A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle

Short communication: Accuracy of whole-genome sequence imputation in Angus cattle using within-breed and multi breed reference populations

Genotype imputation is a standard approach used in the field of genetics. It can be used to fill in missing genotypes or to increase genotype density. Accurate imputed genotypes are required for downstream analyses. In this study, the accuracy of whole-genome sequence imputation for Angus beef cattle was examined using two different ways to form the reference panel, a within-breed reference population and a multi breed reference population. A stepwise imputation was conducted by imputing medium-density (50k) genotypes to high-density, and then to the whole genome sequence (WGS). The reference population consisted of animals with WGS information from the 1 000 Bull Genomes project. The within-breed reference panel comprised 396 Angus cattle, while an additional 2 380 Taurine cattle were added to the reference population for the multi breed reference scenario. Imputation accuracies were variant-wise average accuracies from a 10-fold cross-validation and expressed as concordance rates (CR) and Pearson's correlations (PR). The two imputation scenarios achieved moderate to high imputation accuracies ranging from 0.896 to 0.966 for CR and from 0.779 to 0.834 for PR. The accuracies from two different scenarios were similar, except for PR from WGS imputation, where the within-breed scenario outperformed the multi breed scenario. The result indicated that including a large number of animals from other breeds in the reference panel to impute purebred Angus did not improve the accuracy and may negatively impact the results. In conclusion, the imputed WGS in Angus cattle can be obtained with high accuracy using a within-breed reference panel.

Genomic loci involved in sensing environmental cues and metabolism affect seasonal coat shedding in Bos taurus and Bos indicus cattle

Seasonal shedding of winter hair at the start of summer is well studied in wild and domesticated populations. However, the genetic influences on this trait and their interactions are poorly understood. We use data from 13,364 cattle with 36,899 repeated phenotypes to investigate the relationship between hair shedding and environmental variables, single nucleotide polymorphisms, and their interactions to understand quantitative differences in seasonal shedding. Using deregressed estimated breeding values from a repeated records model in a genome-wide association analysis (GWAA) and meta-analysis of year-specific GWAA gave remarkably similar results. These GWAA identified hundreds of variants associated with seasonal hair shedding. There were especially strong associations between chromosomes 5 and 23. Genotype-by-environment interaction GWAA identified 1,040 day length-by-genotype interaction associations and 17 apparent temperature-by-genotype interaction associations with hair shedding, highlighting the importance of day length on hair shedding. Accurate genomic predictions of hair shedding were created for the entire dataset, Angus, Hereford, Brangus, and multibreed datasets. Loci related to metabolism and light-sensing have a large influence on seasonal hair shedding. This is one of the largest genetic analyses of a phenological trait and provides insight into both agriculture production and basic science.

Uncovering the architecture of selection in two Bos taurus cattle breeds

Directional selection alters the genome via hard sweeps, soft sweeps, and polygenic selection. However, mapping polygenic selection is difficult because it does not leave clear signatures on the genome like a selective sweep. In populations with temporally stratified genotypes, the Generation Proxy Selection Mapping (GPSM) method identifies variants associated with generation number (or appropriate proxy) and thus variants undergoing directional allele frequency changes. Here, we use GPSM on two large datasets of beef cattle to detect associations between an animal's generation and 11 million imputed SNPs. Using these datasets with high power and dense mapping resolution, GPSM detected a total of 294 unique loci actively under selection in two cattle breeds. We observed that GPSM has a high power to detect selection in the very recent past (<10 years), even when allele frequency changes are small. Variants identified by GPSM reside in genomic regions associated with known breed-specific selection objectives, such as fertility and maternal ability in Red Angus, and carcass merit and coat color in Simmental. Over 60% of the selected loci reside in or near (<50 kb) annotated genes. Using haplotype-based and composite selective sweep statistics, we identify hundreds of putative selective sweeps that likely occurred earlier in the evolution of these breeds; however, these sweeps have little overlap with recent polygenic selection. This makes GPSM a complementary approach to sweep detection methods when temporal genotype data are available. The selected loci that we identify across methods demonstrate the complex architecture of selection in domesticated cattle.

The SWine IMputation (SWIM) haplotype reference panel enables nucleotide resolution genetic mapping in pigs

Genetic mapping to identify genes and alleles associated with or causing economically important quantitative trait variation in livestock animals such as pigs is a major goal in animal genetic improvement. Despite recent advances in high-throughput genotyping technologies, the resolution of genetic mapping in pigs remains poor due in part to the low density of genotyped variant sites. In this study, we overcame this limitation by developing a reference haplotype panel for pigs based on 2259 whole genome-sequenced animals representing 44 pig breeds. We evaluated software combinations and breed composition to optimize the imputation procedure and achieved an average concordance rate in excess of 96%, a non-reference concordance rate of 88%, and an r² of 0.85. We demonstrated in two case studies that genotype imputation using this resource can dramatically improve the resolution of genetic mapping. A public web server has been developed to allow the pig genetics community to fully utilize this resource. We expect this resource to facilitate genetic mapping and accelerate genetic improvement in pigs.

