrium is achieved). In the first segregating
generation of a cross of two populations or genotypes, however, the
frequency of haplotype A.sub.mB.sub.n is greater than the product of the
A.sub.m allele frequency and the B.sub.n allele frequency; i.e., linkage
disequilibrium is observed. The approach to equilibrium is a function of
the recombination frequency in a randomly mating population. For unlinked
loci, the haplotype frequency goes halfway to the equilibrium value each
generation; the more tightly the loci are linked, the longer the
disequilibrium persists in the population. Association studies taking
advantage of linkage disequilibrium can thus incorporate many past
generations of recombination to achieve high-resolution, fine scale gene
localization (see, e.g., Xiong and Guo (1997) "Fine-scale mapping of
quantitative trait loci using historical recombinations" Genetics 145:
1201-1218).
[0069] Design and execution of various types of association studies have
been described in the art; see, e.g., Rao and Province, eds., (2001)
Advances in Genetics volume 42, Genetic Dissection of Complex Traits;
Balding et al., eds. (2001) Handbook of Statistical Genetics, John Wiley
and Sons Ltd.; Borecki and Suarez (2001) "Linkage and association: basic
concepts" Adv Genet 42: 45-66; Cardon and Bell (2001) "Association study
designs for complex diseases" Nat Rev Genet 2: 91-99; and Risch (2000)
"Searching for genetic determinants for the new millennium" Nature 405:
847-856. Association studies have been used both to evaluate candidate
genes for association with a phenotypic trait (e.g., Thornsberry et al.
(2001) "Dwarf8 polymorphisms associate with variation in flowering time"
Nature Genetics 28: 286-289) and to perform whole genome scans to
identify genes that contribute to phenotypic variation (e.g., Paunio et
al. (2001) "Genome-wide scan in a nationwide study sample of
schizophrenia families in Finland reveals susceptibility loci on
chromosomes 2q and 5q" Human Molecular Genetics 10: 3037-3048 and Liu et
al. (2002) "Genomewide linkage analysis of celiac disease in Finnish
families" Am. J. Hum. Genet. 70: 51-59).
[0070] As will be evident, linkage disequilibrium must exist in the
region(s) of interest for association studies to be powerful (if no
linkage disequilibrium exists, an association study can identify only a
marker that is itself an actual functional variant). The rate at which
(number of base pairs over which) linkage disequilibrium declines thus
affects the resolution of an association study and the number of markers
required. Such considerations can, for example, affect the choice of
population to be used in the analysis. A number of studies have examined
linkage disequilibrium in humans (e.g., Reich et al. (2001) "Linkage
disequilibrium in the human genome" Nature 411: 199-204 and Daly et al.
(2001) "High-resolution haplotype structure in the human genome" Nature
Genetics 29: 229-232). Linkage disequilibrium has also been analyzed in
plants; for example, a recent study by the authors and others indicates
that strong linkage disequilibrium between SNP loci extends at least 500
bp in maize (Ching et al. (2002) "SNP frequency, haplotype structure and
linkage disequilibrium in elite maize inbred lines" BMC Genetics 3: 19;
see also Remington et al. (2001) "Structure of linkage disequilibrium and
phenotypic associations in the maize genome" Proc. Natl. Assoc. Sci. 98:
11479-11484; Tenaillon et al. (2001) "Patterns of DNA sequence
polymorphism along chromosome 1 of maize" Proc Natl Acad Sci USA 98:
9161-9166; and Jannoo et al. (1999) "Linkage disequilibrium among modern
sugarcane cultivars" Theor App Genet 99: 1053-1060).
[0071] Although a number of association studies involving humans and
animals have been performed (see, e.g., Paunio et al. (2001) "Genome-wide
scan in a nationwide study sample of schizophrenia families in Finland
reveals susceptibility loci on chromosomes 2q and 5q" Human Molecular
Genetics 10: 3037-3048; Liu et al. (2002) "Genomewide linkage analysis of
celiac disease in Finnish families" Am. J. Hum. Genet. 70: 51-59;
Terwilliger (2001) "On the resolution and feasibility of genome scanning
approaches" Adv. Genet. 42: 351-391; and Grupe et al. (2001) "In silico
mapping of complex disease-related traits in mice" Science 292:
1915-1918), fewer studies have been performed involving plants. Plant
pedigrees present several challenges that require modification or
extension of methods used for humans and animals (see, e.g., Yi and Xu
(2001) "Bayesian mapping of quantitative trait loci under complicated
mating designs" Genetics 157: 1759-1771). For example, QTL mapping
methods applicable to plants may need to deal with both selfing and
sexual crossing, pure inbred lines as breeding population founders, and
large family sizes.
[0072] Bayesian methods have been proposed for association studies in
plants that account for these factors. For example, Yi and Xu (2001)
"Bayesian mapping of quantitative trait loci under complicated mating
designs" Genetics 157: 1759-1771 and Bink et al. (2002) "Multiple QTL
mapping in related plant populations via a pedigree-analysis approach"
Theor. Appl. Genet. 104: 751-762 describe Bayesian methods for QTL
mapping in complex plant populations. These methods incorporate
genotypic, phenotypic, and family pedigree information for complex plant
populations (e.g., a first plant population). Use of such complex
populations offers a number of advantages. For example, a large number of
single cross hybrids (or a large number of segregating F2 progeny from a
biparental cross, or the like) need not be generated and phenotyped to
perform the analysis; instead, plants and/or lines can be chosen from the
breeding population, where phenotypic evaluation of large numbers of
progeny of different types is a normal part of the breeding program.
Breeding programs typically evaluate the phenotypes of a large number of
progeny, often replicated at two or more locations (thus providing data
on environmental effects). Since considerable time and effort is required
to accurately assess most of the economically important phenotypic
traits, using data generated as part of an ongoing breeding program
offers considerable time and cost savings as well as potentially more
reliable phenotypic data and thus a better map. See, e.g., Rafalski
(2002) "Applications of single nucleotide polymorphisms in crop genetics"
Curr. Opin. Plant Bio. 5: 94-100 and Rafalski (2002) "Novel genetic
mapping tools in plants: SNPs and LD-based approaches" Plant Sci 162:
329-333.
[0073] The present invention provides methods for using genetic marker
genotype, phenotypic information, and family relationship data for plants
in a first plant population (e.g., a breeding population or a subset
thereof) to identify an association between at least one genetic marker
and a phenotypic trait, for example, using Bayesian methods such as those
referenced above. The methods include prediction of the value of the
phenotypic trait in one or more members of a second, target plant
population based on their genotype for the one or more genetic markers
associated with the trait.
[0074] The methods have a number of applications, e.g., in applied
breeding programs in plants (e.g., hybrid crop plants; similar methods
can be applied for animals). For example, the methods can be used to
predict the phenotypic performance of hybrid progeny, e.g., a single
cross hybrid produced (actually or hypothetically) by crossing a given
pair of inbred lines of known marker genotype. Similarly, by allowing
prediction of phenotypic performance of the potential progeny from a
cross, the methods can facilitate selection of plants (e.g., inbred
plants, hybrid plants, etc.) for use as parents in one or more crosses;
the methods permit selection of parental plants whose offspring have the
highest probability of possessing the desired phenotype.
[0075] A first general class of embodiments provides methods of predicting
a value of a phenotypic trait in a target plant population. In the
methods, an association between at least one genetic marker and the
phenotypic trait is provided. The association is evaluated in a first
plant population, which first plant population is an established breeding
population or a portion thereof. The association is evaluated in the
first plant population according to a statistical model that incorporates
a genotype of the first plant population for a set of genetic markers and
a value of the phenotypic trait in the first plant population. The value
of the phenotypic trait in at least one member of the target plant
population is then provided. The value is predicted from the association
and from a genotype of the at least one member for the at least one
genetic marker associated with the phenotypic trait. The value is
typically predicted in advance of or instead of experimentally
determining the value.
[0076] The phenotypic trait can be a quantitative trait, e.g., for which a
quantitative value is provided. Alternatively, the phenotypic trait can
be a qualitative trait, e.g., for which a qualitative value is provided.
The trait can be determined by a single gene, or it can be determined by
two or more genes.
[0077] The methods optionally include selecting at least one of the
members of the target plant population having a desired predicted value
of the phenotypic trait, and optionally also include breeding at least
one selected member of the target plant population with at least one
other plant (or selfing the at least one selected member, e.g., to create
an inbred line).
[0078] The first plant population typically comprises a plurality of
inbreds, single cross F1 hybrids, or a combination thereof. For example,
in one class of embodiments, the first plant population comprises a
plurality of inbreds. In another class of embodiments, the first plant
population comprises a plurality of single cross F1 hybrids. In yet
another class of embodiments, the first plant population comprises a
plurality of a combination of inbreds and single cross F1 hybrids. The
first plant population optionally consists of inbreds, single cross F1
hybrids, or a combination thereof. The inbreds can be from inbred lines
that are related and/or unrelated to each other, and the single cross F1
hybrids can be produced from single crosses of said inbred lines and/or
one or more additional inbred lines.
[0079] As noted, the members of the first plant population are sampled
from an existing, established breeding population (e.g., a commercial
breeding population). The members of an established breeding population
are typically descendents of a relatively small number of founders and
are thus typically highly inter-related. The ancestry of each member
other than the founders is generally known. Thus, for example, an
established breeding population can comprise at least three founders and
their descendents, where the ancestry of the descendents is known (e.g.,
at least 10 founders, at least 50 founders, at least 100 founders, or at
least 200 founders). For example, the established breeding population can
comprise between about 100 and about 200 founders (e.g., about 30-40
female founders and 80-150 male founders) and their descendents of known
ancestry. The breeding population typically spans a large number of
generations and breeding cycles. For example, an established breeding
population can span three, four, five, six, seven, eight, nine or more
breeding cycles. The members of the first plant population can thus have
the same characteristics. In some embodiments, the members of the first
plant population span at least three breeding cycles (e.g., at least
four, five, six, seven, eight, or nine breeding cycles). In one class of
example embodiments, the first plant population comprises a plurality of
inbreds, single cross F1 hybrids, or a combination thereof, the ancestry
of each inbred and/or single cross F1 hybrid is known, and each inbred
and/or single cross F1 hybrid is a descendent of at least one of three or
more founders (e.g., 10, 50, or 100 or more founders). The first
population optionally comprises one or more founders, e.g., from which
other members of the population are descended.
[0080] The first plant population can comprise essentially any number of
members. For example, the first plant population optionally comprises
between about 50 and about 5000 members (e.g., the first plant population
can include 50-5000 inbreds and/or single cross F1 hybrids). As another
example, the first plant population can comprise at least about 50, 100,
200, 500, 1000, 2000, 3000, 4000, 5000, or even 6000 or more members. As
just one specific example, the first plant population can comprise about
1000 inbreds and between about 3000 and 5000 single cross hybrids.
[0081] It is worth noting that the first plant population optionally has
any combination of the above characteristics. As just one example, the
first plant population can comprise between 50 and 5000 members,
including a plurality of inbreds and/or single cross F1 hybrids, each of
known ancestry and descended from at least one of three or more founders.
