The development of genomics applied to dairy breeding

Diagnostic applications and limitations for the use of cell-free fetal DNA (cffDNA) in animal husbandry and wildlife management

In animal breeding, a species sex can influence the value of the animal. For example, in the horse breeding industry, mares are preferred as polo horses, while in wildlife breeding males with larger horns are more valuable. Therefore, the economic advantages of knowing the unborn fetus' sex are important to successful animal management. Ultrasonography is used to determine the sex of unborn fetuses, but this method places additional stress on the animal and require specialized equipment and expertise. Conversely, molecular-based sexing techniques require less invasive sampling and can determine sex more reliably. Although in humans, various studies have evaluated the use of cell-free fetal DNA (cffDNA) for prenatal sexing, very few animal studies have been published in this field. Several factors can affect the sensitivity of cffDNA-based sex determination, for example the gestational age. These factors are often not optimized and validated when establishing a protocol for prenatal sexing. In this review, we summarize the current literature on cffDNA in animals. We discuss the diagnostic applications and limitations in the use thereof in animal husbandry and wildlife management. Lastly, the feasibility of implementing diagnostic tests is evaluated and solutions are given to the current drawbacks of the technology.

An Appropriate Genetic Approach for Improving Reproductive Traits in Crossbred Thai-Holstein Cattle under Heat Stress Conditions

Thailand is a tropical country affected by global climate change and has high temperatures and humidity that cause heat stress in livestock. A temperature−humidity index (THI) is required to assess and evaluate heat stress levels in livestock. One of the livestock types in Thailand experiencing heat stress due to extreme climate change is crossbred dairy cattle. Genetic evaluations of heat tolerance in dairy cattle have been carried out for reproductive traits. Heritability values for reproductive traits are generally low (<0.10) because environmental factors heavily influence them. Consequently, genetic improvement for these traits would be slow compared to production traits. Positive and negative genetic correlations were found between reproductive traits and reproductive traits and yield traits. Several selection methods for reproductive traits have been introduced, i.e., the traditional method, marker-assisted selection (MAS), and genomic selection (GS). GS is the most promising technique and provides accurate results with a high genetic gain. Single-step genomic BLUP (ssGBLUP) has higher accuracy than the multi-step equivalent for fertility traits or low-heritability traits.

Genomic Prediction of Complex Traits in Animal Breeding with Long Breeding History, the Dairy Cattle Case

In accordance with the infinitesimal model for quantitative traits, a very large number of genes affect nearly all economic traits. In only two cases has the causative polymorphism been determined for genes affecting economic traits in dairy cattle. Most current methods for genomic evaluation are based on the "two-step" method. Genetic evaluations are computed by the individual animal model, and functions of the evaluations of progeny-tested sires are the dependent variable for estimation of marker effects. With the adoption of genomic evaluation in 2008, annual rates of genetic gain in the US increased from ∼50-100% for yield traits and from threefold to fourfold for lowly heritable traits, including female fertility, herd-life and somatic cell concentration. Gradual elimination of the progeny test scheme has led to a reduction in the number of sires with daughter records and less genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of "single-step" methodologies. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and "environmentally friendly" production. Genetic variance for economic traits is maintained by increase in frequency of rare alleles, new mutations, and changes in selection goals and management.

ssGBLUP Method Improves the Accuracy of Breeding Value Prediction in Huacaya Alpaca

Simple Summary

Alpaca breeding takes place in the most entrenched areas of the Andes, where the conditions to implement genetic improvement programs are very difficult. Likewise, taking phenotypic records is limited in its ability to predict genetic merit accurately. For this reason, genomic information is shown as an alternative that helps to predict the genetic values of fiber traits more precisely. This study showed how genomic information increased precision by 2.623% for the fiber diameter, 6.442% for the standard deviation of the fiber diameter, and 1.471% for the percentage of medullation compared to traditional methods for predicting genetic merit, suggesting that adding genomic data in prediction models could be beneficial for alpaca breeding programs in the future.

Abstract

Improving textile characteristics is the main objective of alpaca breeding. A recently developed SNP chip for alpacas could potentially be used to implement genomic selection and accelerate genetic progress. Therefore, this study aimed to compare the increase in prediction accuracy of three important fiber traits: fiber diameter (FD), standard deviation of fiber diameter (SD), and percentage of medullation (PM) in Huacaya alpacas. The data contains a total pedigree of 12,431 animals, 24,169 records for FD and SD, and 8386 records for PM and 60,624 SNP markers for each of the 431 genotyped animals of the Pacomarca Genetic Center. Prediction accuracy of breeding values was compared between a classical BLUP and a single-step Genomic BLUP (ssGBLUP). Deregressed phenotypes were predicted. The accuracies of the genetic and genomic values were calculated using the correlation between the predicted breeding values and the deregressed values of 100 randomly selected animals from the genotyped ones. Fifty replicates were carried out. Accuracies with ssGBLUP improved by 2.623%, 6.442%, and 1.471% on average for FD, SD, and PM, respectively, compared to the BLUP method. The increase in accuracy was relevant, suggesting that adding genomic data could benefit alpaca breeding programs.

