Humanexome beadchip

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The HumanExome BeadChip is a high-throughput microarray platform designed for genotyping known exonic variants across the human genome. It provides comprehensive coverage of protein-coding regions, including rare and common variants, to support a wide range of genetic association studies.

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BeadChips (68 citations) HumanExome BeadChip array (11 citations) HumanExome-12 v1.1 or v1.2 DNA Analysis BeadChip (4 citations) HumanExome Beadchip (“Exome Chip”) array (3 citations) OmniExpress + HumanExome BeadChip (2 citations) HumanExome Beadchip v1.0 or v1.1 (2 citations) HumanExome BeadChip-12-1_A (2 citations) HumanExome Beadchip v1.0 array (2 citations) Infinium HumanExome BeadChip kit (2 citations) HumanExome BeadChip 12v1-1 system (2 citations)

88 protocols using humanexome beadchip

Exome Array Genotyping of Lung Function

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All 3487 cases and 1032 GS:SFHS controls were genotyped together using the Illumina Human Exome BeadChip with additional custom content for regions which have previously shown modest association with lung function (description of custom content design in online supplementary methods). The remaining discovery analyses control samples were genotyped separately using the Illumina Human Exome BeadChip.
The UK BiLEVE samples were genotyped using the Affymetrix UK BiLEVE array, which includes rare variants selected from the same sequencing project as the Illumina Human Exome BeadChip alongside additional content.16 (link) Of the 807 411 SNPs included on the Affymetrix UK BiLEVE array, 74 891 were also present on the Illumina Human Exome BeadChip; this subset of SNPs, which were directly genotyped on both arrays, was selected for the discovery meta-analysis.

Jackson V.E., Ntalla I., Sayers I., Morris R., Whincup P., Casas J.P., Amuzu A., Choi M., Dale C., Kumari M., Engmann J., Kalsheker N., Chappell S., Guetta-Baranes T., McKeever T.M., Palmer C.N., Tavendale R., Holloway J.W., Sayer A.A., Dennison E.M., Cooper C., Bafadhel M., Barker B., Brightling C., Bolton C.E., John M.E., Parker S.G., Moffat M.F., Wardlaw A.J., Connolly M.J., Porteous D.J., Smith B.H., Padmanabhan S., Hocking L., Stirrups K.E., Deloukas P., Strachan D.P., Hall I.P., Tobin M.D, & Wain L.V. (2016). Exome-wide analysis of rare coding variation identifies novel associations with COPD and airflow limitation in MOCS3, IFIT3 and SERPINA12. Thorax, 71(6), 501-509.

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Asian Exome Variant Genotyping

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All study participants were genotyped on the HumanExome Bead-Chip (Illumina), and most cohorts (83%) also included the custom Asian Vanderbilt content. This custom content was added to the standard Illumina HumanExome BeadChip to improve the coverage of low frequency variants in Asian populations. The variants were selected from 1077 (581 Chinese women and 496 Singapore Chinese) whole exome sequenced East Asian samples generously provided by Wei Zheng and Jianjun Liu^{30 (link)}. Additional approximately 29K common variants were added to the array including previously identified GWAS variants selected from the GWAS catalogue. Genotype calling was performed with GenTrain version 2.0 in GenomeStudio V2011.1 (Illumina) in combination with zCall version 2.2^{31 (link)}. Within each study, individuals with low genotype completion rates, individuals expressing gender mismatches or a high level of heterozygosity, and related individuals, and PCA outliers were excluded from further analysis (Supplementary Table 16). In addition, variants that did not meet the 95% or 98% genotyping threshold or showed deviation from the Hardy-Weinberg equilibrium were removed.

Lu X., Peloso G.M., Liu D.J., Wu Y., Zhang H., Zhou W., Li J., Tang C.S., Dorajoo R., Li H., Long J., Guo X., Xu M., Spracklen C.N., Chen Y., Liu X., Zhang Y., Khor C.C., Liu J., Sun L., Wang L., Gao Y.T., Hu Y., Yu K., Wang Y., Cheung C.Y., Wang F., Huang J., Fan Q., Cai Q., Chen S., Shi J., Yang X., Zhao W., Sheu W.H., Cherny S.S., He M., Feranil A.B., Adair L.S., Gordon-Larsen P., Du S., Varma R., da Chen Y.D., Shu X., Lam K.S., Wong T.Y., Ganesh S.K., Mo Z., Hveem K., Fritsche L., Nielsen J.B., Tse H.F., Huo Y., Cheng C.Y., Chen Y.E., Zheng W., Tai E.S., Gao W., Lin X., Huang W., Abecasis G., Consortium G., Kathiresan S., Mohlke K.L., Wu T., Sham P.C., Gu D, & Willer C.J. (2017). Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants contributing to lipid levels and coronary artery disease. Nature genetics, 49(12), 1722-1730.

