Source: UNIVERSITY OF CALIFORNIA, RIVERSIDE submitted to
GENOME SELECTION AND MOLECULAR BREEDING USING HIGH DENSITY MARKERS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
TERMINATED
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
0223418
Grant No.
(N/A)
Project No.
CA-R-BPS-5028-H
Proposal No.
(N/A)
Multistate No.
(N/A)
Program Code
(N/A)
Project Start Date
Oct 1, 2010
Project End Date
Sep 30, 2015
Grant Year
(N/A)
Project Director
Xu, S.
Recipient Organization
UNIVERSITY OF CALIFORNIA, RIVERSIDE
(N/A)
RIVERSIDE,CA 92521
Performing Department
Botany and Plant Sciences
Non Technical Summary
This is a five year project aiming to develop advanced statistical methods and computer programs for detecting quantitative trait loci (QTL) and using detected quantitative trait loci for molecular breeding. We propose to develop Bayesian method of genome selection for quantitative traits (normally and continuously distributed traits), disease and draught resistance traits (non-normal and discrete traits), and count traits (traits with Poisson distribution). We also propose to develop an optimal breeding strategy to best utilize high density markers of the whole genome. Finally, we will incorporate all these new statistical methods into our existing software package, the QTL procedure in SAS
Animal Health Component
(N/A)
Research Effort Categories
Basic
(N/A)
Applied
(N/A)
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2012410108025%
2022410108025%
2032499108025%
2062499108025%
Knowledge Area
206 - Basic Plant Biology; 203 - Plant Biological Efficiency and Abiotic Stresses Affecting Plants; 202 - Plant Genetic Resources; 201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
2410 - Cross-commodity research--multiple crops; 2499 - Plant research, general;

Field Of Science
1080 - Genetics;
Goals / Objectives
The long term goal of the project is to develop advanced statistical methods and computer programs for detecting quantitative trait loci (QTL) and using detected quantitative trait loci for molecular breeding. Five specific objectives will be addressed in the five year project. (1) Developing Bayesian method of QTL mapping for quantitative traits (2) Developing Bayesian method of QTL mapping for traits with non-normal distribution (3) Predicting genomic values of plants using markers of the entire genome (4) Molecular breeding for genetic improvement of crops (5) Developing software package (the QTL procedure in SAS) for genomic data analysis
Project Methods
This project is mainly for basic research on developing statistical methods and computer programs for genomic data analysis and plant breeding. Bayesian and empirical Bayesian methods will be developed to study the association of markers with quantitative traits and qualitative traits of agricultural crops. The Bayesian method will be implemented via the Markov chain Monte Carlo (MCMC) algorithm. The empirical Bayesian method will be implemented via the expectation maximization (EM) algorithm. Permutation test will be used to detect quantitative trait loci (QTL). Cross validation will be used to decide how many QTL should be used to predict the total genetic values (molecular breeding values) for plants. Optimal breeding strategy will be developed after trait associated markers are identified. The optimal breeding strategy will be achieved by selecting the best individuals with high molecular breeding values and choosing the best crosses among the selected plants to generate progeny of the next generation. For the normal trait genome selection, standard linear mixed model will be used as the basic model for the Bayesian method. For traits with non-normal distribution, the generalized linear mixed model will be used as the basis for method development. The computer program will be developed in C++ and with a SAS window interface. The program will be released as a SAS procedure called PROC QTL. Both the methods and computer program will be tested and validated using six datasets we have collected and more data searchable on the public database. We will also simulate data and use them to test the methods and program.

Progress 10/01/10 to 09/30/15

Outputs
Target Audience: Nothing Reported Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? The long-term goal of the project was to develop advanced statistical methods and computer programs for detecting quantitative trait loci (QTL) and using detected quantitative trait loci for molecular breeding. Specific objectives included (1) Developing Bayesian method of QTL mapping for quantitative traits; (2) Developing Bayesian method of QTL mapping for traits with non-normal distribution; (3) Predicting genomic values of plants using markers of the entire genome; (4) Molecular breeding for genetic improvement of crops; (5) Developing software package (the QTL procedure in SAS) for genomic data analysis We published 28 technical articles in total for the five-year project, all of which appeared in refereed technical journals. The publications include methodology development for genomic data analysis, applications to crop improvement and hybrid breeding, computer programs, reviews, book chapters and textbook. All the five specific aims have been accomplished.

