Source: UNIVERSITY OF CALIFORNIA, RIVERSIDE submitted to
BAYESIAN ANALYSIS OF QUANTITATIVE TRAITS UNDER THE PLANT MODEL
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
TERMINATED
Funding Source
NRI COMPETITIVE GRANT
Reporting Frequency
Annual
Accession No.
0186016
Grant No.
00-35300-9245
Project No.
CA-R*-BPS-6760-CG
Proposal No.
2000-01716
Multistate No.
(N/A)
Program Code
(N/A)
Project Start Date
Sep 15, 2000
Project End Date
Sep 30, 2003
Grant Year
2000
Project Director
Xu, S.
Recipient Organization
UNIVERSITY OF CALIFORNIA, RIVERSIDE
(N/A)
RIVERSIDE,CA 92521
Performing Department
BOTANY AND PLANT SCIENCES
Non Technical Summary
The long-term goal of this project is to develop statistical models for studying the genetic architecture of quantitative traits in plants using molecular markers. The statistical technique is called the plant model. Specifically, we will accomplish the following objectives: (1) establish the infrastructure for the plant model quantitative genetic analysis and QTL (quantitative trait loci) mapping using a Bayesian statistic, (2) develop a general algorithm for QTL analysis in arbitrarily complex pedigrees under arbitrary mating designs, (3) extend the general algorithm to QTL mapping with epistatic effects and pleiotropic effects for multiple traits, and (4) develop an algorithm for QTL mapping in polyploid plants. Most QTL mapping techniques require designed experiments of line crosses. These crosses do not exist in natural populations and are rarely used in plant breeding. The proposed plant model has shed new light on quantitative genetics because it can handle models with almost arbitrary complexity. Result of this research will greatly increasing the power of genetic studies and thus enhance our understanding of the genetic architecture of quantitative traits. The proposed research is relevant to the area specified by the USDA Plant Genome Program : development of new technologies for genome mapping and the result will benefit the US agriculture.
Animal Health Component
(N/A)
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
20124991080100%
Knowledge Area
201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
2499 - Plant research, general;

Field Of Science
1080 - Genetics;
Goals / Objectives
The long-term goal of this project is to develop statistical models for studying the genetic architecture of quantitative traits in plants using molecular markers. The statistical technique is called the plant model. Specifically, we will accomplish the following objectives: (1) establish the infrastructure for the plant model quantitative genetic analysis and QTL (quantitative trait loci) mapping using a Bayesian statistic, (2) develop a general algorithm for QTL analysis in arbitrarily complex pedigrees under arbitrary mating designs, (3) extend the general algorithm to QTL mapping with epistatic effects and pleiotropic effects for multiple traits, and (4) develop an algorithm for QTL mapping in polyploid plants. A Bayesian statistical approach will be used to develop the method of QTL mapping. The Bayesian method will be implemented via Markov chain Monte Carlo (MCMC), a computer simulation based algorithm to generate samples of parameters from their posterior distribution. The computer programs for testing the model will be written in C++ in the Unix platform. The programs will be tests using simulated data as well as data collected from fields.
Project Methods
A Bayesian statistical approach will be used to develop the method of QTL mapping. The Bayesian method will be implemented via Markov chain Monte Carlo (MCMC), a computer simulation based algorithm to generate samples of parameters from their posterior distribution. The computer programs for testing the model will be written in C++ in the Unix platform. The programs will be tests using simulated data as well as data collected from fields.