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Asian Exome Variant Genotyping

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HLA-DRB1 Allele Frequency Comparison

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Genetic testing for major histocompatibility complex alleles HLA-DRB1*03:01 and HLA-DRB1*04:01 was performed by imputation from genotype data obtained by using the Illumina Human Exome BeadChip with HIBAG software (Illumina, San Diego, CA) for patients with available DNA samples at the Duke Center for Human Genome Variation.^{23 (link)} Frequencies were compared with allele frequencies from 1,242,890 European whites included in the National Marrow Donor Program, previously described by Gregart et al^{24 (link)} and publicly available at http://allelefrequencies.net.²⁵

de Boer Y.S., Kosinski A.S., Urban T.J., Zhao Z., Long N., Chalasani N., Kleiner D.E, & Hoofnagle J.H. (2016). Features of Autoimmune Hepatitis in Patients With Drug-induced Liver Injury. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association, 15(1), 103-112.e2.

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Genotyping and Ancestry Analysis of a Large Cohort

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All samples included in this study population (n = 3,265) were genotyped using a genome-wide platform, either the Illumina^® HumanOmni1-QUAD (n = 2,430 [74%]) or HumanOmni5-QUAD BeadChip (n = 835 [26%]). The HumanOmni1-QUAD contains 11,675 SNPs in the HLA region and the HumanOmni5-QUAD contains 26,952 SNPs in the HLA region (GRCh37 chr6:28,477,797–33,448,354). In addition, 96% of the samples (n = 3,152) were also typed using the Illumina^® HumanExome BeadChip, which contains putative functional exonic variants and a small amount of non-exonic content including 2,061 HLA tagging SNPs. SNP data from both the HumanExome BeadChip and GWAS platforms were cleaned using the quality control (QC) pipeline developed by the eMERGE Genomics Working Group.[18 (link),19 (link)] Samples were classified as being of European or African descent (≥90% European ancestry for European decent and ≥80% African ancestry for African decent) using ancestry informative markers (AIMs) from genome-wide platforms input into STRUCTURE using Hapmap reference populations.[20 (link)] To further assess admixture, principal components analysis (PCA) was also performed on GWAS data and compared to PCA generated using 1000 Genomes samples.

Karnes J.H., Shaffer C.M., Bastarache L., Gaudieri S., Glazer A.M., Steiner H.E., Mosley J.D., Mallal S., Denny J.C., Phillips E.J, & Roden D.M. (2017). Comparison of HLA allelic imputation programs. PLoS ONE, 12(2), e0172444.

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APOC3 Mutations and Plasma Lipid Levels

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The gene most strongly associated with plasma triglyceride levels in the discovery sample was the gene encoding apolipoprotein C3 (APOC3). To replicate this finding, we genotyped four APOC3 mutations (R19X, IVS2+1G→A, A43T, and IVS3+1G→T), using the HumanExome BeadChip (Illumina) in 41,671 participants of African or European ancestry in seven replication studies (Table S2 in the Supplementary Appendix). This cohort was separate from the discovery cohort. We tested the association of these four mutations with plasma lipid levels. The methods used for genotyping and association testing are described in the Supplementary Appendix.

Loss-of-Function Mutations in APOC3, Triglycerides, and Coronary Disease. (2014). The New England journal of medicine, 371(1), 22-31.

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Detailed Metabolic Profiling and Genomics in Fenland Cohort

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The Fenland study is a longitudinal cohort consisting of 12,435 participants born between 1950 and 1975. Participants were recruited from the general population through GP surgeries in Cambridge, Ely and Wisbech (Cambridgeshire, East Anglia)[32 (link)] and underwent detailed metabolic phenotyping and genome-wide genotyping. Ethical approval for the study was was given by the Cambridge Local Ethics committee (ref. 04/Q0108/19) and all participants gave their written consent prior to entering the study.
A total of 1,145 individuals from the Fenland cohort had both measures of fatmass with GE Lunar iDXA[9 (link)] and Illumina Human Exome BeadChip genotypes after QC checks (Fenland-ExomeChip, Table 1), a further 997 had Illumina Infinium Core Exome data and 7,363 had Affymetrix UK Biobank Axiom array genotype data from which overlapping ExomeChip data was extracted for the purpose of this study (Table 1).

Neville M.J., Wittemans L.B., Pinnick K.E., Todorčević M., Kaksonen R., Pietiläinen K.H., Luan J., Scott R.A., Wareham N.J., Langenberg C, & Karpe F. (2019). Regional fat depot masses are influenced by protein-coding gene variants. PLoS ONE, 14(5), e0217644.

