Genome-wide association and expression quantitative trait loci in cattle reveals common genes regulating mammalian fertility

Most genetic variants associated with fertility in mammals fall in non-coding regions of the genome and it is unclear how these variants affect fertility. Here we use genome-wide association summary statistics for Heifer puberty (pubertal or not at 600 days) from 27,707 Bos indicus, Bos taurus and crossbred cattle; multi-trait GWAS signals from 2119 indicine cattle for four fertility traits, including days to calving, age at first calving, pregnancy status, and foetus age in weeks (assessed by rectal palpation of the foetus); and expression quantitative trait locus for whole blood from 489 indicine cattle, to identify 87 putatively functional genes affecting cattle fertility. Our analysis reveals a significant overlap between the set of cattle and previously reported human fertility-related genes, impling the existence of a shared pool of genes that regulate fertility in mammals. These findings are crucial for developing approaches to improve fertility in cattle and potentially other mammals.

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.

Adaptive sampling during sequencing reveals the origins of the bovine reproductive tract microbiome across reproductive stages and sexes

Cattle enterprises are one of the major livestock production systems globally and are forecasted to have stable growth in the next decade. To facilitate sustainable live weight production, optimal reproductive performance is essential. Microbial colonisation in the reproductive tract has been demonstrated as one of the factors contributing to bovine reproductive performance. Studies also implied that reproductive metagenomes are different at each stage of the estrous cycle. This study applied Oxford Nanopore Technologies’ adaptive long-read sequencing to profile the bovine reproductive microbiome collected from tropical cattle in northern Queensland, Australia. The microbiome samples were collected from cattle of different sexes, reproductive status and locations to provide a comprehensive view of the bovine reproductive microbiome in northern Australian cattle. Ascomycota, Firmicutes and Proteobacteria were abundant phyla identified in the bovine reproductive metagenomes of Australian cattle regardless of sexes, reproductive status and location. The species level taxonomical investigation suggested that gastrointestinal metagenome and the surrounding environment were potentially the origins of the bovine reproductive metagenome. Functional profiles further affirmed this implication, revealing that the reproductive metagenomes of the prepubertal and postpartum animals were dominated by microorganisms that catabolise dietary polysaccharides as an energy substrate while that of the pregnant animals had the function of harvesting energy from aromatic compounds. Bovine reproductive metagenome investigations can be employed to trace the origins of abnormal metagenomes, which is beneficial for disease prevention and control. Additionally, our results demonstrated different reproductive metagenome diversities between cattle from two different locations. The variation in diversity within one location can serve as the indicator of abnormal reproductive metagenome, but between locations inferences cannot be made. We suggest establishing localised metagenomic indices that can be used to infer abnormal reproductive metagenomes which contribute to abortion or sub-fertility.

Environmental variation effects fertility in tropical beef cattle

The northern Australia beef cattle industry operates in harsh environmental conditions which consistently suppress female fertility. To better understand the environmental effect on cattle raised extensively in northern Australia, new environmental descriptors were defined for 54 commercial herds located across the region. Three fertility traits, based on the presence of a corpus luteum at 600 d of age, indicating puberty, (CL Presence, n = 25,176), heifer pregnancy (n = 20,989) and first lactation pregnancy (n = 10,072) were recorded. Temperature, humidity, and rainfall were obtained from publicly available data based on herd location. Being pubertal at 600 d (i.e. CL Presence) increased the likelihood of success at heifer pregnancy and first lactation pregnancy (P < 0.05), underscoring the importance of early puberty in reproductive success. A temperature humidity index (THI) of 65–70 had a significant (P < 0.05) negative effect on first lactation pregnancy rate, heifer pregnancy and puberty at 600 d of age. Area under the curve of daily THI was significant (P < 0.05) and reduced the likelihood of pregnancy at first lactation and puberty at 600 days. Deviation from long-term average rainfall was not significant (P < 0.05) for any trait. Average daily weight gain had a significant and positive relationship (P < 0.05) for heifer and first lactation pregnancy. The results indicate that chronic or cumulative heat load is more determinantal to reproductive performance than acute heat stress. The reason for the lack of a clear relationship between acute heat stress and reproductive performance is unclear but may be partially explained by peak THI and peak nutrition coinciding at the same time. Sufficient evidence was found to justify the use of average daily weight gain and chronic heat load as descriptors to define an environmental gradient.

Sustainable Intensification of Beef Production in the Tropics: The Role of Genetically Improving Sexual Precocity of Heifers

Simple Summary

Tropical pasture-based beef production systems play a vital role in global food security. The importance of promoting sustainable intensification of such systems has been debated worldwide. Demand for beef is growing together with concerns over the impact of its production on the environment. Implementing sustainable livestock intensification programs relies on animal genetic improvement. In tropical areas, the lack of sexual precocity is a bottleneck for cattle efficiency, directly impacting the sustainability of production systems. In the present review we present and discuss the state of the art of genetic evaluation for sexual precocity in Bos indicus beef cattle, covering the definition of measurable traits, genetic parameter estimates, genomic analyses, and a case study of selection for sexual precocity in Nellore breeding programs.

