Enhancing genome-wide populus trait prediction through deep convolutional neural networks

As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genotype nor providing good ideas for modification. Nonlinear models are well characterized in capturing complex nonadditive effects, filling this gap under traditional methods. Taking populus as the research object, this paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes. The method was trained on three datasets, and compared with other four classic models-Bayesian ridge regression (BRR), Elastic Net, support vector regression, and dualCNN. The results show that DCNGP has five typical advantages in performance: strong prediction ability on multiple experimental datasets; the incorporation of batch normalization layers and Early-Stopping technology enhancing the generalization capabilities and prediction stability on test data; learning potent features from the data and thus circumventing the tedious steps of manual production; the introduction of a Gaussian Noise layer enhancing predictive capabilities in the case of inherent uncertainties or perturbations; fewer hyperparameters aiding to reduce tuning time across datasets and improve auto-search efficiency. In this way, DCNGP shows powerful predictive ability from genotype to phenotype, which provide an important theoretical reference for building more robust populus breeding programs.

AI-assisted selection of mating pairs through simulation-based optimized progeny allocation strategies in plant breeding

Emerging technologies such as genomic selection have been applied to modern plant and animal breeding to increase the speed and efficiency of variety release. However, breeding requires decisions regarding parent selection and mating pairs, which significantly impact the ultimate genetic gain of a breeding scheme. The selection of appropriate parents and mating pairs to increase genetic gain while maintaining genetic diversity is still an urgent need that breeders are facing. This study aimed to determine the best progeny allocation strategies by combining future-oriented simulations and numerical black-box optimization for an improved selection of parents and mating pairs. In this study, we focused on optimizing the allocation of progenies, and the breeding process was regarded as a black-box function whose input is a set of parameters related to the progeny allocation strategies and whose output is the ultimate genetic gain of breeding schemes. The allocation of progenies to each mating pair was parameterized according to a softmax function, whose input is a weighted sum of multiple features for the allocation, including expected genetic variance of progenies and selection criteria such as different types of breeding values, to balance genetic gains and genetic diversity optimally. The weighting parameters were then optimized by the black-box optimization algorithm called StoSOO via future-oriented breeding simulations. Simulation studies to evaluate the potential of our novel method revealed that the breeding strategy based on optimized weights attained almost 10% higher genetic gain than that with an equal allocation of progenies to all mating pairs within just four generations. Among the optimized strategies, those considering the expected genetic variance of progenies could maintain the genetic diversity throughout the breeding process, leading to a higher ultimate genetic gain than those without considering it. These results suggest that our novel method can significantly improve the speed and efficiency of variety development through optimized decisions regarding the selection of parents and mating pairs. In addition, by changing simulation settings, our future-oriented optimization framework for progeny allocation strategies can be easily implemented into general breeding schemes, contributing to accelerated plant and animal breeding with high efficiency.

Searching for homozygous haplotype deficiency in Manech Tête Rousse dairy sheep revealed a nonsense variant in the MMUT gene affecting newborn lamb viability

BACKGROUND

Recessive deleterious variants are known to segregate in livestock populations, as in humans, and some may be lethal in the homozygous state.

RESULTS

We used phased 50 k single nucleotide polymorphism (SNP) genotypes and pedigree data to scan the genome of 6845 Manech Tête Rousse dairy sheep to search for deficiency in homozygous haplotypes (DHH). Five Manech Tête Rousse deficient homozygous haplotypes (MTRDHH1 to 5) were identified, with a homozygous deficiency ranging from 84 to 100%. These haplotypes are located on Ovis aries chromosome (OAR)1 (MTRDHH2 and 3), OAR10 (MTRDHH4), OAR13 (MTRDHH5), and OAR20 (MTRDHH1), and have carrier frequencies ranging from 7.8 to 16.6%. When comparing at-risk matings between DHH carriers to safe matings between non-carriers, two DHH (MTRDHH1 and 2) were linked with decreased insemination success and/or increased stillbirth incidence. We investigated the MTRDHH1 haplotype, which substantially increased stillbirth rate, and identified a single nucleotide variant (SNV) inducing a premature stop codon (p.Gln409*) in the methylmalonyl-CoA mutase (MMUT) gene by using a whole-genome sequencing approach. We generated homozygous lambs for the MMUT mutation by at-risk mating between heterozygous carriers, and most of them died within the first 24 h after birth without any obvious clinical symptoms. Reverse transcriptase-qPCR and western blotting on post-mortem liver and kidney biological samples showed a decreased expression of MMUT mRNA in the liver and absence of a full-length MMUT protein in the mutant homozygous lambs.

