Research Topics
| S XuSummaryAffiliation: University of California Country: USA Publications
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Detail Information
Publications
QTL analysis in plantsShizhong Xu
Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
Methods Mol Biol 195:283-310. 2002
Genome-wide evaluation for quantitative trait loci under the variance component modelLide Han
Department of Botany and Plant Science, University of California, Riverside, CA 92521, USA
Genetica 138:1099-109. 2010..While the Bayesian method produced the optimal result, the ML method is computationally more efficient than the Bayesian method. Simulation experiments were conducted to demonstrate the efficacy of the new methods...- Mapping quantitative trait loci for complex binary diseases using line crossesS Xu
Department of Botany and Plant Sciences, University of California, Riverside 92521 0124, USA
Genetics 143:1417-24. 1996..Potential utilization of the QTL mapping procedure for resolving alternative genetic models (e.g., single- or two-trait-locus model) is discussed... 
Mapping QTL for multiple traits using Bayesian statisticsChenwu Xu
Jiangsu Provincial Key Laboratory of Crop Genetics and Physiology, Key Laboratory of Plant Functional Genomics of the Ministry of Education, Yangzhou University, Yangzhou 225009, People s Republic of China
Genet Res (Camb) 91:23-37. 2009..We also apply the method to mapping QTLs responsible for multiple disease resistances to the blast fungus of rice. A computer program written in SAS/IML that implements the method is freely available, on request, to academic researchers...- Derivation of the shrinkage estimates of quantitative trait locus effectsShizhong Xu
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA
Genetics 177:1255-8. 2007..An important lemma regarding the posterior mean of a normal likelihood combined with a normal prior is introduced. The lemma is then used to derive the Bayesian shrinkage estimates of the QTL effects... - Mixed model analysis of quantitative trait lociS Xu
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Proc Natl Acad Sci U S A 97:14542-7. 2000..This unified QTL mapping algorithm treats the fixed and random model approaches as special cases of the general mixed model methodology. Utility and flexibility of the method are demonstrated by using a set of simulated data... - An empirical Bayes method for estimating epistatic effects of quantitative trait lociShizhong Xu
Department of Botany and Plant Sciences, University of California, Riverside, Riverside, California 92521, USA
Biometrics 63:513-21. 2007..However, E-BAYES appears to outperform all other methods in terms of minimizing the mean-squared error (MSE) with relatively short computing time. Application of the new method to real data was demonstrated using a barley dataset... - Genomewide analysis of epistatic effects for quantitative traits in barleyShizhong Xu
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA
Genetics 175:1955-63. 2007..This invalidates the common practice of epistatic analysis in which epistatic effects are estimated only for pairs of loci of which both have main effects... - A multivariate model for ordinal trait analysisS Xu
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Heredity (Edinb) 97:409-17. 2006..The advantages of the EM algorithm over other methods are addressed. Application of the method to QTL mapping for ordinal traits is demonstrated using a simulated baclcross (BC) population... - Estimating polygenic effects using markers of the entire genomeShizhong Xu
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA
Genetics 163:789-801. 2003..Similar results were found from simulated data sets of F(2) and backcross (BC) families... - Mapping quantitative trait loci underlying triploid endosperm traitsC Xu
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Heredity (Edinb) 90:228-35. 2003..With these methods, we are now ready to map endosperm traits, as we can for regular quantitative trait under diploid control... - Theoretical basis of the Beavis effectShizhong Xu
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA
Genetics 165:2259-68. 2003..The theoretical prediction agrees well with the observations reported in Beavis's original simulation study. Application of the theory to meta-analysis of QTL mapping is discussed... - Maximum likelihood analysis of quantitative trait loci under selective genotypingS Xu
Department of Botany and Plant Sciences, University of California, Riverside 92521, USA
Heredity (Edinb) 84:525-37. 2000..If it is impossible to analyse the full data, e.g. sample sizes are too large, phenotypic values of ungenotyped individuals are missing or composite interval mapping is to be performed, the proposed method can be applied... - Phylogenetic analysis under reticulate evolutionS Xu
