Data from: Multivariate analysis of genotype-phenotype association

With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated—in terms of effect size—with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for more than 70% of genetic variation present in all the 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3—the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or non-additive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms.

随着现代成像与测量技术的发展，复杂表型愈发依赖大量测量数据进行表征，但此类测量数据往往无法逐一赋予生物学意义。针对这类多变量表型，若对所有测量指标与所有等位基因开展两两关联分析，不仅效率极低，更无法深入解析观测表型背后的遗传模式。本研究提出一种新型分析方法，可识别与表型变异模式（表型潜变量，phenotypic latent variables）具有最大效应量关联的等位基因变异模式（遗传潜变量，genetic latent variables）。该多变量基因型-表型映射方法（multivariate genotype-phenotype mapping, MGP）可将受强遗传调控的表型特征与遗传决定度较低的表型特征区分开来，进而能够分析基因型-表型关联的多变量结构，包括其维度以及关联框架内遗传变量与表型变量的聚类情况。MGP存在多种变体，可分别最大化不同的基因型-表型关联衡量指标：遗传效应、遗传方差或遗传力。在针对一份完成了353个单核苷酸多态性（single nucleotide polymorphism, SNP）与11项表型性状分型检测的小鼠样本的应用中，遗传与表型潜变量的第一维度可解释全部11项测量指标中超过70%的遗传变异；该表型模式中43%的变异可由对应的遗传潜变量解释。前三个维度累计可解释测量数据中近90%的遗传变异，以及全部可解读的基因型-表型关联。可将每个维度作为整体开展无关联假设检验，由此将统计检验次数从7766次降至3次——这是具备生物学阐释价值的独立检验的最大可行数量。可基于等位基因对表型潜变量的效应量（加性或非加性效应）筛选出关键等位基因。基因型-表型映射的低维度特性对基因鉴定具有重要意义，同时可为解析生物体的进化潜能提供新视角。