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Supplementary Figures 1-5, Supplementary Tables 1-3, Full List of Authors and Affiliations from Saliva-Derived DNA Performs Well in Large-Scale, High-Density Single-Nucleotide Polymorphism Microarray Studies

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posted on 2023-03-31, 13:25 authored by Melanie Bahlo, Jim Stankovich, Patrick Danoy, Peter F. Hickey, Bruce V. Taylor, Sharon R. Browning, Matthew A. Brown, Justin P. Rubio
Supplementary Figures 1-5, Supplementary Tables 1-3, Full List of Authors and Affiliations from Saliva-Derived DNA Performs Well in Large-Scale, High-Density Single-Nucleotide Polymorphism Microarray Studies

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ARTICLE ABSTRACT

As of June 2009, 361 genome-wide association studies (GWAS) had been referenced by the HuGE database. GWAS require DNA from many thousands of individuals, relying on suitable DNA collections. We recently performed a multiple sclerosis (MS) GWAS where a substantial component of the cases (24%) had DNA derived from saliva. Genotyping was done on the Illumina genotyping platform using the Infinium Hap370CNV DUO microarray. Additionally, we genotyped 10 individuals in duplicate using both saliva- and blood-derived DNA. The performance of blood- versus saliva-derived DNA was compared using genotyping call rate, which reflects both the quantity and quality of genotyping per sample and the “GCScore,” an Illumina genotyping quality score, which is a measure of DNA quality. We also compared genotype calls and GCScores for the 10 sample pairs. Call rates were assessed for each sample individually. For the GWAS samples, we compared data according to source of DNA and center of origin. We observed high concordance in genotyping quality and quantity between the paired samples and minimal loss of quality and quantity of DNA in the saliva samples in the large GWAS sample, with the blood samples showing greater variation between centers of origin. This large data set highlights the usefulness of saliva DNA for genotyping, especially in high-density single-nucleotide polymorphism microarray studies such as GWAS. Cancer Epidemiol Biomarkers Prev; 19(3); 794–8

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    Cancer Epidemiology, Biomarkers & Prevention

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