Department of Botany and Plant Sciences, University of California at Riverside, 92521, USA
Mol Biol Evol 17:897-907. 2000..A least-squares method is developed for reconstructing a reticulate phylogeny using gene frequency data. The efficacy of the method under the pure drift model is verified via Monte Carlo simulations... - Computation of the full likelihood function for estimating variance at a quantitative trait locusS Xu
Department of Botany and Plant Sciences, University of California, Riverside 92521, USA
Genetics 144:1951-60. 1996..Given the joint distribution of the unknown IBDs, a method to compute the full likelihood function is developed for families of arbitrary sizes... - Mapping quantitative trait loci using multiple families of line crossesS Xu
Department of Botany and Plant Sciences, University of California, Riverside 92521, USA
Genetics 148:517-24. 1998..number of individuals per family) in which QTL mapping reaches its maximum power and minimum estimation error. Deviation from the optimal strategy reduces the efficiency of the method... - Further investigation on the regression method of mapping quantitative trait lociS Xu
Department of Botany and Plant Sciences, University of California, Riverside 92521, USA
Heredity (Edinb) 80:364-73. 1998..Like the existing regression method, the weighted least squares method can be useful in QTL mapping in conjunction with the permutation tests and construction of confidence intervals by bootstrapping... - An EM algorithm for mapping binary disease loci: application to fibrosarcoma in a four-way cross mouse familyShizhong Xu
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Genet Res 82:127-38. 2003..All the QTLs detected primarily show dominance effects... - Multipoint genetic mapping of quantitative trait loci using a variable number of sibs per familyS Xu
Department of Botany and Plant Sciences, University of California, Riverside 92521, USA
Genet Res 71:73-83. 1998..This is further reflected in simulations with variable family sizes, where variance in family size improves the statistical power of QTL detection relative to a constant size control... - Mapping quantitative trait loci for binary traits using a heterogeneous residual variance model: an application to Marek's disease susceptibility in chickensS Xu
Department of Botany and Plant Sciences, University of California, Riverside 92521, USA
Genetica 104:171-8. 1998..Using the heterogeneous residual variance model, we identified a QTL on chromosome IV that controls Marek's disease susceptibility in chickens. The QTL alone explains 7.2% of the total disease variation... - An expectation-maximization algorithm for the Lasso estimation of quantitative trait locus effectsS Xu
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Heredity (Edinb) 105:483-94. 2010..In the context of quantitative trait loci (QTL) mapping, this new EM algorithm can estimate both genotypic values and QTL effects expressed as linear contrasts of the genotypic values... - Mapping quantitative trait loci using the MCMC procedure in SASS Xu
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Heredity (Edinb) 106:357-69. 2011..One QTL was identified on the second chromosome. This QTL appears to control the switch of seed-producing ability of female plants but does not affect the number of seeds produced once the switch is turned on... - Clustering expressed genes on the basis of their association with a quantitative phenotypeZhenyu Jia
Department of Botany and Plant Sciences, University of California, Riverside, 92521, USA
Genet Res 86:193-207. 2005..The method was verified by analysing two simulated datasets, and further demonstrated using real data generated in a microarray experiment for the study of gene expression associated with Alzheimer's disease... - Bayesian mapping of quantitative trait loci under complicated mating designsN Yi
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA
Genetics 157:1759-71. 2001..Finally, we are able to simultaneously infer the posterior distribution of the number, the additive and dominance variances, and the chromosomal locations of all identified QTL... - Multipoint genetic mapping of quantitative trait loci with dominant markers in outbred populationsD D Gessler
Department of Botany and Plant Sciences, University of California, Riverside 92521, USA
Genetica 105:281-91. 1999..Yet despite this, other situations show a large standard deviation in the estimate of the QTL position, thus making QTL mapping with dominant markers in outbred populations a useful detection tool, albeit limited in its resolution... - Sib mating designs for mapping quantitative trait lociC Xie
Department of Botany and Plant Science, University of California, Riverside 92521, USA
