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Risk prediction and marker selection in nonsynonymous single nucleotide polymorphisms using whole genome sequencing data.

ABSTRACT: Despite the various existing studies about nonsynonymous single nucleotide polymorphisms (nsSNPs), genome-wide studies based on nsSNPs are rare. NsSNPs alter amino acid sequences, affect protein structure and function, and have deleterious effects. By predicting the deleterious effect of nsSNPs, we determined the total risk score per individual. Additionally, the machine learning technique was utilized to find an optimal nsSNP subset that best explains the complete nsSNP effect. A total of 16,100 nsSNPs were selected as the best representatives among 89,519 regressed nsSNPs. In the gene ontology analysis encompassing the 16,100 nsSNPs, DNA metabolic process, chemokine- and immune-related, and reproduction were the most enriched terms. We expect that our risk score prediction and nsSNP marker selection will contribute to future development of extant genome-wide association studies and breeding science more broadly.

SUBMITTER: Lee YS

PROVIDER: S-EPMC7781907 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Risk prediction and marker selection in nonsynonymous single nucleotide polymorphisms using whole genome sequencing data.

Lee Young-Sup YS Won KyeongHye K Shin Donghyun D Oh Jae-Don JD

Animal cells and systems 20201224 6

Despite the various existing studies about nonsynonymous single nucleotide polymorphisms (nsSNPs), genome-wide studies based on nsSNPs are rare. NsSNPs alter amino acid sequences, affect protein structure and function, and have deleterious effects. By predicting the deleterious effect of nsSNPs, we determined the total risk score per individual. Additionally, the machine learning technique was utilized to find an optimal nsSNP subset that best explains the complete nsSNP effect. A total of 16,10 ...[more]

PMID: 33456716

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