Data from: 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 (Model-E), a kinship matrix defining the degree of relatedness among the genotypes (Model-F), or a kinship matrix and principal coordinates (Model-G). The tested models were conceptually different and were also found to differ in terms of power to detect QTL. Model-B with the cofactors and a population effect, effectively controlled population structure and possessed a high predictive power. The varying allele substitution effects in different populations suggest as a promising strategy for JLAM to use Model-B for the detection of QTL and then to estimate their effects by applying Model-C.

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双重优势，是解析复杂性状遗传结构的有力工具。本研究的核心目标为：采用交叉验证、重采样模型平均及实证数据分析策略，对比七种不同的JLAM生物统计模型在群体结构校正与数量性状位点（quantitative trait loci，QTL）检测效力上的表现。本次评估涵盖3种线性模型与4种针对群体分层控制策略各异的线性混合模型：模型A、B、C均为线性模型，其中模型A仅纳入协因子，模型B同时包含协因子与群体效应，模型C则将协因子与单核苷酸多态性（single-nucleotide polymorphism）效应设定为嵌套于群体之内的形式；混合模型D、E、F、G分别为：模型D纳入随机群体效应，模型E包含带有明确方差结构的随机群体效应，模型F借助亲缘关系矩阵定义基因型间的亲缘关联程度，模型G同时引入亲缘关系矩阵与主坐标。经检验，各测试模型在原理上存在显著差异，且QTL检测效力亦各不相同。其中，纳入协因子与群体效应的模型B可有效控制群体结构，且具备较高的预测效力。不同群体间等位基因替换效应存在差异这一现象提示，JLAM可采用兼具潜力的研究策略：先借助模型B完成QTL检测，再通过模型C估算其效应值。