Improving the resolution of canine genome-wide association studies using genotype imputation: A study of two breeds

Abstract

Genotype imputation using a reference panel that combines high-density array data and publicly available whole genome sequence consortium variant data is potentially a cost-effective method to increase the density of extant lower-density array datasets. In this study, three datasets (two Border Collie; one Italian Spinone) generated using a legacy array (Illumina CanineHD, 173 662 SNPs) were utilised to assess the feasibility and accuracy of this approach and to gather additional evidence for the efficacy of canine genotype imputation. The cosmopolitan reference panels used to impute genotypes comprised dogs of 158 breeds, mixed breed dogs, wolves and Chinese indigenous dogs, as well as breed-specific individuals genotyped using the Axiom Canine HD array. The two Border Collie reference panels comprised 808 individuals including 79 Border Collies and 426 326 or 426 332 SNPs; and the Italian Spinone reference panel comprised 807 individuals including 38 Italian Spinoni and 476 313 SNPs. A high accuracy for imputation was observed, with the lowest accuracy observed for one of the Border Collie datasets (mean R² = 0.94) and the highest for the Italian Spinone dataset (mean R² = 0.97). This study's findings demonstrate that imputation of a legacy array study set using a reference panel comprising both breed-specific array data and multi-breed variant data derived from whole genomes is effective and accurate. The process of canine genotype imputation, using the valuable growing resource of publicly available canine genome variant datasets alongside breed-specific data, is described in detail to facilitate and encourage use of this technique in canine genetics.

Keywords: Border Collie; Italian Spinone; genome-wide association study; imputation accuracy; whole genome sequencing.

Figure 1

Flowchart to illustrate dataset processing for imputation study sets and reference panels. WGS, whole genome sequencing

Figure 2

Accuracy of imputation for each chromosome in Italian Spinone and Border Collie datasets. The graph shows the R² of imputed calls and known genotypes. Boxes are 25th–75th percentiles, with lines for the median. Whiskers indicate upper and lower adjacent values; outliers are shown using dots. Truncated y-axis starts at 0.7.

Figure 3

Accuracy of imputation for each concordance-tested individual (n = 8 for each set) in Italian Spinone and Border Collie datasets. The graph shows the R² of imputed calls and known genotypes. Boxes are 25th–75th percentiles, with lines for the median. Whiskers indicate upper and lower adjacent values; outliers are shown using dots. Truncated y-axis starts at 0.7.

Figure 4

Accuracy of imputation for each concordance tested dog from Border Collie Set 1 and each of three reference panels containing decreasing numbers of Border Collies. The graph shows the R² of imputed calls and known genotypes. Boxes are 25th–75th percentiles, with lines for the median. Whiskers indicate upper and lower adjacent values; outliers are shown using dots. Lines show mean R² for each reference panel. Truncated y-axis starts at 0.7.

Figure 5

A comparison of imputation accuracy and predicted certainty. Top: percent of concordant genotypes for SNPs with heterozygous or homozygous known genotypes grouped by impute2’s Info metric (imputation certainty). Bottom: percent of total imputed calls within each Info group.