Sex Differences in Disease Genetics

Abstract There is long‐standing evidence for the gene‐by‐sex interactions in disease risk, which can now be tested in genome‐wide association studies with participant numbers in the hundreds of thousands. The current methods start with a separate test for each sex, but a more powerful approach is to use sex as an interaction term in a combined sample. The most compelling evidence is for adiposity (predictive of cardiac disease) as well as type II diabetes, asthma and inflammatory bowel disease. Autism exhibits a different kind of sex difference, with hypermasculinisation of the brain, and the intriguing enrichment of structural variants in females. Sexually dimorphic gene expression varies exquisitely and unexpectedly, by tissue, age and chromosome, so sex‐dependent genetic effects are expected for a wide range of diseases. Because natural selection against sex‐dependent risk alleles is in one sex only, their effect size is expected to be greater than conventional risk loci. Key Concepts Compelling findings of sex‐dependent genetic effects on disease have been made in adiposity‐related anthropometric traits, type II diabetes and inflammatory bowel disease, and other disorders remain to be more fully investigated, regardless of what sexual dimorphism they exhibit in prevalence and presentation. Initial evidence indicates that sex difference in gene expression is not required for a sex‐dependent genetic effect on gene expression. However, sex differences in expression levels vary dynamically by tissue type and age, so such generalisations may be inaccurate, without more comprehensive data. Sex‐dependent risk alleles are predicted to be of greater effect size than conventional ones, because natural selection acts only against the sex which has the disease. There is evidence for this from a high‐powered GWAS of adiposity‐related traits. Sexual dimorphism in gene expression seems likely to fall into different categories, and thus categorisation of sex‐dependent genetic effects is expected to follow the same patterns. Many of the large GWAS meta‐analyses look for sex‐dependent genetic effects by testing male and female groups separately. However, this may be underpowered compared to a whole‐sample, gene‐by‐sex interaction test.