Bereket, M. | Search bioRxiv

bioRxiv2024

HistoGWAS: An AI-enabled Framework for Automated Genetic Analysis of Tissue Phenotypes in Histology Cohorts

Understanding how genetic variation affects tissue structure and function is crucial for deciphering disease mechanisms, yet comprehensive methods for genetic analysis of tissue histology are lacking. We address this gap with HistoGWAS, a framework integrating AI tools for representation learning and image generation with fast variance component models to enable scalable and interpretable genome-wide association studies of histological traits. HistoGWAS employs histology foundation models for automated trait characterization and generative models to visually interpret the genetic influences on these traits. Applied to eleven tissue types from the GTEx cohort, HistoGWAS identifies four genome-wide significant loci, which we linked to specific tissue histological and gene expression changes. A power analysis confirms the effectiveness of HistoGWAS in analyses of large-scale histological data, underscoring its potential to transform imaging genetic studies.

genetics↗

bioRxiv2022

An allelic series rare variant association test for candidate gene discovery

Allelic series are of candidate therapeutic interest due to the existence of a dose-response relationship between the functionality of a gene and the degree or severity of a phenotype. We define an allelic series as a gene in which increasingly deleterious mutations lead to increasingly large phenotypic effects, and develop a gene-based rare variant association test specifically targeted for the identification of allelic series. Building on the well-known burden and sequence kernel association (SKAT) tests, we specify a variety of association models, covering different genetic architectures, and integrate these into a COding-variant Allelic Series Test (COAST). Through extensive simulations, we confirm that COAST maintains the type I error and improves power when the pattern of coding-variant effect sizes increases monotonically with mutational severity. We applied COAST to identify allelic series for 4 circulating lipid traits and 5 cell count traits among 145,735 subjects with available whole exome sequencing data from the UK Biobank. Compared with optimal SKAT (SKAT-O), COAST identified 29% more Bonferroni significant associations with circulating lipid traits, on average, and 82% more with cell count traits. All of the gene-trait associations identified by COAST have corroborating evidence either from rare-variant associations in the full cohort (Genebass, N = 400K), or from common variant associations in the GWAS catalog. In addition to detecting many gene-trait associations present in Genebass using only a fraction (36.9%) of the sample, COAST detects associations, such as ANGPTL4 with triglycerides, that are absent from Genebass but which have clear common variant support.

genetics↗