Competing analytical strategies of combining associated SNPs for estimating genetic risks

Majumdar, Arunabha and Ghosh, Saurabh (2022) Competing analytical strategies of combining associated SNPs for estimating genetic risks. Journal of Genetics, 101 (1). ISSN 0022-1333

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Abstract

In genomewide association study (GWAS) of a complex phenotype, a large number of variants, many with small effect sizes, are found to contribute to the variability of the phenotype. Subsequent to the identification of such variants in a GWAS, it is of interest to estimate the risk jointly conferred by the variants. We propose three different strategies of combining the risk SNPs to calculate an allele dosage score. Using simulations, we evaluate the different measures of allele dosage score with respect to the risk prediction accuracy of a binary trait and the proportion of variance explained for a quantitative trait. For a binary trait, an allele dosage score defined based on log odds ratio performs marginally better than the other two measures. For a quantitative trait, the measure based on the standardized slope coefficient in linear regression of the trait on single-nucleotide polymorphism (SNP) genotypes performs better than the measures using the weights proportional to log P-value and the proportion of variance explained. We demonstrate the utility of these measures using a real data on type 2 diabetes and fasting blood sugar level in a south Indian population. © 2022, Indian Academy of Sciences

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IITH Creators:

IITH Creators	ORCiD
Majumdar, Arunabha	https://orcid.org/0000-0002-5609-075X

Item Type:

Article

Additional Information:

The authors are grateful to Prof. V. Mohan and Dr Radha Venkatesan of Madras Diabetes Research Foundation (MDRF) for providing access to an apriori analysed portion of the CURES data through a mutual collaboration with SG.

Uncontrolled Keywords:

allele dosage score; genomewide association study; prediction; regression; variance explained

Subjects:

Mathematics

Divisions:

Department of Mathematics

Depositing User:

. LibTrainee 2021

Date Deposited:

27 Jun 2022 05:24

Last Modified:

30 Jun 2022 05:45