Publications

  • Type: Journal Articles Status: Published Year Published: 2011 Citation: Cai, X., A. Huang and S. Xu. 2011. Fast empirical Bayesian Lasso for multiple quantitative trait locus mapping. BMC Bioinformatics 12: 211, doi:10.1186/1471-2105-12-211
  • Type: Journal Articles Status: Published Year Published: 2011 Citation: Zhan, H. and S. Xu. 2011. Generalized linear mixed model for segregation distortion analysis. BMC Genetics 12:97, http://www.biomedcentral.com/1471-2156/12/97
  • Type: Journal Articles Status: Published Year Published: 2012 Citation: Hu, Z., J. D. Ehlers, P. A. Roberts, T. J. Close, M. R. Lucas, S. Wanamaker, and S. Xu. 2012. ParentChecker: a computer program for automated inference of missing parental genotype calls and linkage phase correction. BMC Genetics 13:9, doi:10.1186/1471-2156-13-9.
  • Type: Journal Articles Status: Published Year Published: 2012 Citation: Zhao, F. and S. Xu. 2012. An expectation and maximization algorithm for estimating Q�E interaction effects. Theoretical and Applied Genetics 124(8):1375-1387. doi:10.1007/s00122-012-1794-x
  • Type: Journal Articles Status: Published Year Published: 2012 Citation: Xing, J., J. Li, R. Yang, X. Zhou and S. Xu. 2012. Bayesian B-spline mapping for dynamic quantitative traits. Genetics Research, Cambridge 94: 85-95. doi:10.1017/S0016672312000249
  • Type: Journal Articles Status: Published Year Published: 2012 Citation: Che, X. and S. Xu. 2012. Generalized linear mixed models for mapping multiple quantitative trait loci. Heredity 109:41-49. doi:10.1038/hdy.2012.10
  • Type: Journal Articles Status: Published Year Published: 2012 Citation: Zhao, F. and S. Xu. 2012. Genotype by environment interaction of quantitative traits  A case study in barley. G3 2:779-788. doi: 10.1534/g3.112.002980
  • Type: Journal Articles Status: Published Year Published: 2012 Citation: Hu, Z., Z. Wang and S. Xu. 2012. An infinitesimal model for quantitative trait genomic value prediction. PLoS One 7(7): e41336. doi:10.1371/journal.pone.0041336
  • Type: Journal Articles Status: Published Year Published: 2011 Citation: Xu, S. and Z. Hu. 2011. Mapping quantitative trait loci using the MCMC procedure in SAS. Heredity 106:357-369, doi:10.1038/hdy.2010.77
  • Type: Journal Articles Status: Published Year Published: 2011 Citation: Zhan, H., X. Chen and S. Xu. 2011. A stochastic expectation and maximization (SEM) algorithm for detecting quantitative trait associated genes. Bioinformatics 27: 63-69, doi:10.1093/bioinformatics/btq558
  • Type: Journal Articles Status: Published Year Published: 2011 Citation: Sharma, S., S. Xu, B. Ehdaie, A. Hoops, T. Close, A. Lukaszewski and J. Waines. 2011. Dissection of QTL effects for root traits using a chromosome arm-specific mapping population in bread wheat. Theoretical and Applied Genetics 122: 759769, doi 10.1007/s00122-010-1484-5
  • Type: Journal Articles Status: Published Year Published: 2011 Citation: Hu, Z., Y. Li, X. Song, Y. Han, X. Cai, S. Xu and W. Li. 2011. Genomic value prediction for quantitative traits under the epistatic model. BMC Genetics 12:15 (11 pages), doi:10.1186/1471-2156-12-15.
  • Type: Journal Articles Status: Published Year Published: 2012 Citation: Zhan, H. and S. Xu. 2012. Adaptive ridge regression for rare variant detection. PLoS ONE 7(8): e44173. doi:10.1371/journal.pone.0044173
