Sex-specific differences in effect size estimates at established complex trait loci is a research paper published in International Journal of Epidemiology (2012). On theSindex it has a DataRank of 0.470. It has been cited 22 times.
BackgroundGenetic differences between men and women may contribute to sex differences in prevalence and progression of many common complex diseases. Using the WTCCC GWAS, we analysed whether there are sex-specific differences in effect size estimates at 142 established loci for seven complex diseases: rheumatoid arthritis, type 1 diabetes (T1D), Crohn's disease, type 2 diabetes (T2D), hypertension, coronary artery disease and bipolar disorder.MethodsFor each Single nucleotide polymorphism (SNP), we calculated the per-allele odds ratio for each sex and the relative odds ratios (RORs; the effect size is higher in men with ROR greater than one). RORs were then meta-analysed across loci within each disease and across diseases.ResultsFor each disease, summary RORs were not different from one, but there was between-SNP heterogeneity in the RORs for T1D and T2D. Four loci in T1D, three in Crohn's disease and three in T2D showed differences in the genetic effect between men and women (PConclusionOur results exclude the presence of large and frequent differences in the effect size estimates between men and women for the established loci in the seven common diseases explored. Documenting small differences in genetic effects between men and women requires large studies and systematic evaluation.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.470
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.