Research Project
10197181
7. PROJECT SUMMARY / ABSTRACT We will conduct the largest and most comprehensive study to date on the genetics of female sexual orientation (SO), a genetically complex trait of major scientific and social interest. Our research will uncover fundamental biological aspects of female SO. We have built a large GWAS collaboration to conduct our specific aims: 1. Conduct a meta-analysis of available GWAS datasets totaling ~10,900 homosexual, ~6,800 bisexual, and ~359,700 heterosexual women. We propose a study of unprecedented size and scope for the genetic study of female SO. We will assemble, harmonize, and analyze available genome-wide genotyping data in order to perform a meta-analysis on these women. 2. Conduct a GWAS on a deeply phenotyped, newly collected sample of women (N=8,000) combined with a previous sample (N=1,949), totaling 3,754 homosexual, 2,843 bisexual, and 3,352 heterosexual women. This represents a ~40% increase in the number of non-heterosexual women for genetic research over Aim 1. The proposed research will fill several gaps as it will provide more detailed phenotyping for SO and correlated traits than previous studies, allow for recontact of subjects for future studies, and be rapidly shared through an NIH data-sharing mechanism. This new sample set will serve an important statistical role by increasing the overall sample size and better balancing the ratio of nonheterosexual to heterosexual women. Aims 1 & 2 combined GWAS will be >387,000 women (plus >347,000 men for combined analyses). 3. Perform multi-locus analyses on the data from Aims 1 & 2, providing insight into functional implications of the loci and identifying additional possible genic targets. We will perform pathway analyses to test for the cumulative effect of functionally related loci, and polygenic score analysis to characterize the overall genetic architecture of female SO. 4. Incorporate correlated phenotypes/traits into analyses to assess confounding and potential genetic correlations and define the shared genetic landscape. We will focus on phenotypic correlations?, including suicidality, depression, anxiety, substance use, gender dysphoria?from the literature and our preliminary data. Several analyses will be conducted: (a) test top variants/genes including correlated phenotypes as covariates to assess potential confounding, (b) evaluate heritability and genetic correlations to quantify genetic effects and identify shared genetic components, and (c) conduct multi-trait analysis to leverage information in correlated phenotypes. By mapping and characterizing genetic variants contributing to trait variation, we will provide important novel and confirmatory insights regarding genetic contributions to female SO, a notoriously understudied area. Finding associated and eventually trait-influencing genetic variants will open a gateway to additional research on both genetic and environmental mechanisms of SO development.