  • Type: Journal Articles Status: Published Year Published: 2012 Citation: Chen X, Xu S, McClelland M, Rahmatpanah F, Sawyers A, Z. Jia and D. Mercola. 2012. An accurate prostate cancer prognosticator using a seven-gene signature plus gleason score and taking cell type heterogeneity into account. PLoS ONE 7(9):e45178.doi:10.1371/journal.pone.0045178.
  • Type: Journal Articles Status: Published Year Published: 2012 Citation: Xu, S. 2012. Testing Hardy-Weinberg disequilibrium using the generalized linear model. Genetics Research, Cambridge 94: 319-330, doi:10.1017/S0016672312000511
  • Type: Books Status: Published Year Published: 2012 Citation: Xu, S. 2012. Principles of Statistical Genomics. Springer, New York
  • Type: Journal Articles Status: Published Year Published: 2013 Citation: Huang, A., S. Xu and X. Cai. 2013. Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping. BMC Genetics 14:5, http://www.biomedcentral.com/1471-2156/14/5.
  • Type: Journal Articles Status: Published Year Published: 2013 Citation: Xu, S. 2013. Genetic mapping and genomic selection using recombination breakpoint data. Genetics 195:1103-1115, doi: 10.1534/genetics.113.155309
  • Type: Journal Articles Status: Published Year Published: 2013 Citation: Xu, S. 2013. Mapping quantitative trait loci by controlling polygenic background effects. Genetics 195:1209-1222, doi:10.1534/genetics.113.157032/-/DC1
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Yi, N., S. Xu, H. Mallick and X. Y. Lou. 2014. Multiple comparisons in genetic association studies: a hierarchical modeling approach. Statistical Applications in Genetics and Molecular Biology (SAGMB) 13(1) 35-48. doi: 10.1515/sagmb-2012-0040.
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Xu, P., S. Xu, X. Wu, Y. Tao, B. Wang, S. Wang, D. Qin, Z. Lu and G. Li. 2014. Population genomic analyses from low-coverage RAD-Seq data: A case study on the non-model cucurbit gourd. The Plant Journal 77:430-442. doi: 10.1111/tpj.12370.
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Huang, A., S. Xu and X. Cai. 2014. Whole-genome quantitative trait locus mapping reveals major role of epistasis on yield of rice. PLoS ONE 9(1): e87330. doi: 10.1371/journal.pone.0087330.
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Xu, Shizhong, Dan Zhu and Qifa Zhang. 2014. Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proc. Natl. Acad. Sci. USA 111: 12456-12461.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Huang, Anhui, Shizhong Xu and Xiaodong Cai. 2015. Empirical Bayesian elastic net for multiple quantitative trait locus mapping. Heredity 114:107-115.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Ma, Shujie and Shizhong Xu. 2015. Semiparametric nonlinear regression for detecting gene and environment interactions. Journal of Statistical Planning and Inference 156:31-47.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Ma, Shujie, Raymond J. Carroll, Hua Liang and Shizhong Xu. 2015. Estimation and inference in generalized additive coefficient models for nonlinear interactions with high-dimensional covariates. The Annals of Statistics 43(5): 2102-2131
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2015 Citation: Xavier, Alencar, Shizhong Xu, William M. Muir and Katy Martin Rainey. 2015. NAM: association studies in multiple populations. Bioinformatics In press (accepted July 25, 2015, 3 manuscript pages)
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2015 Citation: Wang, Qishan, Julong Wei, Yuchun Pan and Shizhong Xu. 2015. An efficient empirical Bayes method for genomewide association studies. Journal of Animal Breeding and Genetics. In press (accepted October 15, 2015, 11 manuscript pages)