CONCLUSIONS

We identified five homozygous deficient haplotypes that are likely to harbor five independent deleterious recessive variants in sheep. One of these was detected in the MMUT gene, which is associated with lamb lethality in the homozygous state. A specific management of these haplotypes/variants in the MTR dairy sheep selection program would help enhance the overall fertility and lamb survival.

Unveiling the Genetic Landscape of Feed Efficiency in Holstein Dairy Cows: Insights into Heritability, Genetic Markers, and Pathways via Meta-Analysis

Improving the feeding efficiency of dairy cows is a key component to improve the utilization of land resources and meet the demand for high-quality protein. Advances in genomic methods and omics techniques have made it possible to breed more efficient dairy cows through genomic selection. The aim of this review is to obtain a comprehensive understanding of the biological background of feed efficiency (FE) complex traits in purebred Holstein dairy cows including heritability estimate, and genetic markers, genes, and pathways participating in FE regulation mechanism. Through a literature search, we systematically reviewed the heritability estimation, molecular genetic markers, genes, biomarkers, and pathways of traits related to feeding efficiency in Holstein dairy cows. A meta-analysis based on a random-effects model was performed to combine reported heritability estimates of FE complex. The heritability of residual feed intake, dry matter intake, and energy balance was 0.20, 0.34, and 0.22, respectively, which proved that it was reasonable to include the related traits in the selection breeding program. For molecular genetic markers, a total of 13 single-nucleotide polymorphisms and copy number variance loci, associated genes, and functions were reported to be significant across populations. A total of 169 reported candidate genes were summarized on a large scale, using a higher threshold (adjusted P value < 0.05). Then, the subsequent pathway enrichment of these genes was performed. The important genes reported in the articles were included in a gene list and the gene list was enriched by gene ontology (GO):biological process (BP), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis. Three GO:BP terms and four KEGG terms were statistically significant, which mainly focused on adenosine triphosphate (ATP) synthesis, electron transport chain, and OXPHOS pathway. Among these pathways, involved genes such as ATP5MC2, NDUFA, COX7A2, UQCR, and MMP are particularly important as they were previously reported. Twenty-nine reported biological mechanisms along with involved genes were explained mainly by four biological pathways (insulin-like growth factor axis, lipid metabolism, oxidative phosphorylation pathways, tryptophan metabolism). The information from this study will be useful for future studies of genomic selection breeding and genetic structures influencing animal FE. A better understanding of the underlying biological mechanisms would be beneficial, particularly as it might address genetic antagonism.

A dimensionality-reduction genomic prediction method without direct inverse of the genomic relationship matrix for large genomic data

KEY MESSAGE

A new genomic prediction method (RHPP) was developed via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. Computational efficiency is becoming a hot issue in the practical application of genomic prediction due to the large number of data generated by the high-throughput genotyping technology. In this study, we developed a fast genomic prediction method RHPP via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. The simulation results demonstrated similar prediction accuracy between RHPP and GBLUP, and significantly higher computational efficiency of the former with the increase of individuals. The results of real datasets of both bread wheat and loblolly pine demonstrated that RHPP had a similar or better predictive accuracy in most cases compared with GBLUP. In the future, RHPP may be an attractive choice for analyzing large-scale and high-dimensional data.

A review of machine learning models applied to genomic prediction in animal breeding

The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there's no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.

Toward Genome-Based Selection in Asian Seabass: What Can We Learn From Other Food Fishes and Farm Animals?

