Polygenic prediction of atopic dermatitis improves with atopic training and filaggrin factors

Abstract

Background: While numerous genetic loci associated with atopic dermatitis (AD) have been discovered, to date, work leveraging the combined burden of AD risk variants across the genome to predict disease risk has been limited.

Objectives: This study aims to determine whether polygenic risk scores (PRSs) relying on genetic determinants for AD provide useful predictions for disease occurrence and severity. It also explicitly tests the value of including genome-wide association studies of related allergic phenotypes and known FLG loss-of-function (LOF) variants.

Methods: AD PRSs were constructed for 1619 European American individuals from the Atopic Dermatitis Research Network using an AD training dataset and an atopic training dataset including AD, childhood onset asthma, and general allergy. Additionally, whole genome sequencing data were used to explore genetic scoring specific to FLG LOF mutations.

Results: Genetic scores derived from the AD-only genome-wide association studies were predictive of AD cases (PRS_AD: odds ratio [OR], 1.70; 95% CI, 1.49-1.93). Accuracy was first improved when PRSs were built off the larger atopy genome-wide association studies (PRS_AD+: OR, 2.16; 95% CI, 1.89-2.47) and further improved when including FLG LOF mutations (PRS_AD++: OR, 3.23; 95% CI, 2.57-4.07). Importantly, while all 3 PRSs correlated with AD severity, the best prediction was from PRS_AD++, which distinguished individuals with severe AD from control subjects with OR of 3.86 (95% CI, 2.77-5.36).

Conclusions: This study demonstrates how PRSs for AD that include genetic determinants across atopic phenotypes and FLG LOF variants may be a promising tool for identifying individuals at high risk for developing disease and specifically severe disease.

Keywords: Atopic dermatitis; allergic disease; atopic march; disease prediction; filaggrin; genetic architecture; genetic predisposition; polygenic risk score.

FIG 1.

Workflow diagram for PRS_AD (orange), PRS_AD+ (green), and PRS_AD++ (yellow) PRS derivation and validation. GRS, Genetic risk score.

FIG 2.

PRS_AD PRS_AD+, and PRS_AD++. There is increased separation between cases and controls from left to right with the addition of related GWAS (PRS_AD+) and FLG mutations (PRS_AD++) and from top to bottom with increasing AD severity. The bottom right plot of PRS_AD++ for severe AD versus controls has no interquartile overlap and an AUC of 0.82.

FIG 3.

Quantile plots describe ORs and 95% CIs for each quantile relative to the median quantile (40%, 50%) as a predictor of AD case status. Noting the nonlinear spacing of tick marks on the log-transformed y-axis, the ORs illustrate the most distinction at the extreme quantiles for the PRS_AD++. There is notable ability of PRS–especially at the extremes–to distinguish between cases and controls for this complex disease.

FIG 4.

The top 3 × 3 stacked bar plots present the tally of cases and controls within each quantile of these PRS distributions. Cases tend to increase in frequency toward the upper quantiles while controls are increasingly common among the lower quantiles. This trend strengthens rightward (AD to AD+ to AD++) and downward (increased severity). In the bottom 3 plots, the gray bars in the background illustrate how roughly 50% of the individuals within each quantile had WGS data for investigation of the 4 FLG LOF genotypes. Among individuals with WGS data, the colored lines detail the percent of subjects who are carriers for R501X, 2282del4, R2447X, and S3247X; solid lines indicate percentage of cases, and dashed lines represent percentage of controls who are carriers within each quantile.