Progress and opportunities through use of genomics in animal production

The rearing of farmed animals is a vital component of global food production systems, but its impact on the environment, human health, animal welfare, and biodiversity is being increasingly challenged. Developments in genetic and genomic technologies have had a key role in improving the productivity of farmed animals for decades. Advances in genome sequencing, annotation, and editing offer a means not only to continue that trend, but also, when combined with advanced data collection, analytics, cloud computing, appropriate infrastructure, and regulation, to take precision livestock farming (PLF) and conservation to an advanced level. Such an approach could generate substantial additional benefits in terms of reducing use of resources, health treatments, and environmental impact, while also improving animal health and welfare.

Model Comparison of Heritability Enrichment Analysis in Livestock Population

Heritability enrichment analysis is an important means of exploring the genetic architecture of complex traits in human genetics. Heritability enrichment is typically defined as the proportion of an SNP subset explained heritability, divided by the proportion of SNPs. Heritability enrichment enables better study of underlying complex traits, such as functional variant/gene subsets, biological networks and metabolic pathways detected through integrating explosively increased omics data. This would be beneficial for genomic prediction of disease risk in humans and genetic values estimation of important economical traits in livestock and plant species. However, in livestock, factors affecting the heritability enrichment estimation of complex traits have not been examined. Previous studies on humans reported that the frequencies, effect sizes, and levels of linkage disequilibrium (LD) of underlying causal variants (CVs) would affect the heritability enrichment estimation. Therefore, the distribution of heritability across the genome should be fully considered to obtain the unbiased estimation of heritability enrichment. To explore the performance of different heritability enrichment models in livestock populations, we used the VanRaden, GCTA and α models, assuming different α values, and the LDAK model, considering LD weight. We simulated three types of phenotypes, with CVs from various minor allele frequency (MAF) ranges: genome-wide (0.005 ≤ MAF ≤ 0.5), common (0.05 ≤ MAF ≤ 0.5), and uncommon (0.01 ≤ MAF < 0.05). The performances of the models with two different subsets (one of which contained known CVs and the other consisting of randomly selected markers) were compared to verify the accuracy of heritability enrichment estimation of functional variant sets. Our results showed that models with known CV subsets provided more robust enrichment estimation. Models with different α values tended to provide stable and accurate estimates for common and genome-wide CVs (relative deviation 0.5−2.2%), while tending to underestimate the enrichment of uncommon CVs. As the α value increased, enrichments from 15.73% higher than true value (i.e., 3.00) to 48.93% lower than true value for uncommon CVs were observed. In addition, the long-range LD windows (e.g., 5000 kb) led to large bias of the enrichment estimations for both common and uncommon CVs. Overall, heritability enrichment estimations were sensitive for the α value assumption and LD weight consideration of different models. Accuracy would be greatly improved by using a suitable model. This study would be helpful in understanding the genetic architecture of complex traits and provides a reference for genetic analysis in the livestock population.

Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle

BACKGROUND

In cattle, genome-wide association studies (GWAS) have largely focused on European or Asian breeds, using genotyping arrays that were primarily designed for European cattle. Because there is growing interest in performing GWAS in African breeds, we have assessed the performance of 23 commercial bovine genotyping arrays for capturing the diversity across African breeds and performing imputation. We used 409 whole-genome sequences (WGS) spanning global cattle breeds, and a real cohort of 2481 individuals (including African breeds) that were genotyped with the Illumina high-density (HD) array and the GeneSeek bovine 50 k array.

RESULTS

We found that commercially available arrays were not effective in capturing variants that segregate among African indicine animals. Only 6% of these variants in high linkage disequilibrium (LD) (r² > 0.8) were on the best performing arrays, which contrasts with the 17% and 25% in African and European taurine cattle, respectively. However, imputation from available HD arrays can successfully capture most variants (accuracies up to 0.93), mainly when using a global, not continent-specific, reference panel, which partially reflects the unusually high levels of admixture on the continent. When considering functional variants, the GGPF250 array performed best for tagging WGS variants and imputation. Finally, we show that imputation from low-density arrays can perform almost as well as HD arrays, if a two-stage imputation approach is adopted, i.e. first imputing to HD and then to WGS, which can potentially reduce the costs of GWAS.

CONCLUSIONS

Our results show that the choice of an array should be based on a balance between the objective of the study and the breed/population considered, with the HD and BOS1 arrays being the best choice for both taurine and indicine breeds when performing GWAS, and the GGPF250 being preferable for fine-mapping studies. Moreover, our results suggest that there is no advantage to using the indicus-specific arrays for indicus breeds, regardless of the objective. Finally, we show that using a reference panel that better represents global bovine diversity improves imputation accuracy, particularly for non-European taurine populations.

Evaluation of Whole-Genome Sequence Imputation Strategies in Korean Hanwoo Cattle

Simple Summary

In this study, we evaluated various imputation strategies for the Korean Hanwoo cattle. We observed that a large reference panel consisting of many cattle breeds did not improve the imputation accuracy when compared to a proportionally small purebred Hanwoo reference. This was because the multi-breed reference did not contain animals sufficiently related to the Hanwoo to improve the accuracies and, although not detrimental, in effect, only added to the computational burden of the imputation. Despite the large multi-breed reference, when the Hanwoo were removed from the reference, the imputation accuracies were low. These results suggest additional sequencing efforts are needed for underrepresented breeds, particularly those less genetically related to the main European breeds.