Due to the steadily increasing need for seafood and the plateauing output of fisheries, more fish need to be produced by aquaculture production. In parallel with the improvement of farming methods, elite food fish lines with superior traits for production must be generated by selection programs that utilize cutting-edge tools of genomics. The purpose of this review is to provide a historical overview and status report of a selection program performed on a catadromous predator, the Asian seabass (Lates calcarifer, Bloch 1790) that can change its sex during its lifetime. We describe the practices of wet lab, farm and lab in detail by focusing onto the foundations and achievements of the program. In addition to the approaches used for selection, our review also provides an inventory of genetic/genomic platforms and technologies developed to (i) provide current and future support for the selection process; and (ii) improve our understanding of the biology of the species. Approaches used for the improvement of terrestrial farm animals are used as examples and references, as those processes are far ahead of the ones used in aquaculture and thus they might help those working on fish to select the best possible options and avoid potential pitfalls.

Opportunities to Apply Precision Livestock Management on Rangelands

Precision livestock management has become a new field of study as the result of recent advancements in real-time global positioning system (GPS) tracking, accelerometer and other sensor technologies. Real-time tracking and accelerometer monitoring has the potential to remotely detect livestock disease, animal well-being and grazing distribution issues and notify ranchers and graziers so that they can respond as soon as possible. On-going research has shown that accelerometers can remotely monitor livestock behavior and detect activity changes that are associated with disease and parturition. GPS tracking can also detect parturition by monitoring the distance between a ewe and the remainder of the flock. Tracking also has the potential to detect water system failures. Combinations of GPS tracking and accelerometer monitoring may be more accurate than either device used by itself. Real-time GPS tracking can identify when livestock congregate in environmental sensitive areas which may allow managers the chance to respond before resource degradation occurs. Identification of genetic markers associated with terrain use, decreased cost of GPS tracking and novel tracking data processing should facilitate development of tools needed for genetic selection for cattle grazing distribution. Precision livestock management has potential to improve welfare of livestock grazing rangelands and forested lands, reduce labor costs and improve ranch profitability and improve the condition and sustainability of riparian areas and other environmental sensitive areas on grazing lands around the world.

Assessment of sire contribution and breed-of-origin of alleles in a three-way crossbred broiler dataset

Broiler breeding programs rely on crossbreeding. With genomic selection, widespread use of crossbred performance in breeding programs comes within reach. Commercial crossbreds, however, may have unknown pedigrees and their genomes may include DNA from 2 to 4 different breeds. Our aim was, for a broiler dataset with a limited number of sires having both purebred and crossbred offspring generated using natural mating, to rapidly derive parentage, assess the distribution of the sire contribution to the offspring generation, and to assess breed-of-origin of alleles in crossbreds. The dataset contained genotypes for 56,075 SNPs for 5,882 purebred and 10,943 3-way crossbred offspring generated by natural mating of 164 purebred sires to 1,016 purebred and 1,386 F1 crossbred hens. Using our algorithm FindParents, joint parentage derivation for the offspring and parent generations required only 1 m 29 s to retrieve parentage for 20,253 animals considering 4,504 possible parents. FindParents was similarly accurate as a maximum likelihood based method, apart from situations where settings of FindParents did not match the genotyping error rate in the data. Numbers of offspring per sire had a very skewed distribution, ranging from 1 to 270 crossbreds and 1 to 154 purebreds. Derivation of breed-of-origin of alleles relied on phasing all genotypes, including 8,205, 372, and 720 animals from the 3 pure lines involved, and allocating haplotypes in the crossbreds to purebred lines based on observed frequencies in the purebred lines. Breed-of-origin could be derived for 96.94% of the alleles of the 1,386 F1 crossbred hens and for 91.88% of the alleles of the 10,943 3-way crossbred offspring, of which 49.49% to the sire line. The achieved percentage of assignment to the sire line was sufficient to proceed with subsequent analyses requiring only the breed-of-origin of the paternal alleles to be known. Although required number of animals may be population dependent, to increase the total percentage of assigned alleles, it seems advisable to use at least approx. 1,000 genotyped purebred animals for each of the lines involved.