Progress 09/15/00 to 09/30/03

Outputs
There are five specific aims in the current project: (1) Establish the infrastructure for the plant model quantitative genetic analysis and QTL mapping under the Bayesian framework, (2) Develop a general algorithm for QTL analysis in arbitrarily complex pedigrees under arbitrary mating designs, (3) Extend the general algorithm to QTL mapping with epistatic effects and pleiotropic effects for multiple traits, (4) Develop an algorithm for QTL mapping in polyploid plants, and (5) Write a unified QTL mapping program in C++ for general use. Aims (1), (2), (3) and (4) have been completed. Aim (5) is still under development, but will be completed shortly. We have published a total of 26 technical articles and book chapters, most of which were fully or partially supported by this grant (see the list in the back). Publications # 1 and #4 laid the foundation for the Bayesian mapping methodology. The major contributions of these two papers are the successful utilization of the reversible jump MCMC algorithm to handle models with various number of QTL and the formulation of the threshold model for mapping binary traits. With these two papers, we have unified QTL mapping for both quantitative traits and qualitative traits into a single model. A single program can map QTL for either type of traits. Publications # 6, #7, #11, #12, #13 and #14 deal with QTL mapping in plant pedigrees, where we defined a generalized plant pedigree to be a group of genetically related plants. The generalized pedigrees include families of line. The mixed model approach to QTL mapping (pub #6) partitions the allelic variance into between-population and within-population variance components. The between-population variance component is treated as fixed and the within-population variance component as random. These publications have fulfilled aims (1) and (2) of the current project. Publications #10 and #25 fulfill aim (3). The model has been verified by simulation studies where we showed that epistatic QTL can be detected even if there are no main effects. Detecting QTL for ploeitropic effects for multiple traits (part of aim 3) has been complete and a manuscript is being prepared (C. Xu, Z. Li and S. Xu, submitted to Genetics). Publication # 3 introduced a weighted least square method to map QTL in tetroploid populations. With this publication, we have fulfilled aim (4). Aim (5) of the current project is to release a computer program to implement the plant model QTL mapping methodology. We have already written a set of FORTRAN programs for testing the methods and analyzed some real data. We have recoded some of the programs into SAS (publications #21 and #26). We decided not to recode the programs into C++ because the SAS programs can be as efficient as the C++. So, aim (5) has been fulfilled. So far, we have fulfilled all specific aims of the current project.

Impacts
The project will significantly increase the efficiency of genetic mapping and thus improve our understanding of the genetic architecture of complex traits.

Publications

  • Xu, S. 2002. Linkage Analysis of Quantitative Traits. In Tao, J., Xu, Y., and Zhang, M. Q. (eds), Current Topics in Computational Biology, Tsinghua University Press and Massachusetts Institute of Technology, pp 175-199.
  • Xu, S. 2002. QTL Analysis in Plants. In Camp, N. and Cox, A (eds), Quantitative Trait Loci: Methods and Protocols, Humana Press, Totowa, NJ, pp 283-310.
  • Xu, S. 2003. Advanced Statistical Methods for Estimating Genetic Variances in Plants. Plant Breeding Reviews 22:113-163.
  • Xu, C. and S. Xu. 2003. A SAS/IML program for mapping QTL in line crosses. Proceedings of the Twenty-Eighth Annual SAS Users Group International Conference (SUGI), March 30-April 2, 2003, Cary, NC, SAS Institute, Inc., Paper 235-28:1-6.
  • Luo, L., Y. Mao and S. Xu. 2003. Correcting the bias in estimation of genetic variances contributed by individual QTL. Genetica (in press).
  • Yi, N., S. Xu, V. George and D. B. Allison. 2003. Mapping multiple quantitative trait loci for ordinal traits. Behavior Genetics (in press).
  • Xu, S., N. Yi, D. Burke, A. Galecki, and R. A. Miller. 2003. An EM algorithm for mapping binary disease loci: Application to fibrosarcoma in a four-way cross mouse family. Genetical Research (in press).
  • Yi, N. and S. Xu. 2000. Bayesian mapping of quantitative trait loci under the IBD-based variance component model. Genetics 156:411-422
  • Gessler, D. D. G. and S. Xu. 2000. Meiosis and recombination at low mutation rates. Genetics 156:449-456.
  • Xu, S. and N. Yi. 2000. Mixed model analysis of quantitative trait loci. Proc. Natl. Acad. Sci. USA 97:14542-14547.
  • Yi, N. and S. Xu. 2001. Bayesian mapping of quantitative trait loci under complicated mating designs. Genetics 157:1759-1771.
  • Li, X., W. Gu, G. Masinde, M. Hamilton-Ulland, S. Xu, S. Mohan and D. J. Baylink. 2001. Genetic control of the rate of wound healing in mice. Heredity 86: 668-674
  • Li, X., G. Masinde, W. Gu, J. Wergedal, M. Hamilton-Ulland, S. Xu, S. Mohan and D. J. Baylink. 2002. Chromosomal regions harboring genes for the work to femur failure in mice. Functional and Integrative Genomics 1:367-374.
  • Yi, N. and S. Xu. 2002. Mapping quantitative trait loci with epistatic effects. Genetical Research 79:185-198
  • Yi, N. and S. Xu. 2002. Linkage analysis of quantitative trait loci in multiple line crosses. Genetica 114:217-230.
  • Yi, N., S. Xu and D. B. Allison. 2003. Bayeisan model choice and search strategies for mapping interacting quantitative trait loci. Genetics (in press).
  • Xu, C. and S. Xu. 2003. Extended Sib-Pair Mapping for Quantitative Triats. In Saxton, R. (ed), Genetic Analysis of Complex Traits with SAS. SAS Institute, Inc. Cary, North Carolina (in press).
  • Yi, N. and S. Xu. 2000. Bayesian mapping of quantitative trait loci for complex binary traits. Genetics 155:1391-1403
  • Vogl, C. and S. Xu. 2000. Multipoint mapping of segregation distorting loci using molecular markers. Genetics 155:1439-1447
  • Xie, C. and S. Xu. 2000. Mapping quantitative trait loci in tetroploid populations. Genetical Reseach 76:105-115


