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

A general and efficient representation of ancestral recombination graphs

As a result of recombination, adjacent nucleotides can have different paths of genetic inheritance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). Classical formalisms have focused on mapping coalescence and recombination events to the nodes in an ARG. However, this approach is out of step with some modern developments, which do not represent genetic inheritance in terms of these events or explicitly infer them. We present a simple formalism that defines an ARG in terms of specific genomes and their intervals of genetic inheritance, and show how it generalizes these classical treatments and encompasses the outputs of recent methods. We discuss nuances arising from this more general structure, and argue that it forms an appropriate basis for a software standard in this rapidly growing field.

A general and efficient representation of ancestral recombination graphs

As a result of recombination, adjacent nucleotides can have different paths of genetic inheritance and therefore the genealogical trees for a sample of DNA sequences vary along the genome. The structure capturing the details of these intricately interwoven paths of inheritance is referred to as an ancestral recombination graph (ARG). Classical formalisms have focused on mapping coalescence and recombination events to the nodes in an ARG. This approach is out of step with modern developments, which do not represent genetic inheritance in terms of these events or explicitly infer them. We present a simple formalism that defines an ARG in terms of specific genomes and their intervals of genetic inheritance, and show how it generalises these classical treatments and encompasses the outputs of recent methods. We discuss nuances arising from this more general structure, and argue that it forms an appropriate basis for a software standard in this rapidly growing field.

A cautionary tale of low-pass sequencing and imputation with respect to haplotype accuracy

BACKGROUND

Low-pass whole-genome sequencing and imputation offer significant cost savings, enabling substantial increases in sample size and statistical power. This approach is particularly promising in livestock breeding, providing an affordable means of screening individuals for deleterious alleles or calculating genomic breeding values. Consequently, it may also be of value in companion animal genomics to support pedigree breeding. We sought to evaluate in dogs the impact of low coverage sequencing and reference-guided imputation on genotype concordance and association analyses.

RESULTS

DNA isolated from saliva of 30 Labrador retrievers was sequenced at low (0.9X and 3.8X) and high (43.5X) coverage, and down-sampled from 43.5X to 9.6X and 17.4X. Genotype imputation was performed using a diverse reference panel (1021 dogs), and two subsets of the former panel (256 dogs each) where one had an excess of Labrador retrievers relative to other breeds. We observed little difference in imputed genotype concordance between reference panels. Association analyses for a locus acting as a disease proxy were performed using single-marker (GEMMA) and haplotype-based (XP-EHH) tests. GEMMA results were highly correlated (r ≥ 0.97) between 43.5X and ≥ 3.8X depths of coverage, while for 0.9X the correlation was lower (r ≤ 0.8). XP-EHH results were less well correlated, with r ranging from 0.58 (0.9X) to 0.88 (17.4X). Across a random sample of 10,000 genomic regions averaging 17 kb in size, we observed a median of three haplotypes per dog across the sequencing depths, with 5% of the regions returning more than eight haplotypes. Inspection of one such region revealed genotype and phasing inconsistencies across sequencing depths.

CONCLUSIONS

We demonstrate that saliva-derived canine DNA is suitable for whole-genome sequencing, highlighting the feasibility of client-based sampling. Low-pass sequencing and imputation require caution as incorrect allele assignments result when the subject possesses alleles that are absent in the reference panel. Larger panels have the capacity for greater allelic diversity, which should reduce the potential for imputation error. Although low-pass sequencing can accurately impute allele dosage, we highlight issues with phasing accuracy that impact haplotype-based analyses. Consequently, if accurately phased genotypes are required for analyses, we advocate sequencing at high depth (> 20X).