Disease structure of milk cows and the effect of the mass fractions ratio of fat and protein in milk on the level of the metabolites

The analysis of diseases occurring is given in animals in the first 100 DIM. Determined milk content: MF, MP, pH, Ur, BHBA, Ac, calculated FPR. INCD in milk cows in the first 100 DIM is the second most widespread – 30.72 %. Among INCD, DSD and RSD predominate in the former – 38.8 % each, DMEO accounts for 20.4 % of disease cases. In 17.05 % of the examined animals, the FPR corresponded to optimal values, and in 82.95 % it was 1.10 or less, which may indicate the spread of subacute subclinical rumen acidosis in the animals of the studied population. Exceeding the upper limit of FPR, indicating the presence of ketosis in animals, has not been established. Studies revealed a double excess of Ur content in milk, and in animals with normal FPR values, the Ur content was 11.15 % (p < 0.001) higher than in animals with reduced FPR. The pH of milk generally corresponded to the values of the physiological norm. The level of BHBA in milk was below the threshold values, but in the animal’s group with FPR 1.10 or less, the BHBA content in milk significantly (by 80.0 %, p < 0.01) exceeded the BHBA content in animals with normal FPR values. Cows with a normal FPR value, the Ac level in milk was found to exceed the threshold value by 28.57 %, and in animals with low FPR values, the established excess was 141.43 % (p <0.05).

Investigation of myostatin and calpain 3 gene polymorphisms and their association with milk-production traits in Sfakia sheep

Context Genetic selection based on genetic markers for economically important traits in Sfakia sheep. Aims The aim of the present study was to investigate variation in the ovine myostatin gene (MSTN) and calpain 3 gene (CAPN3), and their association with milk-production traits. Methods Records for milk yield, milk fat content, protein content, lactose content, and non-fat solid content, pH and somatic-cell score (log), were obtained from 376 Sfakia ewes. Polymerase chain reaction–single-strand conformational polymorphism (PCR–SSCP) analyses were used to detect variation in intron 1 of MSTN and exon 10 of CAPN3. General linear models were then used to test for associations between the variation in MSTN and CAPN3, and milk-production traits. Key results The SSCP banding patterns for MSTN showed four variants (A1, A2, A3 and A4), which contained nine nucleotide sequence differences. Four SSCP banding patterns (C1, C2, C3 and C4) were observed for CAPN3 and these contained eight nucleotide-sequence differences. The MSTN variation was associated (P &lt; 0.05) with variation in milk yield and non-fat milk solid content. Variation in CAPN3 was associated with milk yield (P &lt; 0.001), fat content (P &lt; 0.05) and lactose content (P &lt; 0.05). Association analyses between the presence/absence of MSTN and CAPN3 variants and milk-production traits showed that a variant of MSTN that had previously between associated with muscle hypertrophy was associated with decreased milk yield (P &lt; 0.05) and a lower non-fat milk solid content (P &lt; 0.01). A CAPN3 variant that had previously been associated with increased sheep-carcass loin lean-meat yield was associated with a decreased milk yield (P &lt; 0.01) and a decreased milk fat content (P &lt; 0.05). Conclusions Our results have provided an insight into the effects of variation in ovine MSTN and CAPN3 on milk-production traits in sheep. Implications To preserve the dual-purpose characteristics of Sfakia sheep, breeding goals should take into account the possible antagonism between meat and milk traits.

Effect and Interaction of β-Lactoglobulin, Kappa Casein, and Prolactin Genes on Milk Production and Composition of Awassi Sheep

Simple Summary

Beta-lactoglobulin (β-LG), prolactin (PRL), and Kappa casein (CSN3) all contribute to the determination of milk production and composition, but have not been assessed in local Awassi sheep. Therefore, our aim was to analyze the contribution of these genes in milk production and composition traits in commercial Awassi ewe population by genotyping and sequencing these genes. Our results showed the prevalence of the different variations (alleles) of the tested genes in the Awassi population, and no association among β-LG and CSN3 polymorphic genotypes and milk production, or PRL and fat%. Also, all 3 genes help determine the milk production potential of Awassi ewes and help assess milk components, and thus can be used in breeding programs to select for milk potential.