Progress 01/01/02 to 12/31/02

Outputs
The long-term goal of this project is to develop a general statistical model for studying the genetic architecture of quantitative traits in plants using molecular markers. In the current project period, we have accomplished the following objectives: (1) Continue developing statistical methods for QTL mapping in outbred pedigrees; (2) Wrote a book chapter reviewing the random model methods of QTL mapping applied to poultry; (3) Completed a viability mapping project; (4) Developed a new Bayesian mapping statistic which estimate QTL effects of the entire genome; and (5) Wrote two SAS programs for QTL mapping using both the fixed and random model methods.

Impacts
The project will significantly increase the efficiency of genetic mapping and thus improve our understanding of the genetic architecture of complex traits.

Publications

  • Kopp, A., R. M. Graze, S. Xu, S. B. Carroll and S. V. Nuzhdin. 2003. Quantitative trait loci responsible for variation in sexually dimorphic traits in Drosophila melanogaster. Genetics 163:771-787.
  • Luo, L. and S. Xu. 2003. Mapping viability loci using molecular markers. Heredity (in press).
  • Luo, L., Y. Mao and S. Xu. 2003. Correcting the bias in estimation of genetic variances contributed by individual QTL. Genetica (in press).
  • Vogl, C. and S. Xu. 2002. QTL analysis in arbitrary pedigrees with incomplete marker information. Heredity 89:339-345.
  • Xu, C. and S. Xu. 2003. A SAS IML Program for Mapping QTL in Line Crosses. Proceedings of the 28th Annual SAS Users Group International (SUGI) Conference, March 30-April 2, 2003, Seattle, SAS Institute, Inc., Cary, NC. (in press)
  • Xu, C. and S. Xu. 2003. Extended Sib-Pair Mapping for Quantitative Traits. In Saxton, R. (ed), Genetic Analysis of Complex Traits with SAS. SAS Institute, Inc. Cary, North Carolina.
  • Xu, C., X. He and S. Xu. 2003. Mapping quantitative trait loci underlying triploid endosperm traits in cereals. Heredity (in press).
  • Xu, S. 2003. Estimating polygenic effects using markers of the entire genome. Genetics 163:789-801.
  • Yi, N. and S. Xu. 2003. Designs and Methods to Detect QTLs for Production Traits Based on Random Genetic Models. In Muir, W. M. and Aggrey, S. E. (eds), Poultry Genetics, Breeding and Biotechnology, CABI Publishing (in press)


