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

Hybrid de novo and haplotype-resolved genome assembly of Vechur cattle - elucidating genetic variation

Cattle contribute to the nutritional needs and economy of a place. The performance and fitness of cattle depend on the response and adaptation to local climatic conditions. Genomic and genetic studies are important for advancing cattle breeding, and availability of relevant reference genomes is essential. In the present study, the genome of a Vechur calf was sequenced on both short-read Illumina and long-read Nanopore sequencing platforms. The hybrid de novo assembly approach was deployed to obtain an average contig length of 1.97 Mbp and an N50 of 4.94 Mbp. By using a short-read genome sequence of the corresponding sire and dam, a haplotype-resolved genome was also assembled. In comparison to the taurine reference genome, we found 28,982 autosomal structural variants and 16,926,990 SNVs, with 883,544 SNVs homozygous in the trio samples. Many of these SNPs have been reported to be associated with various QTLs including growth, milk yield, and milk fat content, which are crucial determinants of cattle production. Furthermore, population genotype data analysis indicated that the present sample belongs to an Indian cattle breed forming a unique cluster of Bos indicus. Subsequent FST analysis revealed differentiation of the Vechur cattle genome at multiple loci, especially those regions related to whole body growth and cell division, especially IGF1, HMGA2, RRM2, and CD68 loci, suggesting a possible role of these genes in its small stature and better disease resistance capabilities in comparison with the local crossbreeds. This provides an opportunity to select and engineer cattle breeds optimized for local conditions.

GWABLUP: genome-wide association assisted best linear unbiased prediction of genetic values

BACKGROUND

Since the very beginning of genomic selection, researchers investigated methods that improved upon SNP-BLUP (single nucleotide polymorphism best linear unbiased prediction). SNP-BLUP gives equal weight to all SNPs, whereas it is expected that many SNPs are not near causal variants and thus do not have substantial effects. A recent approach to remedy this is to use genome-wide association study (GWAS) findings and increase the weights of GWAS-top-SNPs in genomic predictions. Here, we employ a genome-wide approach to integrate GWAS results into genomic prediction, called GWABLUP.

RESULTS

GWABLUP consists of the following steps: (1) performing a GWAS in the training data which results in likelihood ratios; (2) smoothing the likelihood ratios over the SNPs; (3) combining the smoothed likelihood ratio with the prior probability of SNPs having non-zero effects, which yields the posterior probability of the SNPs; (4) calculating a weighted genomic relationship matrix using the posterior probabilities as weights; and (5) performing genomic prediction using the weighted genomic relationship matrix. Using high-density genotypes and milk, fat, protein and somatic cell count phenotypes on dairy cows, GWABLUP was compared to GBLUP, GBLUP (topSNPs) with extra weights for GWAS top-SNPs, and BayesGC, i.e. a Bayesian variable selection model. The GWAS resulted in six, five, four, and three genome-wide significant peaks for milk, fat and protein yield and somatic cell count, respectively. GWABLUP genomic predictions were 10, 6, 7 and 1% more reliable than those of GBLUP for milk, fat and protein yield and somatic cell count, respectively. It was also more reliable than GBLUP (topSNPs) for all four traits, and more reliable than BayesGC for three of the traits. Although GWABLUP showed a tendency towards inflation bias for three of the traits, this was not statistically significant. In a multitrait analysis, GWABLUP yielded the highest accuracy for two of the traits. However, for SCC, which was relatively unrelated to the yield traits, including yield trait GWAS-results reduced the reliability compared to a single trait analysis.

CONCLUSIONS

GWABLUP uses GWAS results to differentially weigh all the SNPs in a weighted GBLUP genomic prediction analysis. GWABLUP yielded up to 10% and 13% more reliable genomic predictions than GBLUP for single and multitrait analyses, respectively. Extension of GWABLUP to single-step analyses is straightforward.