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ADIPOQ Locus SNP Genotyping

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Single nucleotide polymorphism (SNP) genotype data from a variety of sources were collected. These included individually genotyped SNPs at the ADIPOQ locus (n=33)(An et al. 2012 (link); An et al. 2013 (link)) which were combined with data from 7497 SNPs in the ADIPOQ region (chr3:161,560,463-197,838,262, hg19, 25 MB proximal and to the distal end of the chromosome) derived from Illumina OmniExpress (GWAS chip) genome-wide genotyping as part of the GUARDIAN Consortium(Goodarzi et al. 2013 ) and data from all chromosome 3 SNPs (n=3428) on the Illumina HumanExome Beadchip(Hellwege et al. 2014 (link)) for a total of 10,958 non-redundant, polymorphic SNPs. Quality control for each group of SNP data has previously been described in detail(An et al. 2012 (link); Hellwege et al. 2014 (link)). All datasets underwent Mendelian error checking using PedCheck(O'Connell and Weeks 1998 (link)).

Hellwege J.N., Palmer N.D., Brown W.M., Ziegler J.T., An S.S., Guo X., Chen Y.D., Taylor K., Hawkins G.A., Ng M.C., Speliotes E.K., Lorenzo C., Norris J.M., Rotter J.I., Wagenknecht L.E., Langefeld C.D, & Bowden D.W. (2014). Empirical Characteristics of Family-Based Linkage to a Complex Trait: the ADIPOQ Region and Adiponectin Levels. Human genetics, 134(2), 203-213.

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SNP Selection and Genotyping in PPP2CA

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The HapMap database (HapMap Data Rel 27 phase II+III, Feb09) was used to retrieve SNP information in PPP2CA (including 10kb upstream region) in the Chinese Han population (CHB). Haploview 4.2 software was used to select tag SNPs with r² threshold of 0.80 and minor allele frequency (MAF) ≥ 0.05. Finally, 3 SNPs including rs13187105 (C > A), rs2292283 (G > A) and rs254057 (G > A) were selected for further analyses.
Genomic DNA was extracted from peripheral leukocytes using phenol-chloroform method. The tag SNPs were genotyped in all participants using Illumina Human Exome BeadChip, in which these three loci were customer-designed. Genotype calling was done using the Illumina GenomeStudio software.

Huang T., He K., Mao Y., Zhu M., Yan C., Yu F., Qi Q., Wang T., Wang Y., Du J, & Liu L. (2017). Genetic variants in PPP2CA are associated with gastric cancer risk in a Chinese population. Scientific Reports, 7, 11499.

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Genotyping Variants in Diverse Populations

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All participants from the DHS were previously genotyped for the variants using an Illumina Infinium HumanExome BeadChip^{12 (link),14 (link)}.
Participants from the LBC and from the central European independent replication cohort were genotyped by TaqMan 5′ nuclease assays (Life Technologies). The allelic discrimination probe for PSD3 rs71519934 was not commercially available. A custom assay for this variant has been designed (supplementary material).
Nine individuals denoted as AC homozygotes (n = 3), CT homozygotes (n = 3) and AC/CT heterozygous (n = 3) by the TaqMan assay were Sanger sequenced with consistent results (Supplementary Fig. 2).
UK Biobank participants were genotyped using two highly similar UK BiLEVE or UK Biobank Axiom arrays (>95% overlap). Genotyped data were then imputed based on the 1000 Genomes Phase 3, UK10K haplotype and Haplotype Reference Consortium reference panels^{47 (link)}. Genotype data for the rs71519934 dinucleotide change were not available in the UK Biobank; the rs7003060 (identifying the first nucleotide change of the rs71519934) was among directly genotyped variants and has been used instead.

Mancina R.M., Sasidharan K., Lindblom A., Wei Y., Ciociola E., Jamialahmadi O., Pingitore P., Andréasson A.C., Pellegrini G., Baselli G., Männistö V., Pihlajamäki J., Kärjä V., Grimaudo S., Marini I., Maggioni M., Becattini B., Tavaglione F., Dix C., Castaldo M., Klein S., Perelis M., Pattou F., Thuillier D., Raverdy V., Dongiovanni P., Fracanzani A.L., Stickel F., Hampe J., Buch S., Luukkonen P.K., Prati D., Yki-Järvinen H., Petta S., Xing C., Schafmayer C., Aigner E., Datz C., Lee R.G., Valenti L., Lindén D, & Romeo S. (2022). PSD3 downregulation confers protection against fatty liver disease. Nature Metabolism, 4(1), 60-75.

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