[0082] FIG. 1 is a pedigree schematically illustrating the relationships
between various inbred lines and single cross hybrids that could, for
example, comprise the first plant population. In FIG. 1, SX followed by a
number represents a single cross hybrid, while other character
combinations designate various inbred lines (except LANC, which
represents a population from which inbred line LNC1 was derived). In this
figure, the founders include MP1, FP3, FP1, MA1, FP2, MB5, LNC1, and DRS,
for example. A line connecting two individuals indicates that one is an
ancestor of the other. For example, inbred lines MFP2 and MA21 were
crossed to produce, after several generations of selfing, inbred line
MA32. (In this example, the line connecting MFP2 and MA32 or MA21 and
MA32 represents a distance of one breeding cycle.) As another example,
inbred lines F39 and MA32 were crossed to produce single cross F1 hybrid
SX34. (In this example, the line connecting F39 and SX34 or MA32 and SX34
represents a distance of less than one breeding cycle.)
[0083] FIG. 2 schematically illustrates an example commercial plant
breeding program, for corn in this example. Inbred lines are developed,
e.g., from two populations (one male and one female). In a topcross and
hybrid testing phase, topcrosses are performed with testers from the
opposite population (TC1 and TC2, first and second year topcrosses; MET,
multiple environment test).
[0084] Typically, the first plant population exhibits variability for the
phenotypic trait of interest (e.g., quantitative variability for a
quantitative phenotypic trait).
[0085] The value of the phenotypic trait in the first plant population is
obtained, e.g., by evaluating the phenotypic trait among the members of
the first plant population (e.g., quantifying a quantitative phenotypic
trait among the members of the population). The phenotype can be
evaluated in the members (e.g., the inbreds and/or single cross F1
hybrids) comprising the first plant population. Alternatively, the value
of the phenotypic trait in the first plant population can be obtained by
evaluating the phenotypic trait among the members of the first plant
population in at least one topcross combination with at least one tester
parent (e.g., for phenotypic traits which can only be evaluated in
hybrids).
[0086] The phenotypic trait can be essentially any quantitative or
qualitative phenotypic trait, e.g., one of agronomic and/or economic
importance. For example, the phenotypic trait can be selected from the
group consisting of: yield, grain moisture content, grain oil content,
root lodging resistance, stalk lodging resistance, plant height, ear
height, disease resistance, insect resistance, drought resistance, grain
protein content, test weight, visual or aesthetic appearance, and cob
color. These traits, and techniques for evaluating (e.g., quantifying)
them, are well known in the art. For example, grain yield is a
traditional measure of crop performance. Test weight is a measure of
quality. Grain moisture content is important in storage, while root and
stalk lodging resistance affect standability and are important during
harvest. The methods are similarly applicable to other phenotypic traits,
for example, grain phytate content.
[0087] The set of genetic markers can comprise essentially any convenient
genetic markers. For example, the set of genetic markers can comprise one
or more of: a single nucleotide polymorphism (SNP), a multinucleotide
polymorphism, an insertion or a deletion of at least one nucleotide
(indel), a simple sequence repeat (SSR), a restriction fragment length
polymorphism (RFLP), a random amplified polymorphic DNA (RAPD) marker, or
an arbitrary fragment length polymorphism (AFLP). As will be evident to
one of skill, the number of markers required can vary, e.g., depending on
the rate at which linkage disequilibrium declines in the plant species of
interest and/or on the type of association analysis performed. The set of
genetic markers can include, for example, from 1 to 50,000 markers (e.g.,
between 1 and 10,000 markers). In one class of embodiments, the set of
genetic markers comprises between about 50 and about 2500 markers. For
example, the set of genetic markers can comprise at least about 50, 100,
250, 500, 1000, 2000, or even 2500 or more genetic markers. In certain
embodiments, the set of genetic markers comprises between one and ten
markers (e.g., for candidate gene studies, in which relatively few
markers are needed). In other embodiments, the set of genetic markers
comprises between 500 and 50,000 markers (e.g., for whole genome scans).
[0088] The genotype of the first plant population for the set of genetic
markers can be determined experimentally, predicted, or a combination
thereof. For example, in one class of embodiments, the genotype of each
inbred present in the plant population is experimentally determined and
the genotype of each single cross F1 hybrid present in the first plant
population is predicted (e.g., from the experimentally determined
genotypes of the two inbred parents of each single cross hybrid). Plant
genotypes can be experimentally determined by essentially any convenient
technique. Many applicable techniques for discovering and/or genotyping
genetic markers are known in the art (e.g., those described below in the
section entitled "Genetic Markers"). In one preferred class of
embodiments, a set of DNA segments from each inbred is sequenced to
experimentally determine the genotype of each inbred. Since sequence
polymorphisms (e.g., genetic markers) are typically more common in
noncoding regions (e.g., introns and untranslated regions), in one class
of embodiments the set of DNA segments that is sequenced comprises the
5'-untranslated regions and/or the 3'-untranslated regions of one or more
(e.g., two or more) genes. Sequencing techniques (e.g., direct sequencing
of PCR amplicons) are well known (see, e.g., Ching et al. (2002) "SNP
frequency, haplotype structure and linkage disequilibrium in elite maize
inbred lines" BMC Genetics 3: 19).
[0089] In some embodiments, a single genetic marker is associated with the
phenotypic trait, while in other embodiments, two or more genetic markers
(and/or chromosome regions) are associated with the phenotypic trait.
Thus, in one class of embodiments, an association between a haplotype
comprising two or more genetic markers and the phenotypic trait is
provided. The genetic markers comprising a haplotype can be unlinked
(e.g., two or more QTL affecting the phenotypic trait can be identified,
each of which is associated with one of the markers), or the genetic
markers can be physically linked (e.g., the genetic markers can comprise
a haplotype block associated with the phenotypic trait, e.g., a SNP
haplotype tagged haplotype block).
[0090] As noted, the association is evaluated in the first plant
population according to a statistical model that incorporates genotypic
and phenotypic information about the first plant population. The
statistical model typically also exploits relationships among the plants
in the first population by incorporating family relationships among the
members of the first plant population along with the genetic marker and
phenotypic trait data. The model can incorporate family relationships by,
for example, including an indication of whether a particular allele is of
maternal or paternal origin, or by any other means that permits use of
pedigree relationship information to track alleles that are identical by
descent in different individuals.
[0091] In a preferred class of embodiments, the association between the at
least one genetic marker and the phenotypic trait is evaluated by
performing Bayesian analysis using a linear model, a mixed linear model,
or a nonlinear model. The Bayesian analysis can be implemented, e.g., via
a reversible jump Markov chain Monte Carlo algorithm, a delta method, or
a profile likelihood algorithm. For example, in one such preferred class
of embodiments, the association is evaluated by performing Bayesian
analysis using a linear model, the Bayesian analysis being implemented
via a reversible jump Markov chain Monte Carlo algorithm. Typically,
evaluating the association includes (and/or permits) determining identity
by descent information for founder alleles of the at least one genetic
marker in one or more pedigrees of related inbreds and hybrids, and
permits tracking of the at least one genetic marker throughout such
pedigrees. Typically, the Bayesian analysis (e.g., implemented via a
reversible jump Markov chain Monte Carlo algorithm) is implemented via a
computer program or system.
[0092] Bayesian methods, Monte Carlo algorithms, and the like are well
known in the art. General references that are useful in understanding
relevant concepts include: Gibas and Jambeck (2001) Bioinformatics
Computer Skills, O'Reilly, Sebastipol, Calif.; Pevzner (2000)
Computational Molecular Biology and Algorithmic Approach, The MIT Press,
Cambridge Mass.; Durbin et al. (1998) Biological Sequence Analysis:
Probabilistic Models of Proteins and Nucleic Acids, Cambridge University
Press, Cambridge, UK; Hinchliffe (1996) Modeling Molecular Structures
John Wiley and Sons, NY, N.Y.; and Rashidi and Buehler (2000)
Bioinformatic Basics: Applications in Biological Science and Medicine CRC
Press LLC, Boca Raton, Fla. Detailed discussions of Monte Carlo
statistical analyses are provided in various resources that include,
e.g., Robert et al. (1999) Monte Carlo Statistical Methods,
Springer-Verlag; Chen et al. (2000) Monte Carlo Methods in Bayesian
Computation, Springer-Verlag; Sobol et al. (1994) A Primer for the Monte
Carlo Method, CRC Press, LLC; Manno (1999) Introduction to the
Monte-Carlo Method, Akademiai Kiado; and Rubinstein (1981) Simulation and
the Monte Carlo Method, John Wiley & Sons, Inc. Additional details
relating to these statistical methods are found in, e.g., Carlin et al.
(1995) "Bayesian model choice via Markov chain Monte Carlo methods" J.
Royal Stat. Soc. Series B, 57: 473-84; Carlin et al. (1991) "An iterative
Monte Carlo method for nonconjugate Bayesian analysis" Statistics and
Computing 1: 119-28; and Pillardy et al. (2001) "Conformation-family
Monte Carlo: A new method for crystal structure prediction" Proc. Natl.
Acad. Sci. USA 98(22): 12351-6.
[0093] In particular, Bayesian methods for QTL mapping (i.e., for
evaluating association between a set of genetic markers and a phenotypic
trait) are known in the art. For example, Bink et al. (2002) "Multiple
QTL mapping in related plant populations via a pedigree-analysis
approach" Theor. Appl. Genet. 104: 751-762 and Yi and Xu (2001) "Bayesian
mapping of quantitative trait loci under complicated mating designs"
Genetics 157: 1759-1771 describe Bayesian analysis implemented via
reversible jump Markov chain Monte Carlo algorithms and using linear
models, and are hereby incorporated by reference in their entirety. The
model presented in Bink et al., for example, incorporates the genotype of
two or more plants for a set of genetic markers, values of the phenotypic
trait observed in the plants, and family relationships between the plants
(by using segregation indicators that indicate maternal or paternal
derivation, e.g., of genetic marker and therefore of linked QTL alleles).
This model also includes non-genetic factors affecting the trait (e.g.,
environmental effects).
[0094] Bayesian analysis, QTL mapping, and the like are also described in,
e.g., Sorensen and Gianola (2002) Likelihood, Bayesian and MCMC methods
in quantitative genetics, Springer, N.Y.; Jannink and Fernando (2004) "On
the metropolis-hastings acceptance probability to add or drop a
quantitative trait locus in markov chain monte carlo-based bayesian
analyses" Genetics 166: 641-643; Wu and Jannink (2004) "Optimal sampling
of a population to determine QTL location, variance, and allelic number"
Theor Appl Genet 108: 1434-42; Jannink (2003) "Selection dynamics and
limits under additive-by-additive epistatic gene action" Crop Sci 43:
489-497; Yi and Xu (2000) "Bayesian mapping of quantitative trait loci
under the identity-by-descent-based variance component model" Genetics
156: 411-422; Berry et al. (2002) "Assessing probability of ancestry
using simple sequence repeat profiles: Applications to maize hybrids and
inbreds" Genetics 161: 813-824; Berry et al. (2003) "Assessing
probability of ancestry using simple sequence repeat profiles:
Applications to maize inbred lines and soybean varieties" Genetics 165:
331-342; and Jannink and Wu (2003) "Estimating allelic number and
identity in state of QTLs in interconnected families" Genet Res 81:
133-44. An example software package for Bayesian analysis of QTL in
interconnected populations is publicly available at
www.public.iastate.edu/.about.jjannink/Research/Software.htm.
