Recent advances in molecular genetics techniques have made dense marker maps available, and the prediction of breeding value at the genome level has been employed in genetics research. However, an increasingly large number of markers raise both statistical and computational issues in genomic selection （GS）, and many methods have been developed for genomic prediction to address these problems, including ridge regression-best linear unbiased prediction （RR-BLUP）, genomic best linear unbiased prediction, BayesA, BayesB, BayesCπ, and Bayesian LASSO. In this paper, these methods were compared regarding inference under different conditions, using real data from a wheat data set and simulated scenarios with a small number of quantitative trait loci （QTL） （20）, a moderate number of QTL （60, 180） and an extreme number of QTL （540）. This study showed that the genetic architecture of a trait should be fully considered when a GS method is chosen. If a small amount of loci had a large effect on a trait, great differences were found between the predictive ability of various methods and BayesCπ was recommended. Although there was almost no significant difference between the predictive ability of BayesCπ andBayesB, BayesCπ is more feasible than BayesB for real data analysis. If a trait was controlled by a moderate number of genes, the absolute differences between the various methods were small, but BayesA was also found to be the most accurate method. Furthermore, BayesA was widely adaptable and could perform well with different numbers of QTL. If a trait was controlled by an extreme number of minor genes, almost no significant differences were detected between the predictive ability of various methods, but RR-BLUP slightly outperformed the others in both simulated scenarios and real data analysis, thus demonstrating its robustness and indicating that it was quite effective in this case.
Chromosome segment substitution lines have been created in several experimental models,including many plant and animal species,and are useful tools for the genetic analysis and mapping of complex traits.The traditional t-test is usually applied to identify a quantitative trait locus (QTL) that is contained within a chromosome segment to estimate the QTL's effect.However,current methods cannot uncover the entire genetic structure of complex traits.For example,current methods cannot distinguish between main effects and epistatic effects.In this paper,a linear epistatic model was constructed to dissect complex traits.First,all the long substituted segments were divided into overlapping small bins,and each small bin was considered a unique independent variable.The genetic model for complex traits was then constructed.When considering all the possible main effects and epistatic effects,the dimensions of the linear model can become extremely high.Therefore,variable selection via stepwise regression (Bin-REG) was proposed for the epistatic QTL analysis in the present study.Furthermore,we tested the feasibility of using the LASSO (least absolute shrinkage and selection operator) algorithm to estimate epistatic effects,examined the fully Bayesian SSVS (stochastic search variable selection) approach,tested the empirical Bayes (E-BAYES) method,and evaluated the penalized likelihood (PENAL) method for mapping epistatic QTLs.Simulation studies suggested that all of the above methods,excluding the LASSO and PENAL approaches,performed satisfactorily.The Bin-REG method appears to outperform all other methods in terms of estimating positions and effects.
Epistasis between cytoplasmic and nuclear genes is the primary genetic component of complex quantitative traits.Genetic dissection of cytonuclear epistasis is fundamentally important to understand the genetic architecture of complex traits.In this study,a two-dimensional genome scan strategy was employed to evaluate the contribution of cytoplasm,quantitative trait loci (QTL),QTL×QTL interactions and QTL×QTL×cytoplasm interactions to the phenotypic variation.The p-value and parameter value for each genetic effect were calculated by multiple regression analysis.A stepwise approach was suggested to build confidence in candidate QTL on the basis of q-value estimation,false discovery rate calculation and Bonferroni adjustment.A fine-scale grid scan strategy was proposed for further analysis of peaks of interest.Plant height in maize was used as an example to illustrate the efficiency of the two-dimensional genome scan strategy.
Many complex traits are highly correlated rather than independent. By taking the correlation structure of multiple traits into account, joint association analyses can achieve both higher statistical power and more accurate estimation. To develop a statistical approach to joint association analysis that includes allele detection and genetic effect estimation, we combined multivariate partial least squares regression with variable selection strategies and selected the optimal model using the Bayesian Information Criterion(BIC). We then performed extensive simulations under varying heritabilities and sample sizes to compare the performance achieved using our method with those obtained by single-trait multilocus methods. Joint association analysis has measurable advantages over single-trait methods, as it exhibits superior gene detection power, especially for pleiotropic genes. Sample size, heritability,polymorphic information content(PIC), and magnitude of gene effects influence the statistical power, accuracy and precision of effect estimation by the joint association analysis.