Potential negative effects of genomic selection

Initial findings on genomic selection (GS) indicated substantial improvement for major traits, such as performance, and even successful selection for antagonistic traits. However, recent unofficial reports indicate an increased frequency of deterioration of secondary traits. This phenomenon may arise due to the mismatch between the accelerated selection process and resource allocation. Traits explicitly or implicitly accounted for by a selection index move toward the desired direction, whereas neglected traits change according to the genetic correlations with selected traits. Historically, the first stage of commercial genetic selection focused on production traits. After long-term selection, production traits improved, whereas fitness traits deteriorated, although this deterioration was partially compensated for by constantly improving management. Adding these fitness traits to the breeding objective and the used selection index also helped offset their decline while promoting long-term gains. Subsequently, the trend in observed fitness traits was a combination of a negative response due to genetic antagonism, positive response from inclusion in the selection index, and a positive effect of improving management. Under GS, the genetic trends accelerate, especially for well-recorded higher heritability traits, magnifying the negatively correlated responses for fitness traits. Then, the observed trend for fitness traits can become negative, especially because management modifications do not accelerate under GS. Additional deterioration can occur due to the rapid turnover of GS, as heritabilities for production traits can decline and the genetic antagonism between production and fitness traits can intensify. If the genetic parameters are not updated, the selection index will be inaccurate, and the intended gains will not occur. While the deterioration can accelerate for unrecorded or sparsely recorded fitness traits, GS can lead to an improvement for widely recorded fitness traits. In the context of GS, it is crucial to look for unexpected changes in relevant traits and take rapid steps to prevent further declines, especially in secondary traits. Changes can be anticipated by investigating the temporal dynamics of genetic parameters, especially genetic correlations. However, new methods are needed to estimate genetic parameters for the last generation with large amounts of genomic data.

Multi-breed genomic evaluation for tropical beef cattle when no pedigree information is available

BACKGROUND

It has been challenging to implement genomic selection in multi-breed tropical beef cattle populations. If commercial (often crossbred) animals could be used in the reference population for these genomic evaluations, this could allow for very large reference populations. In tropical beef systems, such animals often have no pedigree information. Here we investigate potential models for such data, using marker heterozygosity (to model heterosis) and breed composition derived from genetic markers, as covariates in the model. Models treated breed effects as either fixed or random, and included genomic best linear unbiased prediction (GBLUP) and BayesR. A tropically-adapted beef cattle dataset of 29,391 purebred, crossbred and composite commercial animals was used to evaluate the models.

RESULTS

Treating breed effects as random, in an approach analogous to genetic groups allowed partitioning of the genetic variance into within-breed and across breed-components (even with a large number of breeds), and estimation of within-breed and across-breed genomic estimated breeding values (GEBV). We demonstrate that moderately-accurate (0.30-0.43) GEBV can be calculated using these models. Treating breed effects as random gave more accurate GEBV than treating breed as fixed. A simple GBLUP model where no breed effects were fitted gave the same accuracy (and correlations of GEBV very close to 1) as a model where GEBV for within-breed and the GEBV for (random) across-breed effects were included. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy, with 3% accuracy improvement averaged across traits, especially when the validation population was less related to the reference population. Estimates of heterosis from our models were in line with previous estimates from beef cattle. A method for estimating the number of effective breed comparisons for each breed combination accumulated across contemporary groups is presented.

CONCLUSIONS

When no pedigree is available, breed composition and heterosis for inclusion in multi-breed genomic evaluation can be estimated from genotypes. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy.

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 eQTL colocalization and transcriptome-wide association study identify potentially causal genes responsible for economic traits in Simmental beef cattle

BACKGROUND

A detailed understanding of genetic variants that affect beef merit helps maximize the efficiency of breeding for improved production merit in beef cattle. To prioritize the putative variants and genes, we ran a comprehensive genome-wide association studies (GWAS) analysis for 21 agronomic traits using imputed whole-genome variants in Simmental beef cattle. Then, we applied expression quantitative trait loci (eQTL) mapping between the genotype variants and transcriptome of three tissues (longissimus dorsi muscle, backfat, and liver) in 120 cattle.

RESULTS

We identified 1,580 association signals for 21 beef agronomic traits using GWAS. We then illuminated 854,498 cis-eQTLs for 6,017 genes and 46,970 trans-eQTLs for 1,903 genes in three tissues and built a synergistic network by integrating transcriptomics with agronomic traits. These cis-eQTLs were preferentially close to the transcription start site and enriched in functional regulatory regions. We observed an average of 43.5% improvement in cis-eQTL discovery using multi-tissue eQTL mapping. Fine-mapping analysis revealed that 111, 192, and 194 variants were most likely to be causative to regulate gene expression in backfat, liver, and muscle, respectively. The transcriptome-wide association studies identified 722 genes significantly associated with 11 agronomic traits. Via the colocalization and Mendelian randomization analyses, we found that eQTLs of several genes were associated with the GWAS signals of agronomic traits in three tissues, which included genes, such as NADSYN1, NDUFS3, LTF and KIFC2 in liver, GRAMD1C, TMTC2 and ZNF613 in backfat, as well as TIGAR, NDUFS3 and L3HYPDH in muscle that could serve as the candidate genes for economic traits.