Abstract

Increasing productivity through continued animal genetic improvement is a crucial part of implementing sustainable livestock intensification programs. In Zebu cattle, the lack of sexual precocity is one of the main obstacles to improving beef production efficiency. Puberty-related traits are complex, but large-scale data sets from different “omics” have provided information on specific genes and biological processes with major effects on the expression of such traits, which can greatly increase animal genetic evaluation. In addition, genetic parameter estimates and genomic predictions involving sexual precocity indicator traits and productive, reproductive, and feed-efficiency related traits highlighted the feasibility and importance of direct selection for anticipating heifer reproductive life. Indeed, the case study of selection for sexual precocity in Nellore breeding programs presented here show that, in 12 years of selection for female early precocity and improved management practices, the phenotypic means of age at first calving showed a strong decreasing trend, changing from nearly 34 to less than 28 months, with a genetic trend of almost −2 days/year. In this period, the percentage of early pregnancy in the herds changed from around 10% to more than 60%, showing that the genetic improvement of heifer’s sexual precocity allows optimizing the productive cycle by reducing the number of unproductive animals in the herd. It has a direct impact on sustainability by better use of resources. Genomic selection breeding programs accounting for genotype by environment interaction represent promising tools for accelerating genetic progress for sexual precocity in tropical beef cattle.

Use of whole-genome sequence data and novel genomic selection strategies to improve selection for age at puberty in tropically-adapted beef heifers

Background

In tropically-adapted beef heifers, application of genomic prediction for age at puberty has been limited due to low prediction accuracies. Our aim was to investigate novel methods of pre-selecting whole-genome sequence (WGS) variants and alternative analysis methodologies; including genomic best linear unbiased prediction (GBLUP) with multiple genomic relationship matrices (MGRM) and Bayesian (BayesR) analyses, to determine if prediction accuracy for age at puberty can be improved.

Methods

Genotypes and phenotypes were obtained from two research herds. In total, 868 Brahman and 960 Tropical Composite heifers were recorded in the first population and 3695 Brahman, Santa Gertrudis and Droughtmaster heifers were recorded in the second population. Genotypes were imputed to 23 million whole-genome sequence variants. Eight strategies were used to pre-select variants from genome-wide association study (GWAS) results using conditional or joint (COJO) analyses. Pre-selected variants were included in three models, GBLUP with a single genomic relationship matrix (SGRM), GBLUP MGRM and BayesR. Five-way cross-validation was used to test the effect of marker panel density (6 K, 50 K and 800 K), analysis model, and inclusion of pre-selected WGS variants on prediction accuracy.

Results

In all tested scenarios, prediction accuracies for age at puberty were highest in BayesR analyses. The addition of pre-selected WGS variants had little effect on the accuracy of prediction when BayesR was used. The inclusion of WGS variants that were pre-selected using a meta-analysis with COJO analyses by chromosome, fitted in a MGRM model, had the highest prediction accuracies in the GBLUP analyses, regardless of marker density. When the low-density (6 K) panel was used, the prediction accuracy of GBLUP was equal (0.42) to that with the high-density panel when only six additional sequence variants (identified using meta-analysis COJO by chromosome) were included.

Conclusions

While BayesR consistently outperforms other methods in terms of prediction accuracies, reasonable improvements in accuracy can be achieved when using GBLUP and low-density panels with the inclusion of a relatively small number of highly relevant WGS variants.

Multivariate genomic predictions for age at puberty in tropically adapted beef heifers

Heifers that have an earlier age at puberty often have greater lifetime productivity. Age at puberty is moderately heritable so selection should effectively reduce the number of days to puberty, and improve heifer productivity and profitability as a result. However, recording age at puberty is intensive, requiring repeat ovarian scanning to determine age at first corpus luteum (AGECL). Genomic selection has been proposed as a strategy to select for earlier age at puberty; however, large reference populations of cows with AGECL records and genotypes would be required to generate accurate GEBV for this trait. Reproductive maturity score (RMS) is a proxy trait for age at puberty for implementation in northern Australia beef herds, where large scale recording of AGECL is not feasible. RMS assigns a score of 0 to 5 from a single ovarian scan to describe ovarian maturity at ~600 d. Here we use multivariate genomic prediction to evaluate the value of a large RMS data set to improve accuracy of GEBV for age at puberty (AGECL). There were 882 Brahman and 990 Tropical Composite heifers with AGECL phenotypes, and an independent set of 974 Brahman, 1,798 Santa Gertrudis, and 910 Droughtmaster heifers with RMS phenotypes. All animals had 728,785 real or imputed SNP genotypes. The correlation of AGECL and RMS (h2 = 0.23) was estimated as -0.83 using the genomic information. This result also demonstrates that using genomic information it is possible to estimate genetic correlations between traits collected on different animals in different herds, with minimal or unknown pedigree linkage between them. Inclusion of heifers with RMS in the multi-trait model improved the accuracy of genomic evaluations for AGECL. Accuracy of RMS GEBV generally did not improve by adding heifers with AGECL phenotypes into the reference population. These results suggest that RMS and AGECL may be used together in a multi-trait prediction model to increase the accuracy of prediction for age at puberty in tropically adapted beef cattle.