Moderate effect size genes in autism spectrum disorder - Project Summary Considerable progress has been made in identifying highly penetrant genes for autism spectrum disorder (ASD), particularly those involving deleterious de novo mutations that are evolutionarily constrained and crucial for developmental processes. These mutations typically result in syndromic forms of ASD with severe symptoms, but they are rare and do not account for the majority of ASD cases. In contrast, identifying genes with a moderate effect size (MES), which may explain the more heritable, mild, and broader ASD spectrum, has been limited. This project aims to identify and characterize MES risk genes by integrating both rare and common genetic variation. Our central hypothesis is that MES genes significantly contribute to ASD liability and manifestation, particularly among individuals with a high genetic load of common variation. The central hypothesis will be tested by pursuing four specific aims: (1) Identify and characterize MES risk genes using rare variation stratified by common variation; (2) Identify MES risk genes on chromosome X using rare variation stratified by common variation; (3) Identify MES risk genes contributing to ASD co-occurring conditions; and (4) Cluster MES risk genes to identify biological pathways relevant to ASD heterogeneity. This research is significant because it sheds light on the role of MES risk genes in ASD liability and presentation, enhancing diagnostic accuracy and supporting clinical applications such as genetic counseling and individualized treatment options. The most innovative parts of our proposal include (1) a novel gene discovery approach targeting MES risk genes typically overlooked due to their subtler effects, (2) the integration of both common and rare genetic variations to comprehensively understand their interplay in ASD, (3) investigating sex- and genetic ancestry-specific ASD risk factors, particularly on chromosome X, and (4) identifying genetic factors that correspond to the heterogeneity within ASD. Importantly, although methodologically innovative, we use large, established collections of individuals diagnosed with ASD, their family members, and population controls, re-purposing these datasets to produce novel and clinically useful findings.
