How Imputation Can Mitigate 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 relative to whole genome sequencing (WGS) data is known as SNP ascertainment bias. Correction for this bias requires detailed knowledge of the array design process which is often not available in practice. This study intends to investigate 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 1,566 chickens from 74 populations that were genotyped for the positions of the Affymetrix Axiom™ 580k 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, Wrights Fixation Index and Nei’s Standard Genetic Distance. In contrast, unbalanced reference sets 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.

Sexual dimorphism in body size and some exterior traits of pigeon breed groups

“…Fairbairn (1997) reported an SSI range between 3% and 128% in male-biased bird species, in which the smallest was in Passerines and the highest in Galliformes. In contrast to domesticated chicken breeds, sexual dimorphism is modest in the pigeon breeds in our study (Remeš and Szekely, 2010;Geibel et al, 2016). It is known that Galliformes are polygamous, but most of the Passerines are monogamous, also the Columbidae.…”

“…Relatively high male dimorphisms were found in the body size of the chickens (live weight), which the jungle chickens showed to be extreme with an average difference of 68% (Remeš, Szekely, 2010). Geibel et al (2016) found also great differences in favor of rosters in various chicken breed groups. In modern breeding programs that select for economically important traits, sex variation in traits is eliminated prior to selection.…”

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] which contains a more detailed description of the studied populations. SNP genotypes were retrieved from a previous study [53]. SVs were called by a consensus calling approach, which used three paired-end 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 [74]. The SNPs needed for this study were then extracted from the old callset using bcftools [75] and the duplicate-marked and base quality score recalibrated BAM files were used as starting point for the SV calling process.…”