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Proc Natl Acad Sci U S A. 2019 Jan 15;116(3):950-959. doi: 10.1073/pnas.1808403116. Epub 2018 Dec 27.

Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis.

Author information

1
St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065.
2
Immunology Institute, Graduate School, Icahn School of Medicine at Mount Sinai, New York, NY 10029.
3
Department of Medicine, Division of Clinical Immunology, Icahn School of Medicine at Mount Sinai, New York, NY 10029.
4
Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, 75015 Paris, France.
5
Imagine Institute, Paris Descartes University, 75015 Paris, France.
6
School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.
7
Human Evolutionary Genetics Unit, Pasteur Institute, 75015 Paris, France.
8
CNRS UMR2000, 75015 Paris, France.
9
Center of Bioinformatics, Biostatistics and Integrative Biology, Pasteur Institute, 75015 Paris, France.
10
Helix, San Carlos, CA 94070.
11
Rady Children's Institute for Genomic Medicine, Department of Neurosciences, University of California, San Diego, La Jolla, CA 92093.
12
Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff CF14 4XW, United Kingdom.
13
Howard Hughes Medical Institute, New York, NY 10065.
14
St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065; casanova@rockefeller.edu yuval.itan@mssm.edu.
15
Pediatric Hematology-Immunology Unit, Necker Hospital for Sick Children, 75015 Paris, France.
16
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029.
17
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029.

Abstract

Computational analyses of human patient exomes aim to filter out as many nonpathogenic genetic variants (NPVs) as possible, without removing the true disease-causing mutations. This involves comparing the patient's exome with public databases to remove reported variants inconsistent with disease prevalence, mode of inheritance, or clinical penetrance. However, variants frequent in a given exome cohort, but absent or rare in public databases, have also been reported and treated as NPVs, without rigorous exploration. We report the generation of a blacklist of variants frequent within an in-house cohort of 3,104 exomes. This blacklist did not remove known pathogenic mutations from the exomes of 129 patients and decreased the number of NPVs remaining in the 3,104 individual exomes by a median of 62%. We validated this approach by testing three other independent cohorts of 400, 902, and 3,869 exomes. The blacklist generated from any given cohort removed a substantial proportion of NPVs (11-65%). We analyzed the blacklisted variants computationally and experimentally. Most of the blacklisted variants corresponded to false signals generated by incomplete reference genome assembly, location in low-complexity regions, bioinformatic misprocessing, or limitations inherent to cohort-specific private alleles (e.g., due to sequencing kits, and genetic ancestries). Finally, we provide our precalculated blacklists, together with ReFiNE, a program for generating customized blacklists from any medium-sized or large in-house cohort of exome (or other next-generation sequencing) data via a user-friendly public web server. This work demonstrates the power of extracting variant blacklists from private databases as a specific in-house but broadly applicable tool for optimizing exome analysis.

KEYWORDS:

WES analysis; WES annotation; blacklist; exome; variant

PMID:
30591557
PMCID:
PMC6338851
[Available on 2019-07-15]
DOI:
10.1073/pnas.1808403116

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