Novel methods for genotype imputation to whole-genome sequence and a simple linear model to predict imputation accuracy

Abstract Background A cost-effective strategy to explore the complete DNA sequence in animals for genetic evaluation purposes is to sequence key ancestors of a population, followed by imputation mechanisms to infer marker genotypes that were not originally reported in a target population of animals genotyped with single nucleotide polymorphism (SNP) panels. The feasibility of this process relies on the accuracy of the genotype imputation in that population, particularly for potential causal mutations which may be at low frequency and either within genes or regulatory regions. The objective of the present study was to investigate the imputation accuracy to the sequence level in a Nellore beef cattle population, including that for variants in annotation classes which are more likely to be functional. Methods Information of 151 key sequenced Nellore sires were used to assess the imputation accuracy from bovine HD BeadChip SNP (~ 777 k) to whole-genome sequence. The choice of the sires aimed at optimizing the imputation accuracy of a genotypic database, comprised of about 10,000 genotyped Nellore animals. Genotype imputation was performed using two computational approaches: FImpute3 and Minimac4 (after using Eagle for phasing). The accuracy of the imputation was evaluated using a fivefold cross-validation scheme and measured by the squared correlation between observed and imputed genotypes, calculated by individual and by SNP. SNPs were classified into a range of annotations, and the accuracy of imputation within each annotation classification was also evaluated. Results High average imputation accuracies per animal were achieved using both FImpute3 (0.94) and Minimac4 (0.95). On average, common variants (minor allele frequency (MAF) > 0.03) were more accurately imputed by Minimac4 and low-frequency variants (MAF ≤ 0.03) were more accurately imputed by FImpute3. The inherent Minimac4 Rsq imputation quality statistic appears to be a good indicator of the empirical Minimac4 imputation accuracy. Both software provided high average SNP-wise imputation accuracy for all classes of biological annotations. Conclusions Our results indicate that imputation to whole-genome sequence is feasible in Nellore beef cattle since high imputation accuracies per individual are expected. SNP-wise imputation accuracy is software-dependent, especially for rare variants. The accuracy of imputation appears to be relatively independent of annotation classification.

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On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

Genetics Selection Evolution ◽

10.1186/s12711-021-00607-4 ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Theo Meuwissen ◽

Irene van den Berg ◽

Mike Goddard

Keyword(s):

Variable Selection ◽

Genome Sequence ◽

Genomic Prediction ◽

Milk Fat ◽

Genotype Imputation ◽

Whole Genome Sequence ◽

Genomic Relationship Matrix ◽

Polygenic Effect ◽

Relationship Matrix ◽

Whole Genome

Abstract Background Whole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision. Methods The Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits. Results The prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits. Conclusions Across-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.

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A reference panel of 64,976 haplotypes for genotype imputation

10.1101/035170 ◽

2015 ◽

Cited By ~ 17

Author(s):

Shane McCarthy ◽

Sayantan Das ◽

Warren Kretzschmar ◽

Olivier Delaneau ◽

Andrew R. Wood ◽

...

Keyword(s):

Genome Sequence ◽

Sequence Data ◽

Association Studies ◽

Allele Frequencies ◽

Genotype Imputation ◽

Reference Panel ◽

Whole Genome Sequence ◽

European Ancestry ◽

Whole Genome ◽

Remote Server

We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1%, a large increase in the number of SNPs tested in association studies and can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

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Optimizing Genomic Selection in Dezhou Donkey Using Low Coverage Whole Genome Sequencing

10.21203/rs.3.rs-607740/v1 ◽

2021 ◽

Author(s):

Changheng Zhao ◽

Jun Teng ◽

Xinhao Zhang ◽

Dan Wang ◽

Xinyi Zhang ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genomic Selection ◽