Using residual regressions to quantify and map signal leakage in genomic prediction

BACKGROUND

Most genomic prediction applications in animal breeding use genotypes with tens of thousands of single nucleotide polymorphisms (SNPs). However, modern sequencing technologies and imputation algorithms can generate ultra-high-density genotypes (including millions of SNPs) at an affordable cost. Empirical studies have not produced clear evidence that using ultra-high-density genotypes can significantly improve prediction accuracy. However, (whole-genome) prediction accuracy is not very informative about the ability of a model to capture the genetic signals from specific genomic regions. To address this problem, we propose a simple methodology that detects chromosome regions for which a specific model (e.g., single-step genomic best linear unbiased prediction (ssGBLUP)) may fail to fully capture the genetic signal present in such segments-a phenomenon that we refer to as signal leakage. We propose to detect regions with evidence of signal leakage by testing the association of residuals from a pedigree or a genomic model with SNP genotypes. We discuss how this approach can be used to map regions with signals that are poorly captured by a model and to identify strategies to fix those problems (e.g., using a different prior or increasing marker density). Finally, we explored the proposed approach to scan for signal leakage of different models (pedigree-based, ssGBLUP, and various Bayesian models) applied to growth-related phenotypes (average daily gain and backfat thickness) in pigs.

RESULTS

We report widespread evidence of signal leakage for pedigree-based models. Including a percentage of animals with SNP data in ssGBLUP reduced the extent of signal leakage. However, local peaks of missed signals remained in some regions, even when all animals were genotyped. Using variable selection priors solves leakage points that are caused by excessive shrinkage of marker effects. Nevertheless, these models still miss signals in some regions due to low linkage disequilibrium between the SNPs on the array used and causal variants. Thus, we discuss how such problems could be addressed by adding sequence SNPs from those regions to the prediction model.

CONCLUSIONS

Residual single-marker regression analysis is a simple approach that can be used to detect regional genomic signals that are poorly captured by a model and to indicate ways to fix such problems.

Using pre-selected variants from large-scale whole-genome sequence data for single-step genomic predictions in pigs

BACKGROUND

Whole-genome sequence (WGS) data harbor causative variants that may not be present in standard single nucleotide polymorphism (SNP) chip data. The objective of this study was to investigate the impact of using preselected variants from WGS for single-step genomic predictions in maternal and terminal pig lines with up to 1.8k sequenced and 104k sequence imputed animals per line.

METHODS

Two maternal and four terminal lines were investigated for eight and seven traits, respectively. The number of sequenced animals ranged from 1365 to 1491 for the maternal lines and 381 to 1865 for the terminal lines. Imputation to sequence occurred within each line for 66k to 76k animals for the maternal lines and 29k to 104k animals for the terminal lines. Two preselected SNP sets were generated based on a genome-wide association study (GWAS). Top40k included the SNPs with the lowest p-value in each of the 40k genomic windows, and ChipPlusSign included significant variants integrated into the porcine SNP chip used for routine genotyping. We compared the performance of single-step genomic predictions between using preselected SNP sets assuming equal or different variances and the standard porcine SNP chip.

RESULTS

In the maternal lines, ChipPlusSign and Top40k showed an average increase in accuracy of 0.6 and 4.9%, respectively, compared to the regular porcine SNP chip. The greatest increase was obtained with Top40k, particularly for fertility traits, for which the initial accuracy based on the standard SNP chip was low. However, in the terminal lines, Top40k resulted in an average loss of accuracy of 1%. ChipPlusSign provided a positive, although small, gain in accuracy (0.9%). Assigning different variances for the SNPs slightly improved accuracies when using variances obtained from BayesR. However, increases were inconsistent across the lines and traits.

CONCLUSIONS

The benefit of using sequence data depends on the line, the size of the genotyped population, and how the WGS variants are preselected. When WGS data are available on hundreds of thousands of animals, using sequence data presents an advantage but this remains limited in pigs.

A genome-wide association study for loin depth and muscle pH in pigs from intensely selected purebred lines

BACKGROUND

Genome-wide association studies (GWAS) aim at identifying genomic regions involved in phenotype expression, but identifying causative variants is difficult. Pig Combined Annotation Dependent Depletion (pCADD) scores provide a measure of the predicted consequences of genetic variants. Incorporating pCADD into the GWAS pipeline may help their identification. Our objective was to identify genomic regions associated with loin depth and muscle pH, and identify regions of interest for fine-mapping and further experimental work. Genotypes for ~ 40,000 single nucleotide morphisms (SNPs) were used to perform GWAS for these two traits, using de-regressed breeding values (dEBV) for 329,964 pigs from four commercial lines. Imputed sequence data was used to identify SNPs in strong ([Formula: see text] 0.80) linkage disequilibrium with lead GWAS SNPs with the highest pCADD scores.