Abstract

This study evaluated the accuracy of sequence imputation in Hanwoo beef cattle using different reference panels: a large multi-breed reference with no Hanwoo (n = 6269), a much smaller Hanwoo purebred reference (n = 88), and both datasets combined (n = 6357). The target animals were 136 cattle both sequenced and genotyped with the Illumina BovineSNP50 v2 (50K). The average imputation accuracy measured by the Pearson correlation (R) was 0.695 with the multi-breed reference, 0.876 with the purebred Hanwoo, and 0.887 with the combined data; the average concordance rates (CR) were 88.16%, 94.49%, and 94.84%, respectively. The accuracy gains from adding a large multi-breed reference of 6269 samples to only 88 Hanwoo was marginal; however, the concordance rate for the heterozygotes decreased from 85% to 82%, and the concordance rate for fixed SNPs in Hanwoo also decreased from 99.98% to 98.73%. Although the multi-breed panel was large, it was not sufficiently representative of the breed for accurate imputation without the Hanwoo animals. Additionally, we evaluated the value of high-density 700K genotypes (n = 991) as an intermediary step in the imputation process. The imputation accuracy differences were negligible between a single-step imputation strategy from 50K directly to sequence and a two-step imputation approach (50K-700K-sequence). We also observed that imputed sequence data can be used as a reference panel for imputation (mean R = 0.9650, mean CR = 98.35%). Finally, we identified 31 poorly imputed genomic regions in the Hanwoo genome and demonstrated that imputation accuracies were particularly lower at the chromosomal ends.

Exploring the optimal strategy of imputation from SNP array to whole-genome sequencing data in farm animals

Genotype imputation from BeadChip to whole-genome sequencing (WGS) data is a cost-effective method of obtaining genotypes of WGS variants. Beagle, one of the most popular imputation software programs, has been widely used for genotype inference in humans and non-human species. A few studies have systematically and comprehensively compared the performance of beagle versions and parameter settings of farm animals. Here, we investigated the imputation performance of three representative versions of Beagle (Beagle 4.1, Beagle 5.0, and Beagle 5.4), and the effective population size (Ne) parameter setting for three species (cattle, pig, and chicken). Six scenarios were investigated to explore the impact of certain key factors on imputation performance. The results showed that the default Ne (1,000,000) is not suitable for livestock and poultry in small reference or low-density arrays of target panels, with 2.47%–10.45% drops in accuracy. Beagle 5 significantly reduced the computation time (4.66-fold–13.24-fold) without an accuracy loss. In addition, using a large combined-reference panel or high-density chip provides greater imputation accuracy, especially for low minor allele frequency (MAF) variants. Finally, a highly significant correlation in the measures of imputation accuracy can be obtained with an MAF equal to or greater than 0.05.

Genome-wide association and genotype by environment interactions for growth traits in U.S. Red Angus cattle

Background

Genotypic information produced from single nucleotide polymorphism (SNP) arrays has routinely been used to identify genomic regions associated with complex traits in beef and dairy cattle. Herein, we assembled a dataset consisting of 15,815 Red Angus beef cattle distributed across the continental U.S. and a union set of 836,118 imputed SNPs to conduct genome-wide association analyses (GWAA) for growth traits using univariate linear mixed models (LMM); including birth weight, weaning weight, and yearling weight. Genomic relationship matrix heritability estimates were produced for all growth traits, and genotype-by-environment (GxE) interactions were investigated.

Results

Moderate to high heritabilities with small standard errors were estimated for birth weight (0.51 ± 0.01), weaning weight (0.25 ± 0.01), and yearling weight (0.42 ± 0.01). GWAA revealed 12 pleiotropic QTL (BTA6, BTA14, BTA20) influencing Red Angus birth weight, weaning weight, and yearling weight which met a nominal significance threshold (P ≤ 1e-05) for polygenic traits using 836K imputed SNPs. Moreover, positional candidate genes associated with Red Angus growth traits in this study (i.e., LCORL, LOC782905, NCAPG, HERC6, FAM184B, SLIT2, MMRN1, KCNIP4, CCSER1, GRID2, ARRDC3, PLAG1, IMPAD1, NSMAF, PENK, LOC112449660, MOS, SH3PXD2B, STC2, CPEB4) were also previously associated with feed efficiency, growth, and carcass traits in beef cattle. Collectively, 14 significant GxE interactions were also detected, but were less consistent among the investigated traits at a nominal significance threshold (P ≤ 1e-05); with one pleiotropic GxE interaction detected on BTA28 (24 Mb) for Red Angus weaning weight and yearling weight.

Conclusions

Sixteen well-supported QTL regions detected from the GWAA and GxE GWAA for growth traits (birth weight, weaning weight, yearling weight) in U.S. Red Angus cattle were found to be pleiotropic. Twelve of these pleiotropic QTL were also identified in previous studies focusing on feed efficiency and growth traits in multiple beef breeds and/or their composites. In agreement with other beef cattle GxE studies our results implicate the role of vasodilation, metabolism, and the nervous system in the genetic sensitivity to environmental stress.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08667-6.

Development and validation of a horse reference panel for genotype imputation

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00740-8.

Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa

Imputation may be used to rescue genomic data from animals that would otherwise be eliminated due to a lower than desired call rate. The aim of this study was to compare the accuracy of genotype imputation for Afrikaner, Brahman, and Brangus cattle of South Africa using within- and multiple-breed reference populations. A total of 373, 309, and 101 Afrikaner, Brahman, and Brangus cattle, respectively, were genotyped using the GeneSeek Genomic Profiler 150 K panel that contained 141,746 markers. Markers with MAF ≤ 0.02 and call rates ≤ 0.95 or that deviated from Hardy Weinberg Equilibrium frequency with a probability of ≤ 0.0001 were excluded from the data as were animals with a call rate ≤ 0.90. The remaining data included 99,086 SNPs and 360 Afrikaner, 75,291 SNPs and 288 animals Brahman, and 97,897 SNPs and 99 Brangus animals. A total of 7986, 7002, and 7000 SNP from 50 Afrikaner and Brahman and 30 Brangus cattle, respectively, were masked and then imputed using BEAGLE v3 and FImpute v2. The within-breed imputation yielded accuracies ranging from 89.9 to 96.6% for the three breeds. The multiple-breed imputation yielded corresponding accuracies from 69.21 to 88.35%. The results showed that population homogeneity and numerical representation for within and across breed strategies, respectively, are crucial components for improving imputation accuracies.

Genotype imputation and polygenic score estimation in northwestern Russian population

Numerous studies demonstrated the lack of transferability of polygenic score (PGS) models across populations and the problem arising from unequal presentation of ancestries across genetic studies. However, even within European ancestry there are ethnic groups that are rarely presented in genetic studies. For instance, Russians, being one of the largest, diverse, and yet understudied group in Europe. In this study, we evaluated the reliability of genotype imputation for the Russian cohort by testing several commonly used imputation reference panels (e.g. HRC, 1000G, HGDP). HRC, in comparison with two other panels, showed the most accurate results based on both imputation accuracy and allele frequency concordance between masked and imputed genotypes. We built polygenic score models based on GWAS results from the UK biobank, measured the explained phenotypic variance in the Russian cohort attributed to polygenic scores for 11 phenotypes, collected in the clinic for each participant, and finally explored the role of allele frequency discordance between the UK biobank and the study cohort in the resulting PGS performance.

Design and performance of a bovine 200 k SNP chip developed for endangered German Black Pied cattle (DSN)

BACKGROUND

German Black Pied cattle (DSN) are an endangered dual-purpose breed which was largely replaced by Holstein cattle due to their lower milk yield. DSN cattle are kept as a genetic reserve with a current herd size of around 2500 animals. The ability to track sequence variants specific to DSN could help to support the conservation of DSN's genetic diversity and to provide avenues for genetic improvement.

RESULTS

Whole-genome sequencing data of 304 DSN cattle were used to design a customized DSN200k SNP chip harboring 182,154 variants (173,569 SNPs and 8585 indels) based on ten selection categories. We included variants of interest to DSN such as DSN unique variants and variants from previous association studies in DSN, but also variants of general interest such as variants with predicted consequences of high, moderate, or low impact on the transcripts and SNPs from the Illumina BovineSNP50 BeadChip. Further, the selection of variants based on haplotype blocks ensured that the whole-genome was uniformly covered with an average variant distance of 14.4 kb on autosomes. Using 300 DSN and 162 animals from other cattle breeds including Holstein, endangered local cattle populations, and also a Bos indicus breed, performance of the SNP chip was evaluated. Altogether, 171,978 (94.31%) of the variants were successfully called in at least one of the analyzed breeds. In DSN, the number of successfully called variants was 166,563 (91.44%) while 156,684 (86.02%) were segregating at a minor allele frequency > 1%. The concordance rate between technical replicates was 99.83 ± 0.19%.

CONCLUSION

The DSN200k SNP chip was proved useful for DSN and other Bos taurus as well as one Bos indicus breed. It is suitable for genetic diversity management and marker-assisted selection of DSN animals. Moreover, variants that were segregating in other breeds can be used for the design of breed-specific customized SNP chips. This will be of great value in the application of conservation programs for endangered local populations in the future.

Taurine and Indicine Haplotype Representation in Advanced Generation Individuals From Three American Breeds

Development of the American Breeds of beef cattle began in the 1920s as breeders and U. S. Experiment Station researchers began to create Bos taurus taurus × Bos taurus indicus hybrids using Brahman as the B. t. indicus source. By 1954, U.S. Breed Associations had been formed for Brangus (5/8 Angus × 3/8 Brahman), Beefmaster (½ Brahman × ¼ Shorthorn × ¼ Hereford), and Santa Gertrudis (5/8 Shorthorn × 3/8 Brahman). While these breeds were developed using mating designs expected to create base generation animals with the required genome contributions from progenitor breeds, each association has now registered advanced generation animals in which selection or drift may have caused the realized genome compositions to differ from initial expected proportions. The availability of high-density SNP genotypes for 9,161 Brangus, 3,762 Beefmaster, and 1,942 Santa Gertrudis animals allowed us to compare the realized genomic architectures of breed members to the base generation expectations. We used RFMix to estimate local ancestry and identify genomic regions in which the proportion of Brahman ancestry differed significantly from a priori expectations. For all three breeds, lower than expected levels of Brahman composition were found genome-wide, particularly in early-generation animals where we demonstrate that selection on beef production traits was likely responsible for the taurine enrichment. Using a proxy for generation number, we also contrasted the genomes of early- and advanced-generation animals and found that the indicine composition of the genome has increased with generation number likely due to selection on adaptive traits. Many of the most-highly differentiated genomic regions were breed specific, suggesting that differences in breeding objectives and selection intensities exist between the breeds. Global ancestry estimation is commonly performed in admixed animals to control for stratification in association studies. However, local ancestry estimation provides the opportunity to investigate the evolution of specific chromosomal segments and estimate haplotype effects on trait variation in admixed individuals. Investigating the genomic architecture of the American Breeds not only allows the estimation of indicine and taurine genome proportions genome-wide, but also the locations within the genome where either taurine or indicine alleles confer a selective advantage.

