Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Leveraging pleiotropy identifies common-variant associations with selective IgA deficiency

Selective IgA deficiency (SIgAD) is the most common inborn error of immunity (IEI). Unlike many IEIs, evidence of a role for highly penetrant rare variants in SIgAD is lacking. Previous SIgAD studies have had limited power to identify common variants due to their small sample size. We overcame this problem first through meta-analysis of two existing GWAS. This identified four novel common-variant associations and enrichment of SIgAD-associated variants in genes linked to Mendelian IEIs. SIgAD showed evidence of shared genetic architecture with serum IgA and a number of immune-mediated diseases. We leveraged this pleiotropy through the conditional false discovery rate procedure, conditioning our SIgAD meta-analysis on large GWAS of asthma and rheumatoid arthritis, and our own meta-analysis of serum IgA. This identified an additional 18 variants, increasing the number of known SIgAD-associated variants to 27 and strengthening the evidence for a polygenic, common-variant aetiology for SIgAD.

Shared genetic links between bladder cancer and obesity-related traits: A conjunctional false discovery rate study

Bladder cancer (BCa) is a common cancer worldwide and is often linked with obesity-related comorbidities, but little is known about the underlying genetic mechanisms. To investigate these mechanisms, we used various quantitative tools, including conditional quantile-quantile (Q-Q) plots, conditional false discovery rate (cFDR), and conjunctional conditional false discovery rate (ccFDR), to explore the pleiotropic enrichment of risk loci between BCa and obesity-related traits. We also performed an expression quantitative trait locus (eQTL) analysis to assess the relationship between shared risk loci and gene expression. Finally, we conducted functional annotation using Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analysis. Our findings indicated that there was successive enrichment for a range of obesity-related traits, including body fat percentage, body mass index, fasting insulin, type 2 diabetes mellitus, fasting glucose, high-density lipoprotein cholesterol, total triglycerides, and waist-to-hip ratio. Using the tools mentioned above, we identified 18 significant SNPs and 18 closely related genes (cFDR<0.01) under the condition of 8 obesity-related traits. The SNPs included rs143004880, rs73301337, rs10798572, rs11594929, rs17019138, rs2877, rs149795948, rs142509736, rs12727575, rs1571277, rs12131828, rs635634, rs76895963, rs118081211, rs7044247, rs138895564, rs4135275, and rs148023060. Additionally, we identified 15 novel loci using ccFDR, including rs143004880, rs73301337, rs10798572, rs11594929, rs17019138, rs2877, rs142509736, rs1571277, rs635634, rs76895963, rs12131828, rs118081211, rs7044247, rs138895564, and rs4135275. Of the 2 significant loci that modify gene expression, rs12131828 and rs635634 were identified. The functional annotation indicated that the conditional risk genes mainly participated in the regulation of gene silencing. Our study provided evidence of pleiotropic enrichment between BCa and 8 obesity-related traits, and we identified potential genetic mechanisms underlying this relationship. These findings may help in developing targeted clinical treatments for BCa.

fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS

BACKGROUND

Genome-wide association studies (GWAS) are limited in power to detect associations that exceed the stringent genome-wide significance threshold. This limitation can be alleviated by leveraging relevant auxiliary data, such as functional genomic data. Frameworks utilising the conditional false discovery rate have been developed for this purpose, and have been shown to increase power for GWAS discovery whilst controlling the false discovery rate. However, the methods are currently only applicable for continuous auxiliary data and cannot be used to leverage auxiliary data with a binary representation, such as whether SNPs are synonymous or non-synonymous, or whether they reside in regions of the genome with specific activity states.

RESULTS

We describe an extension to the cFDR framework for binary auxiliary data, called "Binary cFDR". We demonstrate FDR control of our method using detailed simulations, and show that Binary cFDR performs better than a comparator method in terms of sensitivity and FDR control. We introduce an all-encompassing user-oriented CRAN R package ( https://annahutch.github.io/fcfdr/ ; https://cran.r-project.org/web/packages/fcfdr/index.html ) and demonstrate its utility in an application to type 1 diabetes, where we identify additional genetic associations.

CONCLUSIONS

Our all-encompassing R package, fcfdr, serves as a comprehensive toolkit to unite GWAS and functional genomic data in order to increase statistical power to detect genetic associations.