Genetic Features of Reproductive Traits in Bovine and Buffalo: Lessons From Bovine to Buffalo

Bovine and buffalo are important livestock species that have contributed to human lives for more than 1000 years. Improving fertility is very important to reduce the cost of production. In the current review, we classified reproductive traits into three categories: ovulation, breeding, and calving related traits. We systematically summarized the heritability estimates, molecular markers, and genomic selection (GS) for reproductive traits of bovine and buffalo. This review aimed to compile the heritability and genome-wide association studies (GWASs) related to reproductive traits in both bovine and buffalos and tried to highlight the possible disciplines which should benefit buffalo breeding. The estimates of heritability of reproductive traits ranged were from 0 to 0.57 and there were wide differences between the populations. For some specific traits, such as age of puberty (AOP) and calving difficulty (CD), the majority beef population presents relatively higher heritability than dairy cattle. Compared to bovine, genetic studies for buffalo reproductive traits are limited for age at first calving and calving interval traits. Several quantitative trait loci (QTLs), candidate genes, and SNPs associated with bovine reproductive traits were screened and identified by candidate gene methods and/or GWASs. The IGF1 and LEP pathways in addition to non-coding RNAs are highlighted due to their crucial relevance with reproductive traits. The distribution of QTLs related to various traits showed a great differences. Few GWAS have been performed so far on buffalo age at first calving, calving interval, and days open traits. In addition, we summarized the GS studies on bovine and buffalo reproductive traits and compared the accuracy between different reports. Taken together, GWAS and candidate gene approaches can help to understand the molecular genetic mechanisms of complex traits. Recently, GS has been used extensively and can be performed on multiple traits to improve the accuracy of prediction even for traits with low heritability, and can be combined with multi-omics for further analysis.

Discovery and validation of candidate SNP markers associated to heat stress response in pregnant ewes managed inside a climate-controlled chamber

Sheep production in desert environments during summer is challenging due to heat stress which reduces feed intake, growth, and fertility. Despite warm conditions, some ewes are able to maintain a normal performance suggesting the existence of genetic bases underlying heat tolerance. Our objective was to discover and validate genetic markers associated with thermo-tolerance in pregnant ewes exposed to warm environmental conditions. Using a well-defined model laboratory of heat stress in sheep, pregnant Columbia-Rambouillet crossbred ewes (n = 100) were examined. Following acclimation to the laboratory at thermo-neutral conditions, heat stress was induced in ewes by increasing the temperature-humidity index in a control environmental chamber during mid-gestation. Feed intake, water consumption, and rectal temperature were recorded daily and used to establish the heat stress tolerance index (HSTI) for each ewe. Rectal temperature was a predictor (P < 0.05) of feed intake, and the regression coefficient was used to classify the HSTI. In a subset of 24 ewes, a genome-wide association study (GWAS) was performed using the Illumina OvineSNP50 BeadChip. Single-marker analysis detected 3 intragenic SNPs associated with HSTI (P value = 10^-5). Bayesian multi-marker approach discovered 26 chromosomal regions across the genome which accounted for 9.8% of the variation associated with HSTI. In an independent sheep population (n = 42), the three discovered SNPs were validated as molecular markers associated with thermo-tolerance phenotypic traits. These SNPs were located within the genes F13A1, PAM, and PRELID2. In conclusion, three SNPs appear to be novel molecular markers associated with heat stress tolerance in pregnant ewes providing new knowledge about genetic foundations of thermo-tolerance.

Principal component and cluster analyses of production and fertility traits in Red Sindhi dairy cattle breed in Brazil

The objective of this study was to investigate the relationship among functional traits (age at first calving (AFC), calving interval (CI), reproductive efficiency (RE), total milk yield (TMY), and lactation period (LP)) in Red Sindhi breed through multivariate techniques. For this goal, performance data provided by the Brazilian Association of Zebu Breeders related to 560 Red Sindhi dairy cattle from 28 different herds in Brazil, born in the period from 1987 to 2011, were used. Principal component analysis with correlation matrix was used to find the relationship among AFC, CI, RE, TMY, and LP. It was found that for all functional traits, first 3 principal components explained more than 90% of the total variation. Clustering analysis was performed based on Tocher method, and results showed physiological relationships among functional traits. By cluster analysis, twelve different groups were generated from the pool of Sindhi herds analyzed, with a great homogeneity among females for the traits evaluated and only few females generating separate groups. Four hundred and twenty-nine females were clustered in one group, representing 76.60% of the genotypes. Total milk yield (TMY) showed 71.92% of the total variation, and age at first calving (AFC) contributed with 23.06% of the variation, being the two most important traits for the variability of the data set. In conclusion, the multivariate procedures were effective in generating the correlations among the functional traits, showing that CI is correlated with RE and all these functional traits are related with total milk yield.