Abstract

A participatory animal-breeding program was applied to 9 commercial Awassi sheep flocks in Jordan. This study aimed to assess the influence of Beta-lactoglobulin (β-LG), Prolactin (PRL), and Kappa casein (CSN3) genes, genotypes and their interaction on milk production and composition traits of 167 genotyped Awassi ewes via Polymerase Chain Reaction (PCR) followed by sequencing. Allele frequencies for the two variants were 0.42 and 0.58 for β-LG, 0.82 and 0.18 for PRL, and 0.92 and 0.08 for CSN3. No association was found among β-LG and CSN3 polymorphic genotypes with milk production traits. However, ewes with PRL AA genotype showed higher milk production, β-LG AB was associated with lowest fat%, high solid not fat (SNF)%, protein%, and lactose%. β-LG BB was associated with highest milk density. PRL, β-LG, and CSN3 polymorphic genotypes were differentially associated with milk production and component traits. Furthermore, β-LG × PRL interaction showed the highest milk production and fat%; β-LG × PRL recorded the highest SNF%, protein%, lactose%, and milk density, while the PRL × CSN3 had the highest fat% and SNF%. The enhancing effects of these gene interactions can be incorporated in Awassi breeding programs to improve milk production and composition.

Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters

Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement programs and production systems. Genomic selection exploits the use of realized genomic relationships among individuals and information from genome-wide markers in close linkage disequilibrium with genes of biological and economic importance. We discuss the technical advances, practical requirements, and commercial applications that have made genomic selection feasible in a range of aquaculture industries, with a particular focus on molluscs (pearl oysters, Pinctada maxima) and marine shrimp (Litopenaeus vannamei and Penaeus monodon). The use of low-cost genome sequencing has enabled cost-effective genotyping on a large scale and is of particular value for species without a reference genome or access to commercial genotyping arrays. We highlight the pitfalls and offer the solutions to the genotyping by sequencing approach and the building of appropriate genetic resources to undertake genomic selection from first-hand experience. We describe the potential to capture large-scale commercial phenotypes based on image analysis and artificial intelligence through machine learning, as inputs for calculation of genomic breeding values. The application of genomic selection over traditional aquatic breeding programs offers significant advantages through being able to accurately predict complex polygenic traits including disease resistance; increasing rates of genetic gain; minimizing inbreeding; and negating potential limiting effects of genotype by environment interactions. Further practical advantages of genomic selection through the use of large-scale communal mating and rearing systems are highlighted, as well as presenting rate-limiting steps that impact on attaining maximum benefits from adopting genomic selection. Genomic selection is now at the tipping point where commercial applications can be readily adopted and offer significant short- and long-term solutions to sustainable and profitable aquaculture industries.

Optimising cattle grazing distribution on rangeland: a systematic review and network analysis

Optimising beef cattle (Bos taurus and Bos indicus) distribution, both spatially and temporally, is one of the most significant challenges associated with managing extensive grazed rangelands. Landscape variability and behavioural patterns of cattle may lead to non-uniform and inefficient forage utilisation, damage to critical habitats, and water quality impairment. In order to overcome these distribution challenges, a large suite of tools have been developed and researched to optimise grazing patterns. The objectives of this synthesis paper are 2-fold: (i) to survey and categorise distribution tools; and (ii) to analyse the connectivity of existing research across academic disciplines to identify and isolate knowledge gaps. A systematic literature review revealed specific types of tools and strategies to improve cattle distribution, which were categorised as either ‘animal’ or ‘environmental manipulations’. Animal manipulations utilise aspects of individual behaviour and herd dynamics to alter grazing patterns, whereas environmental manipulations involve transforming aspects of the animal’s surroundings to overcome challenges associated with inefficient distribution. This review reveals that strategies are overwhelmingly studied in isolation, and that there is potential to increase efficacy by integrating multiple strategies to achieve a desired outcome. Motivated by these findings, an author collaboration network analysis was conducted to investigate connectivity within and among author fields of expertise to understand why more integrated management strategies are not currently studied. Authors were classified into five fields of research: animal behaviour science, animal production science, biophysical rangeland science, economics, and other. The network analysis revealed that communities of authors contributing to papers on enhancing cattle distribution are disjointed. These results suggest that in order to fulfil knowledge gaps about the efficacy and cost of management strategies, there needs to be interdisciplinary engagement with particular attention to strategies that integrate animal and environmental manipulations to enhance cattle grazing distribution on extensively grazed landscapes.

Evaluation of 17 microsatellite markers for parentage testing and individual identification of domestic yak (Bos grunniens)

BACKGROUND

Yak (Bos grunniens) is the most important domestic animal for people living at high altitudes. Yak ordinarily feed by grazing, and this behavior impacts the accuracy of the pedigree record because it is difficult to control mating in grazing yak. This study aimed to evaluate the pedigree system and individual identification in polled yak.

