Development and validation of a horse reference panel for genotype imputation

Abstract

Background Genotype imputation is a cost-effective method to generate sequence-level genotypes for a large number of animals. Its application can improve the power of genomic studies, provided that the accuracy of imputation is sufficiently high. The purpose of this study was to develop an optimal strategy for genotype imputation from genotyping array data to sequence level in German warmblood horses, and to investigate the effect of different factors on the accuracy of imputation. Publicly available whole-genome sequence data from 317 horses of 46 breeds was used to conduct the analyses. Results Depending on the size and composition of the reference panel, the accuracy of imputation from medium marker density (60K) to sequence level using the software Beagle 5.1 ranged from 0.64 to 0.70 for horse chromosome 3. Generally, imputation accuracy increased as the size of the reference panel increased, but if genetically distant individuals were included in the panel, the accuracy dropped. Imputation was most precise when using a reference panel of multiple but related breeds and the software Beagle 5.1, which outperformed the other two tested computer programs, Impute 5 and Minimac 4. Genome-wide imputation for this scenario resulted in a mean accuracy of 0.66. Stepwise imputation from 60K to 670K markers and subsequently to sequence level did not improve the accuracy of imputation. However, imputation from higher density (670K) was considerably more accurate (about 0.90) than from medium density. Likewise, imputation in genomic regions with a low marker coverage resulted in a reduced accuracy of imputation. Conclusions The accuracy of imputation in horses was influenced by the size and composition of the reference panel, the marker density of the genotyping array, and the imputation software. Genotype imputation can be used to extend the limited amount of available sequence-level data from horses in order to boost the power of downstream analyses, such as genome-wide association studies, or the detection of embryonic lethal variants.

Genomic analyses of withers height and linear conformation traits in German Warmblood horses using imputed sequence-level genotypes

“…However, inaccurate imputation can affect the results of GWAS based on imputed data [17]. The accuracy of imputing the warmblood horse variants included in our study was estimated to be rather low, i.e., 0.66 [55]. The main reason for this low accuracy is a shortage of WGS data from horses in general and from specific breeds, preventing the establishment of a larger haplotype reference panel, which could make imputation in horses more accurate.…”

“…For these horses, genotype imputation to sequence level was performed using a reference panel of 175 horses that were compiled in a previous study [55]. Briefly, publicly available WGS data for 317 horses from 46 breeds were mapped against the reference genome EquCab3.0 and variant calling was performed using the Genome Analysis Toolkit (GATK) version 4.1.7.0 [56] according to the GATK best practices recommendations [57].…”

“…Subsequent investigations showed that the accuracy of imputing warmblood horses from medium marker density to sequence level was highest using the Beagle 5.1 software and a reference panel that included only a subset of the horses from the variant call set, i.e., 175 of the initial 317 horses. For this approach, the genome-wide imputation accuracy was 0.66 [55]. The 175 horses represented in the panel used were chosen based on their genetic relationship to warmblood horses and included mainly warmblood and Quarter horses, Arabians, Thoroughbreds, and Standardbreds.…”

“…GWAS for withers height revealed two genome-wide significant association signals between 55.91 and 59.04 Mb on ECA1 and between 97.84 and 117.98 Mb on ECA3 (Fig. 2a).…”

Evaluation of the selection of key individuals for genotype imputation in Chinese yellow-feathered chicken

“…The accuracy of imputation is influenced by various factors, with the reference panel being a crucial consideration ( Reich et al, 2022 ). While the accuracy of imputation tends to increase with an expanding number of samples in the reference panel, the inclusion of genetically distant animals may lead to a higher error ratio ( Pook et al, 2020 ).…”