Genome-wide association analysis in diverse inbred mice: power and population structure

The discovery of quantitative trait loci (QTL) in model organisms has relied heavily on the ability to perform controlled breeding to generate genotypic and phenotypic diversity. Recently, we and others have demonstrated the use of a set of diverse inbred mice as a QTL mapping population. The use of this population has many advantages, including increased phenotypic diversity, a higher recombination frequency and the ability to collect genotype data in community databases. However, these methods are complicated by inherent population structure and the inability to accurately assess statistical power. To address these issues, we measured gene expression levels in hypothalamus across the diverse inbred mapping population. We then mapped these phenotypes as quantitative traits with our association algorithm, resulting in a large set of expression QTLs. We utilized these eQTLs (and specifically the cis-eQTLs) to devise a relative measure of statistical power which does not rely on parametrically simulated data. Finally, we utilized this approach to develop and optimize a novel method of accounting for population structure in the Mouse Diversity Panel. Keywords: strain and gender samples were taken from naive mice of different strains and both genders模式生物中数量性状位点（quantitative trait loci, QTL）的发现，高度依赖于通过可控育种产生基因型与表型多样性的能力。近期，我们与其他研究团队均已证实，可采用一组多样化近交系小鼠作为QTL定位群体。该定位群体具备诸多优势：表型多样性更丰富、重组频率更高，且基因型数据可从公共社区数据库中获取。然而，此类方法仍存在局限：受固有群体结构的干扰，且无法准确评估统计效力，导致分析流程复杂化。为解决上述问题，我们对该多样化近交系定位群体的下丘脑组织进行了基因表达水平检测。随后，我们通过关联算法将这些表型作为数量性状进行定位分析，获得了大量表达数量性状位点（expression QTLs, eQTLs）。我们利用这些eQTL，尤其是顺式作用eQTL（cis-eQTLs），设计了一种无需依赖参数化模拟数据的统计效力相对评估方法。最终，我们依托该方法开发并优化了一种针对小鼠多样性面板（Mouse Diversity Panel）的群体结构校正新方法。关键词：样本取自不同品系、不同性别的未接受过实验处理的（naive）小鼠。