Discordant calls across genotype discovery approaches elucidate variants with systematic errors,Genome Research

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Discordant calls across genotype discovery approaches elucidate variants with systematic errors
Genome Research ( IF 6.2 ) Pub Date : 2023-06-01 , DOI: 10.1101/gr.277908.123
Elizabeth G Atkinson _{1,

2,

3} , Mykyta Artomov _{1,

4,

5,

6} , Alexander A Loboda _{4,

7,

8,

9} , Heidi L Rehm _{4,

7,

10} , Daniel G MacArthur _{2,

11} , Konrad J Karczewski _{2,

4,

7} , Benjamin M Neale _{2,

4,

7} , Mark J Daly _{2,

7,

12}

Affiliation

Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA;
Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.
Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio 43215, USA.
Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio 43210, USA.
Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
ITMO University, Saint-Petersburg, 197101, Russia.
Almazov National Medical Research Center, St. Petersburg, 197341, Russia.
Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
Centre for Population Genomics, Garvan Institute of Medical Research and Murdoch Children's Research Institute, Darlinghurst, New South Wales 2010, Australia.
Institute for Molecular Medicine Finland, University of Helsinki, FI-00290 Helsinki, Finland.

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.

更新日期：2023-06-01

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