CONCLUSIONS

The extensive atlas of GWAS, eQTL, fine-mapping, and transcriptome-wide association studies aid in the suggestion of potentially functional variants and genes in cattle agronomic traits and will be an invaluable source for genomics and breeding in beef cattle.

Molecular Cytogenetics in Domestic Bovids: A Review

The discovery of the Robertsonian translocation (rob) involving cattle chromosomes 1 and 29 and the demonstration of its deleterious effects on fertility focused the interest of many scientific groups on using chromosome banding techniques to reveal chromosome abnormalities and verify their effects on fertility in domestic animals. At the same time, comparative banding studies among various species of domestic or wild animals were found useful for delineating chromosome evolution among species. The advent of molecular cytogenetics, particularly the use of fluorescence in situ hybridization (FISH), has allowed a deeper investigation of the chromosomes of domestic animals through: (a) the physical mapping of specific DNA sequences on chromosome regions; (b) the use of specific chromosome markers for the identification of the chromosomes or chromosome regions involved in chromosome abnormalities, especially when poor banding patterns are produced; (c) better anchoring of radiation hybrid and genetic maps to specific chromosome regions; (d) better comparisons of related and unrelated species by comparative FISH mapping and/or Zoo-FISH techniques; (e) the study of meiotic segregation, especially by sperm-FISH, in some chromosome abnormalities; (f) better demonstration of conserved or lost DNA sequences in chromosome abnormalities; (g) the use of informatic and genomic reconstructions, in addition to CGH arrays, to predict conserved or lost chromosome regions in related species; and (h) the study of some chromosome abnormalities and genomic stability using PCR applications. This review summarizes the most important applications of molecular cytogenetics in domestic bovids, with an emphasis on FISH mapping applications.

Fitting Genomic Prediction Models with Different Marker Effects among Prefectures to Carcass Traits in Japanese Black Cattle

We fitted statistical models, which assumed single-nucleotide polymorphism (SNP) marker effects differing across the fattened steers marketed into different prefectures, to the records for cold carcass weight (CW) and marbling score (MS) of 1036, 733, and 279 Japanese Black fattened steers marketed into Tottori, Hiroshima, and Hyogo prefectures in Japan, respectively. Genotype data on 33,059 SNPs was used. Five models that assume only common SNP effects to all the steers (model 1), common effects plus SNP effects differing between the steers marketed into Hyogo prefecture and others (model 2), only the SNP effects differing between Hyogo steers and others (model 3), common effects plus SNP effects specific to each prefecture (model 4), and only the effects specific to each prefecture (model 5) were exploited. For both traits, slightly lower values of residual variance than that of model 1 were estimated when fitting all other models. Estimated genetic correlation among the prefectures in models 2 and 4 ranged to 0.53 to 0.71, all <0.8. These results might support that the SNP effects differ among the prefectures to some degree, although we discussed the necessity of careful consideration to interpret the current results.

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.

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.

Genetic parameters and genome-wide association for milk production traits and somatic cell score in different lactation stages of Shanghai Holstein population

The aim of this study was to investigate the genetic parameters and genetic architectures of six milk production traits in the Shanghai Holstein population. The data used to estimate the genetic parameters consisted of 1,968,589 test-day records for 305,031 primiparous cows. Among the cows with phenotypes, 3,016 cows were genotyped with Illumina Bovine SNP50K BeadChip, GeneSeek Bovine 50K BeadChip, GeneSeek Bovine LD BeadChip v4, GeneSeek Bovine 150K BeadChip, or low-depth whole-genome sequencing. A genome-wide association study was performed to identify quantitative trait loci and genes associated with milk production traits in the Shanghai Holstein population using genotypes imputed to whole-genome sequences and both fixed and random model circulating probability unification and a mixed linear model with rMVP software. Estimated heritabilities (h2) varied from 0.04 to 0.14 for somatic cell score (SCS), 0.07 to 0.22 for fat percentage (FP), 0.09 to 0.27 for milk yield (MY), 0.06 to 0.23 for fat yield (FY), 0.09 to 0.26 for protein yield (PY), and 0.07 to 0.35 for protein percentage (PP), respectively. Within lactation, genetic correlations for SCS, FP, MY, FY, PY, and PP at different stages of lactation estimated in random regression model were ranged from -0.02 to 0.99, 0.18 to 0.99, 0.04 to 0.99, 0.04 to 0.99, 0.01 to 0.99, and 0.33 to 0.99, respectively. The genetic correlations were highest between adjacent DIM but decreased as DIM got further apart. Candidate genes included those related to production traits (DGAT1, MGST1, PTK2, and SCRIB), disease-related (LY6K, COL22A1, TECPR2, and PLCB1), heat stress-related (ITGA9, NDST4, TECPR2, and HSF1), and reproduction-related (7SK and DOCK2) genes. This study has shown that there are differences in the genetic mechanisms of milk production traits at different stages of lactation. Therefore, it is necessary to conduct research on milk production traits at different stages of lactation as different traits. Our results can also provide a theoretical basis for subsequent molecular breeding, especially for the novel genetic loci.

