How imputation can mitigate SNP ascertainment Bias

Johannes Geibel,Henner Simianer,Steffen Weigend,Christian Reimer,Annett Weigend,Torsten Pook

doi:10.1186/s12864-021-07663-6

Johannes Geibel, Henner Simianer + Show 4 more

Open Access

https://doi.org/10.1186/s12864-021-07663-6

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Journal: BMC Genomics	Publication Date: May 12, 2021
Citations: 7	License type: open-access

Affiliation: University of Göttingen, Friedrich-Loeffler-Institut

Abstract

BackgroundPopulation genetic studies based on genotyped single nucleotide polymorphisms (SNPs) are influenced by a non-random selection of the SNPs included in the used genotyping arrays. The resulting bias in the estimation of allele frequency spectra and population genetics parameters like heterozygosity and genetic distances relative to whole genome sequencing (WGS) data is known as SNP ascertainment bias. Full correction for this bias requires detailed knowledge of the array design process, which is often not available in practice. This study suggests an alternative approach to mitigate ascertainment bias of a large set of genotyped individuals by using information of a small set of sequenced individuals via imputation without the need for prior knowledge on the array design.ResultsThe strategy was first tested by simulating additional ascertainment bias with a set of 1566 chickens from 74 populations that were genotyped for the positions of the Affymetrix Axiom™ 580 k Genome-Wide Chicken Array. Imputation accuracy was shown to be consistently higher for populations used for SNP discovery during the simulated array design process. Reference sets of at least one individual per population in the study set led to a strong correction of ascertainment bias for estimates of expected and observed heterozygosity, Wright’s Fixation Index and Nei’s Standard Genetic Distance. In contrast, unbalanced reference sets (overrepresentation of populations compared to the study set) introduced a new bias towards the reference populations. Finally, the array genotypes were imputed to WGS by utilization of reference sets of 74 individuals (one per population) to 98 individuals (additional commercial chickens) and compared with a mixture of individually and pooled sequenced populations. The imputation reduced the slope between heterozygosity estimates of array data and WGS data from 1.94 to 1.26 when using the smaller balanced reference panel and to 1.44 when using the larger but unbalanced reference panel. This generally supported the results from simulation but was less favorable, advocating for a larger reference panel when imputing to WGS.ConclusionsThe results highlight the potential of using imputation for mitigation of SNP ascertainment bias but also underline the need for unbiased reference sets.

Highlights

Population genetic studies based on genotyped single nucleotide polymorphisms (SNPs) are influenced by a non-random selection of the SNPs included in the used genotyping arrays
To perform cost- and computationally efficient, many of the population genetic studies of the last 10 years for humans [1, 2], as well as for model- [3, 4] and agricultural species [5,6,7,8] were based on single nucleotide polymorphisms (SNP), which were genotyped by commercially available SNP arrays
This results in allele frequency spectra of arrays showing a shift towards common SNPs as compared to allele frequency spectra of whole genome sequencing (WGS), which typically contain a high share of rare SNPs [12]

Summary

Introduction

Population genetic studies based on genotyped single nucleotide polymorphisms (SNPs) are influenced by a non-random selection of the SNPs included in the used genotyping arrays. To perform cost- and computationally efficient, many of the population genetic studies of the last 10 years for humans [1, 2], as well as for model- [3, 4] and agricultural species [5,6,7,8] were based on single nucleotide polymorphisms (SNP), which were genotyped by commercially available SNP arrays Those arrays are based on a non-random selection (ascertainment) of SNPs, and come with a bias relative to whole genome re-sequencing (WGS) data, widely known as SNP Ascertainment Bias [9,10,11]. E.g. when used for samples of other species, this can result in a lack of variable and informative SNPs on the array and a shift of the frequency spectrum towards rare variants [16]

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Conclusion