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Allele Balance Bias Identifies Systematic Genotyping Errors and False Disease Associations

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 allele balance 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 (RVAS).

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Dataset ID Description Technology Samples
EGAD00001004132 1217
Publications Citations
Allele balance bias identifies systematic genotyping errors and false disease associations.
Hum Mutat 40: 2019 115-126
15
Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation.
PLoS Comput Biol 17: 2021 e1007784
3