Powerful detection of polygenic selection and evidence of environmental adaptation in US beef cattle

Selection on complex traits can rapidly drive evolution, especially in stressful environments. This polygenic selection does not leave intense sweep signatures on the genome, rather many loci experience small allele frequency shifts, resulting in large cumulative phenotypic changes. Directional selection and local adaptation are changing populations; but, identifying loci underlying polygenic or environmental selection has been difficult. We use genomic data on tens of thousands of cattle from three populations, distributed over time and landscapes, in linear mixed models with novel dependent variables to map signatures of selection on complex traits and local adaptation. We identify 207 genomic loci associated with an animal's birth date, representing ongoing selection for monogenic and polygenic traits. Additionally, hundreds of additional loci are associated with continuous and discrete environments, providing evidence for historical local adaptation. These candidate loci highlight the nervous system's possible role in local adaptation. While advanced technologies have increased the rate of directional selection in cattle, it has likely been at the expense of historically generated local adaptation, which is especially problematic in changing climates. When applied to large, diverse cattle datasets, these selection mapping methods provide an insight into how selection on complex traits continually shapes the genome. Further, understanding the genomic loci implicated in adaptation may help us breed more adapted and efficient cattle, and begin to understand the basis for mammalian adaptation, especially in changing climates. These selection mapping approaches help clarify selective forces and loci in evolutionary, model, and agricultural contexts.

Genome-wide association analyses identify genotype-by-environment interactions of growth traits in Simmental cattle

Understanding genotype-by-environment interactions (G × E) is crucial to understand environmental adaptation in mammals and improve the sustainability of agricultural production. Here, we present an extensive study investigating the interaction of genome-wide SNP markers with a vast assortment of environmental variables and searching for SNPs controlling phenotypic variance (vQTL) using a large beef cattle dataset. We showed that G × E contribute 10.1%, 3.8%, and 2.8% of the phenotypic variance of birth weight, weaning weight, and yearling weight, respectively. G × E genome-wide association analysis (GWAA) detected a large number of G × E loci affecting growth traits, which the traditional GWAA did not detect, showing that functional loci may have non-additive genetic effects regardless of differences in genotypic means. Further, variance-heterogeneity GWAA detected loci enriched with G × E effects without requiring prior knowledge of the interacting environmental factors. Functional annotation and pathway analysis of G × E genes revealed biological mechanisms by which cattle respond to changes in their environment, such as neurotransmitter activity, hypoxia-induced processes, keratinization, hormone, thermogenic and immune pathways. We unraveled the relevance and complexity of the genetic basis of G × E underlying growth traits, providing new insights into how different environmental conditions interact with specific genes influencing adaptation and productivity in beef cattle and potentially across mammals.

How imputation can mitigate SNP ascertainment Bias

Background

Population genetic studies based on genotyped single nucleotide polymorphisms (SNPs) are influenced by a non-random selection of the SNPs included in the used genotyping arrays. The resulting bias in the estimation of allele frequency spectra and population genetics parameters like heterozygosity and genetic distances relative to whole genome sequencing (WGS) data is known as SNP ascertainment bias. Full correction for this bias requires detailed knowledge of the array design process, which is often not available in practice. This study suggests an alternative approach to mitigate ascertainment bias of a large set of genotyped individuals by using information of a small set of sequenced individuals via imputation without the need for prior knowledge on the array design.

Results

The strategy was first tested by simulating additional ascertainment bias with a set of 1566 chickens from 74 populations that were genotyped for the positions of the Affymetrix Axiom™ 580 k Genome-Wide Chicken Array. Imputation accuracy was shown to be consistently higher for populations used for SNP discovery during the simulated array design process. Reference sets of at least one individual per population in the study set led to a strong correction of ascertainment bias for estimates of expected and observed heterozygosity, Wright’s Fixation Index and Nei’s Standard Genetic Distance. In contrast, unbalanced reference sets (overrepresentation of populations compared to the study set) introduced a new bias towards the reference populations. Finally, the array genotypes were imputed to WGS by utilization of reference sets of 74 individuals (one per population) to 98 individuals (additional commercial chickens) and compared with a mixture of individually and pooled sequenced populations. The imputation reduced the slope between heterozygosity estimates of array data and WGS data from 1.94 to 1.26 when using the smaller balanced reference panel and to 1.44 when using the larger but unbalanced reference panel. This generally supported the results from simulation but was less favorable, advocating for a larger reference panel when imputing to WGS.

Conclusions

The results highlight the potential of using imputation for mitigation of SNP ascertainment bias but also underline the need for unbiased reference sets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07663-6.

