DOI: 10.48802/owey.8o28ij71
title: Genome-wide genotype data in the Milieu Intérieur cohort

description: To identify the effects of genetic factors on variation in the immune measurements, we performed genome-wide DNA genotyping of 1000 healthy adults from the Milieu Intérieur cohort. SNP array-based genotype calls for (i) 663,396 SNPs and 1,000 donors (Illumina HumanOmniExpress-24 BeadChip) and (ii) 87,960 SNPs in exonic regions and 955 donors (Illumina HumanExome-12 BeadChip) were combined, to obtain 732,341 polymorphic SNPs in the 1,000 donors; 5,699,237 genotyped and imputed SNPs were obtained in the 1,000 donors, after imputation with IMPUTE2. We imputed HLA alleles from the SNP array data, using the SNP2HLA tool.
Analyzed datasets are provided in PLINK bed, bim, fam formats, including post-QC pre-imputation SNP array data, for the whole genome or the HLA locus, and the post-imputation data. Imputed HLA alleles are provided as a txt file specifically, in the form of a matrix with 956 donors as rows and 8 HLA loci as columns.

description: The 1,000 subjects of the Milieu Intérieur cohort were genotyped at 719,665 SNPs by the HumanOmniExpress-24 BeadChip (Illumina, California). 966 of the 1,000 donors were also genotyped at 245,766 exonic SNPs by the HumanExome-12 BeadChip (Illumina, California). Genotype calling was performed with GenomeStudio. We filtered out from both data sets SNPs that: (i) were unmapped on dbSNP138, (ii) were duplicated, (iii) had a low genotype clustering quality (GenTrain score < 0.35), (iv) had a call rate of < 99%, (v) were monomorphic, (vi) were on sex chromosomes and (vii) were in Hardy-Weinberg disequilibrium (HWE) (P < 10−7). These SNP quality-control filters yielded a total of 661,332 SNPs and 87,960 SNPs for the HumanOmniExpress and HumanExome BeadChips, respectively. The two data sets were then merged, after excluding triallelic SNPs, SNPs with discordant alleles between arrays (even after allele flipping), SNPs with discordant chromosomal position, and SNPs shared between arrays that presented a genotype concordance rate of < 99%. Average concordance rate for the 16,753 SNPs shared between the two genotyping platforms was 99.9925%, and individual concordance rates ranged from 99.80% to 100%, which confirmed that no problem occurred during DNA sample processing. Prior to imputation, we phased the final SNP data set with SHAPEIT2 using 500 conditioning haplotypes, 50 MCMC iterations, and 10 burn-in and 10 pruning iterations. Genotype imputation was performed by IMPUTE v.2, considering 1-Mb windows and a buffer region of 1 Mb. Out of the 37,895,612 SNPs obtained after imputation, 37,164,442 were imputed. We removed 26,005,463 imputed SNPs with information ≤ 0.8, 43,737 duplicated SNPs, 955 monomorphic SNPs, and 449,903 SNPs with missingness > 5% (individual genotype probabilities < 0.8 were considered as missing data). After quality-control filters, a total of 11,395,554 high-quality SNPs were further filtered for minor allele frequencies > 5%, yielding a final set of 5,699,237 SNPs for association analyses. Files for the corresponding datasets are named “LabExMI_imputation_1000x11395554.*” and “LabExMI_imputation_1000x5699237.*”.