Dataset Information

High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is achievable in sheep1.

ABSTRACT: The objective of the present study was to quantify the accuracy of imputing medium-density single nucleotide polymorphism (SNP) genotypes from lower-density panels (384 to 12,000 SNPs) derived using alternative selection methods to select the most informative SNPs. Four different selection methods were used to select SNPs based on genomic characteristics (i.e., minor allele frequency (MAF) and linkage disequilibrium (LD)) within five sheep breeds (642 Belclare, 645 Charollais, 715 Suffolk, 440 Texel, and 620 Vendeen) separately. Selection methods evaluated included (i) random, (ii) splitting the genome into blocks of equal length and selecting SNPs within block based on MAF and LD patterns, (iii) equidistant location while optimizing MAF, (iv) a combination of MAF, distance from already selected SNPs, and weak LD with the SNP(s) already selected. All animals were genotyped on the Illumina OvineSNP50 Beadchip containing 51,135 SNPs of which 44,040 remained after edits. Within each breed separately, the youngest 100 animals were assumed to represent the validation population; the remaining animals represented the reference population. Imputation was undertaken under three different conditions: (i) SNPs were selected within a given breed and imputed for all breeds individually, (ii) all breeds were collectively used to select SNPs and were included as the reference population, and (iii) the SNPs were selected for each breed separately and imputation was undertaken for all breeds but excluding from the reference population, the breed from which the SNPs were selected. Regardless of SNP selection method, mean animal allele concordance rate improved at a diminishing rate while the variability in mean animal allele concordance rate reduced as the panel density increased. The SNP selection method impacted the accuracy of imputation although the effect reduced as the density of the panel increased. Overall, the most accurate SNP selection method for panels with <9,000 SNPs was that based on MAF and LD pattern within genomic blocks. The mean animal allele concordance rate varied from 0.89 in Texel to 0.97 in Vendeen. Greater imputation accuracy was achieved when SNPs were selected and imputed within each breed individually compared with when SNPs were selected across all breeds and imputed using a multi-breed reference population. In all, results indicate that accurate genotype imputation to medium density is achievable with low-density genotype panels with at least 6,000 SNPs.

SUBMITTER: O'Brien AC

PROVIDER: S-EPMC6448760 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is achievable in sheep1.

O'Brien Aine C AC Judge Michelle M MM Fair Sean S Berry Donagh P DP

Journal of animal science 20190401 4

The objective of the present study was to quantify the accuracy of imputing medium-density single nucleotide polymorphism (SNP) genotypes from lower-density panels (384 to 12,000 SNPs) derived using alternative selection methods to select the most informative SNPs. Four different selection methods were used to select SNPs based on genomic characteristics (i.e., minor allele frequency (MAF) and linkage disequilibrium (LD)) within five sheep breeds (642 Belclare, 645 Charollais, 715 Suffolk, 440 T ...[more]

PMID: 30722011

Similar Datasets

Project description:Genomic selection has the potential to increase genetic progress. Genotype imputation of high-density single-nucleotide polymorphism (SNP) genotypes can improve the cost efficiency of genomic breeding value (GEBV) prediction for pig breeding. Consequently, the objectives of this work were to: (1) estimate accuracy of genomic evaluation and GEBV for three traits in a Yorkshire population and (2) quantify the loss of accuracy of genomic evaluation and GEBV when genotypes were imputed under two scenarios: a high-cost, high-accuracy scenario in which only selection candidates were imputed from a low-density platform and a low-cost, low-accuracy scenario in which all animals were imputed using a small reference panel of haplotypes. Phenotypes and genotypes obtained with the PorcineSNP60 BeadChip were available for 983 Yorkshire boars. Genotypes of selection candidates were masked and imputed using tagSNP in the GeneSeek Genomic Profiler (10K). Imputation was performed with BEAGLE using 128 or 1800 haplotypes as reference panels. GEBV were obtained through an animal-centric ridge regression model using de-regressed breeding values as response variables. Accuracy of genomic evaluation was estimated as the correlation between estimated breeding values and GEBV in a 10-fold cross validation design. Accuracy of genomic evaluation using observed genotypes was high for all traits (0.65-0.68). Using genotypes imputed from a large reference panel (accuracy: R(2) = 0.95) for genomic evaluation did not significantly decrease accuracy, whereas a scenario with genotypes imputed from a small reference panel (R(2) = 0.88) did show a significant decrease in accuracy. Genomic evaluation based on imputed genotypes in selection candidates can be implemented at a fraction of the cost of a genomic evaluation using observed genotypes and still yield virtually the same accuracy. On the other side, using a very small reference panel of haplotypes to impute training animals and candidates for selection results in lower accuracy of genomic evaluation.

