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• W2137492849 abstract "Compared with an arbitrary reference, the protein-coding sequence of any human genome contains about 20,000 single-nucleotide variants, most of which are heterozygous, and far fewer variants of other types. When their frequency is >1% in a given population, variants are designated as common. The others are rare, including some that appear to be private to the individual or kindred studied. In each individual, at most two variations can underlie a monogenic disorder. It has thus been hoped that computational methods would be able to prioritize these variants and point at a handful of candidate culprits in the exome of any patient, not only for diagnostic but also more ambitiously for research purposes (1). This vision culminated in the utopia, or dystopia, of genetic medicine relying only on a dry laboratory to draw conclusions from genome sequencing (2). To work, such methods should show both a low false-positive (FP) prediction rate, to filter out irrelevant variants (the smaller the size of the haystack the better), and a low false-negative (FN) rate, to avoid filtering out the disease-causing mutations (the needle must stay in the haystack). Many “variant-level” approaches predict the biochemical impact of variants. Examples include sorting intolerant from tolerant (SIFT), which is based on protein sequence homology (3), polymorphism phenotyping v2 (PolyPhen-2), which is based on a combination of sequence conservation and biochemical properties of proteins and was trained by a set of known disease-causing mutations (4), and combined annotation-dependent depletion (CADD), which combines existing variant-level methods (including SIFT and PolyPhen-2) with an analysis of the impact of actual versus simulated human variants (5). In an important study, Miosge et al. show experimentally that the current software generate a low rate of FNs but a high rate of FPs (6)." @default.
• W2137492849 created "2016-06-24" @default.
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• W2137492849 date "2015-09-08" @default.
• W2137492849 modified "2023-10-14" @default.
• W2137492849 title "Can the impact of human genetic variations be predicted?" @default.
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• W2137492849 doi "https://doi.org/10.1073/pnas.1515057112" @default.
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