Integrative analyses of genomic and metabolomic data reveal genetic mechanisms associated with carcass merit traits in beef cattle

Improvement of carcass merit traits is a priority for the beef industry. Discovering DNA variants and genes associated with variation in these traits and understanding biological functions/processes underlying their associations are of paramount importance for more effective genetic improvement of carcass merit traits in beef cattle. This study integrates 10,488,742 imputed whole genome DNA variants, 31 plasma metabolites, and animal phenotypes to identify genes and biological functions/processes that are associated with carcass merit traits including hot carcass weight (HCW), rib eye area (REA), average backfat thickness (AFAT), lean meat yield (LMY), and carcass marbling score (CMAR) in a population of 493 crossbred beef cattle. Regression analyses were performed to identify plasma metabolites associated with the carcass merit traits, and the results showed that 4 (3-hydroxybutyric acid, acetic acid, citric acid, and choline), 6 (creatinine, L-glutamine, succinic acid, pyruvic acid, L-lactic acid, and 3-hydroxybutyric acid), 4 (fumaric acid, methanol, D-glucose, and glycerol), 2 (L-lactic acid and creatinine), and 5 (succinic acid, fumaric acid, lysine, glycine, and choline) plasma metabolites were significantly associated with HCW, REA, AFAT, LMY, and CMAR (P-value < 0.1), respectively. Combining the results of metabolome-genome wide association studies using the 10,488,742 imputed SNPs, 103, 160, 83, 43, and 109 candidate genes were identified as significantly associated with HCW, REA, AFAT, LMY, and CMAR (P-value < 1 × 10^-5), respectively. By applying functional enrichment analyses for candidate genes of each trait, 26, 24, 26, 24, and 28 significant cellular and molecular functions were predicted for HCW, REA, AFAT, LMY, and CMAR, respectively. Among the five topmost significantly enriched biological functions for carcass merit traits, molecular transport and small molecule biochemistry were two top biological functions associated with all carcass merit traits. Lipid metabolism was the most significant biological function for LMY and CMAR and it was also the second and fourth highest biological function for REA and HCW, respectively. Candidate genes and enriched biological functions identified by the integrative analyses of metabolites with phenotypic traits and DNA variants could help interpret the results of previous genome-wide association studies for carcass merit traits. Our integrative study also revealed additional potential novel genes associated with these economically important traits. Therefore, our study improves understanding of the molecular and biological functions/processes that influence carcass merit traits, which could help develop strategies to enhance genomic prediction of carcass merit traits with incorporation of metabolomic data. Similarly, this information could guide management practices, such as nutritional interventions, with the purpose of boosting specific carcass merit traits.

Genomic evaluation with multibreed and crossbred data

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We found low accuracy of genomic evaluation of crossbreds based on purebred data.

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We found higher accuracy for crossbreds based on crossbred data.

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Use of putative sequence variants had a small impact on genomic accuracy.

Several types of multibreed genomic evaluation are in use. These include evaluation of crossbreds based on purebred SNP effects, joint evaluation of all purebreds and crossbreds with a single additive effect, and treating each purebred and crossbred group as a separate trait. Additionally, putative quantitative trait nucleotides can be exploited to increase the accuracy of prediction. Existing studies indicate that the prediction of crossbreds based on purebred data has low accuracy, that a joint evaluation can potentially provide accurate evaluations for crossbreds but could lower accuracy for purebreds compared with single-breed evaluations, and that the use of putative quantitative trait nucleotides only marginally increases the accuracy. One hypothesis is that genomic selection is based on estimation of clusters of independent chromosome segments. Subsequently, predicting a particular group type would require a reference population of the same type, and crosses with same breed percentage but different type (F₁ vs. F₂) would, at best, use separate reference populations. The genomic selection of multibreed population is still an active research topic.