Discovery of novel variants in genotyping arrays improves genotype retention and reduces ascertainment bias

Abstract

BackgroundHigh-density genotyping arrays that measure hybridization of genomic DNA fragments to allele-specific oligonucleotide probes are widely used to genotype single nucleotide polymorphisms (SNPs) in genetic studies, including human genome-wide association studies. Hybridization intensities are converted to genotype calls by clustering algorithms that assign each sample to a genotype class at each SNP. Data for SNP probes that do not conform to the expected pattern of clustering are often discarded, contributing to ascertainment bias and resulting in lost information - as much as 50% in a recent genome-wide association study in dogs.ResultsWe identified atypical patterns of hybridization intensities that were highly reproducible and demonstrated that these patterns represent genetic variants that were not accounted for in the design of the array platform. We characterized variable intensity oligonucleotide (VINO) probes that display such patterns and are found in all hybridization-based genotyping platforms, including those developed for human, dog, cattle, and mouse. When recognized and properly interpreted, VINOs recovered a substantial fraction of discarded probes and counteracted SNP ascertainment bias. We developed software (MouseDivGeno) that identifies VINOs and improves the accuracy of genotype calling. MouseDivGeno produced highly concordant genotype calls when compared with other methods but it uniquely identified more than 786000 VINOs in 351 mouse samples. We used whole-genome sequence from 14 mouse strains to confirm the presence of novel variants explaining 28000 VINOs in those strains. We also identified VINOs in human HapMap 3 samples, many of which were specific to an African population. Incorporating VINOs in phylogenetic analyses substantially improved the accuracy of a Mus species tree and local haplotype assignment in laboratory mouse strains.ConclusionThe problems of ascertainment bias and missing information due to genotyping errors are widely recognized as limiting factors in genetic studies. We have conducted the first formal analysis of the effect of novel variants on genotyping arrays, and we have shown that these variants account for a large portion of miscalled and uncalled genotypes. Genetic studies will benefit from substantial improvements in the accuracy of their results by incorporating VINOs in their analyses.

A Novel Intronic Single Nucleotide Polymorphism in the Myosin heavy polypeptide 4 Gene Is Responsible for the Mini-Muscle Phenotype Characterized by Major Reduction in Hind-Limb Muscle Mass in Mice

“…The Minimsc candidate interval on MMU11 was refined using the Hartmann et al 2008) to fine-map boundaries of the candidate region ( Figure 1). Hybridization intensities at every probe within the Minimsc candidate interval were compared between the minimuscle and normal mice in an effort to identify potential deletions and variable intensity oligonucleotides (Yang et al 2011;Didion et al 2012). Intensity plots spanning from 66.924 to 70.027 Mb on MMU11 among all pairwise comparisons of a mini-muscle and a normal sample were analyzed.…”

Detecting Natural Selection in Genomic Data

“…When the SNP discovery protocol is known, statistical measures can be taken to counteract the effect of ascertainment bias (86,87,106). In addition, genotyping assays that incorporate variable intensity oligonucleotide (VINO) probes can be used to mitigate the number of polymorphisms overlooked due to ascertainment bias (25).…”

Genetic mapping in polyploids

“…by exome sequencing (Ng et al, 2009) or target enrichment (Mamanova et al, 2010)). The disadvantages of targeted approaches have been well explored (particularly regarding ascertainment bias, where the set of targeted SNPs on an array poorly represents the diversity in the samples under investigation due to biased methods of SNP discovery) (Albrechtsen et al, 2010;Moragues et al, 2010;Didion et al, 2012;Lachance and Tishkoff, 2013), although there are advantages and disadvantages to both methods (Mason et al, 2017). Apart from costs, differences exist in the ease of data analysis following genotyping, with sequencing data requiring greater curation and bioinformatics skills (Spindel et al, 2013;Bajgain et al, 2016) as well as potentially containing more erroneous and missing data (Spindel et al, 2013;Jones et al, 2017).…”