Progress 10/01/13 to 09/30/14

Outputs
Target Audience: Nothing Reported Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? We published four technical articles in total for the current year of the project, all of which appeared in refereed technical journals. We developed a new method for genetic mapping and genomic selection incorporating gene-gene interaction (epistatic) effects. Using this method, we analyzed yield of rice in a hybrid population and detected many epitstaic effects for yield (Huang et al. 2014). We also predicted hybrid rice yield using whole genome SNP markers. We predicted that we can increase hybrid yield by 16% using genomic data relative to using phenotypic data (Xu et al. 2014). We also developed a new empirical Bayesian method to detect multiple quantitative trait loci with efficiency higher than the best available method (Huang et al. 2015). Finally, collaborating with my colleague in Statistics, we developed a semiparametric method for detecting gene and environment interactions (Ma and Xu 2015). All specific aims have been accomplished except that the SAS program for genomic data analysis has not been formally released. We will recode the program in R and release both the SAS and R simultaneously.

Publications

  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Huang, A., S. Xu and X. Cai. 2014. Whole-genome quantitative trait locus mapping reveals major role of epistasis on yield of rice. PLoS ONE 9(1): e87330. doi: 10.1371/journal.pone.0087330.
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Xu, Shizhong, Dan Zhu and Qifa Zhang. 2014. Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proc. Natl. Acad. Sci. USA 111: 12456-12461.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Huang, Anhui, Shizhong Xu and Xiaodong Cai. 2015. Empirical Bayesian elastic net for multiple quantitative trait locus mapping. Heredity 114:107-115.
  • Type: Journal Articles Status: Published Year Published: 2015 Citation: Ma, Shujie and Shizhong Xu. 2015. Semiparametric nonlinear regression for detecting gene and environment interactions. Journal of Statistical Planning and Inference 156:31-47.


Progress 01/01/13 to 09/30/13

Outputs
Target Audience: Nothing Reported Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? Nothing Reported How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals? Nothing Reported

Impacts
What was accomplished under these goals? We published four technical articles in total for the current year of the project, all of which appeared in refereed technical journals. We developed a new method for genetic mapping and genomic selection using recombination breakpoint data (Xu, 2013a). The method combines markers with the same segregation pattern into a bin. When the marker density is very high, we can reduce the number of markers into the number of bins. This is a model dimension reduction approach via biological reduction rather than statistical reduction. We also developed a method of genetic mapping that incorporates dominance and epistatic (marker by marker interaction) effects into the model (Xu 3013b). This is the first model that correctly incorporates epistatic polygenic effect into the genetic mapping model to control the background information. Both papers are published in Genetics. Collaborating with my former postdoc, we jointly published a paper addressing the problem of multiple tests in genome-wide association studies (Yi et al. 2014). In this study, we developed a Bayesian hierarchical modeling approach and used the “effective number of tests” to control the critical value for the test statistic. Finally, I developed a new statistical method to test population differentiation in cucurbit gourd. The study was published jointly with my Chinese collaborators (Xu et al. 2014). All specific aims have been accomplished except that the SAS program for genomic data analysis has not been formally released. We will continue the software development in the new academic year. We will also recode the program in R package for general release.

Publications

  • Type: Journal Articles Status: Published Year Published: 2013 Citation: Xu, S. 2013a. Genetic mapping and genomic selection using recombination breakpoint data. Genetics 195:1103-1115, doi: 10.1534/genetics.113.155309
  • Type: Conference Papers and Presentations Status: Published Year Published: 2013 Citation: Xu, S. 2013b. Mapping quantitative trait loci by controlling polygenic background effects. Genetics 195:1209-1222, doi:10.1534/genetics.113.157032/-/DC1
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Yi, N., S. Xu, H. Mallick and X. Y. Lou. 2014. Multiple comparison in genetic association studies: A hierarchical modeling approach. Statistical Applications in Genetics and Molecular Biology (SAGMB) 13(1) 35-48. doi: 10.1515/sagmb-2012-0040.
  • Type: Journal Articles Status: Published Year Published: 2014 Citation: Xu, P., S. Xu, X. Wu, Y. Tao, B. Wang, S. Wang, D. Qin, Z. Lu and G. Li. 2014. Population genomic analyses from low-coverage RAD-Seq data: A case study on the non-model cucurbit gourd. The Plant Journal 77:430-442. doi: 10.1111/tpj.12370.


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

Outputs
OUTPUTS: The long-term goal of the project was to develop advanced statistical methods and computer programs for detecting quantitative trait loci (QTL) and using detected quantitative trait loci for molecular breeding. Specific objectives included (1) Developing Bayesian method of QTL mapping for quantitative traits; (2) Developing Bayesian method of QTL mapping for traits with non-normal distribution; (3) Predicting genomic values of plants using markers of the entire genome; (4) Molecular breeding for genetic improvement of crops; (5) Developing software package (the QTL procedure in SAS) for genomic data analysis We published six technical articles in total for the current year of the project, all of which appeared in refereed technical journals and four of them come from my own lab. We developed a fast empirical Bayesian method and program to map quantitative trait loci (QTL) with epistatic effects for binary traits (Huang, Xu and Cai 2013). We also developed a generalized linear model for detecting genome-wide Hardy-Weinberg equilibrium (Xu 2012). Previously, we developed a Bayesian method for estimating and testing QTL by environment interaction (QxE). However, this method is time consuming. Therefore, we developed a fast algorithm called the EM algorithm to estimate QxE interaction and applied this method to test QxE interaction in barley (Zhao and Xu 2012). We developed a fast and more efficient genomic selection algorithm called the bin model analysis. The method can handle virtually unlimited number of markers (Hu, Wang and Xu 2012). Finally, we developed an adaptive ridge regression method to detect rare variants associated with quantitative traits (Zhan and Xu 2012). PARTICIPANTS: Zhiqiu Hu, Postdoctoral research associate Fuping Zhao, Postdoctoral research associate Haimao Zhan, Ph.D student TARGET AUDIENCES: No new audience PROJECT MODIFICATIONS: No modification

Impacts
Methods and software package developed in the project will significantly increase the efficiency of genetic mapping. The project eventually will improve our understanding of the genetic architecture of complex traits.