RESULTS

Fifteen distinct regions were associated with loin depth and one with loin pH at genome-wide significance. Regions on chromosomes 1, 2, 5, 7, and 16, explained between 0.06 and 3.55% of the additive genetic variance and were strongly associated with loin depth. Only a small part of the additive genetic variance in muscle pH was attributed to SNPs. The results of our pCADD analysis suggests that high-scoring pCADD variants are enriched for missense mutations. Two close but distinct regions on SSC1 were associated with loin depth, and pCADD identified the previously identified missense variant within the MC4R gene for one of the lines. For loin pH, pCADD identified a synonymous variant in the RNF25 gene (SSC15) as the most likely candidate for the muscle pH association. The missense mutation in the PRKAG3 gene known to affect glycogen content was not prioritised by pCADD for loin pH.

CONCLUSIONS

For loin depth, we identified several strong candidate regions for further statistical fine-mapping that are supported in the literature, and two novel regions. For loin muscle pH, we identified one previously identified associated region. We found mixed evidence for the utility of pCADD as an extension of heuristic fine-mapping. The next step is to perform more sophisticated fine-mapping and expression quantitative trait loci (eQTL) analysis, and then interrogate candidate variants in vitro by perturbation-CRISPR assays.

Multi-line ssGBLUP evaluation using preselected markers from whole-genome sequence data in pigs

Genomic evaluations in pigs could benefit from using multi-line data along with whole-genome sequencing (WGS) if the data are large enough to represent the variability across populations. The objective of this study was to investigate strategies to combine large-scale data from different terminal pig lines in a multi-line genomic evaluation (MLE) through single-step GBLUP (ssGBLUP) models while including variants preselected from whole-genome sequence (WGS) data. We investigated single-line and multi-line evaluations for five traits recorded in three terminal lines. The number of sequenced animals in each line ranged from 731 to 1,865, with 60k to 104k imputed to WGS. Unknown parent groups (UPG) and metafounders (MF) were explored to account for genetic differences among the lines and improve the compatibility between pedigree and genomic relationships in the MLE. Sequence variants were preselected based on multi-line genome-wide association studies (GWAS) or linkage disequilibrium (LD) pruning. These preselected variant sets were used for ssGBLUP predictions without and with weights from BayesR, and the performances were compared to that of a commercial porcine single-nucleotide polymorphisms (SNP) chip. Using UPG and MF in MLE showed small to no gain in prediction accuracy (up to 0.02), depending on the lines and traits, compared to the single-line genomic evaluation (SLE). Likewise, adding selected variants from the GWAS to the commercial SNP chip resulted in a maximum increase of 0.02 in the prediction accuracy, only for average daily feed intake in the most numerous lines. In addition, no benefits were observed when using preselected sequence variants in multi-line genomic predictions. Weights from BayesR did not help improve the performance of ssGBLUP. This study revealed limited benefits of using preselected whole-genome sequence variants for multi-line genomic predictions, even when tens of thousands of animals had imputed sequence data. Correctly accounting for line differences with UPG or MF in MLE is essential to obtain predictions similar to SLE; however, the only observed benefit of an MLE is to have comparable predictions across lines. Further investigation into the amount of data and novel methods to preselect whole-genome causative variants in combined populations would be of significant interest.

The size and composition of haplotype reference panels impact the accuracy of imputation from low-pass sequencing in cattle

BACKGROUND

Low-pass sequencing followed by sequence variant genotype imputation is an alternative to the routine microarray-based genotyping in cattle. However, the impact of haplotype reference panels and their interplay with the coverage of low-pass whole-genome sequencing data have not been sufficiently explored in typical livestock settings where only a small number of reference samples is available.

METHODS

Sequence variant genotyping accuracy was compared between two variant callers, GATK and DeepVariant, in 50 Brown Swiss cattle with sequencing coverages ranging from 4- to 63-fold. Haplotype reference panels of varying sizes and composition were built with DeepVariant based on 501 individuals from nine breeds. High-coverage sequence data for 24 Brown Swiss cattle were downsampled to between 0.01- and 4-fold to mimic low-pass sequencing. GLIMPSE was used to infer sequence variant genotypes from the low-pass sequencing data using different haplotype reference panels. The accuracy of the sequence variant genotypes that were inferred from low-pass sequencing data was compared with sequence variant genotypes called from high-coverage data.

