Using a Machine Learning Approach to Identify Low-Frequency and Rare FLG Alleles Associated with Remission of Atopic Dermatitis

Abstract

Atopic dermatitis (AD) is a common relapsing inflammatory skin disease. FLG is the gene most consistently associated with AD. Loss-of-function variants in FLG have been previously associated with AD. Low-frequency and rare alleles (minor allele frequency < 5%) in this gene have been given less attention than loss-of-function variants. We fine sequenced the FLG gene in a cohort of individuals with AD. We developed a machine learning‒based algorithm to associate low-frequency and rare alleles with the disease. We then applied this algorithm to the FLG data, searching for associations between groups of low-frequency and rare FLG alleles and AD remission. A group of 46 rare and low-frequency FLG alleles was associated with increased AD remission (P = 2.76e-11). Overall, 16 of these 46 FLG variants were identified in an independent cohort and were associated with decreased AD incidence (P = 0.0007). This study presents an application of statistical methods in AD genetics and suggests that low-frequency and rare alleles may play a larger role in AD pathogenesis than previously appreciated.