METHODS

A total of 71 microsatellite loci were selected from the literature, mostly from the studies on cattle. A total of 35 microsatellite loci generated excellent PCR results and were evaluated for the parentage testing and individual identification of 236 unrelated polled yaks. A total of 17 of these 35 microsatellite loci had polymorphic information content (PIC) values greater than 0.5, and these loci were in Hardy-Weinberg equilibrium without linkage disequilibrium.

RESULTS

Using multiplex PCR, capillary electrophoresis, and genotyping, very high exclusion probabilities were obtained for the combined core set of 17 loci. The exclusion probability (PE) for one candidate parent when the genotype of the other parent is not known was 0.99718116. PE for one candidate parent when the genotype of the other parent is known was 0.99997381. PE for a known candidate parent pair was 0.99999998. The combined PEI (PE for identity of two unrelated individuals) and PESI (PE for identity of two siblings) were >0.99999999 and 0.99999899, respectively. These findings indicated that the combination of 17 microsatellite markers could be useful for efficient and reliable parentage testing and individual identification in polled yak.

DISCUSSION

Many microsatellite loci have been investigated for cattle paternity testing. Nevertheless, these loci cannot be directly applied to yak identification because the two bovid species have different genomic sequences and organization. A total of 17 loci were selected from 71 microsatellite loci based on efficient amplification, unambiguous genotyping, and high PIC values for polled yaks, and were suitable for parentage analysis in polled yak populations.

Use of GPS tracking collars and accelerometers for rangeland livestock production research

Over the last 20 yr, global positioning system (GPS) collars have greatly enhanced livestock grazing behavior research. Practices designed to improve livestock grazing distribution can now be accurately and cost effectively monitored with GPS tracking. For example, cattle use of feed supplement placed in areas far from water and on steep slopes can be measured with GPS tracking and corresponding impacts on distribution patterns estimated. Ongoing research has identified genetic markers that are associated with cattle spatial movement patterns. If the results can be validated, genetic selection for grazing distribution may become feasible. Tracking collars have become easier to develop and construct, making them significantly less expensive, which will likely increase their use in livestock grazing management research. Some research questions can be designed so that dependent variables are measured by spatial movements of livestock, and in such cases, GPS tracking is a practical tool for conducting studies on extensive and rugged rangeland pastures. Similarly, accelerometers are changing our ability to monitor livestock behavior. Today, accelerometers are sensitive and can record movements at fine temporal scales for periods of weeks to months. The combination of GPS tracking and accelerometers appears to be useful tools for identifying changes in livestock behavior that are associated with livestock diseases and other welfare concerns. Recent technological advancements may make real-time or near real-time tracking on rangelands feasible and cost-effective. This would allow development of applications that could remotely monitor livestock well-being on extensive rangeland and notify ranchers when animals require treatment or other management.

Design and validation of a 90K SNP genotyping assay for the water buffalo (Bubalus bubalis)

Background

The availability of the bovine genome sequence and SNP panels has improved various genomic analyses, from exploring genetic diversity to aiding genetic selection. However, few of the SNP on the bovine chips are polymorphic in buffalo, therefore a panel of single nucleotide DNA markers exclusive for buffalo was necessary for molecular genetic analyses and to develop genomic selection approaches for water buffalo. The creation of a 90K SNP panel for river buffalo and testing in a genome wide association study for milk production is described here.

Methods

The genomes of 73 buffaloes of 4 different breeds were sequenced and aligned against the bovine genome, which facilitated the identification of 22 million of sequence variants among the buffalo genomes. Based on frequencies of variants within and among buffalo breeds, and their distribution across the genome, inferred from the bovine genome sequence, 90,000 putative single nucleotide polymorphisms were selected to create an Axiom® Buffalo Genotyping Array 90K.

Results

This 90K “SNP-Chip” was tested in several river buffalo populations and found to have ∼70% high quality and polymorphic SNPs. Of the 90K SNPs about 24K were also found to be polymorphic in swamp buffalo. The SNP chip was used to investigate the structure of buffalo populations, and could distinguish buffalo from different farms. A Genome Wide Association Study identified genomic regions on 5 chromosomes putatively involved in milk production.

