CHARR efficiently estimates contamination from DNA sequencing data

Wenhan Lu; Laura D Gauthier; Timothy Poterba; Edoardo Giacopuzzi; Julia K Goodrich; Christine R Stevens; Daniel King; Mark J Daly; Benjamin M Neale; Konrad J Karczewski

doi:10.1016/j.ajhg.2023.10.011

CHARR efficiently estimates contamination from DNA sequencing data

Am J Hum Genet. 2023 Dec 7;110(12):2068-2076. doi: 10.1016/j.ajhg.2023.10.011. Epub 2023 Nov 23.

Authors

Affiliations

¹ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
² Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
³ Human Technopole, Viale Rita Levi-Montalcini 1, 20157 Milano, Italy.
⁴ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
⁵ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute for Molecular Medicine Finland, Helsinki, Finland.
⁶ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
⁷ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address: konradk@broadinstitute.org.

PMID: 38000370
PMCID: PMC10716339 (available on 2024-06-07)
DOI: 10.1016/j.ajhg.2023.10.011

Abstract

DNA sample contamination is a major issue in clinical and research applications of whole-genome and -exome sequencing. Even modest levels of contamination can substantially affect the overall quality of variant calls and lead to widespread genotyping errors. Currently, popular tools for estimating the contamination level use short-read data (BAM/CRAM files), which are expensive to store and manipulate and often not retained or shared widely. We propose a metric to estimate DNA sample contamination from variant-level whole-genome and -exome sequence data called CHARR, contamination from homozygous alternate reference reads, which leverages the infiltration of reference reads within homozygous alternate variant calls. CHARR uses a small proportion of variant-level genotype information and thus can be computed from single-sample gVCFs or callsets in VCF or BCF formats, as well as efficiently stored variant calls in Hail VariantDataset format. Our results demonstrate that CHARR accurately recapitulates results from existing tools with substantially reduced costs, improving the accuracy and efficiency of downstream analyses of ultra-large whole-genome and exome sequencing datasets.

Keywords: DNA sequencing; contamination; data science; genetic research; quality control; variant calling.

MeSH terms

Animals
DNA*
Genotype
High-Throughput Nucleotide Sequencing / methods
Homozygote
Humans
Sequence Analysis, DNA / methods
Software
Trout*

Substances

DNA

Abstract

MeSH terms

Substances

Grants and funding