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We examined sex differences in the genetics of thirty-three blood and urine lab test levels (biomarkers) in the UK Biobank population. We built a Bayesian Mixture Model to estimate the fraction of genetic variance shared between men and women for a given trait and used an extension of this model to identify genetic variants with sex-specific effects. The majority of these biomarkers did not show sex-related effects, with the exception of testosterone. For testosterone, we found a large number of genetic variants that showed effects in males but not females and vice versa. We built models to predict testosterone level from genetics, and found that sex-specific models outperformed sex combined models. Additionally, we used the sex-specific genetic variants associated with testosterone to examine the relationship between testosterone and several phenotypic and disease traits. We found evidence for a relationship between testosterone and height, body mass index, waist and hip circumference, and type 2 diabetes. Overall, our results demonstrate that while sex has a limited role in most of the genetics of most of the lab tests we examined, it plays a large role in the genetics of testosterone.
Generating effective therapeutic hypotheses from genomic and hospital linkage data
This proposal seeks access to UK Biobank data to support efforts to generate effect therapeutic hypotheses from genomic and hospital in-patient data. We have developed novel statistical methods to assess the impact of genetic variation across a broad range of disease outcomes. We plan to take advantage of the tree structure of the ICD-10 codes to improve inference. By doing so we hope to prioritize genetic effects that are consistent with a protective profile. This will result in a set of therapeutic hypotheses that academics, pharmaceutical companies, and the public may be able to pursue.