Genome Sequencing ◽

Sequence Data ◽

Low Cost ◽

Imputation Accuracy ◽

Genotype Imputation ◽

Whole Genome Sequence ◽

Whole Genome ◽

Low Coverage

Abstract Background Low coverage whole genome sequencing is a low-cost genotyping technology. Combining with genotype imputation approaches, it is likely to become a critical component of cost-efficient genomic selection programs in agricultural livestock. Here, we used the low-coverage sequence data of 617 Dezhou donkeys to investigate the performance of genotype imputation for low coverage whole genome sequence data and genomic selection based on the imputed genotype data. The specific aims were: (i) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, MAFs, and imputation pipelines; and (ii) to assess the accuracy of genomic selection under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single- vs multi-trait models. Results We found that a high imputation accuracy (> 0.95) can be achieved for sequence data with sequencing depth as low as 1x and the number of sequenced individuals equal to 400. For genomic selection, the best performance was obtained by using a marker density of 410K and a G matrix constructed using marker dosage information. Multi-trait GBLUP performed better than single-trait GBLUP. Conclusions Our study demonstrates that low coverage whole genome sequencing would be a cost-effective method for genomic selection in Dezhou Donkey.

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Developing a 670k genotyping array to tag ∼2M SNPs across 24 horse breeds

10.1101/112979 ◽

2017 ◽

Author(s):

Robert J. Schaefer ◽

Mikkel Schubert ◽

Ernest Bailey ◽

Danika L. Bannasch ◽

Eric Barrey ◽

...

Keyword(s):

Genome Sequence ◽

High Density ◽

Genotype Imputation ◽

Whole Genome Sequence ◽

Probe Design ◽

Candidate Snps ◽

Genotype Data ◽

Domestic Horse ◽

Whole Genome ◽

Next Generation

AbstractBackgroundTo date, genome-scale analyses in the domestic horse have been limited by suboptimal single nucleotide polymorphism (SNP) density and uneven genomic coverage of the current SNP genotyping arrays. The recent availability of whole genome sequences has created the opportunity to develop a next generation, high-density equine SNP array.ResultsUsing whole genome sequence from 153 individuals representing 24 distinct breeds collated by the equine genomics community, we cataloged over 23 million de novo discovered genetic variants. Leveraging genotype data from individuals with both whole genome sequence, and genotypes from lower-density, legacy SNP arrays, a subset of ∼5 million high-quality, high-density array candidate SNPs were selected based on breed representation and uniform spacing across the genome. Considering probe design recommendations from a commercial vendor (Affymetrix, now Thermo Fisher Scientific) a set of ∼2 million SNPs were selected for a next-generation high-density SNP chip (MNEc2M). Genotype data were generated using the MNEc2M array from a cohort of 332 horses from 20 breeds and a lower-density array, consisting of ∼670 thousand SNPs (MNEc670k), was designed for genotype imputation.ConclusionsHere, we document the steps taken to design both the MNEc2M and MNEc670k arrays, report genomic and technical properties of these genotyping platforms, and demonstrate the imputation capabilities of these tools for the domestic horse.

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Accuracy of whole-genome sequence imputation using hybrid peeling in large pedigreed livestock populations

10.1101/771576 ◽

2019 ◽

Cited By ~ 1

Author(s):

Roger Ros-Freixedes ◽

Andrew Whalen ◽

Ching-Yi Chen ◽

Gregor Gorjanc ◽

William O Herring ◽

...

Keyword(s):

