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一种对 翅膀形状的多变量全基因组关联研究。

A Multivariate Genome-Wide Association Study of Wing Shape in .

机构信息

Department of Integrative Biology, Program in Ecology, Evolutionary Biology and Behavior, Michigan State University, East Lansing, Michigan.

Department of Biological Science, Florida State University, Tallahassee, Florida.

出版信息

Genetics. 2019 Apr;211(4):1429-1447. doi: 10.1534/genetics.118.301342. Epub 2019 Feb 21.

Abstract

Due to the complexity of genotype-phenotype relationships, simultaneous analyses of genomic associations with multiple traits will be more powerful and informative than a series of univariate analyses. However, in most cases, studies of genotype-phenotype relationships have been analyzed only one trait at a time. Here, we report the results of a fully integrated multivariate genome-wide association analysis of the shape of the wing in the Genetic Reference Panel. Genotypic effects on wing shape were highly correlated between two different laboratories. We found 2396 significant SNPs using a 5% false discovery rate cutoff in the multivariate analyses, but just four significant SNPs in univariate analyses of scores on the first 20 principal component axes. One quarter of these initially significant SNPs retain their effects in regularized models that take into account population structure and linkage disequilibrium. A key advantage of multivariate analysis is that the direction of the estimated phenotypic effect is much more informative than a univariate one. We exploit this fact to show that the effects of knockdowns of genes implicated in the initial screen were on average more similar than expected under a null model. A subset of SNP effects were replicable in an unrelated panel of inbred lines. Association studies that take a phenomic approach, considering many traits simultaneously, are an important complement to the power of genomics.

摘要

由于基因型-表型关系的复杂性,同时分析基因组与多个性状的关联将比一系列单变量分析更强大和更有信息。然而,在大多数情况下,基因型-表型关系的研究一次只分析一个性状。在这里,我们报告了对遗传参考面板中翅膀形状进行全集成多变量全基因组关联分析的结果。在两个不同实验室之间,翅膀形状的基因型效应高度相关。我们在多变量分析中使用 5%的错误发现率截止值发现了 2396 个显著 SNP,但在第一 20 个主成分轴得分的单变量分析中只有 4 个显著 SNP。这些最初显著 SNP 的四分之一在正则化模型中保留了其效应,该模型考虑了种群结构和连锁不平衡。多变量分析的一个主要优势是,估计表型效应的方向比单变量更有信息量。我们利用这一事实表明,在初始筛选中涉及的基因敲低的影响平均比在零假设下更相似。SNP 效应的一部分在一组不相关的近交系中具有可重复性。同时考虑多个性状的表型方法的关联研究是对基因组学力量的重要补充。

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