Transethnic analysis of psoriasis susceptibility in South Asians and Europeans enhances fine mapping in the MHC and genome wide

Journal of Investigative Dermatology(2022)

Cited 7|Views10
No score
Abstract
Because transethnic analysis may facilitate prioritization of causal genetic variants, we performed a genome-wide association study (GWAS) of psoriasis in South Asians (SAS), consisting of 2,590 cases and 1,720 controls. Comparison with our existing European-origin (EUR) GWAS showed that effect sizes of known psoriasis signals were highly correlated in SAS and EUR (Spearman ρ = 0.78; p < 2 × 10−14). Transethnic meta-analysis identified two non-major histocompatibility complex (non-MHC) psoriasis loci (1p36.22 and 1q24.2) not previously identified in EUR, which may have regulatory roles. For these two loci, the transethnic GWAS provided higher genetic resolution and reduced the number of potential causal variants compared to using the EUR sample alone. We then explored multiple strategies to develop reference panels for accurately imputing MHC genotypes in both SAS and EUR populations and conducted a fine mapping of MHC psoriasis associations in SAS and the largest such effort for EUR. HLA-C∗06 was the top-ranking MHC locus in both populations but was even more prominent in SAS based on odds ratio, disease liability, model fit, and predictive power. Transethnic modeling also substantially boosted the probability that the HLA-C∗06 protein variant is causal. Secondary MHC signals included coding variants of HLA-C and HLA-B, but also potential regulatory variants of these two genes as well as HLA-A and several HLA class II genes, with effects on both chromatin accessibility and gene expression. This study highlights the shared genetic basis of psoriasis in SAS and EUR populations and the value of transethnic meta-analysis for discovery and fine mapping of susceptibility loci.
More
Translated text
Key words
genome-wide association study,major histocompatibility complex,human leukocyte antigens,imputation,psoriasis,skin diseases
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined