DOI: 10.5061/dryad.fc55k
title: Data from: Multivariate analysis of genotype-phenotype association

description: 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.
description: AllChromosomesF2F3This file contains genotype scores for 2060 specimens of the F2 and F3 generations of an intercross of inbred LG/J and SM/J mice, obtained from The Jackson Laboratory (for details see Cheverud et al. 1996; Norgard et al. 2008). Each individual was genotyped at 384 polymorphic SNPs. In our study we used only individuals without missing data and the 353 SNPs on the 19 autosomal chromosomes. Cheverud, J. M., E. J. Routman, F. A. Duarte, B. van Swinderen, K. Cothran et al., 1996 Quantitative trait loci for murine growth. Genetics 142: 1305–1319. Norgard, E. A., C. C. Roseman, G. L. Fawcett, M. Pavlicev, C. D. Morgan et al., 2008 Identification of quantitative trait loci affecting murine long bone length in a two-generation intercross of lg/j and sm/j mice. J. Bone Miner. Res. 23(6): 887–895.Chrom6_phenotypesAdditive and dominance genotype scores for each of the 22 screened SNPs on chromosome 6 as well as for 89 loci imputed every 1 cM. The last 11 columns comprise the phenotypic measurements: total body weight (WTN), the weights of the reproductive fat pad (FP), the heart (HT), the kidney (KD), the spleen (SP), and the liver (LV), as well as tail length (TL) and the lengths of the right set of long bones (HUM, ULN, FEM, TIB). Measurements were corrected for the effects of sex, litter size, and age at necropsy.