Blacklisting variants common in private cohorts but not in public databases optimizes human exome analysis.
Proc Natl Acad Sci U S A
; 116(3): 950-959, 2019 01 15.
Article
em En
| MEDLINE
| ID: mdl-30591557
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Variação Genética
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Software
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Genoma Humano
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Análise de Sequência de DNA
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Bases de Dados de Ácidos Nucleicos
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Exoma
Tipo de estudo:
Etiology_studies
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Incidence_studies
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Observational_studies
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Risk_factors_studies
Limite:
Female
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Humans
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Male
Idioma:
En
Revista:
Proc Natl Acad Sci U S A
Ano de publicação:
2019
Tipo de documento:
Article