RESULTS

DeepVariant was used to establish bovine haplotype reference panels because it outperformed GATK in all evaluations. Within-breed haplotype reference panels were more accurate and efficient to impute sequence variant genotypes from low-pass sequencing than equally-sized multibreed haplotype reference panels for all target sample coverages and allele frequencies. F1 scores greater than 0.9, which indicate high harmonic means of recall and precision of called genotypes, were achieved with 0.25-fold sequencing coverage when large breed-specific haplotype reference panels (n = 150) were used. In absence of such large within-breed haplotype panels, variant genotyping accuracy from low-pass sequencing could be increased either by adding non-related samples to the haplotype reference panel or by increasing the coverage of the low-pass sequencing data. Sequence variant genotyping from low-pass sequencing was substantially less accurate when the reference panel lacked individuals from the target breed.

CONCLUSIONS

Variant genotyping is more accurate with DeepVariant than GATK. DeepVariant is therefore suitable to establish bovine haplotype reference panels. Medium-sized breed-specific haplotype reference panels and large multibreed haplotype reference panels enable accurate imputation of low-pass sequencing data in a typical cattle breed.

Imputation to whole-genome sequence and its use in genome-wide association studies for pork colour traits in crossbred and purebred pigs

Imputed whole-genome sequence (WGS) has been proposed to improve genome-wide association studies (GWAS), since all causative mutations responsible for phenotypic variation are expected to be present in the data. This approach was applied on a large number of purebred (PB) and crossbred (CB) pigs for 18 pork color traits to evaluate the impact of using imputed WGS relative to medium-density marker panels. The traits included Minolta A*, B*, and L* for fat (FCOL), quadriceps femoris muscle (QFCOL), thawed loin muscle (TMCOL), fresh ham gluteus medius (GMCOL), ham iliopsoas muscle (ICOL), and longissimus dorsi muscle on the fresh loin (FMCOL). Sequence variants were imputed from a medium-density marker panel (61K for CBs and 50K for PBs) in all genotyped pigs using BeagleV5.0. We obtained high imputation accuracy (average of 0.97 for PBs and 0.91 for CBs). GWAS were conducted for three datasets: 954 CBs and 891 PBs, and the combined CBs and PBs. For most traits, no significant associations were detected, regardless of panel density or population type. However, quantitative trait loci (QTL) regions were only found for a few traits including TMCOL Minolta A* and GMCOL Minolta B* (CBs), FMCOL Minolta B*, FMCOL Minolta L*, and ICOL Minolta B* (PBs) and FMCOL Minolta A*, FMCOL Minolta B*, GMCOL Minolta B*, and ICOL Minolta B* (Combined dataset). More QTL regions were identified with WGS (n = 58) relative to medium-density marker panels (n = 22). Most of the QTL were linked to previously reported QTLs or candidate genes that have been previously reported to be associated with meat quality, pH and pork color; e.g., VIL1, PRKAG3, TTLL4, and SLC11A1, USP37. CTDSP1 gene on SSC15 has not been previously associated with meat color traits in pigs. The findings suggest any added value of WGS was only for detecting novel QTL regions when the sample size is sufficiently large as with the Combined dataset in this study. The percentage of phenotypic variance explained by the most significant SNPs also increased with WGS compared with medium-density panels. The results provide additional insights into identification of a number of candidate regions and genes for pork color traits in different pig populations.

Genomic prediction with whole-genome sequence data in intensely selected pig lines

Background

Early simulations indicated that whole-genome sequence data (WGS) could improve the accuracy of genomic predictions within and across breeds. However, empirical results have been ambiguous so far. Large datasets that capture most of the genomic diversity in a population must be assembled so that allele substitution effects are estimated with high accuracy. The objectives of this study were to use a large pig dataset from seven intensely selected lines to assess the benefits of using WGS for genomic prediction compared to using commercial marker arrays and to identify scenarios in which WGS provides the largest advantage.

Methods

We sequenced 6931 individuals from seven commercial pig lines with different numerical sizes. Genotypes of 32.8 million variants were imputed for 396,100 individuals (17,224 to 104,661 per line). We used BayesR to perform genomic prediction for eight complex traits. Genomic predictions were performed using either data from a standard marker array or variants preselected from WGS based on association tests.

