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) 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. Since the landmark study by Lander and Botstein [1] in the field of quantitative genetics, interest in the genetic analysis of complex traits has increased enormously. During the past century, numerous investigators have inferred the action of a polygene to be the underlying cause of a complex phenotype with continuous variation. The quantitative trait loci (QTLs) responsible for phenotypic variation can be mapped within a chromosome interval using conventional segregation populations, such as the F 2 and backcross mapping populations. Some QTLs have been cloned in rice, mouse, and other model organisms [2][3][4]. These achievements have enhanced our understanding of complex traits and enabled us to use marker-assisted selection (MAS) and genetic engineering to introduce valuable alleles into crops and improve crop breeding more effectively. Genetic studies can provide important insights into the detailed molecular mechanisms that underlie the variation of complex traits. However, conventional populations have several limitations for accurate identification and fine mapping of QTLs [5,6]. One of the shortcomings of these populations is that a major QTL can overshadow a small-effect QTL by increasing the total phenotypic variation; thus, the

Modern quantitative genetics: Dissecting complex polygenic systems into individual genetic factors

“…The first article [1] is a review of statistical approaches for QTL mapping and marker-assisted breeding that were developed by the authors and their group. In subsequent articles, several new QTL mapping methods are proposed for specific experimental designs, including (1) the use of two backcross populations [2], (2) the use of a NCIII design based on recombinant inbred lines [3], and (3) the use of chromosome segment substitution lines [4]; or for specific purposes, namely, (1) to detect epistasis between nuclear genes (QTLs) and the cytoplasm [5], and (2) to map sterile genes (QTLs) with epistasis in remote hybridization of plant [6]. In another article [7], an improved approach for QTL mapping in farm animals based on general pedigrees, termed pedigree transmission disequilibrium test, is proposed; this method performs better than existing statistical methods.…”