نبذة مختصرة : Large-scale high-throughput sequencing data sets have been transformative for informing clinical variant interpretation and for use as reference panels for statistical and population genetic efforts. Although such resources are often treated as ground truth, we find that in widely used reference data sets such as the Genome Aggregation Database (gnomAD), some variants pass gold-standard filters, yet are systematically different in their genotype calls across genotype discovery approaches. The inclusion of such discordant sites in study designs involving multiple genotype discovery strategies could bias results and lead to false-positive hits in association studies owing to technological artifacts rather than a true relationship to the phenotype. Here, we describe this phenomenon of discordant genotype calls across genotype discovery approaches, characterize the error mode of wrong calls, provide a list of discordant sites identified in gnomAD that should be treated with caution in analyses, and present a metric and machine learning classifier trained on gnomAD data to identify likely discordant variants in other data sets. We find that different genotype discovery approaches have different sets of variants at which this problem occurs, but there are characteristic variant features that can be used to predict discordant behavior. Discordant sites are largely shared across ancestry groups, although different populations are powered for the discovery of different variants. We find that the most common error mode is that of a variant being heterozygous for one approach and homozygous for the other, with heterozygous in the genomes and homozygous reference in the exomes making up the majority of miscalls. ; Peer reviewed
Relation: We thank the members of the gnomAD and Hail teams for their assistance with this project. This project was supported by the National Institute of Mental Health (K01 MH121659 and T32 MH017119 to E.G.A.) and the National Human Genome Research Institute (U24HG011450). E.G.A. was additionally supported by the Caroline Wiess Law Fund for Research in Molecular Medicine and the ARCO Foundation Young Teacher-Investigator Fund at Baylor College of Medicine. A.A.L. was supported by the Ministry of Science and Higher Education of the Russian Federation (agreement no. 075-15-2022-301, institutional grant to Almazov National Medical Research Center). M.A. was supported by the Aging Biology Foundation and Nationwide Foundation Pediatric Innovation Fund. The All of Us Research Program is supported by the National Institutes of Health, Office of the Director: Regional Medical Centers: 1 OT2 OD026549, 1 OT2 OD026554, 1 OT2 OD026557, 1 OT2 OD026556, 1 OT2 OD026550, 1 OT2 OD 026552, 1 OT2 OD026553, 1 OT2 OD026548, 1 OT2 OD026551, 1 OT2 OD026555; IAA #: AOD 16037; Federally Qualified Health Centers: HHSN 263201600085U; Data and Research Center: 5 U2C OD023196; Biobank: 1 U24 OD023121; The Participant Center: U24 OD023176; Participant Technology Systems Center: 1 U24 OD023163; Communications and Engagement: 3 OT2 OD023205, 3 OT2 OD023206; and Community Partners: 1 OT2 OD025277, 3 OT2 OD025315, 1 OT2 OD025337, 1 OT2 OD025276. In addition, the All of Us Research Program would not be possible without the partnership of its participants.; Atkinson , E G , Artomov , M , Loboda , A A , Rehm , H L , MacArthur , D G , Karczewski , K J , Neale , B M & Daly , M J 2023 , ' Discordant calls across genotype discovery approaches elucidate variants with systematic errors ' , Genome Research , vol. 33 , no. 6 , pp. 999-1005 . https://doi.org/10.1101/gr.277908.123; http://hdl.handle.net/10138/571294; a2d804ed-750c-4005-bba3-7782cac2458a; 85165521323
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