酒精使用障碍鉴定试验
审计
全基因组关联研究
外显子组
遗传关联
荟萃分析
外显子组测序
心理学
医学
遗传学
生物
内科学
表型
毒物控制
基因型
基因
会计
伤害预防
医疗急救
单核苷酸多态性
业务
作者
Mohammad Ahangari,Amanda Elswick Gentry,Mohammed Falih Hassan,Tan Hoang Nguyen,Kenneth S. Kendler,Silviu‐Alin Bacanu,Roseann E. Peterson,Brien P. Riley,Bradley T. Webb
标识
DOI:10.1101/2023.09.11.557163
摘要
Alcohol use disorder (AUD) is moderately heritable with significant social and economic impact. Genome-wide association studies (GWAS) have identified common variants associated with AUD, however, rare variant investigations have yet to achieve well-powered sample sizes. In this study, we conducted an interval-based exome-wide analysis of the Alcohol Use Disorder Identification Test Problems subscale (AUDIT-P) using both machine learning (ML) predicted risk and empirical functional weights. This research has been conducted using the UK Biobank Resource (application number 30782.) Filtering the 200k exome release to unrelated individuals of European ancestry resulted in a sample of 147,386 individuals with 51,357 observed and 96,029 unmeasured but predicted AUDIT-P for exome analysis. Sequence Kernel Association Test (SKAT/SKAT-O) was used for rare variant (Minor Allele Frequency (MAF) < 0.01) interval analyses using default and empirical weights. Empirical weights were constructed using annotations found significant by stratified LD Score Regression analysis of predicted AUDIT-P GWAS, providing prior functional weights specific to AUDIT-P. Using only samples with observed AUDIT-P yielded no significantly associated intervals. In contrast,
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