Discordant calls across genotype discovery approaches elucidate variants with systematic errors
Year: 2023
Type: preprint
Abstract: 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.
Source: Genome Research
Institutions: Broad Institute, Baylor College of Medicine, Massachusetts General Hospital, Stanley Center for Psychiatric Research, Nationwide Children's Hospital, The Ohio State University, ITMO University, Federal Almazov North-West Medical Research Centre, Garvan Institute of Medical Research, Murdoch Children's Research Institute, University of Helsinki, Institute for Molecular Medicine Finland
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: bronze
Awards: Illinois Precision Medicine Consortium , G1640808343, G1717111205, Healthy Americas: All of Us Research Program, G1882701011, Empowerment-focused Outreach, Education and Enrollment of Tennessee Older Adults in the All of Us Research Program through Community Efforts by FiftyForward, Research Matters: Creating Possibilities to Achieve Health and Wellness for All of Us Research Program, Training Program in Psychiatric Genetics and Translational Research, G3206782191, Precision Medicine Initiative Cohort Program Biobank, Communications Design and Development for NIH Precision Medicine Initiative Research Projects, A framework enabling the genomic analysis of psychiatric traits across admixed populations., New York City Consortium for Precision Medicine, Technology to Empower Changes in Health (TECH) Network Participant Technologies Center, University of Arizona-Banner Health All of Us Research Program , The New England Precision Medicine Consortium of the All of Us Research Program, G597338630, All of Us Wisconsin , Data and Research Support Center , All of Us Pennsylvania, Trans-America Consortium of the Health Care Systems Research Network for the All of Us Research Program, G6444257319, Southern All of Us Network, G735271137, SouthEast Enrollment Center (SEEC) , Adaptive Platform for Personalized Engagement, California Precision Medicine Research Program Consortium, Communication Implementation and Coordination for NIH Precision Medicine Initiative Research Projects, The Genome Aggregation Database (gnomAD), PRIDEnet for the All of Us Research Program
OpenAlex ID: W4378783768