[0095] In another preferred class of embodiments, the association is
evaluated by performing a transmission disequilibrium test (see, e.g.,
the Examples and the references therein). In another class of
embodiments, the association is evaluated by a maximum likelihood mixed
linear or nonlinear model analysis (see, e.g., Lynch and Walsh (1998)
Genetic Analysis of Quantitative Traits, Sinauer Associates, Inc.,
Sunderland M A, pp 746-755). In yet another class of embodiments, the
association is evaluated in the first plant population via an artificial
neural network. Such networks are known in the art; see, e.g., Gurney
(1999) An Introduction to Neural Networks, UCL Press, 1 Gunpowder Square,
London EC4A 3DE, UK; Bishop (1995) Neural Networks for Pattern
Recognition, Oxford Univ Press; ISBN: 0198538642; Ripley, Hjort (1995)
Pattern Recognition and Neural Networks, Cambridge University Press
(Short); and Masters (1993) Practical Neural Network Recipes in C++
(Book&Disk edition) Academic Press.
[0096] The target plant population can comprise essentially any number of
members that are related and/or unrelated to each other and to the
members of the first plant population. The members of the target plant
population typically do not themselves comprise the first plant
population.
[0097] Thus, the target plant population can comprise, e.g., inbred
plants, hybrid plants, or a combination thereof. The hybrid plants can
comprise, e.g., single cross hybrids, double cross hybrids, hybrid
progeny of three-way crosses, or essentially any other hybrids. In a
preferred class of embodiments, the target plant population comprises
hybrid plants that comprise F1 progeny produced from single crosses
between inbred lines. These F1 progeny can be produced, e.g., from single
crosses between inbreds comprising the first plant population (where the
hybrid plants do not comprise the first plant population), from single
crosses between new inbreds that contain preferred alleles (genetic
marker and/or QTL alleles) identical by descent or identical by state to
those inbreds used in the association mapping analysis, or a combination
thereof. Similarly, in one class of embodiments, the target plant
population comprises an advanced generation produced from breeding
crosses comprising at least one of the members of the first plant
population (i.e., the target plant population comprises F2 or later
descendants of at least one member of the first plant population).
[0098] It is worth noting that the target plant population can comprise
actual living plants and/or hypothetical plants (e.g., hypothetical
single cross hybrids produced by crossing given pairs of inbred lines of
known genetic marker genotype). Typically, if the methods are applied to
a hypothetical target plant population, at least one actual plant (e.g.,
one having the most desirable predicted value of the phenotypic trait)
will actually be produced as a living plant.
[0099] The genotype of the member(s) of the target plant population for
the at least one genetic marker associated with the phenotypic trait can
be determined experimentally and/or predicted. Thus, in one class of
embodiments, the genotype of the at least one member of the target plant
population for the at least one genetic marker is determined
experimentally, e.g., by high throughput screening. In another class of
embodiments, the genotype of the at least one member of the target plant
population for the at least one genetic marker is predicted. For example,
the genotype of a single cross F1 hybrid member of the target population
can be predicted if the genotypes of its inbred parents are known.
[0100] The value of the phenotypic trait in at least one member of the
target plant population can be predicted, for example, by a method that
incorporates both pedigree and genetic marker information (e.g., both
genetic marker genotype and identity by descent and/or identity by state
information for genetic marker alleles).
[0101] In a preferred class of embodiments, the value of the phenotypic
trait in the at least one member of the target plant population is
predicted using a best linear unbiased prediction method. Best linear
unbiased prediction methods are known in the art; see, e.g., Gianola et
al. (2003) "On Marker-Assisted Prediction of Genetic Value: Beyond the
Ridge" Genetics 163: 347-365 and Bink et al. (2002) "Multiple QTL mapping
in related plant populations via a pedigree-analysis approach" Theor.
Appl. Genet. 104: 751-762. Alternatively, other methods can be used to
predict the value of the phenotypic trait in the at least one member of
the target plant population, e.g., a multiple regression method, a
selection index technique, a ridge regression method, a linear
optimization method, or a non-linear optimization method. Such methods
are well known; see, e.g., Johnson, B. E. et al. (1988) "A model for
determining weights of traits in simultaneous multitrait selection" Crop
Sci. 28: 723-728.
[0102] The first and target plant populations can comprise essentially any
type of plants. For example, in a preferred class of embodiments, the
first and target plant populations comprise (e.g., consist of) diploid
plants. As noted previously, the methods are particularly applicable to
hybrid crop plants. Thus, in preferred embodiments, the first and target
plant populations are selected from the group consisting of: maize (e.g.,
Zea mays), soybean, sorghum, wheat, sunflower, rice, canola, cotton, and
millet.
[0103] A QTL identified by the methods herein (e.g., a QTL allele linked
to the at least one genetic marker associated with the phenotypic trait)
can optionally be cloned and expressed, e.g., to create a transgenic
plant having a desirable value of the phenotypic trait. Thus, in one
class of embodiments, the methods include cloning a gene that is linked
to the at least one genetic marker associated with the phenotypic trait,
wherein expression of the gene affects the phenotypic trait. The methods
optionally also include constructing a transgenic plant by expressing the
cloned gene in a host plant.
[0104] Digital Systems
[0105] In general, various automated systems can be used to perform some
or all of the method steps as noted herein. In addition to practicing
some or all of the method steps herein, digital or analog systems, e.g.,
comprising a digital or analog computer, can also control a variety of
other functions such as a user viewable display (e.g., to permit viewing
of method results by a user) and/or control of output features (e.g., to
assist in marker assisted selection or control of automated field
equipment).
[0106] For example, certain of the methods described above are optionally
(and typically) implemented via a computer program or programs (e.g.,
that perform or assist in performing a transmission disequilibrium test,
Bayesian analysis and/or phenotype prediction). Thus, the present
invention provides digital systems, e.g., computers, computer readable
media, and/or integrated systems comprising instructions (e.g., embodied
in appropriate software) for performing the methods herein. For example,
a digital system comprising instructions for evaluating an association in
the first plant population between at least one genetic marker and a
phenotypic trait and for predicting the value of the phenotypic trait in
at least one member of a second, target plant population, as described
herein, is a feature of the invention. The digital system can also
include information (data) corresponding to plant genotypes for a set of
genetic markers, phenotypic values, and/or family relationships. The
system can also aid a user in performing marker assisted selection
according to the methods herein, or can control field equipment which
automates selection, harvesting, and/or breeding schemes.
[0107] Standard desktop applications such as word processing software
(e.g., Microsoft Word.TM. or Corel WordPerfect.TM.) and/or database
software (e.g., spreadsheet software such as Microsoft Excel.TM., Corel
Quattro Pro.TM., or database programs such as Microsoft Access.TM. or
Paradox.TM.) can be adapted to the present invention by inputting data
which is loaded into the memory of a digital system, and performing an
operation as noted herein on the data. For example, systems can include
the foregoing software having the appropriate pedigree data, phenotypic
information, associations between phenotype and pedigree, etc., e.g.,
used in conjunction with a user interface (e.g., a GUI in a standard
operating system such as a Windows, Macintosh or LINUX system) to perform
any analysis noted herein, or simply to acquire data (e.g., in a
spreadsheet) to be used in the methods herein.
[0108] Software for performing statistical analysis can also be included
in the digital system. For example, Bayesian analysis can be performed
using software such as that described in Bink et al. (2002) "Multiple QTL
mapping in related plant populations via a pedigree-analysis approach"
Theor. Appl. Genet. 104: 751-762, or a modified version thereof. FIG. 3
schematically depicts a software implementation of this Bayesian analysis
of QTLs in a complex pedigree.
[0109] Systems typically include, e.g., a digital computer with software
for performing association analysis and/or phenotypic value prediction,
or for performing Bayesian analysis, e.g., implemented via a reversible
jump Markov chain Monte Carlo algorithm, or the like, as well as data
sets entered into the software system comprising plant genotypes for a
set of genetic markers, phenotypic values, family relationships, and/or
the like. The computer can be, e.g., a PC (Intel x86 or Pentium
chip-compatible DOS,.TM. OS2,.TM. WINDOWS,.TM. WINDOWS NT,.TM.
WINDOWS95,.TM. WINDOWS98,.TM. LINUX, Apple-compatible, MACINTOSH.TM.
compatible, Power PC compatible, or a UNIX compatible (e.g., SUN.TM. work
station) machine) or other commercially common computer which is known to
one of skill. Software for performing association analysis and/or
phenotypic value prediction can be constructed by one of skill using a
standard programming language such as Visualbasic, Fortran, Basic, Java,
or the like, according to the methods herein.
[0110] Any system controller or computer optionally includes a monitor
which can include, e.g., a cathode ray tube ("CRT") display, a flat panel
display (e.g., active matrix liquid crystal display, liquid crystal
display), or others. Computer circuitry is often placed in a box which
includes numerous integrated circuit chips, such as a microprocessor,
memory, interface circuits, and others. The box also optionally includes
a hard disk drive, a floppy disk drive, a high capacity removable drive
such as a writeable CD-ROM, and other common peripheral elements.
Inputting devices such as a keyboard or mouse optionally provide for
input from a user and for user selection of genetic marker genotype,
phenotypic value, or the like in the relevant computer system.
[0111] The computer typically includes appropriate software for receiving
user instructions, either in the form of user input into a set parameter
fields, e.g., in a GUI, or in the form of preprogrammed instructions,
e.g., preprogrammed for a variety of different specific operations. The
software then converts these instructions to appropriate language for
instructing the system to carry out any desired operation. For example,
in addition to performing statistical analysis, a digital system can
instruct selection of plants comprising certain markers, or control field
machinery for harvesting, selecting, crossing or preserving crops
according to the relevant method herein.
[0112] The invention can also be embodied within the circuitry of an
application specific integrated circuit (ASIC) or programmable logic
device (PLD). In such a case, the invention is embodied in a computer
readable descriptor language that can be used to create an ASIC or PLD.
The invention can also be embodied within the circuitry or logic
processors of a variety of other digital apparatus, such as PDAs, laptop
computer systems, displays, image editing equipment, etc.
[0113] Identifying New Allelic Variants
[0114] The present invention also provides methods that can be used to
identify new allelic variants of a QTL affecting a phenotypic trait.
Association analysis can be performed to identify at least one genetic
marker associated with the phenotypic trait. Novel alleles of the genetic
marker, and thus possibly of a QTL associated with the genetic marker,
can be identified in non-adapted germplasm. Such novel allelic variants
can then, e.g., be bred into the adapted germplasm (e.g., a commercial
breeding population).
