How array design creates SNP ascertainment bias

Abstract

Single nucleotide polymorphisms (SNPs), genotyped with arrays, have become a widely used marker type in population genetic analyses over the last 10 years. However, compared to whole genome re-sequencing data, arrays are known to lack a substantial proportion of globally rare variants and tend to be biased towards variants present in populations involved in the development process of the respective array. This affects population genetic estimators and is known as SNP ascertainment bias. We investigated factors contributing to ascertainment bias in array development by redesigning the Axiom™ Genome-Wide Chicken Array in silico and evaluating changes in allele frequency spectra and heterozygosity estimates in a stepwise manner. A sequential reduction of rare alleles during the development process was shown. This was mainly caused by the identification of SNPs in a limited set of populations and a within-population selection of common SNPs when aiming for equidistant spacing. These effects were shown to be less severe with a larger discovery panel. Additionally, a generally massive overestimation of expected heterozygosity for the ascertained SNP sets was shown. This overestimation was 24% higher for populations involved in the discovery process than not involved populations in case of the original array. The same was observed after the SNP discovery step in the redesign. However, an unequal contribution of populations during the SNP selection can mask this effect but also adds uncertainty. Finally, we make suggestions for the design of specialized arrays for large scale projects where whole genome re-sequencing techniques are still too expensive.

How imputation can mitigate SNP ascertainment Bias

“…SNPs that are common in those discovery populations are not necessarily globally common. As a consequence, allele frequency spectra of discovery populations are systematically skewed towards higher minor allele frequencies (MAF) than those of non-discovery populations [ 12 , 16 ]. In extreme cases, e.g.…”

“…In extreme cases, e.g. when used for samples of other species, this can result in a lack of variable and thus informative SNPs on the array and therefore a shift of the frequency spectrum towards rare variants [ 16 ].…”

“…The shift in the allele frequency spectra has an effect on population genetic estimators that depend on the allele frequency estimates. Exemplarily, the shift in allele frequencies towards common variants leads to an systematic overestimation of the heterozygosity of populations [ 16 , 17 ]. The relative effect is stronger for populations that were part of the discovery set compared to populations that were not part of the discovery set [ 16 ].…”

“…Exemplarily, the shift in allele frequencies towards common variants leads to an systematic overestimation of the heterozygosity of populations [ 16 , 17 ]. The relative effect is stronger for populations that were part of the discovery set compared to populations that were not part of the discovery set [ 16 ]. Since commercially used breeds tend to be overrepresented in discovery sets [ 14 , 15 ], their diversity thus tends to be overestimated compared to non-commercial breeds not included in the discovery set [ 16 ].…”

“…The relative effect is stronger for populations that were part of the discovery set compared to populations that were not part of the discovery set [ 16 ]. Since commercially used breeds tend to be overrepresented in discovery sets [ 14 , 15 ], their diversity thus tends to be overestimated compared to non-commercial breeds not included in the discovery set [ 16 ]. Systematic differences in allele frequency spectra further increase estimates of genetic distances between populations which were part of the discovery set and those which were not [ 10 ].…”