How imputation can mitigate SNP ascertainment Bias

Abstract

Background 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. 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. Results The 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. Conclusions The results highlight the potential of using imputation for mitigation of SNP ascertainment bias but also underline the need for unbiased reference sets.

Assessment of linkage disequilibrium patterns between structural variants and single nucleotide polymorphisms in three commercial chicken populations

“…The raw data was first published by Qanbari et al [52] who described the studied populations in more detail. SNP genotypes were retrieved from a previous study [53]. SVs were called by a consensus calling approach, which used three pairedend and split-read-based tools, followed by a strict filtering procedure that further utilized read-depth and SNP information.…”

“…Alignment on the reference genome galGal6/ GRGC6a and SNP calling were conducted in a previous study [53] following GATK best practices pipeline [75]. The SNPs needed for this study were then extracted from the old callset using bcftools [76] and the duplicate-marked and base quality score recalibrated BAM files were used as starting point for the SV calling process.…”

Imputation of Low-density Marker Chip Data in Plant Breeding: Evaluation of Methods Based on Sugar Beet

“…This value is supported by a number of publications which estimate effective population sizes for crop and livestock populations much closer to 100 than to 1 million which is the default setting (Cowling, 2007; Gorssen et al, 2020; Leroy et al, 2013; Makanjuola et al, 2020; Saura et al, 2021; Zhao et al, 2021). After the publication of Pook et al (2020), some studies have already adopted lower values for ‘ne’ (Thorn et al, 2021; Whalen & Hickey, 2020) and a few are also increasing the window size (Arouisse et al, 2020; Geibel et al, 2021; Lamb et al, 2021).…”

“…This would lead to a biased comparison between approaches. Only a few studies use imputation parameters other than the default (Arouisse et al, 2020;Geibel et al, 2021;Lamb et al, 2021;Nyine et al, 2019;Thorn et al, 2021;.…”

“…This would lead to a biased comparison between approaches. Only a few studies use imputation parameters other than the default (Arouisse et al, 2020; Geibel et al, 2021; Lamb et al, 2021; Nyine et al, 2019; Thorn et al, 2021; Whalen & Hickey, 2020). These studies lower the parameter for the effective population size and some also increase the window size.…”