Project description:BackgroundGenomic selection accuracy increases with the use of high SNP (single nucleotide polymorphism) coverage. However, such gains in coverage come at high costs, preventing their prompt operational implementation by breeders. Low density panels imputed to higher densities offer a cheaper alternative during the first stages of genomic resources development. Our study is the first to explore the imputation in a tree species: black poplar. About 1000 pure-breed Populus nigra trees from a breeding population were selected and genotyped with a 12K custom Infinium Bead-Chip. Forty-three of those individuals corresponding to nodal trees in the pedigree were fully sequenced (reference), while the remaining majority (target) was imputed from 8K to 1.4 million SNPs using FImpute. Each SNP and individual was evaluated for imputation errors by leave-one-out cross validation in the training sample of 43 sequenced trees. Some summary statistics such as Hardy-Weinberg Equilibrium exact test p-value, quality of sequencing, depth of sequencing per site and per individual, minor allele frequency, marker density ratio or SNP information redundancy were calculated. Principal component and Boruta analyses were used on all these parameters to rank the factors affecting the quality of imputation. Additionally, we characterize the impact of the relatedness between reference population and target population.ResultsDuring the imputation process, we used 7540 SNPs from the chip to impute 1,438,827 SNPs from sequences. At the individual level, imputation accuracy was high with a proportion of SNPs correctly imputed between 0.84 and 0.99. The variation in accuracies was mostly due to differences in relatedness between individuals. At a SNP level, the imputation quality depended on genotyped SNP density and on the original minor allele frequency. The imputation did not appear to result in an increase of linkage disequilibrium. The genotype densification not only brought a better distribution of markers all along the genome, but also we did not detect any substantial bias in annotation categories.ConclusionsThis study shows that it is possible to impute low-density marker panels to whole genome sequence with good accuracy under certain conditions that could be common to many breeding populations.

Project description:BackgroundCurrently, genome-wide evaluation of cattle populations is based on SNP-genotyping using ~ 54,000 SNP. Increasing the number of markers might improve genomic predictions and power of genome-wide association studies. Imputation of genotypes makes it possible to extrapolate genotypes from lower to higher density arrays based on a representative reference sample for which genotypes are obtained at higher density.MethodsGenotypes using 639 214 SNP were available for 797 bulls of the Fleckvieh cattle breed. The data set was divided into a reference and a validation population. Genotypes for all SNP except those included in the BovineSNP50 Bead chip were masked and subsequently imputed for animals of the validation population. Imputation of genotypes was performed with Beagle, findhap.f90, MaCH and Minimac. The accuracy of the imputed genotypes was assessed for four different scenarios including 50, 100, 200 and 400 animals as reference population. The reference animals were selected to account for 78.03%, 89.21%, 97.47% and > 99% of the gene pool of the genotyped population, respectively.ResultsImputation accuracy increased as the number of animals and relatives in the reference population increased. Population-based algorithms provided highly reliable imputation of genotypes, even for scenarios with 50 and 100 reference animals only. Using MaCH and Minimac, the correlation between true and imputed genotypes was > 0.975 with 100 reference animals only. Pre-phasing the genotypes of both the reference and validation populations not only provided highly accurate imputed genotypes but was also computationally efficient. Genome-wide analysis of imputation accuracy led to the identification of many misplaced SNP.ConclusionsGenotyping key animals at high density and subsequent population-based genotype imputation yield high imputation accuracy. Pre-phasing the genotypes of the reference and validation populations is computationally efficient and results in high imputation accuracy, even when the reference population is small.