[0115] Thus, one general class of embodiments provides methods of
selecting a plant. In the methods, an association between at least one
genetic marker and the phenotypic trait is provided. The association is
evaluated in a first plant population, which first plant population is an
established breeding population or a portion thereof. The association is
evaluated in the first plant population according to a statistical model
that incorporates a genotype of the first plant population for a set of
genetic markers and a value of the phenotypic trait in the first plant
population. The statistical model can also incorporate family
relationships among the members of the first plant population. One or
more plants from one or more non-adapted lines are then provided. The one
or more plants are selected for a selected genotype comprising the at
least one genetic marker associated with the phenotypic trait. The
selected genotype can comprise, e.g., at least one allele of at least one
of the genetic markers associated with the phenotypic trait that is novel
with respect to the genetic marker alleles found in the first population.
The genotype of the one or more plants for the at least one genetic
marker is typically determined experimentally, by any convenient
technique.
[0116] A novel genetic marker genotype can indicate the presence of a
novel allele of a QTL associated with the genetic marker (and with the
phenotypic trait). To determine if this putative novel QTL allele is one
that favorably affects the phenotypic trait, the methods can include
evaluating the phenotypic trait (e.g., quantifying a quantitative
phenotypic trait) in the one or more plants having the selected genotype.
At least one plant having the selected genotype and a desirable value of
the phenotypic trait can be selected. In addition, the at least one
selected plant having the selected genotype and the desirable value of
the phenotypic trait can be bred with at least one other plant (e.g., to
introduce the genetic marker allele and thus the putative novel QTL
allele into the adapted germplasm).
[0117] The first plant population typically comprises a plurality of
inbreds, single cross F1 hybrids, or a combination thereof. For example,
in one class of embodiments, the first plant population comprises a
plurality of inbreds. In another class of embodiments, the first plant
population comprises a plurality of single cross F1 hybrids. In yet
another class of embodiments, the first plant population comprises a
plurality of a combination of inbreds and single cross F1 hybrids. The
first plant population optionally consists of inbreds, single cross F1
hybrids, or a combination thereof. The inbreds can be related and/or
unrelated to each other, and the single cross F1 hybrids can be produced
from single crosses of said inbred lines and/or one or more additional
inbred lines.
[0118] As noted, the members of the first plant population are sampled
from an established breeding population (e.g., a commercial breeding
population). FIG. 1 is a pedigree schematically illustrating the
relationships between various inbred lines and single cross hybrids that
could, for example, comprise the first plant population. Characteristics
of established breeding populations and/or first plant populations noted
for the embodiments described above apply to these embodiments as well.
Thus, for example, in one class of embodiments, the first plant
population comprises a plurality of inbreds, single cross F1 hybrids, or
a combination thereof, the ancestry of each inbred and/or single cross F1
hybrid is known, and each inbred and/or single cross F1 hybrid is a
descendent of at least one of three or more founders (e.g., 10, 50, or
100 or more founders). Similarly, in some embodiments, the members of the
first plant population span at least three breeding cycles (e.g., at
least four, five, six, seven, eight, or nine breeding cycles). In one
class of embodiments, the established breeding population comprises at
least three founders and their descendents (e.g., at least 10 founders,
at least 50 founders, at least 100 founders, or at least 200 founders,
e.g., between about 100 and about 200 founders and their descendents),
where the ancestry of the descendents is known. The established breeding
population can span, e.g., three, four, five, six, seven, eight, nine or
more breeding cycles.
[0119] The first plant population can comprise essentially any number of
members. For example, the first plant population optionally comprises
between about 50 and about 5000 members (e.g., the first plant population
can include 50-5000 inbreds and/or single cross F1 hybrids). As another
example, the first plant population can comprise at least about 50, 100,
200, 500, 1000, 2000, 3000, 4000, 5000, or even 6000 or more members.
[0120] It is worth noting that the first plant population optionally has
any combination of the above characteristics. As just one example, the
first plant population can comprise between 50 and 5000 members,
including a plurality of inbreds and/or single cross F1 hybrids, each of
known ancestry and descended from at least one of three or more founders.
[0121] The phenotypic trait can be a quantitative trait, e.g., for which a
quantitative value can be provided. Alternatively, the phenotypic trait
can be a qualitative trait, e.g., for which a qualitative value can be
provided. The trait can be determined by a single gene, or it can be
determined by two or more genes.
[0122] Typically, the first plant population exhibits variability for the
phenotypic trait of interest (e.g., quantitative variability for a
quantitative phenotypic trait).
[0123] The value of the phenotypic trait in the first plant population is
obtained, e.g., by evaluating the phenotypic trait among the members of
the first plant population (e.g., quantifying a quantitative trait). The
phenotype can be evaluated in the plants (e.g., the inbreds and/or single
cross hybrids) comprising the first plant population. Alternatively, the
value of the phenotypic trait in the first plant population can be
obtained by evaluating the phenotypic trait among the members of the
first plant population in at least one topcross combination with at least
one tester parent, and optionally calculating Best Linear Unbiased
Predictors of the phenotype for the genotype of interest.
[0124] The phenotypic trait can be essentially any qualitative or
quantitative phenotypic trait, e.g., one of agronomic and/or economic
importance. For example, the phenotypic trait can be selected from the
group consisting of: yield, grain moisture content, grain oil content,
root lodging resistance, stalk lodging resistance, plant height, ear
height, disease resistance, insect resistance, drought resistance, grain
protein content, test weight, visual and/or aesthetic appearance, and cob
color. These traits, and techniques for quantifying them, are well known
in the art. For example, grain yield is a traditional measure of crop
performance. Test weight is a measure of quality. Grain moisture content
is important in storage, while root and stalk lodging resistance affect
standability and are important during harvest. The methods are similarly
applicable to other phenotypic traits, for example, grain phytate
content.
[0125] The set of genetic markers can comprise essentially any convenient
genetic markers. For example, the set of genetic markers can comprise one
or more of: a single nucleotide polymorphism (SNP), a multinucleotide
polymorphism, an insertion or a deletion of at least one nucleotide
(indel), a simple sequence repeat (SSR), a restriction fragment length
polymorphism (RFLP), an EST sequence or a unique nucleotide sequence of
20-40 bases used as a probe (oligonucleotides), a random amplified
polymorphic DNA (RAPD) marker, or an arbitrary fragment length
polymorphism (AFLP). As will be evident to one of skill, the number of
markers required can vary, e.g., depending on the rate at which linkage
disequilibrium declines in the plant species of interest and/or on the
type of association analysis performed. The set of genetic markers can
include, for example, from 1 to 50,000 markers (e.g., between 1 and
10,000 markers). In one class of embodiments, the set of genetic markers
comprises between about 50 and about 2500 markers. For example, the set
of genetic markers can comprise at least about 50, 100, 250, 500, 1000,
2000, or even 2500 or more genetic markers. In certain embodiments, the
set of genetic markers comprises between one and ten markers (e.g., for
candidate gene studies, in which relatively few markers are needed). In
other embodiments, the set of genetic markers comprises between 500 and
50,000 markers (e.g., for whole genome scans).
[0126] The genotype of the first plant population for the set of genetic
markers can be determined experimentally, predicted, or a combination
thereof. For example, in one class of embodiments, the genotype of each
inbred present in the first plant population is experimentally determined
and the genotype of each F1 hybrid present in the first plant population
is predicted (e.g., from the experimentally determined genotypes of the
two inbred parents of each single cross hybrid). Plant genotypes can be
experimentally determined by essentially any convenient technique. Many
applicable techniques for discovering and/or genotyping genetic markers
are known in the art (e.g., those described below in the section entitled
"Genetic Markers"). In one preferred class of embodiments, a set of DNA
segments from each inbred is sequenced to experimentally determine the
genotype of each inbred. Since sequence polymorphisms (e.g., genetic
markers) are typically more common in noncoding regions (e.g., introns
and untranslated regions), in one class of embodiments the set of DNA
segments that is sequenced comprises the 5'-untranslated regions and/or
the 3'-untranslated regions of one or more (e.g., two or more) genes. As
noted above, sequencing techniques (e.g., direct sequencing of PCR
amplicons) are well known.
[0127] In some embodiments, a single genetic marker is associated with the
phenotypic trait, while in other embodiments, two or more genetic markers
are associated with the phenotypic trait. Thus, in one class of
embodiments, an association between a haplotype comprising two or more
genetic markers and the phenotypic trait is provided. The genetic markers
comprising a haplotype can be unlinked (e.g., two or more QTL affecting
the phenotypic trait can be identified, each of which is associated with
one of the markers), or the genetic markers can be physically linked
(e.g., the genetic markers can comprise a haplotype block associated with
the phenotypic trait, e.g., a SNP haplotype tagged haplotype block).
[0128] In a preferred class of embodiments, the association between the at
least one genetic marker and the phenotypic trait is evaluated by
performing Bayesian analysis using a linear model, a mixed linear model,
or a nonlinear model. The Bayesian analysis can be implemented, e.g., via
a reversible jump Markov chain Monte Carlo algorithm, a delta method, or
a profile likelihood algorithm. For example, in one such preferred class
of embodiments, the association is evaluated by performing Bayesian
analysis using a linear model, the Bayesian analysis being implemented
via a reversible jump Markov chain Monte Carlo algorithm. Typically, the
Bayesian analysis (e.g., implemented via a reversible jump Markov chain
Monte Carlo algorithm) is implemented via a computer program or system.
[0129] As noted above, Bayesian methods, Monte Carlo algorithms, and the
like are well known in the art. In particular, Bayesian methods for QTL
mapping (i.e., for evaluating association between a set of genetic
markers and a phenotypic trait) are known; see, e.g., Bink et al. and Yi
and Xu, both supra.
[0130] In another preferred class of embodiments, the association is
evaluated by performing a transmission disequilibrium test. In another
class of embodiments, the association is evaluated by a maximum
likelihood mixed linear or nonlinear model analysis. In yet another class
of embodiments, the association is evaluated in the first plant
population via an artificial neural network. As noted, such networks are
known in the art; see, e.g., the references above.
[0131] The first plant population and the one or more non-adapted lines
can comprise essentially any type of plants. For example, in a preferred
class of embodiments, the first plant population and the one or more
non-adapted lines comprise (e.g., consist of) diploid plants. In
preferred embodiments, the first plant population and the one or more
non-adapted lines are selected from the group consisting of: maize (e.g.,
Zea mays), soybean, sorghum, wheat, sunflower, rice, canola, cotton, and
millet.
[0132] A QTL identified by the methods herein (e.g., a QTL allele linked
to the at least one genetic marker associated with the phenotypic trait)
can optionally be cloned and expressed, e.g., to create a transgenic
plant having a desirable value of the phenotypic trait. Thus, in one
class of embodiments, the methods include cloning a gene that is linked
to the at least one genetic marker associated with the phenotypic trait
from the at least one selected plant having the selected genotype and the
desirable value of the phenotypic trait, wherein expression of the gene
affects the phenotypic trait (i.e., cloning the novel QTL allele from the
non-adapted plant). The methods optionally also include constructing a
transgenic plant by expressing the cloned gene in a host plant.