Publications

  • Xing, J., Li, J., Yang, R., Zhou, X., and Xu, S. (2012). Bayesian B-spline mapping for dynamic quantitative traits. Genetics Research, Cambridge 94: 85-95. doi:10.1017/S0016672312000249.
  • 4. Zhan, H. and Xu, S. (2012). Adaptive ridge regression for rare variant detection. PLoS ONE 7(8): e44173. doi:10.1371/journal.pone.0044173.
  • Chen, X., Xu, S., McClelland, M., Rahmatpanah, F., Sawyers, A.,Jia, Z. and Mercola, D. (2012). An accurate prostate cancer prognosticator using a seven-gene signature plus gleason score and taking cell type heterogeneity into account. PLoS ONE 7(9):e45178.doi:10.1371/journal.pone.0045178.
  • Zhao, F. and Xu, S. (2012). Genotype by environment interaction of quantitative traits - A case study in barley. G3 2:779-788. doi: 10.1534/g3.112.002980. Hu, Z., Wang, Z., and Xu, S. (2012). An infinitesimal model for quantitative trait genomic value prediction. PLoS One 7(7): e41336. doi:10.1371/journal.pone.0041336.
  • Xu, S. (2012). Testing Hardy-Weinberg disequilibrium using generalized linear model. Genetics Research, Cambridge 94: 319-330, doi:10.1017/S0016672312000511.
  • Huang, A., Xu, S. and Cai, X. (2013). Empirical Bayesian Lasso-logistic regression for mapping multiple quantitative trait loci for binary traits. BMC Genetics 14:5, http://www.biomedcentral.com/1471-2156/14/5


Progress 01/01/11 to 12/31/11

Outputs
OUTPUTS: The long-term goal of the project was to develop advanced statistical methods and computer programs for detecting quantitative trait loci (QTL) and using detected quantitative trait loci for molecular breeding. Specific objectives included (1) Developing Bayesian method of QTL mapping for quantitative traits; (2) Developing Bayesian method of QTL mapping for traits with non-normal distribution; (3) Predicting genomic values of plants using markers of the entire genome; (4) Molecular breeding for genetic improvement of crops; (5) Developing software package (the QTL procedure in SAS) for genomic data analysis We published four technical articles in total for the current year of the project, all of which appeared in refereed technical journals and all from my own lab. Three more manuscripts are currently under review. We developed a fast empirical Bayesian method and program to map quantitative trait loci (QTL) with epistatic effects (Cai, Huang and Xu 2011). We also developed a generalized linear mixed model for mapping segregation distortion loci (Zhan and Xu 2011). Previously, we developed a Bayesian method for estimating and testing QTL by environment interaction (QxE). However, this method is time consuming. Therefore, we developed a fast algorithm called the EM algorithm to estimate QxE interaction. This work has been published in Theoretical and Applied Genetics (Zhao and Xu 2012). Finally, we developed a generalized linear mixed model to map multiple QTL for quantitative traits (Che and Xu 2012). With these publications, half of the proposed aims have been accomplished. PARTICIPANTS: Zhiqiu Hu, Postdoctoral research associate Fuping Zhao, Postdoctoral research associate Haimao Zhan, Ph.D student Xiaohonh Che, Ph.D student TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.

Impacts
Methods and software package developed in the project will significantly increase the efficiency of genetic mapping. The project eventually will improve our understanding of the genetic architecture of complex traits.

Publications

  • 1. Cai, X., A. Huang and S. Xu. 2011. Fast empirical Bayesian Lasso for multiple quantitative trait locus mapping. BMC Bioinformatics 12: 211, doi:10.1186/1471-2105-12-211
  • 2. Zhan, H. and S. Xu. 2011. Generalized linear mixed model for segregation distortion analysis. BMC Genetics 12:97, doi:10.1186/1471-2156-12-97.
  • 3. Zhao, F. and S. Xu. 2012. An expectation and maximization algorithm for estimating QxE interaction effects. Theoretical and Applied Genetics (In press, accepted Janurary 5, 2012, 39 manuscript pages).
  • 4. Che, X. and S. Xu. 2012. Generalized linear mixed models for mapping multiple quantitative trait loci. Heredity (In press, accepted Janurary 9, 2012, 40 manuscript pages).


