Risk prediction and marker selection in nonsynonymous single nucleotide polymorphisms using whole genome sequencing data
| Autor: | Jae-Don Oh, Donghyun Shin, Kyeong-Hye Won, Young-Sup Lee |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Nonsynonymous substitution Total risk Single-nucleotide polymorphism Computational biology Biology deleterious effect General Biochemistry Genetics and Molecular Biology 03 medical and health sciences risk prediction 0302 clinical medicine Extant taxon nssnp lcsh:QH301-705.5 Genetic association marker selection Whole genome sequencing lcsh:R5-920 Gene ontology Genes & Genomics 030104 developmental biology lcsh:Biology (General) 030220 oncology & carcinogenesis breeding Animal Science and Zoology Marker selection lcsh:Medicine (General) Research Article |
| Zdroj: | Animal Cells and Systems, Vol 0, Iss 0, Pp 1-8 (2020) Animal Cells and Systems article-version (VoR) Version of Record |
| ISSN: | 2151-2485 1976-8354 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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