Project description:BackgroundGenotyping with the medium-density Bovine SNP50 BeadChip® (50K) is now standard in cattle. The high-density BovineHD BeadChip®, which contains 777,609 single nucleotide polymorphisms (SNPs), was developed in 2010. Increasing marker density increases the level of linkage disequilibrium between quantitative trait loci (QTL) and SNPs and the accuracy of QTL localization and genomic selection. However, re-genotyping all animals with the high-density chip is not economically feasible. An alternative strategy is to genotype part of the animals with the high-density chip and to impute high-density genotypes for animals already genotyped with the 50K chip. Thus, it is necessary to investigate the error rate when imputing from the 50K to the high-density chip.MethodsFive thousand one hundred and fifty three animals from 16 breeds (89 to 788 per breed) were genotyped with the high-density chip. Imputation error rates from the 50K to the high-density chip were computed for each breed with a validation set that included the 20% youngest animals. Marker genotypes were masked for animals in the validation population in order to mimic 50K genotypes. Imputation was carried out using the Beagle 3.3.0 software.ResultsMean allele imputation error rates ranged from 0.31% to 2.41% depending on the breed. In total, 1980 SNPs had high imputation error rates in several breeds, which is probably due to genome assembly errors, and we recommend to discard these in future studies. Differences in imputation accuracy between breeds were related to the high-density-genotyped sample size and to the genetic relationship between reference and validation populations, whereas differences in effective population size and level of linkage disequilibrium showed limited effects. Accordingly, imputation accuracy was higher in breeds with large populations and in dairy breeds than in beef breeds. More than 99% of the alleles were correctly imputed if more than 300 animals were genotyped at high-density. No improvement was observed when multi-breed imputation was performed.ConclusionIn all breeds, imputation accuracy was higher than 97%, which indicates that imputation to the high-density chip was accurate. Imputation accuracy depends mainly on the size of the reference population and the relationship between reference and target populations.

Project description:BACKGROUND:The trading of individual animal genotype information often involves only the exchange of the called genotypes and not necessarily the additional information required to effectively call structural variants. The main aim here was to determine if it is possible to impute copy number variants (CNVs) using the flanking single nucleotide polymorphism (SNP) haplotype structure in cattle. While this objective was achieved using high-density genotype panels (i.e., 713,162 SNPs), a secondary objective investigated the concordance of CNVs called with this high-density genotype panel compared to CNVs called from a medium-density panel (i.e., 45,677 SNPs in the present study). This is the first study to compare CNVs called from high-density and medium-density SNP genotypes from the same animals. High (and medium-density) genotypes were available on 991 Holstein-Friesian, 1015 Charolais, and 1394 Limousin bulls. The concordance between CNVs called from the medium-density and high-density genotypes were calculated separately for each animal. A subset of CNVs which were called from the high-density genotypes was selected for imputation. Imputation was carried out separately for each breed using a set of high-density SNPs flanking the midpoint of each CNV. A CNV was deemed to be imputed correctly when the called copy number matched the imputed copy number. RESULTS:For 97.0% of CNVs called from the high-density genotypes, the corresponding genomic position on the medium-density of the animal did not contain a called CNV. The average accuracy of imputation for CNV deletions was 0.281, with a standard deviation of 0.286. The average accuracy of imputation of the CNV normal state, i.e. the absence of a CNV, was 0.982 with a standard deviation of 0.022. Two CNV duplications were imputed in the Charolais, a single CNV duplication in the Limousins, and a single CNV duplication in the Holstein-Friesians; in all cases the CNV duplications were incorrectly imputed. CONCLUSION:The vast majority of CNVs called from the high-density genotypes were not detected using the medium-density genotypes. Furthermore, CNVs cannot be accurately predicted from flanking SNP haplotypes, at least based on the imputation algorithms routinely used in cattle, and using the SNPs currently available on the high-density genotype panel.

Dataset Information

High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is achievable in sheep1.

Publications

High imputation accuracy from informative low-to-medium density single nucleotide polymorphism genotypes is achievable in sheep1.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets