Abstract
In recent years, next-generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state-of-the-art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.
| Original language | English |
|---|---|
| Journal | Human Mutation |
| DOIs | |
| Publication status | Accepted/In press - 1 Jan 2018 |
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Keywords
- allele balance
- false positive variant calls
- genetic variant detection
- systematic NGS errors
ASJC Scopus subject areas
- Genetics
- Genetics(clinical)
Cite this
Allele balance bias identifies systematic genotyping errors and false disease associations. / Muyas, Francesc; Bosio, Mattia; Puig, Anna; Susak, Hana; Domènech, Laura; Escaramis, Georgia; Zapata, Luis; Demidov, German; Estivill, Xavier P.; Rabionet, Raquel; Ossowski, Stephan.
In: Human Mutation, 01.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Allele balance bias identifies systematic genotyping errors and false disease associations
AU - Muyas, Francesc
AU - Bosio, Mattia
AU - Puig, Anna
AU - Susak, Hana
AU - Domènech, Laura
AU - Escaramis, Georgia
AU - Zapata, Luis
AU - Demidov, German
AU - Estivill, Xavier P.
AU - Rabionet, Raquel
AU - Ossowski, Stephan
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In recent years, next-generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state-of-the-art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.
AB - In recent years, next-generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state-of-the-art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.
KW - allele balance
KW - false positive variant calls
KW - genetic variant detection
KW - systematic NGS errors
UR - http://www.scopus.com/inward/record.url?scp=85057097529&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057097529&partnerID=8YFLogxK
U2 - 10.1002/humu.23674
DO - 10.1002/humu.23674
M3 - Article
JO - Human Mutation
JF - Human Mutation
SN - 1059-7794
ER -