Bin-based model construction and analytical strategies for dissecting complex traits with chromosome segment substitution lines

Abstract 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...
Ausführliche Beschreibung

Abstract 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. Ausführliche Beschreibung