期刊
HUMAN GENOMICS
卷 8, 期 -, 页码 -出版社
BMC
DOI: 10.1186/1479-7364-8-11
关键词
SNV; nsSNPs; Disease causing; Disease specific; FATHMM; HMMs; SIFT; PolyPhen; Bioinformatics
资金
- UK Medical Research Council (MRC) [MC_UU_12013/8, G1000427/1]
- Biotechnology and Biological Sciences Research Council (BBSRC) [BB/G022771]
- BIOBASE GmbH
- Biotechnology and Biological Sciences Research Council [BB/G022771/1] Funding Source: researchfish
- Medical Research Council [G1000427, MC_UU_12013/8, MC_UU_12013/2] Funding Source: researchfish
- BBSRC [BB/G022771/1] Funding Source: UKRI
- MRC [MC_UU_12013/2, MC_UU_12013/8, G1000427] Funding Source: UKRI
As the number of non-synonymous single nucleotide polymorphisms (nsSNPs) identified through whole-exome/whole- genome sequencing programs increases, researchers and clinicians are becoming increasingly reliant upon computational prediction algorithms designed to prioritize potential functional variants for further study. A large proportion of existing prediction algorithms are 'disease agnostic' but are nevertheless quite capable of predicting when a mutation is likely to be deleterious. However, most clinical and research applications of these algorithms relate to specific diseases and would therefore benefit from an approach that discriminates between functional variants specifically related to that disease from those which are not. In a whole-exome/whole- genome sequencing context, such an approach could substantially reduce the number of false positive candidate mutations. Here, we test this postulate by incorporating a disease-specific weighting scheme into the Functional Analysis through Hidden Markov Models (FATHMM) algorithm. When compared to traditional prediction algorithms, we observed an overall reduction in the number of false positives identified using a disease-specific approach to functional prediction across 17 distinct disease concepts/categories. Our results illustrate the potential benefits of making disease-specific predictions when prioritizing candidate variants in relation to specific diseases.
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