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Sex differences in disease genetics: evidence, evolution, and detection.

Gilks, William P; Abbott, Jessica LU and Morrow, Edward H (2014) In Trends in Genetics 30(10). p.453-463
Abstract
Understanding the genetic architecture of disease is an enormous challenge, and should be guided by evolutionary principles. Recent studies in evolutionary genetics show that sexual selection can have a profound influence on the genetic architecture of complex traits. Here, we summarise data from heritability studies and genome-wide association studies (GWASs) showing that common genetic variation influences many diseases and medically relevant traits in a sex-dependent manner. In addition, we discuss how the discovery of sex-dependent effects in population samples is improved by joint interaction analysis (rather than separate-sex), as well as by recently developed software. Finally, we argue that although genetic variation that has... (More)
Understanding the genetic architecture of disease is an enormous challenge, and should be guided by evolutionary principles. Recent studies in evolutionary genetics show that sexual selection can have a profound influence on the genetic architecture of complex traits. Here, we summarise data from heritability studies and genome-wide association studies (GWASs) showing that common genetic variation influences many diseases and medically relevant traits in a sex-dependent manner. In addition, we discuss how the discovery of sex-dependent effects in population samples is improved by joint interaction analysis (rather than separate-sex), as well as by recently developed software. Finally, we argue that although genetic variation that has sex-dependent effects on disease risk could be maintained by mutation-selection balance and genetic drift, recent evidence indicates that intra-locus sexual conflict could be a powerful influence on complex trait architecture, and maintain sex-dependent disease risk alleles in a population because they are beneficial to the opposite sex. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Trends in Genetics
volume
30
issue
10
pages
453 - 463
publisher
Elsevier
external identifiers
  • pmid:25239223
  • wos:000342609200004
  • scopus:84921914031
ISSN
1362-4555
DOI
10.1016/j.tig.2014.08.006
language
English
LU publication?
yes
id
7436699d-edd0-42dd-8b16-062cf6592d62 (old id 4691195)
date added to LUP
2014-10-08 13:37:58
date last changed
2017-11-05 03:22:15
@article{7436699d-edd0-42dd-8b16-062cf6592d62,
  abstract     = {Understanding the genetic architecture of disease is an enormous challenge, and should be guided by evolutionary principles. Recent studies in evolutionary genetics show that sexual selection can have a profound influence on the genetic architecture of complex traits. Here, we summarise data from heritability studies and genome-wide association studies (GWASs) showing that common genetic variation influences many diseases and medically relevant traits in a sex-dependent manner. In addition, we discuss how the discovery of sex-dependent effects in population samples is improved by joint interaction analysis (rather than separate-sex), as well as by recently developed software. Finally, we argue that although genetic variation that has sex-dependent effects on disease risk could be maintained by mutation-selection balance and genetic drift, recent evidence indicates that intra-locus sexual conflict could be a powerful influence on complex trait architecture, and maintain sex-dependent disease risk alleles in a population because they are beneficial to the opposite sex.},
  author       = {Gilks, William P and Abbott, Jessica and Morrow, Edward H},
  issn         = {1362-4555},
  language     = {eng},
  number       = {10},
  pages        = {453--463},
  publisher    = {Elsevier},
  series       = {Trends in Genetics},
  title        = {Sex differences in disease genetics: evidence, evolution, and detection.},
  url          = {http://dx.doi.org/10.1016/j.tig.2014.08.006},
  volume       = {30},
  year         = {2014},
}