Progress 01/01/01 to 12/31/01

Outputs
The long-term goal of this project is to develop a general statistical model for studying the genetic architecture of quantitative traits in plants using molecular markers. Specifically, we have accomplished following objectives in the current project: 1. Establish the infrastructure for the plant model quantitative genetic analysis and QTL mapping under the Bayesian framework. 2. Develop a general algorithm for QTL analysis in arbitrarily complex pedigrees under arbitrary mating designs. 3. Extend the general algorithm to QTL mapping with epistatic effects.

Impacts
The project will significantly increase the efficiency of genetic mapping and thus improve our understanding of the genetic architecture of complex traits.

Publications

  • LI, X., G. MASINDE, W. GU, J. WERGEDAL, M. HAMILTON-ULLAND, S. XU, S. MOHAN and D. J. BAYLINK. 2002. Chromosomal regions harboring genes for the work to femur failure in mice. Functional and Integrative Genomics (in press).
  • YI, N. and S. XU. 2002. Mapping quantitative trait loci with epistatic effects. Genetical Research (in press)
  • YI, N. and S. XU. 2002. Linkage analysis of quantitative trait loci in multiple line crosses. Genetica (in press)
  • XU, S. 2002. Advanced Statistical Methods for Estimating Genetic Variances in Plants. Plant Breeding Reviews (in press)
  • XU, S. 2002. Linkage Analysis of Quantitative Traits. In: Tao, J., Xu, Y., and Zhang, M. Q. (eds), Current Topics in Computational Biology, Tsinghua University Press and Massachusetts Institute of Technology, pp 175-199.
  • XU, S. 2002. QTL Analysis in Plants. In Camp, N. and Cox, A (eds), Quantitative Trait Loci: Methods and Protocols, Humana Press, Totowa, NJ (Invited contribution; anticipated date of publication Spring of 2001, 30 manuscript pages).
  • YI, N. and S. XU. 2002. Designs and Methods to Detect QTLs for Production Traits Based on Random Genetic Models. Chicken Breeding? (in press)
  • XU, S. and N. YI. 2001. Mixed model analysis of quantitative trait loci. In: Plant and Animal Genome IX. The International Conference on the Status of Plant Genome Research. January 13-17, 2001. Town & Country Hotel, San Diego, CA. W193.
  • YI, N. and S. XU. 2001. Bayesian mapping of quantitative trait loci under complicated mating designs. Genetics 157:1759-1771.
  • LI, X., W. GU, G. MASINDE, M. HAMILTON-ULLAND, S. XU, S. MOHAN and D. J. BAYLINK. 2001. Genetic control of the rate of wound healing in mice. Heredity 86: 668-674
  • YI, N. and S. XU. 2001. Bayesian mapping of quantitative trait loci in multiple line mating designs. In: Plant and Animal Genome IX. The International Conference on the Status of Plant Genome Research. January 13-17, 2001. Town & Country Hotel, San Diego, CA. P359. LUO, L. and S. XU. 2001. Mapping viability loci using molecular markers. In: Plant and Animal Genome IX. The International Conference on the Status of Plant Genome Research. January 13-17, 2001. Town & Country Hotel, San Diego, CA. P360.
  • YI, N. and S. XU. 2002. Bayesian analysis of quantitative trait loci with epistatic effects for complex traits. In: Plant and Animal Genome X. The International Conference on the Status of Plant and Animal Genome Research. January 12-16, 2002. Town & Country Hotel, San Diego, CA. P359.
  • LU, Y. and S. XU. 2002. A program to compute the genetic relationships of individuals in a complex pedigree In: Plant and Animal Genome X. The International Conference on the Status of Plant and Animal Genome Research. January 12-16, 2002. Town & Country Hotel, San Diego, CA. P360.