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Benchmarking computational variant effect predictors by their ability to infer human traits - Genome Biology
F.P.R., D.K., and R.L. conceived of the project idea and designed the study. D.R.T., D.K., and R.L. analyzed data from the UK Biobank cohort; M.L. analyzed data from the cohort, supervised by D.M.R.. D.R.T., K.L., and Y.W. curated variant effect predictor scores for the variants examined in this study. D.R.T. and D.K. carried out predictor benchmarking in the UK Biobank, with help from R.L. and K.L.; D.R.T. and M.L. carried out predictor benchmarking in the cohort.
Improved pathogenicity prediction for rare human missense variants
Discussion Evidence to evaluate variant pathogenicity is typically less available for rare as opposed to common variants. Detecting population-level correlation between a trait and a genotype has reduced power at lower minor allele frequencies and is futile for extremely rare variants. Experimental functional evidence is also less available for lower-allele-frequency variants.
A proactive genotype-to-patient-phenotype map for cystathionine beta-synthase
Song Sun1,2,3,4,5 na1, Jochen Weile1,2,3,4 na1, Marta Verby1,2,4, Yingzhou Wu1,2,3,4, Yang Wang6,7, Atina G. Cote1,2,4, Iosifina Fotiadou1,2,4, Julia Kitaygorodsky1,2,4, Marc Vidal6,7, Jasper Rine8,9, Pavel Ješina10, Viktor Kožich10 & Frederick P. Roth ORCID: orcid.org/0000-0002-6628-649X1,2,3,4 Genome Medicine volume 12, Article number: 13 (2020) Cite this article Abstract Background For the majority of rare clinical missense variants, pathogenicity status cannot currently be classified.
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