Widespread deviant patterns of heterozygosity in whole-genome sequencing due to autopolyploidy, repeated elements and duplication

Most population genomic tools rely on accurate SNP calling and filtering to meet their underlying assumptions. However, genomic complexity, due to structural variants, paralogous sequences and repetitive elements, presents significant challenges in assembling contiguous reference genomes. Consequently, short-read resequencing studies can encounter mismapping issues, leading to SNPs that deviate from Mendelian expected patterns of heterozygosity and allelic ratio. In this study, we employed the ngsParalog software to identify such deviant SNPs in whole-genome sequencing data from four species: Arctic Char (Salvelinus alpinus), Lake Whitefish (Coregonus clupeaformis), Atlantic Salmon (Salmo salar), and the American Eel (Anguilla rostrata), with low (2X) to intermediate (6X) coverage. The analyses revealed that deviant SNPs accounted for up to 62% of all SNPs in salmonid datasets and approximately 10% in the American Eel dataset. These deviant SNPs were particularly concentrated within repetitive elements and genomic regions that had recently undergone rediploidization in salmonids. Additionally, narrow peaks of elevated coverage were ubiquitous along all four reference genomes, encompassed most deviant SNPs and could be partially attributed to transposons and tandem repeats. Including these deviant SNPs in genomic analyses led to highly distorted site frequency spectra, apparent homogenization of populations and underestimating pairwise FST values. Considering the widespread occurrence of deviant SNPs arising from a variety of source, their important impact in estimating population parameters, and the availability of effective tools to identify them, we propose that excluding deviant-SNPs from WGS datasets is required to improve genomic inferences for a wide range of taxa and sequencing depths. Significance: Genomes can be very repetitive and hard to assemble into a reference, which can lead to biases when genotyping genetic markers in complex genomic regions. Here, we draw attention to this issue in various whole-genome datasets and validate a method to identify problematic SNPs at low coverage. We also explore processes creating such SNPs and their consequences on common population genomics analyses.