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610k quad array

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The 610K-Quad arrays are high-density genotyping products developed by Illumina. They are designed to interrogate approximately 610,000 genetic markers across the human genome. The arrays provide comprehensive coverage for genome-wide association studies and other genetic research applications.

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Lab products found in correlation

Human genome u133 plus 2.0 microarray, by Thermo Fisher Scientific (2 mentions) 7900ht real time pcr system, by Thermo Fisher Scientific (2 mentions) Whole genome expression beadchips, by Illumina (1 mentions) Affymetrix 6.0 array, by Thermo Fisher Scientific (1 mentions)

5 protocols using 610k quad array

1

Adipose Tissue Gene Expression and Copy-Number in Obesity

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The discovery sample included 149 Swedish families (342 subjects) ascertained through an obesity-discordant sib-pair (BMI difference>10kg/m2)6 (link). Gene expression for 29,546 transcripts (16,563 Ensembl genes) was measured in subcutaneous adipose tissue using the Affymetrix Human Genome U133 Plus 2.0 microarray. GWAS signal intensity data from Illumina 610K-Quad arrays were available for 348,150 probes lying within each transcript plus 30kb upstream and downstream to encompass the coding regions and their internal and nearby regulatory regions.
Quantitative real-time PCR (qPCR) was carried out to infer relative copy-number measurements reflecting the underlying copy-number distribution at AMY1 and AMY2, respectively, using the TaqMan assays Hs07226362_cn and Hs04204136_cn on an Applied Biosystems 7900HT Real-Time PCR System. Association analyses were carried out for 481 subjects with complete data on BMI and dual-energy X-ray absorptiometry (DEXA)-derived fat mass.
Falchi M., Moustafa J.S., Takousis P., Pesce F., Bonnefond A., Andersson-Assarsson J.C., Sudmant P.H., Dorajoo R., Al-Shafai M.N., Bottolo L., Ozdemir E., So H.C., Davies R.W., Patrice A., Dent R., Mangino M., Hysi P.G., Dechaume A., Huyvaert M., Skinner J., Pigeyre M., Caiazzo R., Raverdy V., Vaillant E., Field S., Balkau B., Marre M., Visvikis-Siest S., Weill J., Poulain-Godefroy O., Jacobson P., Sjostrom L., Hammond C.J., Deloukas P., Sham P.C., McPherson R., Lee J., Tai E.S., Sladek R., Carlsson L.M., Walley A., Eichler E.E., Pattou F., Spector T.D, & Froguel P. (2014). Low copy number of the salivary amylase gene predisposes to obesity. Nature genetics, 46(5), 492-497.
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2

Adipose Tissue Gene Expression and Copy-Number in Obesity

Cited 1 time
Check if the same lab product or an alternative is used in the 5 most similar protocols
The discovery sample included 149 Swedish families (342 subjects) ascertained through an obesity-discordant sib-pair (BMI difference>10kg/m2)6 (link). Gene expression for 29,546 transcripts (16,563 Ensembl genes) was measured in subcutaneous adipose tissue using the Affymetrix Human Genome U133 Plus 2.0 microarray. GWAS signal intensity data from Illumina 610K-Quad arrays were available for 348,150 probes lying within each transcript plus 30kb upstream and downstream to encompass the coding regions and their internal and nearby regulatory regions.
Quantitative real-time PCR (qPCR) was carried out to infer relative copy-number measurements reflecting the underlying copy-number distribution at AMY1 and AMY2, respectively, using the TaqMan assays Hs07226362_cn and Hs04204136_cn on an Applied Biosystems 7900HT Real-Time PCR System. Association analyses were carried out for 481 subjects with complete data on BMI and dual-energy X-ray absorptiometry (DEXA)-derived fat mass.
Falchi M., Moustafa J.S., Takousis P., Pesce F., Bonnefond A., Andersson-Assarsson J.C., Sudmant P.H., Dorajoo R., Al-Shafai M.N., Bottolo L., Ozdemir E., So H.C., Davies R.W., Patrice A., Dent R., Mangino M., Hysi P.G., Dechaume A., Huyvaert M., Skinner J., Pigeyre M., Caiazzo R., Raverdy V., Vaillant E., Field S., Balkau B., Marre M., Visvikis-Siest S., Weill J., Poulain-Godefroy O., Jacobson P., Sjostrom L., Hammond C.J., Deloukas P., Sham P.C., McPherson R., Lee J., Tai E.S., Sladek R., Carlsson L.M., Walley A., Eichler E.E., Pattou F., Spector T.D, & Froguel P. (2014). Low copy number of the salivary amylase gene predisposes to obesity. Nature genetics, 46(5), 492-497.
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3