Results

The accuracies of genomic predictions based on preselected WGS variants were not robust across traits and lines and the improvements in prediction accuracy that we achieved so far with WGS compared to standard marker arrays were generally small. The most favourable results for WGS were obtained when the largest training sets were available and standard marker arrays were augmented with preselected variants with statistically significant associations to the trait. With this method and training sets of around 80k individuals, the accuracy of within-line genomic predictions was on average improved by 0.025. With multi-line training sets, improvements of 0.04 compared to marker arrays could be expected.

Conclusions

Our results showed that WGS has limited potential to improve the accuracy of genomic predictions compared to marker arrays in intensely selected pig lines. Thus, although we expect that larger improvements in accuracy from the use of WGS are possible with a combination of larger training sets and optimised pipelines for generating and analysing such datasets, the use of WGS in the current implementations of genomic prediction should be carefully evaluated against the cost of large-scale WGS data on a case-by-case basis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00756-0.

Rare and population-specific functional variation across pig lines

BACKGROUND

It is expected that functional, mainly missense and loss-of-function (LOF), and regulatory variants are responsible for most phenotypic differences between breeds and genetic lines of livestock species that have undergone diverse selection histories. However, there is still limited knowledge about the existing missense and LOF variation in commercial livestock populations, in particular regarding population-specific variation and how it can affect applications such as across-breed genomic prediction.

METHODS

We re-sequenced the whole genome of 7848 individuals from nine commercial pig lines (average sequencing coverage: 4.1×) and imputed whole-genome genotypes for 440,610 pedigree-related individuals. The called variants were categorized according to predicted functional annotation (from LOF to intergenic) and prevalence level (number of lines in which the variant segregated; from private to widespread). Variants in each category were examined in terms of their distribution along the genome, alternative allele frequency, per-site Wright's fixation index (F_ST), individual load, and association to production traits.

RESULTS

Of the 46 million called variants, 28% were private (called in only one line) and 21% were widespread (called in all nine lines). Genomic regions with a low recombination rate were enriched with private variants. Low-prevalence variants (called in one or a few lines only) were enriched for lower allele frequencies, lower F_ST, and putatively functional and regulatory roles (including LOF and deleterious missense variants). On average, individuals carried fewer private deleterious missense alleles than expected compared to alleles with other predicted consequences. Only a small subset of the low-prevalence variants had intermediate allele frequencies and explained small fractions of phenotypic variance (up to 3.2%) of production traits. The significant low-prevalence variants had higher per-site F_ST than the non-significant ones. These associated low-prevalence variants were tagged by other more widespread variants in high linkage disequilibrium, including intergenic variants.

CONCLUSIONS

Most low-prevalence variants have low minor allele frequencies and only a small subset of low-prevalence variants contributed detectable fractions of phenotypic variance of production traits. Accounting for low-prevalence variants is therefore unlikely to noticeably benefit across-breed analyses, such as the prediction of genomic breeding values in a population using reference populations of a different genetic background.

An association study on imputed whole-genome resequencing from high-throughput sequencing data for body traits in crossbred pigs

Body traits are important economic factors in the pig industry. Genome-wide association studies (GWASs) have been widely applied using high-density genotype data to detect QTL in pigs. The aim of the present study was to detect the genetic variants significantly associated with body traits in crossbred pigs using the Illumina Porcine SNP50 BeadChip and imputed whole-genome sequence data. A set of seven body traits - body length, body height, chest circumference, cannon bone circumference, leg buttock circumference, back fat thickness and loin muscle depth - were measured. Moderate to high heritabilities were obtained for most traits (from 0.14 to 0.46), and significant genetic and phenotypic correlations among them were observed. GWAS identified 714 significantly associated SNPs located at 39 regions on all autosomes for body traits, and a total of seven functionally related candidate genes: PIK3CD, HOXA, PCGF2, CHST11, COL2A1, BMI1 and OSR2. Functional enrichment analysis revealed that candidate genes were enriched in the estrogen signaling pathway, embryonic skeletal system morphogenesis and embryonic skeletal system development. These results aim to uncover the genetic mechanisms underlying body development and marker-assisted selection programs focusing on body traits in pigs.

