Evolvability of individual traits in a multivariate context: Partitioning the additive genetic variance into common and specific components

McGuigan, Katrina and Blows, Mark W. (2010) Evolvability of individual traits in a multivariate context: Partitioning the additive genetic variance into common and specific components. Evolution, 64 7: 1899-1911. doi:10.1111/j.1558-5646.2010.00968.x

Author McGuigan, Katrina
Blows, Mark W.
Title Evolvability of individual traits in a multivariate context: Partitioning the additive genetic variance into common and specific components
Journal name Evolution   Check publisher's open access policy
ISSN 0014-3820
Publication date 2010-07-01
Year available 2010
Sub-type Article (original research)
DOI 10.1111/j.1558-5646.2010.00968.x
Open Access Status DOI
Volume 64
Issue 7
Start page 1899
End page 1911
Total pages 13
Editor Nicholas Barton
Mark D. Rausher
Jennifer Mahar
Place of publication Lancaster, Pa., U.S.A.
Publisher Society for the Study of Evolution
Language eng
Subject C1
970106 Expanding Knowledge in the Biological Sciences
0604 Genetics
Abstract Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg(2) despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease.
Formatted abstract
Genetic covariation among multiple traits will bias the direction of evolution. Although a trait's phenotypic context is crucial for understanding evolutionary constraints, the evolutionary potential of one (focal) trait, rather than the whole phenotype, is often of interest. The extent to which a focal trait can evolve independently depends on how much of the genetic variance in that trait is unique. Here, we present a hypothesis-testing framework for estimating the genetic variance in a focal trait that is independent of variance in other traits. We illustrate our analytical approach using two Drosophila bunnanda trait sets: a contact pheromone system comprised of cuticular hydrocarbons (CHCs), and wing shape, characterized by relative warps of vein position coordinates. Only 9% of the additive genetic variation in CHCs was trait specific, suggesting individual traits are unlikely to evolve independently. In contrast, most (72%) of the additive genetic variance in wing shape was trait specific, suggesting relative warp representations of wing shape could evolve independently. The identification of genetic variance in focal traits that is independent of other traits provides a way of studying the evolvability of individual traits within the broader context of the multivariate phenotype.
©2010 The Author(s). Journal compilation ©2010 The Society for the Study of Evolution.
Keyword Factor analysis
Sexual selection
Affecting wing shape
Morphological integration
Phenotypic evolution
Covariance matrices
Quantitative genetics
Principal components
Sexual selection
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID F32 GM106584
T32 MH020030
R03 HG006731
R01 AR063759
R01 GM105857
R01 MH101244
U01 MH094432
U01 MH096296
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Biological Sciences Publications
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Citation counts: TR Web of Science Citation Count  Cited 24 times in Thomson Reuters Web of Science Article | Citations
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Created: Sun, 25 Jul 2010, 10:06:53 EST