Simultaneous estimation of genotype error and uncalled deletion rates in whole genome sequence data

TL;DR

This study introduces a new model to estimate genotype errors and uncalled deletions in genetic data, showing that ignoring deletions leads to biased results.

Contribution

A novel model that simultaneously accounts for genotype errors and uncalled deletions in estimating error rates.

Findings

The model reduces bias in genotype error rate estimates when uncalled deletions are present.

77% of genotype errors in the data are attributed to uncalled deletions.

The estimated genotype error rate is 3.2×10−4 for SNVs with minor allele frequency > 0.001.

Abstract

Genotype data include errors that may influence conclusions reached by downstream statistical analyses. Previous studies have estimated genotype error rates from discrepancies in human pedigree data, such as Mendelian inconsistent genotypes or apparent phase violations. However, uncalled deletions, which generally have not been accounted for in these studies, can lead to biased error rate estimates. In this study, we propose a genotype error model that considers both genotype errors and uncalled deletions when calculating the likelihood of the observed genotypes in parent-offspring trios. Using simulations, we show that when there are uncalled deletions, our model produces genotype error rate estimates that are less biased than estimates from a model that does not account for these deletions. We applied our model to SNVs in 77 sequenced White British parent-offspring trios in the UK Biobank. We use the Akaike information criterion to show that our model fits the data better than a model that does not account for uncalled deletions. We estimate the genotype error rate at SNVs with minor allele frequency > 0.001 in these data to be 3.2×10−4(90%CI:[2.8×10−4,6.2×10−4]). We estimate that 77% of the genotype errors at these markers are attributable to uncalled deletions (90%CI:[73%,88%]).

A genotype error occurs when the genotype identified through molecular analysis does not match the actual genotype of the individual being analyzed. Because genotype errors can influence downstream statistical results, previous studies have attempted to estimate the rate of genotype errors in a study sample. However, uncalled deletions, which generally have not been accounted for in these studies, can lead to biased error rate estimates. In this study, we formulate a model adjusting for uncalled deletions when estimating genotype error rates. We show that when uncalled deletions are present, this model results in less biased estimates of genotype error rates compared to a model that does not adjust for uncalled deletions. We apply this model to SNVs in 77 sequenced White British parent-offspring trios in the UK Biobank and estimate the genotype error rate and the proportion of genotype errors that are attributable to uncalled deletions at SNVs with minor allele frequency > 0.001.

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