Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease

Saori Sakaue, Saisriram Gurajala, Michelle Curtis, Yang Luo, Wanson Choi, Kazuyoshi Ishigaki, Joyce B. Kang, Laurie Rumker, Aaron J. Deutsch, Sebastian Schönherr, Lukas Forer, Jonathon LeFaive, Christian Fuchsberger, Buhm Han, Tobias L. Lenz, Paul I. W. de Bakker, Yukinori Okada, Albert V. Smith, Soumya Raychaudhuri

Published: 2023-07-25 DOI: 10.1038/s41596-023-00853-4

quality control

HLA imputation

genetic association

disease mapping

statistical genetics

AI 解读

Extended

Extended Data Fig. 1 The linkage disequilibrium (LD) patterns across the extended MHC region.

A heatmap of LD r2 for pairwise variants across the extended MHC region. We used biallelic markers in our HLA reference panel within European populations and calculated LD r 2 values for exhaustive pairs of these variants. The variants are ordered (both on x -axis and y -axis) and annotated by HLA gene names (on x -axis) based on their genomic coordinates on chromosome 6. The bottom plot shows the detailed LD pattern in the class II region.

Extended Data Fig. 2 Schematic illustration of method used to construct scaffold variants within multi-ancestry HLA reference panel.

We extracted SNP variants within MHC region in 1000 Genomes Project (1KG) samples. We only retained variants that were included in major genotyping arrays (Illumina Multi-Ethnic Genotyping Array, Global Screening Array, OmniExpressExome, and Human Core Exome), colored in teal. We then quality controlled each of the participating cohorts’ MHC SNPs separately, retained overlapping variants with selected SNPs in 1KG, and cross-imputed each cohort’s missing variants by using 1KG genotypes. We finally concatenate all cohorts together to construct scaffold variants for multi-ancestry reference panel.

Extended Data Fig. 3 Michigan Imputation Server.

Example usage of Michigan Imputation Server for HLA imputation at https://imputationserver.sph.umich.edu/index.html .

Extended Data Fig. 4 The runtime benchmark for HLA imputation using different platforms.

a . For SNP2HLA, we used BEAGLE4 for phasing and imputation algorithm (Luo et al. Nat Genet . 2021) with using 10 CPUs. For Minmac4, we used SHAPEIT2 as phasing algorithm with samples <10,000 and EAGLE2 as phasing algorithm with samples > 5,000 as we described in the manuscript both with using 10 CPUs. b . For Michigan Imputation Server, we uploaded the unphased genotype data and standard imputation pipeline was performed with default setting (with 1CPU).

Use of stable isotope-tagged thymidine and multi-isotope imaging mass spectrometry (MIMS) for quantification of human cardiomyocyte division

Graphene- and metal-induced energy transfer for single-molecule imaging and live-cell nanoscopy with (sub)-nanometer axial resolution

References

Overview of the tutorial
HLA nomenclature
HLA imputation
HLA imputation reference panel
Recommendations for collecting genotype and phenotype information
QC of the target genotype data
Per-individual QC
Per-variant QC
Tools for genotype phasing and HLA imputation
SNP2HLA
MIS
Postimputation QC
HLA association and fine-mapping
Single-marker tests
Omnibus tests for fine-mapping amino acid position
Conditional haplotype tests to define a risk sequence of amino acids
Tests for nonadditivity
Tests for interactions among HLA alleles
HLA evolutionary allele divergence
Multitrait analysis
Concluding remarks
Acknowledgements
Extended Data Fig. 1 The linkage disequilibrium (LD) patterns across the extended MHC region.
Extended Data Fig. 2 Schematic illustration of method used to construct scaffold variants within multi-ancestry HLA reference panel.
Extended Data Fig. 3 Michigan Imputation Server.
Extended Data Fig. 4 The runtime benchmark for HLA imputation using different platforms.

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Tutorial: a statistical genetics guide to identifying HLA alleles driving complex disease

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