[0133] All of the various optional configurations and features noted for
the embodiments above apply here as well, to the extent they are
relevant.
[0134] Plants
[0135] Plants selected, provided, or produced by any of the methods herein
form another feature of the invention, as do transgenic plants created by
any of the methods herein.
[0136] Genetic Markers
[0137] In the following discussion, the phrase "nucleic acid,"
"polynucleotide," "polynucleotide sequence" or "nucleic acid sequence"
refers to deoxyribonucleotides or ribonucleotides and polymers thereof in
either single- or double-stranded form. Unless specifically stated, the
term encompasses nucleic acids containing known analogs of natural
nucleotides which have similar binding properties as the reference
nucleic acid.
[0138] The ability to characterize an individual by its genome is due to
the inherent variability of genetic information. Typically, genetic
markers are polymorphic regions of a genome and the complementary
oligonucleotides which bind to these regions. Polymorphic sites are often
located in noncoding regions of DNA (e.g., 5' or 3' untranslated regions,
intergenic regions, and the like). Polymorphic sites are also found in
coding regions, where, for example, a nucleotide change can be silent and
not result in amino acid substitution in the encoded protein, result in
conservative amino acid substitution, or result in nonconservative amino
acid substitution. As would be expected, polymorphic sites (particularly
insertions, deletions, and nucleotide changes resulting in
nonconservative substitutions) are relatively uncommon in regions coding
for proteins whose function is essential. Typically, the presence or
absence of a particular genetic marker identifies individuals by their
unique nucleic acid sequence; in other instances, a genetic marker is
found in all individuals but the individual is identified by where, in
the genome, the genetic marker is located.
[0139] The major causes of genetic variability, and thus the major sources
of genetic markers, are insertions (additions), deletions, nucleotide
substitutions (point mutations), recombination events, and transposable
elements within the genome of individuals in a plant population. As one
example, point mutations can result from errors in DNA replication or
damage to the DNA. As another example, insertions and deletions can
result from inaccurate recombination events. As yet another example,
variability can arise from the insertion or excision of a transposable
element (a DNA sequence that has the ability to move or to jump to new
locations with the genome, autonomously or non-autonomously).
[0140] The net result of such heritable changes in DNA sequences is that
individuals have different sequences. Regions comprising polymorphic
sites (sites where DNA sequences are different among individuals or
between the two chromosomes in a given individual) can be used as genetic
markers.
[0141] Genetic markers can be classified by the type of change (e.g.,
insertion or deletion of one or more nucleotides or substitution of one
or more nucleotides) and/or by the way in which the change is detected
(e.g., a RFLP and an AFLP can each result from insertion, deletion, or
substitution).
[0142] Discovery, detection, and genotyping of various genetic markers has
been well described in the literature. See, e.g., Henry, ed. (2001) Plant
Genotyping. The DNA Fingerprinting of Plants Wallingford: CABI
Publishing; Phillips and Vasil, eds. (2001) DNA-based Markers in Plants
Dordrecht: Kluwer Academic Publishers; Pejic et al. (1998) "Comparative
analysis of genetic similarity among maize inbred lines detected by
RFLPs, RAPDs, SSRs and AFLPs" Theor. App. Genet. 97: 1248-1255;
Bhattramakki et al. (2002) "Insertion-deletion polymorphisms in 3'
regions of maize genes occur frequently and can be used as highly
informative genetic markers" Plant Mol. Biol. 48: 539-47; Nickerson et
al. (1997) "PolyPhred: automating the detection and genotyping of single
nucleotide substitutions using fluorescence-based resequencing" Nucleic
Acids Res. 25: 2745-2751; Underhill et al. (1997) "Detection of numerous
Y chromosome biallelic polymorphisms by denaturing high-performance
liquid chromatography" Genome Res. 7: 996-1005; Shi (2001) "Enabling
large-scale pharmacogenetic studies by high-throughput mutation detection
and genotyping technologies" Clin. Chem. 47: 164-172; Kwok (2000)
"High-throughput genotyping assay approaches" Pharmacogenomics 1: 95-100;
Rafalski et al. (2002) "The genetic diversity of components of rye
hybrids" Cell Mol Biol Lett 7: 471-5; Ching and Rafalski (2002) "Rapid
genetic mapping of ests using SNP pyrosequencing and indel analysis" Cell
Mol Biol Lett. 7: 803-10; and Powell et al. (1996) "The comparison of
RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis"
Mol. Breeding 2: 225-238.
[0143] SNPs
[0144] Sites in the DNA sequence where individuals differ at a single DNA
base are called single nucleotide polymorphisms (SNPs). A SNP can result,
e.g., from a point mutation.
[0145] SNPs can be discovered by any of a number of techniques known in
the art. For example, SNPs can be detected by direct sequencing of DNA
segments, e.g., amplified by PCR, from several individuals (see, e.g.,
Ching et al. (2002) "SNP frequency, haplotype structure and linkage
disequilibrium in elite maize inbred lines" BMC Genetics 3: 19). As
another example, SNPs can be discovered by computer analysis of available
sequences (e.g., ESTs, STSS) derived from multiple genotypes (see, e.g.,
Marth et al. (1999) "A general approach to single-nucleotide polymorphism
discovery" Nature Genetics 23: 452-456 and Beutow et al. (1999) "Reliable
identification of large numbers of candidate SNPs from public EST data"
Nature Genetics 21: 323-325). (Indels, insertions or deletions of one or
more nucleotides, can also be discovered by sequencing and/or computer
analysis, e.g., simultaneously with SNP discovery.)
[0146] Similarly, SNPs can be genotyped by sequencing. SNPs can also be
genotyped by various other methods (including high throughput methods)
known in the art, for example, using DNA chips, allele-specific
hybridization, allele-specific PCR, and primer extension techniques. See,
e.g., Lindblad-Toh et al. (2000) "Large-scale discovery and genotyping of
single-nucleotide polymorphisms in the mouse" Nature Genetics 24:
381-386; Bhattramakki and Rafalski (2001) "Discovery and application of
single nucleotide polymorphism markers in plants" in Plant Genotyping:
The DNA Fingerprinting of Plants, CABI Publishing; Syvanen (2001)
"Accessing genetic variation: genotyping single nucleotide polymorphisms"
Nat. Rev. Genet. 2: 930-942; Kuklin et al. (1998) "Detection of
single-nucleotide polymorphisms with the WAVE TM DNA fragment analysis
system" Genetic Testing 1: 201-206; Gut (2001) "Automation in genotyping
single nucleotide polymorphisms" Hum. Mutat. 17: 475-492; Lemieux (2001)
"Plant genotyping based on analysis of single nucleotide polymorphisms
using microarrays" in Plant Genotyping: The DNA Fingerprinting of Plants,
CABI Publishing; Edwards and Mogg (2001) "Plant genotyping by analysis of
single nucleotide polymorphisms" in Plant Genotyping: The DNA
Fingerprinting of Plants, CABI Publishing; Ahmadian et al. (2000)
"Single-nucleotide polymorphism analysis by pyrosequencing" Anal.
Biochem. 280: 103-110; Useche et al. (2001) "High-throughput
identification, database storage and analysis of SNPs in EST sequences"
Genome Inform Ser Workshop Genome Inform 12: 194-203; Pastinen et al.
(2000) "A system for specific, high-throughput genotyping by
allele-specific primer extension on microarrays" Genome Res. 10:
1031-1042; Hacia (1999) "Determination of ancestral alleles for human
single-nucleotide polymorphisms using high-density oligonucleotide
arrays" Nature Genet. 22: 164-167; and Chen et al. (2000)
"Microsphere-based assay for single-nucleotide polymorphism analysis
using single base chain extension" Genome Res. 10: 549-557.
[0147] Multinucleotide polymorphisms can be discovered and detected by
analogous methods.
[0148] RFLPs
[0149] As noted above, different individuals have different genomic DNA
sequences. Thus, when these DNA sequences are digested with one or more
restriction endonucleases that recognize specific restriction sites, some
of the resulting fragments are of different lengths. The resulting
fragments are restriction fragment length polymorphisms.
[0150] The phrase restriction fragment length polymorphisms or RFLPs
refers to inherited differences in restriction enzyme sites (for example,
caused by base changes in the target site) or additions or deletions in
regions flanked by the restriction enzyme sites that result in
differences in the lengths of the fragments produced by cleavage with a
relevant restriction enzyme. A point mutation leads to either longer
fragments if the mutation is within the restriction site or shorter
fragments if the mutation creates a restriction site. Insertions and
transposable element integration lead to longer fragments, and deletions
lead to shorter fragments.
[0151] Originally, RFLP analysis was performed by Southern blot and
hybridization. RFLP analysis is currently more typically performed by
PCR. A pair of oligonucleotide primers linking the region comprising the
RFLP is used to amplify a fragment from genomic DNA. The size of the PCR
products can be analyzed directly, and if the fragment contains a
polymorphic restriction site, the PCR products can be digested with the
enzyme and the size of the digested products can be analyzed.
[0152] Techniques for discovery and genotyping of RFLPs have been well
described in the literature. See, for example, Gauthier et al. (2002)
"RFLP diversity and relationships among traditional European maize
populations" Theor. Appl. Genet. 105: 91-99; Ramalingam et al. (2003)
"Candidate defense genes from rice, barley, and maize and their
association with qualitative and quantitative resistance in rice" Mol
Plant Microbe Interact 16: 14-24; Guo et al. (2002) "Restriction fragment
length polymorphism assessment of the heterogeneous nature of maize
population GT-MAS:gk and field evaluation of resistance to aflatoxin
production by Aspergillus flavus" J Food Prot 65: 167-71; Pejic et al.
(1998) "Comparative analysis of genetic similarity among maize inbred
lines detected by RFLPs, RAPDs, SSRs and AFLPs" Theor. App. Genet. 97:
1248-1255; and Powell et al. (1996) "The comparison of RFLP, RAPD, AFLP
and SSR (microsatellite) markers for germplasm analysis" Mol. Breeding 2:
225-238.
[0153] RAPDs
[0154] To identify a Random Amplified Polymorphic DNA (RAPD) marker, an
oligonucleotide (e.g., an octanucleotide, a decanucleotide) is randomly
chosen. The complexity of plant genomic DNA is high enough that a pair of
sites complementary to the oligonucleotide may by chance exist in the
correct orientation and close enough together to permit PCR amplification
of a fragment bounded by the pair of sites. With some randomly chosen
oligonucleotides, no sequences are amplified. With other
oligonucleotides, products of the same length are generated from genomic
DNA of different individuals. With yet other oligonucleotides, however,
product lengths are not the same for every individual in a population,
providing a useful RAPD marker. RAPD markers have been described in,
e.g., Pejic et al. (1998) "Comparative analysis of genetic similarity
among maize inbred lines detected by RFLPs, RAPDs, SSRs and AFLPs" Theor.
