Data from: Multivariate analysis of genotype-phenotype association
Mitteroecker, Philipp; Cheverud, James M.; Pavlicev, Mihaela (2016), Data from: Multivariate analysis of genotype-phenotype association, Dryad, Dataset, https://doi.org/10.5061/dryad.fc55k
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.