Progress 01/01/10 to 12/31/10

Outputs
OUTPUTS: The long-term goal of the project was to develop advanced statistical methods and computer programs for detecting quantitative trait loci (QTL) and using detected quantitative trait loci for molecular breeding. Specific objectives included (1) Developing Bayesian method of QTL mapping for quantitative traits; (2) Developing Bayesian method of QTL mapping for traits with non-normal distribution; (3) Predicting genomic values of plants using markers of the entire genome; (4) Molecular breeding for genetic improvement of crops; (5) Developing software package (the QTL procedure in SAS) for genomic data analysis We published nine technical articles in total for the current year of the project, all of which appeared in refereed technical journals and all from my own lab. Objective (1) of the project (developing Bayesian statistics for QTL mapping) has been accomplished (see Che and Xu 2010a,b). We developed the MCMC implemented Bayesian method along with a permutation test to detect QTL (Che and Xu 2010a). We also reviewed the applications of Bayesian method to agricultural experiments (Che and Xu 2010b). The MCMC procedure in SAS developed by Xu and Hu (2011a) also belongs to objective (1). In objective (2), we proposed to develop methods for mapping traits with non-normal distribution. This has been partly fulfilled with the generalized linear model published by Xu and Hu (2010). Two recent publications (Hu et al. 2011; Xu and Hu 2011b) came from the third objective of the project (genome prediction). In addition, we studied genotype by environment interaction (Chen et al. 2010) using the Bayesian method. Overall, we achieved more than what we expected for the first year of the project. PARTICIPANTS: Zhiqiu Hu, Postdoctoral research associate Fuping Zhao, Postdoctoral research associate Haimao Zhan, Ph.D student Xiaohonh Che, Ph.D student TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Not relevant to this project.

Impacts
Methods and software package developed in the project will significantly increase the efficiency of genetic mapping. The project eventually will improve our understanding of the genetic architecture of complex traits.

Publications

  • Che, X. and S. Xu. 2010a. Significance test and genome selection in Bayesian shrinkage analysis. International Journal of Plant Genomics. Volume 2010, Article ID 893206, 11 pages, doi:10.1155/2010/893206.
  • Che, X. and S. Xu. 2010b. Bayesian data analysis for agricultural experiments. Canadian Journal of Plant Science 90: 575-603.
  • Chen, X., F. Zhao and S. Xu. 2010. Mapping environment-specific quantitative trait loci. Genetics 186: 1053-1066, doi: 10.1534/genetics.110.120311.
  • Han, L. and S. Xu. 2010. Genome-wide evaluation for quantitative trait loci under the variance component model. Genetica 138:1099-1109, doi 10.1007/s10709-010-9497-1.
  • Hu, Z. Y. Li, X. Song, Y. Han, X. Cai, S. Xu and W. Li. 2011. Genomic value prediction for quantitative traits under the epistatic model. BMC Genetics 2011, 12:15 (11 pages), doi:10.1186/1471-2156-12-15.
  • Sharma, S., S. Xu, B. Ehdaie, A. Hoops, T. Close, A. Lukaszewski and J. Waines. 2010. Dissection of QTL effects for root traits using a chromosome arm-specific mapping population in bread wheat. Theoretical and Applied Genetics (In press, accepted on 10/22/2010, 30 manuscript pages).
  • Xu, S. and Z. Hu. 2010. Generalized linear model for interval mapping of quantitative trait loci. Thereotical and Applied Genetics 121: 47-63. doi: 10.1007/s00122-1290-0.
  • Xu, S. and Z. Hu. 2011a. Mapping quantitative trait loci using the MCMC procedure in SAS. Heredity 106:357-369, doi:10.1038/hdy.2010.77.
  • Xu, S. and Z. Hu. 2011b. Methods of plant breeding in the genome era. Genetics Research (In press, accepted on 11/11/2010, 60 manucript pages).
  • Zhan, H., X. Chen and S. Xu. 2010. A stochastic expectation and maximization (SEM) algorithm for detecting quantitative trait associated genes. Bioinformatics (In press, accepted on 9/18/2010, 8 preprint pages)