Inference of Ancestries and Heterozygosity Proportion and Genotype Imputation in West African Cattle Populations

Several studies have evaluated computational methods that infer the haplotypes from population genotype data in European cattle populations. However, little is known about how well they perform in African indigenous and crossbred populations. This study investigates: (1) global and local ancestry inference; (2) heterozygosity proportion estimation; and (3) genotype imputation in West African indigenous and crossbred cattle populations. Principal component analysis (PCA), ADMIXTURE, and LAMP-LD were used to analyse a medium-density single nucleotide polymorphism (SNP) dataset from Senegalese crossbred cattle. Reference SNP data of East and West African indigenous and crossbred cattle populations were used to investigate the accuracy of imputation from low to medium-density and from medium to high-density SNP datasets using Minimac v3. The first two principal components differentiated Bos indicus from European Bos taurus and African Bos taurus from other breeds. Irrespective of assuming two or three ancestral breeds for the Senegalese crossbreds, breed proportion estimates from ADMIXTURE and LAMP-LD showed a high correlation (r ≥ 0.981). The observed ancestral origin heterozygosity proportion in putative F1 crosses was close to the expected value of 1.0, and clearly differentiated F1 from all other crosses. The imputation accuracies (estimated as correlation) between imputed and the real data in crossbred animals ranged from 0.142 to 0.717 when imputing from low to medium-density, and from 0.478 to 0.899 for imputation from medium to high-density. The imputation accuracy was generally higher when the reference data came from the same geographical region as the target population, and when crossbred reference data was used to impute crossbred genotypes. The lowest imputation accuracies were observed for indigenous breed genotypes. This study shows that ancestral origin heterozygosity can be estimated with high accuracy and will be far superior to the use of observed individual heterozygosity for estimating heterosis in African crossbred populations. It was not possible to achieve high imputation accuracy in West African crossbred or indigenous populations based on reference data sets from East Africa, and population-specific genotyping with high-density SNP assays is required to improve imputation.

Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review

Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.

Investigating the Effect of Imputed Structural Variants from Whole-Genome Sequence on Genome-Wide Association and Genomic Prediction in Dairy Cattle

Simple Summary

Structural variants are large changes to the DNA sequences that differ from individual to individual. We discovered and quality-controlled a set of 24,908 structural variants and used a technique called imputation to infer them into 35,588 Holstein and Jersey cattle. We then investigated whether the structural variants affected key dairy cattle traits such as milk production, fertility and overall conformation. Structural variants explained generally less than 10 percent of the phenotypic variation in these traits. Four of the structural variants were significantly associated with dairy cattle production traits. However, the inclusion of the structural variants in the genomic prediction model did not increase genomic prediction accuracy.

Abstract

Structural variations (SVs) are large DNA segments of deletions, duplications, copy number variations, inversions and translocations in a re-sequenced genome compared to a reference genome. They have been found to be associated with several complex traits in dairy cattle and could potentially help to improve genomic prediction accuracy of dairy traits. Imputation of SVs was performed in individuals genotyped with single-nucleotide polymorphism (SNP) panels without the expense of sequencing them. In this study, we generated 24,908 high-quality SVs in a total of 478 whole-genome sequenced Holstein and Jersey cattle. We imputed 4489 SVs with R2 > 0.5 into 35,568 Holstein and Jersey dairy cattle with 578,999 SNPs with two pipelines, FImpute and Eagle2.3-Minimac3. Genome-wide association studies for production, fertility and overall type with these 4489 SVs revealed four significant SVs, of which two were highly linked to significant SNP. We also estimated the variance components for SNP and SV models for these traits using genomic best linear unbiased prediction (GBLUP). Furthermore, we assessed the effect on genomic prediction accuracy of adding SVs to GBLUP models. The estimated percentage of genetic variance captured by SVs for production traits was up to 4.57% for milk yield in bulls and 3.53% for protein yield in cows. Finally, no consistent increase in genomic prediction accuracy was observed when including SVs in GBLUP.

Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations

The difficulty in finding causative mutations has hampered their use in genomic prediction. Here, we present a methodology to fine-map potentially causal variants genome-wide by integrating the functional, evolutionary and pleiotropic information of variants using GWAS, variant clustering and Bayesian mixture models. Our analysis of 17 million sequence variants in 44,000+ Australian dairy cattle for 34 traits suggests, on average, one pleiotropic QTL existing in each 50 kb chromosome-segment. We selected a set of 80k variants representing potentially causal variants within each chromosome segment to develop a bovine XT-50K genotyping array. The custom array contains many pleiotropic variants with biological functions, including splicing QTLs and variants at conserved sites across 100 vertebrate species. This biology-informed custom array outperformed the standard array in predicting genetic value of multiple traits across populations in independent datasets of 90,000+ dairy cattle from the USA, Australia and New Zealand.

Genome-Wide Association Analyses of Fertility Traits in Beef Heifers

The ability of livestock to reproduce efficiently is critical to the sustainability of animal agriculture. Antral follicle count (AFC) and reproductive tract scores (RTS) can be used to estimate fertility in beef heifers, but the genetic mechanisms influencing variation in these measures are not well understood. Two genome-wide association studies (GWAS) were conducted to identify the significant loci associated with these traits. In total, 293 crossbred beef heifers were genotyped on the Bovine GGP 50K chip and genotypes were imputed to 836,121 markers. A GWAS was performed with the AFC phenotype for 217 heifers with a multi-locus mixed model, conducted using the year, age at time of sampling and principal component analysis groupings as the covariates. The RTS GWAS was performed with 289 heifers using an additive correlation/trend test comparing prepubertal to pubertal heifers. The loci on chromosomes 2, 3 and 23 were significant in the AFC GWAS and the loci on chromosomes 2, 8, 10 and 11 were significant in the RTS GWAS. The significant region on chromosome 2 was similar between both analyses. These regions contained genes associated with cell proliferation, transcription, apoptosis and development. This study proposes candidate genes for beef cattle fertility, although future research is needed to elucidate the precise mechanisms.

