Comparison of biometrical models for joint linkage association mapping

Joint linkage association mapping (JLAM) combines the advantages of linkage mapping and association mapping, and is a powerful tool to dissect the genetic architecture of complex traits. The main goal of this study was to use a cross-validation strategy, resample model averaging and empirical data analyses to compare seven different biometrical models for JLAM with regard to the correction for population structure and the quantitative trait loci (QTL) detection power. Three linear models and four linear mixed models with different approaches to control for population stratification were evaluated. Models A, B and C were linear models with either cofactors (Model-A), or cofactors and a population effect (Model-B), or a model in which the cofactors and the single-nucleotide polymorphism effect were modeled as nested within population (Model-C). The mixed models, D, E, F and G, included a random population effect (Model-D), or a random population effect with defined variance structure (Mod...

联合连锁关联定位（Joint linkage association mapping, JLAM）兼具连锁定位与关联定位的优势，是解析复杂性状遗传结构的高效工具。本研究旨在采用交叉验证策略、重采样模型平均法及实证数据分析方法，针对群体结构校正与数量性状基因座（quantitative trait loci, QTL）检测效能两个方面，对比7种用于JLAM分析的不同生物统计模型。本次评估共纳入3种线性模型与4种采用不同群体分层控制策略的线性混合模型：模型A、B、C均为线性模型，其中模型A仅包含共因子，模型B同时纳入共因子与群体效应，模型C则将共因子与单核苷酸多态性效应设定为嵌套于群体的效应；混合模型D、E、F、G分别包含随机群体效应（模型D），或带有指定方差结构的随机群体效应（Mod...