A statistical genetics guide to identifying HLA alleles driving complex disease

Abstract

The human leukocyte antigen (HLA) locus is associated with more human complex diseases than any other locus. In many diseases it explains more heritability than all other known loci combined. Investigators have now demonstrated the accuracy of in silico HLA imputation methods. These approaches enable rapid and accurate estimation of HLA alleles in the millions of individuals that are already genotyped on microarrays. HLA imputation has been used to define causal variation in autoimmune diseases, such as type I diabetes, and infectious diseases, such as HIV infection control. However, there are few guidelines on performing HLA imputation, association testing, and fine-mapping. Here, we present comprehensive statistical genetics guide to impute HLA alleles from genotype data. We provide detailed protocols, including standard quality control measures for input genotyping data and describe options to impute HLA alleles and amino acids including a web-based Michigan Imputation Server. We updated the HLA imputation reference panel representing global populations (African, East Asian, European and Latino) available at the Michigan Imputation Server (n = 20,349) and achived high imputation accuracy (mean dosage correlation r = 0.981). We finally offer best practice recommendations to conduct association tests in order to define the alleles, amino acids, and haplotypes affecting human traits. This protocol will be broadly applicable to the large-scale genotyping data and contribute to defining the role of HLA in human diseases across global populations.

Mapping the dynamic genetic regulatory architecture ofHLAgenes at single-cell resolution

“…Using SNP2HLA with our group’s multi-ancestry HLA reference panel ( 24, 33, 34 ) ( Methods, Fig. 1B, Fig.…”

“…All cohorts were genotyped using genotyping arrays, except for PBMC-cultured, which used low-pass whole-genome sequencing (WGS) ( Table S1 ). We processed the genotyping data and performed QC using PLINK v1.90, as described below following the tutorial at https://github.com/immunogenomics/HLA_analyses_tutorial/blob/main/tutorial_HLAQCImputation.ipyn b ( 24 ). Genome-wide variants were used to calculate PCs to control for genetic ancestry in eQTL analysis, and variants in the extended MHC (defined here as chr 6: 28000000-34000000) were used for HLA imputation.…”

“…We used SNP2HLA (https://github.com/immunogenomics/HLA_analyses_tutorial/blob/main/scripts/SNP2HLA.py) to perform HLA imputation using version 2 of our group’s multi-ethnic reference panel described in Sakaue et al ( 24, 33, 34 ). We performed imputation on the full genotyping datasets (i.e., not limited to samples with paired scRNA-seq), then subset the imputed VCF file to the samples with scRNA-seq.…”

“…Reads can fail to align if an individual's allele is dissimilar from the reference allele, resulting in unmapped reads, or reads can "multi-map" to multiple HLA genes due to sequence similarity between genes (24). This bias confounds eQTL analysis, making it difficult to distinguish genuine genetic associations with HLA expression from inaccurate read alignment.…”

“…Because HLA genes are highly polymorphic, standard short-read sequencing pipelines that align reads to a single reference genome are biased when quantifying HLA expression ( 22, 23 ). Reads can fail to align if an individual’s allele is dissimilar from the reference allele, resulting in unmapped reads, or reads can “multi-map” to multiple HLA genes due to sequence similarity between genes ( 24 ). This bias confounds eQTL analysis, making it difficult to distinguish genuine genetic associations with HLA expression from inaccurate read alignment.…”