The impact of reference composition and genome build on the accuracy of genotype imputation in Australian Angus cattle

Context Genotype imputation is an effective method to increase the number of SNP markers available for an animal and thereby increase the overall power of genome-wide associations and accuracy of genomic predictions. It is also the key to achieve a common set of markers for all individuals when the original genotypes are obtained using multiple genotyping platforms. High accuracy of imputed genotypes is crucial to their utility. Aims In this study, we propose a method for the construction of a common set of medium density markers for imputation, which relies on keeping as much information as possible. We also investigated the impact of changing marker coordinates on the basis of the new bovine genome assembly, ARS-UCD 1.2, on imputation accuracy. Methods In total, 49 754 animals with 45 364 single nucleotide polymorphism markers were used in a 10-fold cross-validation to compare four different imputation scenarios. The four scenarios were based on two alternative designs for the reference datasets. (1) A traditional reference panel that was created using the overlapping SNP from five medium density arrays and (2) a composite reference panel created by combining SNPs across the five arrays. Each of the reference datasets was used to test imputation accuracy when the SNPs were aligned on the basis of two genome assemblies (UMD 3.1 and ARS-UCD 1.2). Key results Our results showed that a composite reference panel can achieve higher imputation accuracies than does a traditional overlap reference. Incorporating mapping information on the basis of the recent genome build slightly improved the imputation accuracies, especially for lower density chips. Conclusions Markers with unreliable mapping information and animals with low connectedness to the imputation reference dataset benefited the most from the ARS-UCD 1.2 assembly and composite reference respectively. Implications The presented method is straightforward and can be used to setup an optimal imputation for accurate inference of genotypes in Australian Angus cattle.

Investigating the accuracy of imputing autosomal variants in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome

Background

Imputation accuracy among other things depends on the size of the reference panel, the marker’s minor allele frequency (MAF), and the correct placement of single nucleotide polymorphism (SNP) on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50 K to 777 K SNP density using Minimac3, when map positions were determined according to the bovine genome assemblies UMD3.1 and ARS-UCD1.2. We assessed the effect of reference and target panel sizes on the pre-phasing based imputation quality using ten-fold cross-validation. Further, we compared the reliability of the model-based imputation quality score (Rsq) from Minimac3 to the empirical imputation accuracy.

Results

The overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R²dose) was almost identical using either the UMD3.1 or ARS-UCD1.2 genome assembly. When the size of the reference panel increased from 250 to 2000, R²dose increased from 0.845 to 0.917, and the number of polymorphic markers in the imputed data set increased from 586,701 to 618,660. Advantages in both accuracy and marker density were also observed when larger target panels were imputed, likely resulting from more accurate haplotype inference. Imputation accuracy increased from 0.903 to 0.913, and the marker density in the imputed data increased from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals. The model-based imputation quality scores from Minimac3 (Rsq) were systematically higher than empirically estimated accuracies. However, both metrics were positively correlated and the correlation increased with the size of the reference panel and MAF of imputed variants.

Conclusions

Accurate imputation of BovineHD BeadChip markers is possible in Nellore cattle using the new bovine reference genome assembly ARS-UCD1.2. The use of large reference and target panels improves the accuracy of the imputed genotypes and provides genotypes for more markers segregating at low frequency for downstream genomic analyses. The model-based imputation quality score from Minimac3 (Rsq) can be used to detect poorly imputed variants but its reliability depends on the size of the reference panel and MAF of the imputed variants.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07184-8.

Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers

Decreasing costs are making low coverage sequencing with imputation to a comprehensive reference panel an attractive alternative to obtain functional variant genotypes that can increase the accuracy of genomic prediction. To assess the potential of low-pass sequencing, genomic sequence of 77 steers sequenced to >10X coverage was downsampled to 1X and imputed to a reference of 946 cattle representing multiple Bos taurus and Bos indicus-influenced breeds. Genotypes for nearly 60 million variants detected in the reference were imputed from the downsampled sequence. The imputed genotypes strongly agreed with the SNP array genotypes (r¯=0.99) and the genotypes called from the transcript sequence (r¯=0.97). Effects of BovineSNP50 and GGP-F250 variants on birth weight, postweaning gain, and marbling were solved without the steers' phenotypes and genotypes, then applied to their genotypes, to predict the molecular breeding values (MBV). The steers' MBV were similar when using imputed and array genotypes. Replacing array variants with functional sequence variants might allow more robust MBV. Imputation from low coverage sequence offers a viable, low-cost approach to obtain functional variant genotypes that could improve genomic prediction.

Development of a genetic evaluation for hair shedding in American Angus cattle to improve thermotolerance

BACKGROUND

Heat stress and fescue toxicosis caused by ingesting tall fescue infected with the endophytic fungus Epichloë coenophiala represent two of the most prevalent stressors to beef cattle in the United States and cost the beef industry millions of dollars each year. The rate at which a beef cow sheds her winter coat early in the summer is an indicator of adaptation to heat and an economically relevant trait in temperate or subtropical parts of the world. Furthermore, research suggests that early-summer hair shedding may reflect tolerance to fescue toxicosis, since vasoconstriction induced by fescue toxicosis limits the ability of an animal to shed its winter coat. Both heat stress and fescue toxicosis reduce profitability partly via indirect maternal effects on calf weaning weight. Here, we developed parameters for routine genetic evaluation of hair shedding score in American Angus cattle, and identified genomic loci associated with variation in hair shedding score via genome-wide association analysis (GWAA).

RESULTS

Hair shedding score was moderately heritable (h2 = 0.34 to 0.40), with different repeatability estimates between cattle grazing versus not grazing endophyte-infected tall fescue. Our results suggest modestly negative genetic and phenotypic correlations between a dam's hair shedding score (lower score is earlier shedding) and the weaning weight of her calf, which is one metric of performance. Together, these results indicate that economic gains can be made by using hair shedding score breeding values to select for heat-tolerant cattle. GWAA identified 176 variants significant at FDR < 0.05. Functional enrichment analyses using genes that were located within 50 kb of these variants identified pathways involved in keratin formation, prolactin signalling, host-virus interaction, and other biological processes.

CONCLUSIONS

This work contributes to a continuing trend in the development of genetic evaluations for environmental adaptation. Our results will aid beef cattle producers in selecting more sustainable and climate-adapted cattle, as well as enable the development of similar routine genetic evaluations in other breeds.

Signatures of positive selection underlying beef production traits in Korean cattle breeds

The difference in the breeding programs and population history may have diversely shaped the genomes of Korean native cattle breeds. In the absence of phenotypic data, comparisons of breeds that have been subjected to different selective pressures can aid to identify genomic regions and genes controlling qualitative and complex traits. In this study to decipher genetic variation and identify evidence of divergent selection, 3 Korean cattle breeds were genotyped using the recently developed high-density GeneSeek Genomic Profiler F250 (GGP-F250) array. The three Korean cattle breeds clustered according to their coat color phenotypes and breeding programs. The Heugu breed reliably showed smaller effective population size at all generations considered. Across the autosomal chromosomes, 113 and 83 annotated genes were identified from Hanwoo-Chikso and Hanwoo-Heugu comparisons, respectively of which 16 genes were shared between the two pairwise comparisons. The most important signals of selection were detected on bovine chromosomes 14 (24.39–25.13 Mb) and 18 (13.34–15.07 Mb), containing genes related to body size, and coat color (XKR4, LYN, PLAG1, SDR16C5, TMEM68, CDH15, MC1R, and GALNS). Some of the candidate genes are also associated with meat quality traits (ACSF3, EIF2B1, BANP, APCDD1, and GALM) and harbor quantitative trait locus (QTL) for beef production traits. Further functional analysis revealed that the candidate genes (DBI, ACSF3, HINT2, GBA2, AGPAT5, SCAP, ELP6, APOB, and RBL1) were involved in gene ontology (GO) terms relevant to meat quality including fatty acid oxidation, biosynthesis, and lipid storage. Candidate genes previously known to affect beef production and quality traits could be used in the beef cattle selection strategies.

Assessing genomic diversity and signatures of selection in Original Braunvieh cattle using whole-genome sequencing data

Background

Autochthonous cattle breeds are an important source of genetic variation because they might carry alleles that enable them to adapt to local environment and food conditions. Original Braunvieh (OB) is a local cattle breed of Switzerland used for beef and milk production in alpine areas. Using whole-genome sequencing (WGS) data of 49 key ancestors, we characterize genomic diversity, genomic inbreeding, and signatures of selection in Swiss OB cattle at nucleotide resolution.

Results

We annotated 15,722,811 SNPs and 1,580,878 Indels including 10,738 and 2763 missense deleterious and high impact variants, respectively, that were discovered in 49 OB key ancestors. Six Mendelian trait-associated variants that were previously detected in breeds other than OB, segregated in the sequenced key ancestors including variants causal for recessive xanthinuria and albinism. The average nucleotide diversity (1.6  × 10^− 3) was higher in OB than many mainstream European cattle breeds. Accordingly, the average genomic inbreeding derived from runs of homozygosity (ROH) was relatively low (F_ROH = 0.14) in the 49 OB key ancestor animals. However, genomic inbreeding was higher in OB cattle of more recent generations (F_ROH = 0.16) due to a higher number of long (> 1 Mb) runs of homozygosity. Using two complementary approaches, composite likelihood ratio test and integrated haplotype score, we identified 95 and 162 genomic regions encompassing 136 and 157 protein-coding genes, respectively, that showed evidence (P < 0.005) of past and ongoing selection. These selection signals were enriched for quantitative trait loci related to beef traits including meat quality, feed efficiency and body weight and pathways related to blood coagulation, nervous and sensory stimulus.

Conclusions

We provide a comprehensive overview of sequence variation in Swiss OB cattle genomes. With WGS data, we observe higher genomic diversity and less inbreeding in OB than many European mainstream cattle breeds. Footprints of selection were detected in genomic regions that are possibly relevant for meat quality and adaptation to local environmental conditions. Considering that the population size is low and genomic inbreeding increased in the past generations, the implementation of optimal mating strategies seems warranted to maintain genetic diversity in the Swiss OB cattle population.