Degner, J. F. | Search bioRxiv

bioRxiv2020

Estimating colocalization probability from limited summary statistics

11.1 MotivationA common approach to understanding the mechanisms of noncoding GWAS associations is to test the GWAS variant for association with lower level cellular phenotypes such as gene expression. However, significant association to gene expression will often arise from linkage disequilibrium to a separate causal variant and be unrelated to the mechanism underlying the GWAS association. Colocalization is a statistical genetic method used to determine whether the same variant is causal for multiple phenotypes and is stronger evidence for understanding mechanism than shared significance. Current colocalization methods require full summary statistics for both traits, limiting their use with the majority of reported GWAS associations (e.g. GWAS Catalog). We propose a new approximation to the popular coloc method [1] that can be applied when limited summary statistics are available, as in the common scenario where a GWAS catalog hit would be tested for colocalization with a GTEx eQTL. Our method (POint EstiMation of Colocalization - POEMColoc) imputes missing summary statistics using LD structure in a reference panel, and performs colocalization between the imputed statistics and full summary statistics for a second trait. 1.2 ResultsAs a test of whether we are able to approximate the posterior probability of colocalization, we apply our method to colocalization of UK Biobank phenotypes and GTEx eQTL. We show good correlation between posterior probabilities of colocalization computed from imputed and observed UK Biobank summary statistics. We perform simulations and show that the POEMColoc method can identify shared causality with similar accuracy to the coloc method. We evaluate scenarios that might reduce POEMColoc performance and show that multiple independent causal variants in a region and imputation from a limited subset of typed variants have a larger effect while mismatched ancestry in the reference panel has a modest effect. We apply POEMColoc to estimate colocalization of GWAS Catalog entries and GTEx eQTL. We find evidence for colocalization of ~ 150,000 trait-gene-tissue triplets. We find that colocalized trait-gene pairs are enriched in tissues relevant to the etiology of the disease (e.g., thyroid eQTLs are enriched in colocalized hypothyroidism GWAS signals). Further, we find that colocalized trait-gene pairs are enriched in approved drug target - indication pairs. 1.3 AvailabilityPOEMColoc is freely available as an R package at https://github.com/AbbVie-ComputationalGenomics/POEMColoc

bioinformatics↗

bioRxiv2019

Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval.

1Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing historical pipeline data, Nelson et al. 2015 (Nat. Genet.) concluded pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. Taking advantage of recent clinical development advances and rapid growth in GWAS datasets, we extend the original work using updated data, test whether genetic evidence predicts future successes and introduce statistical models adjusting for target and indication-level properties. Our work confirms drugs with genetically supported targets were more likely to be successful in Phases II and III. When causal genes are clear (Mendelian traits and GWAS associations linked to coding variants), we find the use of human genetic evidence increases approval from Phase I by greater than two-fold, and, for Mendelian associations, the positive association holds prospectively. Our findings suggest investments into genomics and genetics are likely to be beneficial to companies deploying this strategy.

genetics↗