App. Genet. 97: 1248-1255; and Powell et al. (1996) "The comparison of
RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis"
Mol. Breeding 2: 225-238.
[0155] AFLPs
[0156] Arbitrary fragment length polymorphisms (AFLPs) can also be used as
genetic markers (Vos, P., et al., Nucl. Acids Res. 23: 4407 (1995)). The
phrase "arbitrary fragment length polymorphism" refers to selected
restriction fragments which are amplified before or after cleavage by a
restriction endonuclease. The amplification step allows easier detection
of specific restriction fragments rather than determining the size of all
restriction fragments and comparing the sizes to a known control.
[0157] AFLP allows the detection of a large number of polymorphic markers
(see, supra) and has been used for genetic mapping of plants (Becker et
al. (1995) Mol. Gen. Genet. 249: 65; and Meksem et al. (1995) Mol. Gen.
Genet. 249: 74) and to distinguish among closely related bacteria species
(Huys et al. (1996) Int'l J. Systematic Bacteriol. 46: 572).
[0158] SSRs
[0159] Simple sequence repeats (SSRs) are short tandem repeats (e.g., di-,
tri- or tetra-nucleotide tandem repeats). SSRs can occur at high levels
within a genome. For example, dinucleotide repeats have been reported to
occur in the human genome as many as 50,000 times, with n (the number of
times the dinucleotide sequence is tandemly repeated within a given SSR
region) varying from 10 to 60 (Jacob et al. (1991) Cell 67: 213). SSRs
have also been found in higher plants; see, e.g., Taramino and Tingey
(1996) "Simple sequence repeats for germplasm analysis and mapping in
maize" Genome 39: 277-287; Condit and Hubbell (1991) Genome 34: 66;
Peakall et al. (1998) "Cross-species amplification of soybean (Glycine
max) simple sequence repeats (SSRs) within the genus and other legume
genera: implications for the transferability of SSRs in plants" Mol Biol
Evol 15: 1275-87; Morgante et al. (1994) "Genetic mapping and variability
of seven soybean simple sequence repeat loci" Genome 37: 763-9; and
Zietkiewicz et al. (1994) "Genome fingerprinting by simple sequence
repeat (SSR)-anchored polymerase chain reaction amplification" Genomics
20: 176-83.
[0160] Briefly, SSR data can be generated, e.g., by hybridizing primers to
conserved regions of the plant genome which flank an SSR region. PCR is
then used to amplify the nucleotide repeats between the primers. The
amplified sequences are then electrophoresed to determine the size of the
amplified fragment and therefore the number of di-, tri- and
tetra-nucleotide repeats.
[0161] Other Markers
[0162] Other genetic markers and methods of detecting sequence
polymorphisms are known in the art and can be applied to the practice of
the present invention, including, but not limited to, single-stranded
conformation polymorphisms (SSCPs), amplified variable sequences, isozyme
markers, allele-specific hybridization, and self-sustained sequence
replication. See, e.g., Orita et al. (1989) "Detection of polymorphisms
of human DNA by gel electrophoresis as single-strand conformation
polymorphisms" Proc. Natl. Acad. Sci. USA 86: 2766-2770; U.S. Pat. No.
6,399,855 to Beavis, entitled "QTL mapping in plant breeding
populations"; and the references above. Candidate genes identified in
other studies, e.g., gene function studies, studies of biochemical
pathways affecting the phenotypes of interest, physiology of the traits
of interest, and the like, can also be used as markers in the first
population and the target population.
[0163] Haplotype Blocks
[0164] Sets of nearby genetic markers on a given chromosome can be
inherited in blocks. In some situations, the haplotype of such a block
(e.g., a haplotype tag, e.g., comprising the haplotype of a few SNPs
representative of a greater number of polymorphisms in a block) may be
more informative than the haplotype of a single genetic marker within the
block (e.g., a single SNP). See, e.g., the description of haplotype tags
in Rafalski (2002) "Applications of single nucleotide polymorphisms in
crop genetics" Curr. Opin. Plant Bio. 5: 94-100 and Johnson et (2001)
"Haplotype tagging for the identification of common disease genes" Nat.
Genet. 29: 233-237.
[0165] Molecular Biological Techniques
[0166] In practicing the present invention, many conventional techniques
in molecular biology and recombinant DNA technology are optionally used.
These techniques are well known and are explained in, for example, Berger
and Kimmel, Guide to Molecular Cloning Techniques, Methods in Enzymology
volume 152 Academic Press, Inc., San Diego, Calif. ("Berger"); Sambrook
et al., Molecular Cloning--A Laboratory Manual (3rd Ed.), Vol. 1-3, Cold
Spring Harbor Laboratory, Cold Spring Harbor, N.Y., 2000 ("Sambrook") and
Current Protocols in Molecular Biology, F. M. Ausubel et al., eds.,
Current Protocols, a joint venture between Greene Publishing Associates,
Inc. and John Wiley & Sons, Inc., (supplemented through 2004)
("Ausubel")). Other useful references for cell isolation and culture
(e.g., for subsequent nucleic acid isolation) include, e.g., Freshney
(1994) Culture of Animal Cells, a Manual of Basic Technique, third
edition, Wiley-Liss, New York and the references cited therein; Payne et
al. (1992) Plant Cell and Tissue Culture in Liquid Systems John Wiley &
Sons, Inc. New York, N.Y.; Gamborg and Phillips (Eds.) (1995) Plant Cell,
Tissue and Organ Culture; Fundamental Methods Springer Lab Manual,
Springer-Verlag (Berlin Heidelberg N.Y.) and Atlas and Parks (Eds.) The
Handbook of Microbiological Media (1993) CRC Press, Boca Raton, Fla.
[0167] Oligonucleotides (e.g., for use as PCR primers, for use in genetic
marker detection methods, or the like) can be obtained by a number of
well known techniques. For example, oligonucleotides can be synthesized
chemically according to the solid phase phosphoramidite triester method
described by Beaucage and Caruthers (1981), Tetrahedron Letts., 22(20):
1859-1862, e.g., using a commercially available automated synthesizer,
e.g., as described in Needham-VanDevanter et al. (1984) Nucleic Acids
Res., 12: 6159-6168. Oligonucleotides (including, e.g., labeled or
modified oligos) can also be ordered from a variety of commercial sources
known to persons of skill. There are many commercial providers of oligo
synthesis services, and thus, this is a broadly accessible technology.
Any nucleic acid can be custom ordered from any of a variety of
commercial sources, such as The Midland Certified Reagent Company
(www.mcrc.com), The Great American Gene Company (www.genco.com),
ExpressGen Inc. (www.expressgen.com), QIAGEN (http://oligos.qiagen.com)
and many others.
[0168] Positional Cloning
[0169] Positional gene cloning uses the proximity of at least one genetic
marker to physically define a cloned chromosomal fragment that is linked
to a QTL identified using the statistical methods herein. Clones of such
linked nucleic acids have a variety of uses, including as genetic markers
for identification of linked QTLs in subsequent marker assisted selection
protocols, and to improve desired properties in recombinant plants where
expression of the cloned sequences in a transgenic plant affects the
phenotypic trait of interest. Common linked sequences which are desirably
cloned include open reading frames, e.g., encoding proteins which provide
a molecular basis for an observed QTL. If one or more markers are
proximal to an open reading frame, they may hybridize to a given DNA
clone, thereby identifying a clone on which the open reading frame is
located. If flanking markers are more distant, a fragment containing the
open reading frame may be identified by constructing a contig of
overlapping clones.
[0170] In certain applications, it is advantageous to make or clone large
nucleic acids to identify nucleic acids more distantly linked to a given
marker, or isolate nucleic acids linked to or responsible for QTLs as
identified herein. It will be appreciated that a nucleic acid genetically
linked to a polymorphic nucleotide optionally resides up to about 50
centimorgans from the polymorphic nucleic acid, although the precise
distance will vary depending on the cross-over frequency of the
particular chromosomal region. Typical distances from a polymorphic
nucleotide are in the range of 1-50 centimorgans, for example, often less
than 1 centimorgan, less than about 1-5 centimorgans, about 1-5, 1, 5,
10, 15, 20, 25, 30, 35, 40, 45 or 50 centimorgans, etc.
[0171] Many methods of making large recombinant RNA and DNA nucleic acids,
including recombinant plasmids, recombinant lambda phage, cosmids, yeast
artificial chromosomes (YACs), P1 artificial chromosomes, bacterial
artificial chromosomes (BACs), and the like are known. A general
introduction to YACs, BACs, PACs and MACs as artificial chromosomes is
described in Monaco & Larin (1994) Trends Biotechnol. 12: 280-286.
Examples of appropriate cloning techniques for making large nucleic
acids, and instructions sufficient to direct persons of skill through
many cloning exercises are also found in Berger, Sambrook, and Ausubel,
all supra.
[0172] In one aspect; nucleic acids hybridizing to the genetic markers
linked to QTLs identified by the above methods are cloned into large
nucleic acids such as YACs, or are detected in YAC genomic libraries
cloned from the crop of choice. The construction of YACs and YAC
libraries is known. See, e.g., Berger (supra), Ausubel (supra), Burke et
al. (1987) Science 236: 806-812, Anand et al. (1989) Nucleic Acids Res.
17: 3425-3433, Anand et al. (1990) Nucleic Acids Res. 18: 1951-1956, and
Riley (1990) Nucleic Acids Res. 18: 2887-2890. YAC libraries containing
large fragments of soybean DNA have been constructed (see Funke &
Kolchinsky (1994) CRC Press, Boca Raton, Fla. pp. 125-308; Marek &
Shoemaker (1996) Soybean Genet. Newsl. 23: 126-129; Danish et al. (1997)
Soybean Genet. Newsl. 24: 196-198). YAC libraries for many other
commercially important crops are available or can be constructed using
known techniques.
[0173] Similarly, cosmids or other molecular vectors such as BAC and P1
constructs are also useful for isolating or cloning nucleic acids linked
to genetic markers. Cosmid cloning is also known. See, e.g., Ausubel;
Ish-Horowitz & Burke (1981) Nucleic Acids Res. 9: 2989-2998; Murray
(1983) LAMBDA II (Hendrix et al., eds.) pp. 395432, Cold Spring Harbor
Laboratory, N.Y.; Frischauf et al. (1983) J. Mol. Biol. 170: 827-842; and
Dunn & Blattner (1987) Nucleic Acids Res. 15: 2677-2698, and the
references cited therein. Construction of BAC and P1 libraries is known;
see, e.g., Ashworth et al. (1995) Anal. Biochem. 224: 564-571; Wang et
al. (1994) Genomics 24(3): 527-534; Kim et al. (1994) Genomics 22: 336-9;
Rouquier et al. (1994) Anal. Biochem. 217: 205-9; Shizuya et al. (1992)
Proc. Natl Acad. Sci. USA 89: 8794-7; Kim et al. (1994) Genomics 22:
336-9; Woo et al. (1994) Nucleic Acids Res. 22(23): 4922-31; Wang et al.