Genetica 104:9-19. 1998..As a result, the estimated QTL parameters can be applied to a wide statistical inference space relating to the entire reference population... - Linkage analysis of quantitative trait loci in multiple line crossesNengjun Yi
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521-0124, USA
Genetica 114:217-30. 2002..Design I involves two inbred lines and their derived F1, F2, and BC populations. Design II is a half-diallel cross involving three inbred lines. The two designs appear different, but can be handled with the same robust computer program... - A quantitative genetics model for viability selectionL Luo
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Heredity 94:347-55. 2005..The quantitative genetic model has been verified with a series of Monte Carlo simulation experiments... - Bayesian mapping of quantitative trait loci under the identity-by-descent-based variance component modelN Yi
Department of Botany and Plant Sciences, University of California, Riverside, California, 92521 0124, USA
Genetics 156:411-22. 2000..Utilities of the method are demonstrated using a simulated population consisting of multiple full-sib families... - Mapping quantitative trait loci using naturally occurring genetic variance among commercial inbred lines of maize (Zea mays L.)Yuan-Ming Zhang
Department of Botany and Plant Sciences, University of California, Riverside, 92521-0124, USA
Genetics 169:2267-75. 2005..The MAS procedure implemented via BLUP may be routinely used by breeders to select superior lines and line combinations for development of new cultivars... - Joint mapping of quantitative trait Loci for multiple binary charactersChenwu Xu
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA
Genetics 169:1045-59. 2005..Efficiency of the method is demonstrated using simulated data. We also apply the new method to a set of real data and detect several loci responsible for blast resistance in rice... - Generalized linear mixed models for mapping multiple quantitative trait lociX Che
Department of Statistics, University of California, Riverside, CA 92521, USA
Heredity (Edinb) 109:41-9. 2012..The two methods of GLMM were applied to MQM for the female fertility trait of wheat. Multiple QTL were detected to control the variation of the number of seeded spikelets... - A Monte Carlo algorithm for computing the IBD matrices using incomplete marker informationY Mao
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521-0124, USA
Heredity 94:305-15. 2005..With these locus-specific IBD matrices, we are ready to search for quantitative trait loci along the genome in complicated pedigrees... - Mapping viability loci using molecular markersL Luo
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Heredity 90:459-67. 2003..We conclude that mapping viability loci can be accomplished using similar statistical techniques used in quantitative trait locus mapping for quantitative traits... - Mapping quantitative trait loci with epistatic effectsNengjun Yi
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521-0124, USA
Genet Res 79:185-98. 2002..This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data... - Mapping quantitative trait loci in F2 incorporating phenotypes of F3 progenyYuan-Ming Zhang
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA
Genetics 166:1981-93. 2004..Extension of the mixture model to multiple QTL analysis is developed using a Bayesian approach. The computer program performing the Bayesian analysis of the simulated data is available to users for real data analysis... - Bayesian shrinkage estimation of quantitative trait loci parametersHui Wang
Department of Botany and Plant Sciences, University of California, Riverside, 92521, USA
Genetics 170:465-80. 2005..The method was also used to map QTL responsible for wound healing in a family of a (MRL/MPJ x SJL/J) cross with 633 F(2) mice derived from two inbred lines... - Mapping quantitative trait loci for expression abundanceZhenyu Jia
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA
Genetics 176:611-23. 2007..In the experiment, approximately 40,000 transcripts and 145 codominant markers are investigated for their associations. A program written in SAS/IML is available from the authors on request... - QTL analysis in arbitrary pedigrees with incomplete marker informationC Vogl
Department of Botany and Plant Sciences, University of California, Riverside CA 92521 0124, USA
Heredity (Edinb) 89:339-45. 2002..Computer simulations suggest that the algorithm can indeed handle complex pedigrees and detect two QTL on a linkage group, but that the number of individuals in a single extended family is limited to about 50 to 100 individuals... - Using the expectation or the distribution of the identity by descent for mapping quantitative trait loci under the random modelD D Gessler