Genome Sequence ◽

Sequence Data ◽

Imputation Accuracy ◽

High Accuracy ◽

Whole Genome Sequence ◽

Whole Genome ◽

Sequencing Coverage ◽

Sequencing Strategy ◽

Main Factors ◽

The Individual

AbstractBackgroundWe demonstrate high accuracy of whole-genome sequence imputation in large livestock populations where only a small fraction of individuals (2%) had been sequenced, mostly at low coverage.MethodsWe used data from four pig populations of different sizes (18,349 to 107,815 individuals) that were broadly genotyped at densities between 15,000 and 75,000 markers genome-wide. Around 2% of the individuals in each population were sequenced (most at 1x or 2x and a small fraction at 30x; average coverage per individual: 4x). We imputed whole-genome sequence with hybrid peeling. We evaluated the imputation accuracy by removing the sequence data of a total of 284 individuals that had been sequenced at high coverage, using a leave-one-out design. We complemented these results with simulated data that mimicked the sequencing strategy used in the real populations to quantify the factors that affected the individual-wise and variant-wise imputation accuracies using regression trees.ResultsImputation accuracy was high for the majority of individuals in all four populations (median individual-wise correlation was 0.97). Individuals in the earliest generations of each population had lower accuracy than the rest, likely due to the lack of marker array data for themselves and their ancestors. The main factors that determined the individual-wise imputation accuracy were the genotyping status of the individual, the availability of marker array data for immediate ancestors, and the degree of connectedness of an individual to the rest of the population, but sequencing coverage had no effect. The main factors that determined variant-wise imputation accuracy were the minor allele frequency and the number of individuals with sequencing coverage at each variant site. These results were validated with the empirical observations.ConclusionsThe coupling of an appropriate sequencing strategy and imputation method, such as described and validated here, is a powerful strategy for generating whole-genome sequence data in large pedigreed populations with high accuracy. This is a critical step for the successful implementation of whole-genome sequence data for genomic predictions and fine-mapping of causal variants.

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Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions

Heredity ◽

10.1038/hdy.2013.13 ◽

2013 ◽

Vol 112 (1) ◽

pp. 39-47 ◽

Cited By ~ 98

Author(s):

T Druet ◽

I M Macleod ◽

B J Hayes

Keyword(s):

Genome Sequence ◽

Genomic Prediction ◽

Sequence Data ◽

Genotype Imputation ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data

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Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling

10.1101/824631 ◽

2019 ◽

Cited By ~ 1

Author(s):

Roger Ros-Freixedes ◽

Andrew Whalen ◽

Gregor Gorjanc ◽

Alan J Mileham ◽

John M Hickey

Keyword(s):

Genome Sequence ◽

Sequence Data ◽

Imputation Accuracy ◽

Whole Genome Sequence ◽

Whole Genome ◽

Genome Sequence Data ◽

Uniform Coverage ◽

Sequencing Strategy ◽

Four Levels ◽

The Impact

AbstractBackgroundFor assembling large whole-genome sequence datasets to be used routinely in research and breeding, the sequencing strategy should be adapted to the methods that will later be used for variant discovery and imputation. In this study we used simulation to explore the impact that the sequencing strategy and level of sequencing investment have on the overall accuracy of imputation using hybrid peeling, a pedigree-based imputation method well-suited for large livestock populations.MethodsWe simulated marker array and whole-genome sequence data for fifteen populations with simulated or real pedigrees that had different structures. In these populations we evaluated the effect on imputation accuracy of seven methods for selecting which individuals to sequence, the generation of the pedigree to which the sequenced individuals belonged, the use of variable or uniform coverage, and the trade-off between the number of sequenced individuals and their sequencing coverage. For each population we considered four levels of investment in sequencing that were proportional to the size of the population.ResultsImputation accuracy largely depended on pedigree depth. The distribution of the sequenced individuals across the generations of the pedigree underlay the performance of the different methods used to select individuals to sequence. Additionally, it was critical to balance high imputation accuracy in early generations as well as in late generations. Imputation accuracy was highest with a uniform coverage across the sequenced individuals of around 2x rather than variable coverage. An investment equivalent to the cost of sequencing 2% of the population at 2x provided high imputation accuracy. The gain in imputation accuracy from additional investment diminished with larger populations and larger levels of investment. However, to achieve the same imputation accuracy, a proportionally greater investment must be used in the smaller populations compared to the larger ones.ConclusionsSuitable sequencing strategies for subsequent imputation with hybrid peeling involve sequencing around 2% of the population at a uniform coverage around 2x, distributed preferably from the third generation of the pedigree onwards. Such sequencing strategies are beneficial for generating whole-genome sequence data in populations with deep pedigrees of closely related individuals.

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