Increasing calling accuracy, coverage, and read-depth in sequence data by the use of haplotype blocks

High-throughput genotyping of large numbers of lines remains a key challenge in plant genetics, requiring geneticists and breeders to find a balance between data quality and the number of genotyped lines under a variety of different existing genotyping technologies when resources are limited. In this work, we are proposing a new imputation pipeline (“HBimpute”) that can be used to generate high-quality genomic data from low read-depth whole-genome-sequence data. The key idea of the pipeline is the use of haplotype blocks from the software HaploBlocker to identify locally similar lines and subsequently use the reads of all locally similar lines in the variant calling for a specific line. The effectiveness of the pipeline is showcased on a dataset of 321 doubled haploid lines of a European maize landrace, which were sequenced at 0.5X read-depth. The overall imputing error rates are cut in half compared to state-of-the-art software like BEAGLE and STITCH, while the average read-depth is increased to 83X, thus enabling the calling of copy number variation. The usefulness of the obtained imputed data panel is further evaluated by comparing the performance of sequence data in common breeding applications to that of genomic data generated with a genotyping array. For both genome-wide association studies and genomic prediction, results are on par or even slightly better than results obtained with high-density array data (600k). In particular for genomic prediction, we observe slightly higher data quality for the sequence data compared to the 600k array in the form of higher prediction accuracies. This occurred specifically when reducing the data panel to the set of overlapping markers between sequence and array, indicating that sequencing data can benefit from the same marker ascertainment as used in the array process to increase the quality and usability of genomic data.

High-throughput genotyping of large numbers of lines remains a key challenge in plant genetics and breeding. Cost, precision, and throughput must be balanced to achieve optimal efficiency given available technologies and finite resources. Although genotyping arrays are still considered the gold standard in high-throughput quantitative genetics, recent advances in sequencing provide new opportunities. Both the quality and cost of genomic data generated based on sequencing are highly dependent on the used read-depth. In this work, we propose a new imputation pipeline (“HBimpute”) that uses haplotype blocks to detect individuals of the same genetic origin and subsequently uses all reads of those individuals in the variant calling. Thus, the obtained virtual read-depth is artificially increased, leading to higher calling accuracy, coverage, and the ability to call copy number variation based on low read-depth sequencing data. To conclude, our approach makes sequencing a cost-competitive alternative to genotyping arrays with the added benefit of allowing the calling of structural variation.

The construction of a haplotype reference panel using extremely low coverage whole genome sequences and its application in genome-wide association studies and genomic prediction in Duroc pigs

Extremely low coverage whole genome sequencing (lcWGS) is an economical technique to obtain high-density single nucleotide polymorphisms (SNPs). Here, we explored the feasibility of constructing a haplotype reference panel (lcHRP) using lcWGS and evaluated the effects of lcHRP through a genome-wide association study (GWAS) and genomic prediction in pigs. A total of 297 and 974 Duroc pigs were genotyped using lcWGS and a 50 K SNP array, respectively. We obtained 19,306,498 SNPs using lcWGS with an accuracy of 0.984. With the help of lcHRP, the accuracy of imputation from the SNP array to lcWGS was 0.922. Compared to the SNP array findings, those from the imputation-based GWAS identified more signals across four traits. With the integration of the top 1% imputation-based GWAS findings as genomic features, the accuracies of genomic prediction was improved by 6.0% to 13.2%. This study showed the great potential of lcWGS in pigs' molecular breeding.

An imputed whole-genome sequence-based GWAS approach pinpoints causal mutations for complex traits in a specific swine population

Sequencing-based genome-wide association studies (GWAS) have facilitated the identification of causal associations between genetic variants and traits in diverse species. However, it is cost-prohibitive for the majority of research groups to sequence a large number of samples. Here, we carried out genotype imputation to increase the density of single nucleotide polymorphisms in a large-scale Swine F₂ population using a reference panel including 117 individuals, followed by a series of GWAS analyses. The imputation accuracies reached 0.89 and 0.86 for allelic concordance and correlation, respectively. A quantitative trait nucleotide (QTN) affecting the chest vertebrate was detected directly, while the investigation of another QTN affecting the residual glucose failed due to the presence of similar haplotypes carrying wild-type and mutant allelesin the reference panel used in this study. A high imputation accuracy was confirmed by Sanger sequencing technology for the most significant loci. Two candidate genes, CPNE5 and MYH3, affecting meat-related traits were proposed. Collectively, we illustrated four scenarios in imputation-based GWAS that may be encountered by researchers, and our results will provide an extensive reference for future genotype imputation-based GWAS analyses in the future.

Rapid genotype imputation from sequence with reference panels

Inexpensive genotyping methods are essential to modern genomics. Here we present QUILT, which performs diploid genotype imputation using low-coverage whole genome sequence data. QUILT employs Gibbs sampling to partition reads into maternal and paternal sets, facilitating rapid haploid imputation using large reference panels. We show this partitioning to be accurate over many megabases, enabling highly accurate imputation close to theoretical limits and outperforming existing methods. Moreover, QUILT can impute accurately using diverse technologies, including using long reads from Oxford Nanopore Technologies, and a novel form of low-cost barcoded Illumina sequencing called haplotagging, with the latter showing improved accuracy at low coverages. Relative to DNA genotyping microarrays, QUILT offers improved accuracy at reduced cost, particularly for diverse populations that are traditionally underserved in modern genomic analyses, with accuracy nearly doubling at rare SNPs. Finally, QUILT can accurately impute (4-digit) HLA types, the first such method from low-coverage sequence data.

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

BACKGROUND

For assembling large whole-genome sequence datasets for routine use in research and breeding, the sequencing strategy should be adapted to the methods that will be used later 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 that is well suited for large livestock populations.

METHODS

We simulated marker array and whole-genome sequence data for 15 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.

RESULTS

Imputation accuracy depended greatly 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 and it was critical for achieving high imputation accuracy in both early and late generations. Imputation accuracy was highest with a uniform coverage across the sequenced individuals of 2× rather than variable coverage. An investment equivalent to the cost of sequencing 2% of the population at 2× provided high imputation accuracy. The gain in imputation accuracy from additional investment decreased with larger populations and higher 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.

CONCLUSIONS

Suitable sequencing strategies for subsequent imputation with hybrid peeling involve sequencing ~2% of the population at a uniform coverage 2×, distributed preferably across all generations of the pedigree, except for the few earliest generations that lack genotyped ancestors. Such sequencing strategies are beneficial for generating whole-genome sequence data in populations with deep pedigrees of closely related individuals.

Current status of genomic evaluation

Early application of genomic selection relied on SNP estimation with phenotypes or de-regressed proofs (DRP). Chips of 50k SNP seemed sufficient for an accurate estimation of SNP effects. Genomic estimated breeding values (GEBV) were composed of an index with parent average, direct genomic value, and deduction of a parental index to eliminate double counting. Use of SNP selection or weighting increased accuracy with small data sets but had minimal to no impact with large data sets. Efforts to include potentially causative SNP derived from sequence data or high-density chips showed limited or no gain in accuracy. After the implementation of genomic selection, EBV by BLUP became biased because of genomic preselection and DRP computed based on EBV required adjustments, and the creation of DRP for females is hard and subject to double counting. Genomic selection was greatly simplified by single-step genomic BLUP (ssGBLUP). This method based on combining genomic and pedigree relationships automatically creates an index with all sources of information, can use any combination of male and female genotypes, and accounts for preselection. To avoid biases, especially under strong selection, ssGBLUP requires that pedigree and genomic relationships are compatible. Because the inversion of the genomic relationship matrix (G) becomes costly with more than 100k genotyped animals, large data computations in ssGBLUP were solved by exploiting limited dimensionality of genomic data due to limited effective population size. With such dimensionality ranging from 4k in chickens to about 15k in cattle, the inverse of G can be created directly (e.g., by the algorithm for proven and young) at a linear cost. Due to its simplicity and accuracy, ssGBLUP is routinely used for genomic selection by the major chicken, pig, and beef industries. Single step can be used to derive SNP effects for indirect prediction and for genome-wide association studies, including computations of the P-values. Alternative single-step formulations exist that use SNP effects for genotyped or for all animals. Although genomics is the new standard in breeding and genetics, there are still some problems that need to be solved. This involves new validation procedures that are unaffected by selection, parameter estimation that accounts for all the genomic data used in selection, and strategies to address reduction in genetic variances after genomic selection was implemented.