(1995) Plant 3: 525-33; Cai (1995) Genomics 29(2): 413-25; Schmitt et al.
(1996) Genomics 33: 9-20; Kim et al. (1996) Genomics 34(2): 213-8; Kim et
al. (1996) Proc. Natl. Acad. Sci. USA 13: 6297-301; Pusch et al., (1996)
Gene 183(1-2): 29-33; and Wang et al. (1996) Genome Res. 6(7): 612-9.
Improved methods of in vitro amplification to amplify large nucleic acids
linked to the polymorphic nucleic acids herein are summarized in Cheng et
al. (1994) Nature 369: 684-685 and the references therein.
[0174] In addition, any of the cloning or amplification strategies
described herein are useful for creating contigs of overlapping clones,
thereby providing overlapping nucleic acids which show the physical
relationship at the molecular level for genetically linked nucleic acids.
A common example of this strategy is found in whole organism sequencing
projects, in which overlapping clones are sequenced to provide the entire
sequence of a chromosome. In this procedure, a library of the organism's
cDNA or genomic DNA is made according to standard procedures described,
e.g., in the references above. Individual clones are isolated and
sequenced, and overlapping sequence information is ordered to provide the
sequence of the organism. See also, Tomb et al. (1997) Nature 388:
539-547 describing the whole genome random sequencing and assembly of the
complete genomic sequence of Helicobacter pylori; Fleischmann et al.
(1995) Science 269: 496-512 describing whole genome random sequencing and
assembly of the complete Haemophilus influenzae genome; Fraser et al.
(1995) Science 270: 397-403 describing whole genome random sequencing and
assembly of the complete Mycoplasma genitalium genome; and Bult et al.
(1996) Science 273: 1058-1073 describing whole genome random sequencing
and assembly of the complete Methanococcus jannaschii genome. Hagiwara
and Curtis, Nucleic Acids Res. 24: 2460-2461 (1996) developed a "long
distance sequencer" PCR protocol for generating overlapping nucleic acids
from very large clones to facilitate sequencing, and methods of
amplifying and tagging the overlapping nucleic acids into suitable
sequencing templates. The methods can be used in conjunction with shotgun
sequencing techniques to improve the efficiency of shotgun methods
typically used in whole organism sequencing projects. As applied to the
present invention, the techniques are useful for identifying and
sequencing genomic nucleic acids genetically linked to the QTLs as well
as "candidate" genes responsible for QTL expression as identified by the
methods herein. As noted above, the allelic sequences that comprise a QTL
can be cloned and inserted into a transgenic plant. Methods of creating
transgenic plants are well known in the art and are described in brief
below.
[0175] Transgenic Plants
[0176] Nucleic acids derived from those linked to a genetic marker and/or
QTL identified by the statistical methods herein can be introduced into
plant cells, either in culture or in organs of a plant, e.g., leaves,
stems, fruit, seed, etc. The expression of natural or synthetic nucleic
acids can be achieved by operably linking a nucleic acid of interest to a
promoter, incorporating the construct into an expression vector, and
introducing the vector into a suitable host cell.
[0177] Typical vectors (e.g., plasmids) contain transcription and
translation terminators, transcription and translation initiation
sequences, and/or promoters useful for regulation of the expression of
the particular nucleic acid. The vectors optionally comprise generic
expression cassettes containing promoter, gene, and terminator sequences,
sequences permitting replication of the cassette in eukaryotes, or
prokaryotes, or both, (e.g., shuttle vectors) and selection markers for
both prokaryotic and eukaryotic systems. Vectors are suitable for
replication and integration in prokaryotes, eukaryotes, or preferably
both. See, e.g., Berger; Sambrook; and Ausubel.
[0178] Cloning of QTL Allelic Sequences into Bacterial Hosts
[0179] Bacterial cells can be used to increase the number of plasmids
containing the DNA constructs of this invention. The plasmids can be
introduced into bacterial host cells by any of a number of methods known
in the art (e.g., electroporation or calcium chloride). The bacteria are
grown, and the plasmids within the bacteria are isolated by a variety of
methods known in the art (see, for instance, Sambrook). In addition, a
plethora of kits are commercially available for the purification of
plasmids from bacteria (for example, StrataClean.TM. from Stratagene or
QIAprep.TM. from Qiagen). The isolated and purified plasmids can then be
further manipulated to produce other plasmids, used to transfect plant
cells, or incorporated into Agrobacterium tumefaciens to infect plants.
[0180] Alternatively, a cloned plant nucleic acid can be expressed in
bacteria such as E. coli and the resulting protein can be isolated and
purified.
[0181] Transfecting Plant Cells
[0182] Preparation of Recombinant Vectors
[0183] To use isolated sequences in the above techniques, recombinant DNA
vectors suitable for transformation of plant cells are prepared.
Techniques for transforming a wide variety of higher plant species are
well known and described in the technical and scientific literature. See,
for example, Weising et al. (1988) Ann. Rev. Genet. 22: 421-477. A DNA
sequence coding for a desired polypeptide (for example, a cDNA sequence
encoding a full length protein) will preferably be combined with
transcriptional and translational initiation regulatory sequences which
will direct the transcription of the sequence from the gene.
[0184] Promoters can be identified by analyzing the 5' sequences upstream
of the coding sequence of an allele associated with a QTL. Sequences
characteristic of promoter sequences can be used to identify the
promoter. Sequences controlling eukaryotic gene expression have been
extensively studied. For instance, promoter sequence elements include the
TATA box consensus sequence (TATAAT), which is usually 20 to 30 base
pairs upstream of the transcription start site. In most instances the
TATA box is required for accurate transcription initiation. In plants,
further upstream from the TATA box, at positions -80 to -100, there is
typically a promoter element with a series of adenines surrounding the
trinucleotide G (or T) N G. See, e.g., J. Messing et al. (1983) in
Genetic Engineering in Plants, pp. 221-227 (Kosage, Meredith and
Hollaender, eds.). A number of methods are known to those of skill in the
art for identifying and characterizing promoter regions in plant genomic
DNA (see, e.g., Jordano et al. (1989) Plant Cell 1: 855-866; Bustos et
al. (1989) Plant Cell 1: 839-854; Green et al. (1988) EMBO J. 7:
4035-4044; Meier et al. (1991) Plant Cell 3: 309-316; and Zhang et al.
(1996) Plant Physiology 110: 1069-1079).
[0185] In construction of recombinant expression cassettes of the
invention, a plant promoter fragment may be employed which will direct
expression of the gene in all tissues of a regenerated plant. Such
promoters are referred to herein as "constitutive" promoters and are
active under most environmental conditions and states of development or
cell differentiation. Examples of constitutive promoters include the
cauliflower mosaic virus (CaMV) 35 S transcription initiation region, the
ubiquitin promoter, the 1'- or 2'-promoter derived from T-DNA of
Agrobacterium tumefaciens, and other transcription initiation regions
from various plant genes known to those of skill.
[0186] Alternatively, the plant promoter may direct expression of the
polynucleotide of the invention in a specific tissue (tissue-specific
promoters) or may be otherwise under more precise environmental control
(inducible promoters). Examples of tissue-specific promoters under
developmental control include promoters that initiate transcription only
in certain tissues, such as fruit, seeds, or flowers. For example, the
tissue specific E8 promoter from tomato is useful for directing gene
expression so that a desired gene product is located in fruits. Other
suitable promoters include those from genes encoding embryonic storage
proteins. Examples of environmental conditions that may affect
transcription by inducible promoters include anaerobic conditions,
elevated temperature, or the presence of light.
[0187] If proper polypeptide expression is desired, a polyadenylation
region at the 3'-end of the coding region should be included. The
polyadenylation region can be derived from the natural gene, from a
variety of other plant genes, or from T-DNA.
[0188] The vector comprising the sequences (e.g., promoters or coding
regions) from QTL alleles of the invention will typically comprise a
marker gene which confers a selectable phenotype on plant cells. For
example, the marker may encode biocide resistance, particularly
antibiotic resistance, such as resistance to kanamycin, G418, bleomycin,
hygromycin, or herbicide resistance, such as resistance to chlorosluforon
or glufosinate.
[0189] Introduction of the Nucleic Acids into Plant Cells
[0190] The DNA constructs of the invention can be introduced into plant
cells, either in culture or in the organs of a plant, by a variety of
conventional techniques. For example, the DNA construct can be introduced
directly into the plant cell using techniques such as electroporation and
microinjection of plant cell protoplasts, or the DNA constructs can be
introduced directly to plant cells using ballistic methods, such as DNA
particle bombardment. Alternatively, the DNA constructs are combined with
suitable T-DNA flanking regions and introduced into a conventional
Agrobacterium tumefaciens host vector. The virulence functions of the
Agrobacterium tumefaciens host directs the insertion of the construct and
adjacent marker into the plant cell DNA when the cell is infected by the
bacteria.
[0191] Microinjection techniques are known in the art and well described
in the scientific and patent literature. The introduction of DNA
constructs using polyethylene glycol precipitation is described in
Paszkowski et al. (1984) EMBO J. 3: 2717. Electroporation techniques are
described in Fromm et al. (1985) Proc. Nat'l Acad. Sci. USA 82: 5824.
Ballistic transformation techniques are described in Klein et al. (1987)
Nature 327: 70-73. Agrobacterium tumefaciens-mediated transformation
techniques, including disarming and use of binary vectors, are also well
described in the scientific literature. See, for example Horsch et al.
(1984) Science 233: 496-498 and Fraley et al. (1983) Proc. Nat'l Acad.
Sci. USA 80: 4803.
[0192] Generation of Transgenic Plants
[0193] Transformed plant cells (e.g., those derived by any of the above
transformation techniques) can be cultured to regenerate a whole plant
which possesses the transformed genotype and thus the desired phenotype.
Such regeneration techniques rely on manipulation of certain
phytohormones in a tissue culture growth medium, typically relying on a
biocide and/or herbicide marker which has been introduced together with
the desired nucleotide sequences. Plant regeneration from cultured
protoplasts is described in Evans et al. (1983) "Protoplasts Isolation
and Culture" in the Handbook of Plant Cell Culture, pp. 124-176,
Macmillian Publishing Company, N.Y.; and Binding (1985) Regeneration of
Plants, Plant Protoplasts, pp. 21-73, CRC Press, Boca Raton. Regeneration
can also be obtained from plant callus, explants, somatic embryos (e.g.,
Dandekar et al. (1989) J. Tissue Cult. Meth. 12: 145 and McGranahan et
al. (1990) Plant Cell Rep. 8: 512), organs, or parts thereof. Such
regeneration techniques are described generally in Klee et al. (1987)
Ann. Rev. of Plant Phys. 38: 467-486.
[0194] One of skill will recognize that after the expression cassette is
stably incorporated in transgenic plants and confirmed to be operable, it
can be introduced into other plants by sexual crossing. Any of a number
of standard breeding techniques can be used, depending upon the species
to be crossed.
