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Omni 5m snp array

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The OMNI 5M SNP Array is a high-density genotyping platform designed by Illumina for genome-wide association studies and other genomic research applications. The array interrogates over 5 million single nucleotide polymorphisms (SNPs) across the human genome, providing comprehensive coverage and high-resolution genomic data.

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Lab products found in correlation

Snp 6.0 array, by Thermo Fisher Scientific (2 mentions)

Close spelling variants of this product

SNP arrays (14 citations) Omni 2.5M SNP array data (2 citations) Omni Human 2.5M SNP Array (2 citations) OMNI 2.5M SNP array (2 citations)

6 protocols using omni 5m snp array

1

Archaic Haplotypes Affect Gene Expression

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To determine whether the archaic haplotypes have any effect on gene regulation, we analyzed expression data from 52 tissues provided by the GTEx.32 (link) Between 4 and 210 individuals were available for each tissue. We identified nine shared archaic-like SNPs (chr4: 38,777,471; 38,778,903; 38,783,848; 38,802,913; 38,803,063; 38,805,983; 38,806,019; 38,806,096; 38,806,827) from the Illumina OMNI 5M SNP Array32 (link) used to genotype each individual, and we used these to tag the archaic-like core haplotypes. All nine SNPs are in perfect linkage with one another.
In each tissue, we used the DESeq2 package to test for allele-specific differential expression of TLR1, TLR6, and TLR10 between those individuals with and without archaic-like core haplotypes.33 (link) The expression (in normalized counts) of each of the three TLR genes and the corresponding differential expression p values are shown in Figure 3 (additional information in Figures S4 and S7 and Table S7).
Dannemann M., Andrés A.M, & Kelso J. (2016). Introgression of Neandertal- and Denisovan-like Haplotypes Contributes to Adaptive Variation in Human Toll-like Receptors. American Journal of Human Genetics, 98(1), 22-33.
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2

Analyzing Tissue-Specific eQTLs in GTEx and TCGA

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Publicly available data generated by the Genotype-Tissue Expression Project (GTEx)20 (link) and The Cancer Genome Atlas (TCGA) were accessed to examine tissue-specific eQTLs. For GTEx, expression and genotype data were generated from 70 normal uteri from post-mortem biopsies, using an Affymetrix Expression array and Illumina Omni 5M SNP array. GTEx provided processed results, evaluating association between genotype and expression data. The expression levels are represented as a rank normalized score. TCGA genotype and copy number variation (CNV) data were derived from Affymetrix 6.0 SNP arrays. Expression data were from RNAseq arrays (Illumina HiSeq and Illumina GA) for 458 endometrial cancer tissues and 30 adjacent normal endometrial tissues. Association analyses for TCGA datasets were performed as follows. Genes within 500kb flanking our SNPs of interest were selected for analysis. Since there may be significant variation in tumour tissue copy number, somatic CNVs were taken into account by regressing gene expression to average copy number spanning the gene. Residual unexplained variance in gene expression was then regressed on the genotype of the lead SNP at each locus, using genotyped or imputed data. Statistical comparisons were subject to Bonferroni correction for number of tests (number of sample sets, and number of genes assessed).
Cheng T.H., Thompson D.J., O’Mara T.A., Painter J.N., Glubb D.M., Flach S., Lewis A., French J.D., Freeman-Mills L., Church D., Gorman M., Martin L., Hodgson S., Webb P.M., Attia J., Holliday E.G., McEvoy M., Scott R.J., Henders A.K., Martin N.G., Montgomery G.W., Nyholt D.R., Ahmed S., Healey C.S., Shah M., Dennis J., Fasching P.A., Beckmann M.W., Hein A., Ekici A.B., Hall P., Czene K., Darabi H., Li J., Dörk T., Dürst M., Hillemanns P., Runnebaum I., Amant F., Schrauwen S., Zhao H., Lambrechts D., Depreeuw J., Dowdy S.C., Goode E.L., Fridley B.L., Winham S.J., Njølstad T.S., Salvesen H.B., Trovik J., Werner H.M., Ashton K., Otton G., Proietto T., Liu T., Mints M., Tham E., Consortium C., Jun Li M., Yip S.H., Wang J., Bolla M.K., Michailidou K., Wang Q., Tyrer J.P., Dunlop M., Houlston R., Palles C., Hopper J.L., Peto J., Swerdlow A.J., Burwinkel B., Brenner H., Meindl A., Brauch H., Lindblom A., Chang-Claude J., Couch F.J., Giles G.G., Kristensen V.N., Cox A., Cunningham J.M., Pharoah P.D., Dunning A.M., Edwards S.L., Easton D.F., Tomlinson I, & Spurdle A.B. (2016). Five endometrial cancer risk loci identified through genome-wide association analysis. Nature genetics, 48(6), 667-674.
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3