Integrating GWAS and eQTLs for Gene Expression Analysis

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The GWAS meta-analysis summary data of diabetes was input into SMR for single gene expression association analysis of fasting glucose and insulin resistance. SMR is capable of integrating GWAS results with eQTLs annotation information to evaluate the relationships between gene expression levels and complex traits [9 (link)]. We applied the eQTLs annotation dataset built by Westra et al. [14 (link)]. Briefly, these eQTLs datasets were driven from a meta-analysis of 5,311 peripheral blood samples and replicated in another 2,775 samples. Illumina whole-genome Expression BeadChips were used for gene expression profiling. SNP genotyping was conducted using commercial platforms, such as Illumina 610K quad arrays and Illumina HumanHap300 arrays. Imputation was conducted using MACH [11 (link)] or IMPUTE [12 (link)] against the HapMap 2 reference panels. 923,021 cis-eQTL for 14,329 gene expression probes and 4,732 trans-eQTL for 2,612 gene expression probes were identified at false discovery rate (FDR) < 0.05 [14 (link)]. An expression association testing p value for each gene was calculated by SMR. After Bonferroni correction, the genes with SMR p values < 9.28 × 10−6 (0.05/5389) were considered as significant genes in our study.
Liang X., He A., Wang W., Liu L., Du Y., Fan Q., Li P., Wen Y., Hao J., Guo X, & Zhang F. (2017). Integrating Genome-Wide Association and eQTLs Studies Identifies the Genes and Gene Sets Associated with Diabetes. BioMed Research International, 2017, 1758636.
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4

Polygenic Risk Score for Type 2 Diabetes

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ARIC participants were genotyped using the Affymetrix 6.0 array (Affymetrix Inc). Genotyping in the RS was done using the Illumina 550K and 610K quad array (Illumina Inc). Genotyped variants were imputed to the Haplotype Reference Consortium (r1.1 2016).21 Haplotype phasing and imputation was performed using the Michigan Imputation Server, which is available at https://imputationserver.sph.umich.edu.
A recent GWAS based on individuals of European ancestry identified 403 independent genetic variants associated with type 2 diabetes.5 Using the 403 genetic variants identified in this study, we created weighted polygenic score by multiplying the risk allele dosage with the effect estimates reported in the GWAS of type 2 diabetes. An additive weighted polygenic score was calculated by summing the weighted dosages for each individual.22 Separately in ARIC and the RS, all individuals were categorized into low (quintile 1), intermediate (quintiles 2–4) and high (quintile 5) genetic risk categories, with the low genetic risk category as the reference.
Ligthart S., Hasbani N.R., Ahmadizar F., van Herpt T.T., Leening M.J., Uitterlinden A.G., Sijbrands E.J., Morrison A.C., Boerwinkle E., Pankow J.S., Selvin E., Ikram M.A., Kavousi M., de Vries P.S, & Dehghan A. (2021). Genetic susceptibility, obesity and lifetime risk of type 2 diabetes: The ARIC study and Rotterdam Study. Diabetic Medicine, 38(10), e14639.
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5

Genetic Analysis of Asthma Cohort

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DNA extraction, genotyping (Illumina 610K Quad Array) and variant filtering of the 1214 individuals from the SLSJ asthma familial cohort have been described previously [1 (link), 30 (link)]. These samples include the 149 trios sequenced. Genotyping data were used for quality cut-offs assessment, inference and imputation of the sequence for the entire cohort.
Morin A., Madore A.M., Kwan T., Ban M., Partanen J., Rönnblom L., Syvänen A.C., Sawcer S., Stunnenberg H., Lathrop M., Pastinen T, & Laprise C. (2018). Exploring rare and low-frequency variants in the Saguenay–Lac-Saint-Jean population identified genes associated with asthma and allergy traits. European Journal of Human Genetics, 27(1), 90-101.
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