EXAMPLES
[0195] The following sets forth a series of experiments that demonstrate
determination and use of an association between cob color and a genetic
marker haplotype in maize. It is understood that the examples and
embodiments described herein are for illustrative purposes only and that
various modifications or changes in light thereof will be suggested to
persons skilled in the art and are to be included within the spirit and
purview of this application and scope of the appended claims.
Accordingly, the following examples are offered to illustrate, but not to
limit, the claimed invention.
[0196] Cob color (e.g., red or white) in maize is determined in part by
the pericarp color 1 (p1) gene. See, e.g., Neuffer, Coe, and Wessler
(1997) Mutants of Maize, Cold Spring Harbor Laboratory Press, p 107 for a
description of p1-wr, p 363 for a description of the gene and its mode of
action, and p 35 for its map location. The following example describes
determination of an association between cob color and a genetic marker
sequence that is linked to p1.
[0197] Linkage Map
[0198] To generate genetic marker information, a large number of loci
selected from an EST database were sequenced across a set of inbreds
chosen from a multigeneration pedigree (Pioneer's established maize
breeding population). These markers were used to generate a multipoint
linkage map basically as follows.
[0199] The set of genetic markers included 5741 haplotypes (haplotype
blocks) generated by sequencing approximately 450 base pairs from each of
5741 EST sequences from each of the inbreds. For example, marker MZA6914
haplotype was genotyped by sequencing a nested PCR product amplified
using the following primers: outer primers taggtgctttgcggaccttg (SEQ ID
NO:1) and tctgaacagcaaatcgttgttg (SEQ ID NO:2), and inner primers
aggaaacagctatgaccat (SEQ ID NO:3) and gttttcccagtcacgacg (SEQ ID NO:4).
The set of genetic markers also included 505 SSR markers that had been
genotyped in B73/Mol7 and mapped on the public IBM2 map.
[0200] The set of inbreds chosen from the established breeding population
included 320 triplets, each containing two inbred lines and a third
inbred line derived from a cross between those two lines, corresponding
to about 600 inbreds total. Using pedigree information and triplets
containing inbred parents having different marker alleles, a multipoint
linkage map containing the 6246 markers (5741 haplotypes and 505 SSRs)
was developed by assigning the markers to chromosomes and ordering the
markers on the chromosomes. (It will be evident that not every triplet is
informative for every marker, e.g., if the parents have the same marker
allele). The linkage map used the public IBM2 map (http://www.maizegdb.or-
g) as the backbone. Overgo probes were designed for most of the 5741
sequenced loci and hybridized to a physical map, helping link the
physical and genetic maps and permitting markers that were too close to
genetically map to be ordered.
[0201] Likelihood Ratio TDT Test
[0202] Phenotypic data (red or white cob color) for the inbred lines used
to generate the linkage map had been collected as part of Pioneer's
ongoing breeding program. Association analysis was performed using the
third inbred from triplets in which the two parental inbred lines had
different phenotypes for cob color (i.e., one red parent and one white
parent); the third inbreds from these triplets, chosen from the
established breeding population, comprise the first plant population. The
set of genetic markers included 511 markers on chromosome 1 (488
haplotypes and 23 SSRs) whose genotypes had been determined by sequencing
as noted above. (The analysis was limited to the first chromosome since
the p1 locus is on chromosome 1.) Again, it will be evident that not
every triplet is informative for every marker; only triplets in which the
inbred parents have different marker haplotypes are informative. The
genetic marker and phenotypic information, along with pedigree
relationships between the inbreds in the first plant population, were
used in a TDT analysis (see, e.g., Gutin et al. (2001) "Allelic
association in large pedigrees" Genet Epidemiol. 21 Suppl 1: S571-575 and
Spielman et al. (1993) "Transmission test for linkage disequilibrium: The
insulin gene region and insulin-dependent diabetes mellitus (IDDM)"
American Journal of Human Genetics 52: 506-516).
[0203] A TDT-based association test using haplotype data in which each
haplotype can have more than two alleles can be computed from a TDT test
for multiple alleles (originally proposed by Spielman and Ewens (1996)
"The TDT and other family-based tests for linkage disequilibrium and
association" American Journal of Human Genetics 59: 983-989) converted
into a likelihood ratio test, which will be referred to as a Likelihood
Ratio TDT Test (LR-TDT). We first briefly describe the test for bi-allele
marker data and then extend the method to the analysis of multiple allele
data.
[0204] For bi-allele data, we define the conditional probabilities of
transmitting allele M.sub.1 and not transmitting allele M.sub.2 given
parental genotype M.sub.1M.sub.2 to be t.sub.12=P(M.sub.1,M.sub.2.vertlin-
e.g=M.sub.1M.sub.2) and of transmitting allele M.sub.2 but not M.sub.1 be
t.sub.21=P(M.sub.2,M.sub.1.vertline.g=M.sub.1M.sub.2). The maximum
likelihood estimates of t.sub.12 and t.sub.21 are n.sub.12/(n.sub.12+n.su-
b.21) and n.sub.21/(n.sub.12+n.sub.21), respectively. There are n
individuals with informative parents for the marker of interest; n.sub.12
of these inherited the first marker allele and the second trait
phenotype, and n.sub.21 of these inherited the second marker allele and
the first trait phenotype. The log-likelihood function of transmitting a
marker allele from heterozygous parents to affected offspring is then 1
ln L 1 = n 12 ln ( t 12 ) + n 21 ln ( t 21
) = n 12 ln n 12 n 12 + n 21 + n 21 ln
n 21 n 12 + n 21 .
[0205] The corresponding log-likelihood function at the null hypothesis is
2 ln L 0 = ( n 12 + n 21 ) ln 1 2 .
[0206] The likelihood ratio test statistic is
LRT=2(ln L.sub.1-ln L.sub.0);
[0207] it has a chi-square distribution with df=1 (df represents degrees
of freedom).
[0208] To extend the above formula to multiple allele marker data, we
assume k alleles for each marker locus (each marker haplotype in this
example). We designate one allele, M.sub.v, as the M.sub.1 allele. All
other alleles are treated together as allele M.sub.2, and their allele
counts are pooled so the multiple allele data is converted into k
bi-allele data sets. The log likelihood ratio test statistic for k
alleles (LRT.sub.k) is thus the sum of k independent log likelihood ratio
tests (LRT.sub.v): 3 LRT k = k - 1 k v = 1 k LRT k =
k - 1 k v = 1 k 2 ( ln L v1 - ln L v0
) .
[0209] The above multiple allele log likelihood ratio test statistic has
an asymptotic chi-square distribution with degree of freedom df=k-1.
[0210] FIG. 4 plots the TDT likelihood ratio statistic for cob color for
the 511 markers ordered by chromosome position. The horizontal dashed
line on the likelihood profile (FIG. 4) is the threshold or significant
LRT.sub.k value after Bonferroni adjustment for multiple loci testing
.alpha..sub.b=.alpha./m, where m is the number of markers on the
chromosome and .alpha.=0.01. The arrow indicates the position of the p1
locus. Map positions are given with respect to the multipoint linkage map
described above.
[0211] Table 1 presents additional details about the LR-TDT test. For each
of several genetic marker haplotypes (indicated by an MZA number), the
table indicates the sample size (number of third inbreds in the first
plant population, corresponding to the number of triplets informative for
the particular marker), degrees of freedom (df, equal to the number of
marker haplotypes minus one), chi-square value for the TDT test, the
probability associated with that chi-square value, linkage group
(corresponding to the public maize genetic map), and map position in
centimorgans (cm, with respect to the multipoint linkage map described
above). Note that genetic marker haplotypes with a frequency of less than
5% were not included in the analysis. For MZA6914, for example, three
haplotypes each had a frequency less than 5% and were not considered
while three haplotypes each had a frequency greater than 5% and were
considered.
1TABLE 1
LR-TDT results for cob color.
trait
marker sample size df Z_Chi_sq Pval_Z_CHIsq linkage group position
RED MZA6914 100 3 49.08 0 1.03 385.69
RED MZA1241 230 4
14.74 4.38E-07 1.03 389.00
RED MZA9011 246 7 22.68 9.51E-07 1.03
391.98
RED MZA7069 250 7 18.29 3.13E-09 1.03 394.18
RED
MZA3729 282 7 23.72 9.14E-10 1.03 396.25
[0212] As indicated in FIG. 4 and Table 1, a highly significant
association is observed between marker MZA6914 and cob color. MZA6914 is
not the p1 gene but is a sequence tightly linked to p1, based on
information from the physical map.
[0213] Applications
[0214] From the association between MZA6914 and cob color determined in
the first population of inbreds as described above, cob color can be
predicted in other plants based on their MZA6914 genotype, and this
information can be applied to selection and breeding for desired
phenotypes. For example, plants having the desired MZA6914 genotype
(e.g., a MZA6914 haplotype associated with white cobs) can be identified
before pollination and used as parents in white corn product development
programs, e.g., where their offspring (comprising the target plant
population) are predicted to have white cobs. White cob color is desired,
for example, in hybrids having white kernels, since red glumes are
difficult to remove and can add undesirable color to corn chips,
tortillas, etc. produced from the kernels. Selection for plants before
pollination can result in significant labor savings in the development
process. Prediction of an offspring's cob color phenotype prior to
pollination of the plants can thus increase the efficiency of developing
inbred lines and/or hybrids having white cobs and white kernels.
[0215] The association can, if desired, be verified in segregating crosses
prior to use in selecting parents and predicting offspring phenotypes in
a breeding program.
[0216] The example of association analysis and phenotypic trait prediction
described above uses cob color, but this type of analysis and prediction
is equally applicable to any qualitative trait or any simple trait
conditioned by a single gene. For example, single genes condition
resistance to a number of plant diseases, and the strategy outlined in
this example can be used to predict, breed and/or select for offspring
resistant to such diseases. A number of other examples of simple traits
are provided in Mutants of Maize (supra).
[0217] Also as noted herein, related strategies can be applied to
determining associations and predicting phenotypes for traits that have a
continuous phenotypic distribution and that may be controlled by multiple
loci, by using statistical analysis designed to identify genetic regions
associated with continuous traits.
[0218] While the foregoing invention has been described in some detail for
purposes of clarity and understanding, it will be clear to one skilled in
the art from a reading of this disclosure that various changes in form
and detail can be made without departing from the true scope of the
invention. For example, all the techniques and compositions described
above can be used in various combinations. All publications, patents,
patent applications, and/or other documents cited in this application are
incorporated by reference in their entirety for all purposes to the same
extent as if each individual publication, patent, patent application,
and/or other document were individually indicated to be incorporated by
reference for all purposes.
Sequence CWU
1
1
4 1 20 DNA Artificial oligonucleotide primer 1 taggtgcttt gcggaccttg
20 2 22 DNA Artificial
oligonucleotide primer 2 tctgaacagc aaatcgttgt tg
22 3 19 DNA Artificial oligonucleotide primer 3
aggaaacagc tatgaccat 19
4 18 DNA Artificial oligonucleotide primer 4 gttttcccag tcacgacg
18
* * * * *