Estimating variance components in population scale family trees

The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records.

Leveraging Available Resources and Stakeholder Involvement for Improved Productivity of African Livestock in the Era of Genomic Breeding

The African continent is home to diverse populations of livestock breeds adapted to harsh environmental conditions with more than 70% under traditional systems of management. Animal productivity is less than optimal in most cases and is faced with numerous challenges including limited access to adequate nutrition and disease management, poor institutional capacities and lack of adequate government policies and funding to develop the livestock sector. Africa is home to about 1.3 billion people and with increasing demand for animal proteins by an ever growing human population, the current state of livestock productivity creates a significant yield gap for animal products. Although a greater section of the population, especially those living in rural areas depend largely on livestock for their livelihoods; the potential of the sector remains underutilized and therefore unable to contribute significantly to economic development and social wellbeing of the people. With current advances in livestock management practices, breeding technologies and health management, and with inclusion of all stakeholders, African livestock populations can be sustainably developed to close the animal protein gap that exists in the continent. In particular, advances in gene technologies, and application of genomic breeding in many Western countries has resulted in tremendous gains in traits like milk production with the potential that, implementation of genomic selection and other improved practices (nutrition, healthcare, etc.) can lead to rapid improvement in traits of economic importance in African livestock populations. The African livestock populations in the context of this review are limited to cattle, goat, pig, poultry, and sheep, which are mainly exploited for meat, milk, and eggs. This review examines the current state of livestock productivity in Africa, the main challenges faced by the sector, the role of various stakeholders and discusses in-depth strategies that can enable the application of genomic technologies for rapid improvement of livestock traits of economic importance.

Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.

Invited review: A perspective on the future of genomic selection in dairy cattle

Genomic evaluation has been successfully implemented in the United States, Canada, Great Britain, Ireland, New Zealand, Australia, France, the Netherlands, Germany, and the Scandinavian countries. Adoption of this technology in the major dairy producing countries has led to significant changes in the worldwide dairy industry. Gradual elimination of the progeny test system has led to a reduction in the number of sires with daughter records and fewer genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of "single-step" methodologies. Although genomic selection has been successful in increasing rates of genetic gain, we still know very little about the genetic architecture of quantitative variation. Apparently, a very large number of genes affect nearly all economic traits, in accordance with the infinitesimal model for quantitative traits. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and on environmentally friendly production with reduced waste and gas emission. Genetic variance for economic traits is maintained by the increase in frequency of rare alleles, new mutations, and changes in selection goals and management. Thus, it is unlikely that a selection plateau will be reached in the near future.

Genetic mutations potentially cause two novel NCF1 splice variants up-regulated in the mammary gland, blood and neutrophil of cows infected by Escherichia coli

Neutrophil cytosolic factor 1 (NCF1) plays a crucial role in host defense against microbial pathogens. In this study, we investigated the potential alternative splicing patterns, expression and splice-relevant single nucleotide polymorphisms (SNPs) of the bovine NCF1 gene to increase insights into its potential role against bovine mastitis caused by Escherichia coli infection. Using RT-PCR and clone sequencing methods, we found two novel splice variants designed as NCF1-TV1 (retained intron 6) and NCF1-TV2 (retained part of intron 8), respectively, encoding two putative truncated proteins (239AA and 283AA). Two splice variants were drastically up-regulated in the mastitis-infected cows' mammary tissues, blood and neutrophils compared with these of healthy cows using real-time RT-PCR. Genomic sequencing analysis identified four novel SNPs g.10112 G>A, g.10766 T>C, SNPs g.12085 G>A and g.12430 T>C at the ends of intron 6 and intron 8 of NCF1. ESE motif predicted that the SNP (g.10766 T>C) might affect the binding with splicing-related factors and subsequently caused the production of aberrant splice variant NCF1-TV1, which is one of the potential reasons that the functional SNP was associated with increased milk somatic cell score in cow. Our results would help in better understanding the NCF1 gene function in the process against pathogen infection, and the effect of splicing-related SNP on the production of aberrant splice variant and careful functional characterization in dairy cattle.