Abstract
BackgroundGenomic studies such as genome-wide association and genomic selection require genome-wide genotype data. All existing technologies used to create these data result in missing genotypes, which are often then inferred using genotype imputation software. However, existing imputation methods most often make use only of genotypes that are successfully inferred after having passed a certain read depth threshold. Because of this, any read information for genotypes that did not pass the threshold, and were thus set to missing, is ignored. Most genomic studies also choose read depth thresholds and quality filters without investigating their effects on the size and quality of the resulting genotype data. Moreover, almost all genotype imputation methods require ordered markers and are therefore of limited utility in non-model organisms.ResultsHere we introduce LinkImputeR, a software program that exploits the read count information that is normally ignored, and makes use of all available DNA sequence information for the purposes of genotype calling and imputation. It is specifically designed for non-model organisms since it requires neither ordered markers nor a reference panel of genotypes. Using next-generation DNA sequence (NGS) data from apple, cannabis and grape, we quantify the effect of varying read count and missingness thresholds on the quantity and quality of genotypes generated from LinkImputeR. We demonstrate that LinkImputeR can increase the number of genotype calls by more than an order of magnitude, can improve genotyping accuracy by several percent and can thus improve the power of downstream analyses. Moreover, we show that the effects of quality and read depth filters can differ substantially between data sets and should therefore be investigated on a per-study basis.ConclusionsBy exploiting DNA sequence data that is normally ignored during genotype calling and imputation, LinkImputeR can significantly improve both the quantity and quality of genotype data generated from NGS technologies. It enables the user to quickly and easily examine the effects of varying thresholds and filters on the number and quality of the resulting genotype calls. In this manner, users can decide on thresholds that are most suitable for their purposes. We show that LinkImputeR can significantly augment the value and utility of NGS data sets, especially in non-model organisms with poor genomic resources.
Highlights
Genomic studies such as genome-wide association and genomic selection require genome-wide genotype data
Genome-wide association study (GWAS) We aimed to ensure that using low read counts and high levels of missingness would not result in spurious results when performing genetic mapping
All existing genotyping methods produce missing genotype data and filling in these missing genotypes via imputation is a crucial step in most genomic studies
Summary
Genomic studies such as genome-wide association and genomic selection require genome-wide genotype data. Existing imputation methods most often make use only of genotypes that are successfully inferred after having passed a certain read depth threshold. Most genomic studies choose read depth thresholds and quality filters without investigating their effects on the size and quality of the resulting genotype data. Almost all genotype imputation methods require ordered markers and are of limited utility in non-model organisms. Most studies that make use of genome-wide genotype data first fill in the missing genotypes using genotype imputation [10]. Does imputation result in a more complete table of genotype data, but it can improve the power of downstream analyses, such as Genome-Wide Association Studies (GWAS) [11]
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