Department of Botany and Plant Sciences, University of California, Riverside 93531, USA
Am J Hum Genet 59:1382-90. 1996..We discuss this in light of the general ability of the random model to partition these components... - A Fisher scoring algorithm for the weighted regression method of QTL mappingL Han
Department of Botany and Plant Science, University of California, Riverside, CA 92521, USA
Heredity (Edinb) 101:453-64. 2008..To compare the Fisher scoring algorithm with the expectation maximization (EM)-based ML method, we also developed a slightly simplified method to compute the variance-covariance matrix of the estimated parameters under the EM algorithm... - A penalized maximum likelihood method for estimating epistatic effects of QTLY-M Zhang
Department of Botany and Plant Sciences, University of California, Riverside, CA, USA
Heredity 95:96-104. 2005..Simulation studies also show that results of the penalized likelihood method are comparable to the Bayesian shrinkage analysis, but the computational speed of the penalized method is orders of magnitude faster... - An EM algorithm for mapping quantitative resistance lociC Xu
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Heredity 94:119-28. 2005..The method is verified in simulated data under various combinations of the parameters. An SAS program is available to implement the multicycle ECM algorithm. The program can be downloaded from our website at www.statgen.ucr.edu... - On the evolution of recombination and meiosisD D Gessler
Department of Botany and Plant Sciences, University of California, Riverside 92521, USA
Genet Res 73:119-31. 1999..The genetic mechanism lends itself naturally to a model for the evolution of meiosis, where modern-day gametes are seen as derivative of ancient unicellular organisms... - Efficiency of multistage marker-assisted selection in the improvement of multiple quantitative traitsC Xie
Department of Botany and Plant Science, University of California, Riverside 92521 0124, USA
Heredity (Edinb) 80:489-98. 1998..0) and the heritability (h2) is greater than 0.3. The efficiency of MAS increases as r increases and h2 decreases. For MAS to be more effective, it is necessary to decrease further the cost associated with molecular marker assays... - Mapping QTLs for traits measured as percentagesYongcai Mao
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521-0124, USA
Genet Res 83:159-68. 2004..We develop the QTL mapping procedure based on the maximum likelihood methodology implemented via the expectation-maximization algorithm. The efficacy of the new method is demonstrated using Monte Carlo simulation... - Meiosis and the evolution of recombination at low mutation ratesD D Gessler
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA
Genetics 156:449-56. 2000..Interestingly, the allele spreads without repairing even a single DNA mutation... - Multipoint mapping of viability and segregation distorting loci using molecular markersC Vogl
Department of Biology, University of Oulu, FIN 90401 Oulu, Finland
Genetics 155:1439-47. 2000..Both methods are applied to a set of simulated data and real data from a cross of two Scots pine trees... - Quantitative trait locus mapping can benefit from segregation distortionShizhong Xu
Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA
Genetics 180:2201-8. 2008..However, other situations are less benign. A method that can simultaneously map QTL and SDL is discussed, maximizing both use of mapping resources and use by agricultural and evolutionary biologists... - Generalized linear model for interval mapping of quantitative trait lociShizhong Xu
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Theor Appl Genet 121:47-63. 2010..Both algorithms were coded in C++ and interfaced with SAS as a user-defined SAS procedure called PROC QTL... - Supervised cluster analysis for microarray data based on multivariate Gaussian mixtureYi Qu
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Bioinformatics 20:1905-13. 2004..AVAILABILITY: The program written in the SAS language implementing methods I-III in this report is available upon request. The software of SVMs is available in the website http://svm.sdsc.edu/cgi-bin/nph-SVMsubmit.cgi.. - A simple method for calculating the statistical power for detecting a QTL located in a marker intervalZ Hu
Department of Botany and Plant Sciences, University of California, Riverside, CA 98521, USA
Heredity (Edinb) 101:48-52. 2008.... - Correcting the bias in estimation of genetic variances contributed by individual QTLLang Luo
Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Genetica 119:107-13. 2003..Therefore, we also develop a simple method to estimate the standard error of the estimated genetic effect, which is subsequently used to correct the bias in the variance estimate... - Quantitative trait associated microarray gene expression data analysisYi Qu
Department of Botany and Plant Sciences, University of California, Riverside, USA
Mol Biol Evol 23:1558-73. 2006..We give detailed instructions and provide a working program that allows others to directly implement this method in their own analyses... - Box-Cox transformation for QTL mappingRunqing Yang
School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, 201101, PR China
Genetica 128:133-43. 2006.... - Mapping multiple quantitative trait Loci for ordinal traitsNengjun Yi
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama 35294 0022, USA
Behav Genet 34:3-15. 2004..To this end, we provide a unified approach to mapping multiple QTL for continuous, binary, and ordinal traits. Utility and flexibility of the method are demonstrated using simulated data... - Bayesian shrinkage analysis of quantitative trait Loci for dynamic traitsRunqing Yang
School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 201101, People s Republic of China
Genetics 176:1169-85. 2007..We propose several alternative methods to present the results of the Bayesian shrinkage analysis. In particular, we found that the Wald test-statistic profile can serve as a mechanism to test the significance of a putative QTL... - Joint tests for quantitative trait loci in experimental crossesT Mark Beasley
Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL, USA
Genet Sel Evol 36:601-19. 2004..Joint tests were generally more powerful for one-tailed selection under both backcross and F2 intercross situations. However, joint tests cannot be recommended for one-tailed selective genotyping if segregation distortion is suspected... - Bayesian model choice and search strategies for mapping interacting quantitative trait LociNengjun Yi
Department of Biostatistics, University of Alabama, Birmingham, Alabama 35294, USA
Genetics 165:867-83. 2003..Utility and flexibility of the method are demonstrated using both simulated data and a real data set. Sensitivity of posterior inference to prior specifications of the number and genetic effects of QTL is investigated... - Identification of QTL for production traits in chickensChristiane Hansen
USDA, ARS, Avian Disease and Oncology Laboratory, East Lansing, Michigan, USA
Anim Biotechnol 16:67-79. 2005..Two of these regions, one spanning the area of 263/287 cM on GAA01 and the other spanning the area of 23/28 cM on GAA02, were associated with multiple QTL... - Quantitative trait loci responsible for variation in sexually dimorphic traits in Drosophila melanogasterArtyom Kopp
Howard Hughes Medical Institute and Laboratory of Molecular Biology, University of Wisconsin, Madison, Wisconsin 53706, USA
Genetics 163:771-87. 2003.... - Mapping quantitative trait loci for traits defined as ratiosRunqing Yang
School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 201101, PR China
Genetica 132:323-9. 2008..A real example of QTL mapping for relative growth rate in soybean demonstrates that the new method can detect more QTL than existing methods of QTL mapping for traits defined as ratios... - Mapping quantitative trait loci for longitudinal traits in line crossesRunqing Yang
School of Agriculture and Biology, Shanghai Jiaotong University, People's Republic of China
Genetics 173:2339-56. 2006..The method is verified with simulated data and demonstrated with experimental data from a pseudobackcross family of Populus (poplar) trees... - Chromosomal regions harboring genes for the work to femur failure in miceXinmin Li
Molecular Genetics Division, Musculoskeletal Disease Center, JL Pettis VA Medical Center, 11201 Benton Street 151, Loma Linda, CA 92357, USA
Funct Integr Genomics 1:367-74. 2002..If this is also true in humans, this finding will challenge the predictive value of BMD for the risk of fracture... - Mouse chromosome 9 quantitative trait loci for soft tissue regeneration: congenic analysis and fine mappingHongrun Yu
Musculoskeletal Disease Center, Jerry L Pettis Memorial VA Medical Center, Loma Linda, California 92357, USA
Wound Repair Regen 15:922-7. 2007..Based on the 2-LOD intervals, the total QTL region was confined to a combined length of no more than 28.2 Mb. Application of a Bayesian shrinkage estimation indicated that a major locus was located in a region of just 1.3 Mb... - Bayesian LASSO for quantitative trait loci mappingNengjun Yi
Department of Biostatistics, University of Alabama, Birmingham, AL 35294 0022, USA
Genetics 179:1045-55. 2008..Markov chain Monte Carlo (MCMC) algorithms are developed to simulate the parameters from the posteriors. The methods are illustrated using well-known barley data...