Analyzing Tissue-Specific eQTLs in GTEx and TCGA

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Publicly available data generated by the Genotype-Tissue Expression Project (GTEx)20 (link) and The Cancer Genome Atlas (TCGA) were accessed to examine tissue-specific eQTLs. For GTEx, expression and genotype data were generated from 70 normal uteri from post-mortem biopsies, using an Affymetrix Expression array and Illumina Omni 5M SNP array. GTEx provided processed results, evaluating association between genotype and expression data. The expression levels are represented as a rank normalized score. TCGA genotype and copy number variation (CNV) data were derived from Affymetrix 6.0 SNP arrays. Expression data were from RNAseq arrays (Illumina HiSeq and Illumina GA) for 458 endometrial cancer tissues and 30 adjacent normal endometrial tissues. Association analyses for TCGA datasets were performed as follows. Genes within 500kb flanking our SNPs of interest were selected for analysis. Since there may be significant variation in tumour tissue copy number, somatic CNVs were taken into account by regressing gene expression to average copy number spanning the gene. Residual unexplained variance in gene expression was then regressed on the genotype of the lead SNP at each locus, using genotyped or imputed data. Statistical comparisons were subject to Bonferroni correction for number of tests (number of sample sets, and number of genes assessed).
Cheng T.H., Thompson D.J., O’Mara T.A., Painter J.N., Glubb D.M., Flach S., Lewis A., French J.D., Freeman-Mills L., Church D., Gorman M., Martin L., Hodgson S., Webb P.M., Attia J., Holliday E.G., McEvoy M., Scott R.J., Henders A.K., Martin N.G., Montgomery G.W., Nyholt D.R., Ahmed S., Healey C.S., Shah M., Dennis J., Fasching P.A., Beckmann M.W., Hein A., Ekici A.B., Hall P., Czene K., Darabi H., Li J., Dörk T., Dürst M., Hillemanns P., Runnebaum I., Amant F., Schrauwen S., Zhao H., Lambrechts D., Depreeuw J., Dowdy S.C., Goode E.L., Fridley B.L., Winham S.J., Njølstad T.S., Salvesen H.B., Trovik J., Werner H.M., Ashton K., Otton G., Proietto T., Liu T., Mints M., Tham E., Consortium C., Jun Li M., Yip S.H., Wang J., Bolla M.K., Michailidou K., Wang Q., Tyrer J.P., Dunlop M., Houlston R., Palles C., Hopper J.L., Peto J., Swerdlow A.J., Burwinkel B., Brenner H., Meindl A., Brauch H., Lindblom A., Chang-Claude J., Couch F.J., Giles G.G., Kristensen V.N., Cox A., Cunningham J.M., Pharoah P.D., Dunning A.M., Edwards S.L., Easton D.F., Tomlinson I, & Spurdle A.B. (2016). Five endometrial cancer risk loci identified through genome-wide association analysis. Nature genetics, 48(6), 667-674.
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4

Replicating Insomnia-Associated Genes

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To further replicate the authenticity of these identified insomnia-associated genes, we re-performed the Sherlock integrative analysis with the use of an independent eQTL dataset (i.e. 136 brain cortex samples) from GTEx portal (data release v7) [40 (link)]. RNA-sequencing was used for quantifying the level of RNA expression, and the Illumina OMNI 5M SNP Array was used for SNP genotyping. Based on the additive genetic model, eQTL analysis was conducted through the tool of Matrix eQTL [41 (link)] using linear regression analysis. For Sherlock Bayesian-based analysis, all the parameters were set to be the same with those in discovery eQTL data. The P values calculated from Sherlock were corrected with the application of the Bonferroni correction.
Sun H., Zhang J., Ma Y, & Liu J. (2020). Integrative genomics analysis identifies five promising genes implicated in insomnia risk based on multiple omics datasets. Bioscience Reports, 40(9), BSR20201084.
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5

Cis-association analysis of eQTLs

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Cis-association data of the influence of significant SNPs on the expression of nearby genes were extracted from the Genotype-Tissue Expression (GTEx) project, a public source (http://www.gtexportal.org/home/) with available expression Quantative Trait Loci (eQTL) data of 7051 samples from 44 different tissues and for genome-wide genetic variations in the general population (Illumina OMNI 5M SNP Array) (27) .
Delacrétaz A., Zdralovic A., Vandenberghe F., Saigi-Morgui N., Glatard A., Quteineh L., Gholam-Rezaee M., Raffoul W., Applegate L.A., Jafari P., Gamma F., von Gunten A., Conus P, & Eap C.B. (2017). Association of variants in SH2B1 and RABEP1 with worsening of low-density lipoprotein and glucose parameters in patients treated with psychotropic drugs. Gene, 628.
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6

Genotyping and Imputation of 5M Dataset

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A subset 162 individuals were genotyped as part of the 5M dataset using the Illumina Omni5M SNP array, which includes approximately 4.5 million SNPs. The full 5M dataset was phased using Beagle 4.0 [73] and the 1kGP reference panel [19] . These data were further imputed using minimac3 [74] and a reference panel consisting of the 1kGP and 180 WGS from the Tishkoff lab (unpublished).
Kelly D.E., Ramdas S., Ma R., Rawlings-Goss R., Grant G.R., Ranciaro A., Hirbo J., Beggs W., Yeager M., Chanock S.J., Nyambo T., Omar S.A., Woldemeskel D., Belay G., Li H., Brown C.D., & Tishkoff S.A. (2022). The Genetic and Evolutionary Basis of Gene Expression Variation in East Africans.
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