Conclusion

The 90K buffalo SNP chip described here is suitable for the analysis of the genomes of river buffalo breeds, and could be used for genetic diversity studies and potentially as a starting point for genome-assisted selection programmes. This SNP Chip could also be used to analyse swamp buffalo, but many loci are not informative and creation of a revised SNP set specific for swamp buffalo would be advised.

Origin and phylogenetic status of the local Ashanti Dwarf pig (ADP) of Ghana based on genetic analysis

Background

The Ashanti Dwarf Pig (ADP) of Ghana is an endangered pig breed with hardy and disease resistant traits. Characterisation of animal genetic resources provides relevant data for their conservation and sustainable use for food security and economic development. We investigated the origin and phylogenetic status of the local ADP of Ghana and their crosses with modern commercial breeds based on mtDNA, MC1R, Y-chromosome sequence polymorphisms, and genome-wide SNP genotyping.

Results

The study involved 164 local pigs sampled from the three agro-ecological zones of Ghana. Analyses of the mitochondrial D-loop region and Y-chromosome sequences revealed both European and Asian genetic signatures, with differences between the geographical zones. Black coat colour is the most predominant within the breed, with black MC1R alleles of both Asian and European origin. European alleles for spotting are present at a low frequency in the sample set, and may account for the occurrence of spotted piglets in some APD litters. PCA analysis of SNP data revealed a strong location and breed effect on clustering of local Ghanaian pigs. On a global level, Ghanaian local pigs cluster closely with European pigs of commercial origin, but we identified intervals via F_ST analyses that may elucidate loci for ADP specific traits.

Conclusions

The presence of both European and Asian contributions, with differences between geographical zones probably reflects trading and colonial influences. Understanding the effects of admixture on important adaptive and economic traits of the ADP and other local breeds in Africa is critical for developing sustainable conservation programmes to prevent the decline of these genetic resources.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3536-6) contains supplementary material, which is available to authorized users.

Genetic parameters of subclinical macromineral disorders and major clinical diseases in postparturient Holstein cows

The main objective of this study was to assess the genetic parameters of subclinical disorders associated with subclinical hypocalcemia, hypophosphatemia, subclinical hypomagnesemia, hypokalemia, and hyperphosphatemia, as well as major clinical diseases after calving in Holstein cows. The secondary objective was to estimate the associated genetic and phenotypic correlations among these subclinical and clinical conditions after calving in Holstein cows. The study was conducted in 9dairy herds located in Northern Greece. None of the herds used any kind of preventive measures for milk fever (MF). A total of 1,021 Holstein cows with pedigree information were examined from November 2010 until November 2012. The distribution across parities was 466 (parity 1), 242 (parity 2), 165 (parity 3), and 148 (parity 4 and above) cows. All cows were subjected to a detailed clinical examination and blood was sampled on d 1, 2, 4, and 8 after calving. Serum concentrations of Ca, P, Mg, and K were measured in all samples, whereas β-hydroxybutyrate (BHB) was measured only for d 8. The final data set included 4,064 clinical and 16,848 biochemical records (4,020 Ca, 4,019 P, 4,020Mg, 3,792K, and 997 BHB). Data of 1,988 observations of body condition score at d 1 and 8 were also available. All health traits were analyzed with a univariate random regression model. The genetic analysis for macromineral-related disorders included 986 cows with no obvious signs of MF (35 cows with MF were excluded). Analysis for other health traits included all 1,021 cows. A similar single record model was used for the analysis of BHB. Genetic correlations among traits were estimated with a series of bivariate analyses. Statistically significant daily heritabilities of subclinical hypocalcemia (0.13-0.25), hypophosphatemia (0.18-0.33), subclinical hypomagnesemia (0.11-0.38), and hyperphosphatemia (0.14-0.22) were low to moderate, whereas that of hypokalemia was low (0.08-0.10). The heritability of body condition score was 0.20±0.10. Statistically significant daily heritabilities of clinical diseases were those of MF (0.07-0.11), left displaced abomasum (0.19-0.31), and mastitis (0.15-0.41). Results suggest that these health disorders are heritable traits and could be minimized with proper genetic selection. Statistically significant phenotypic correlations were estimated for the first time between macromineral concentrations and almost all transition cow metabolic and infectious health disorders.

Genomic selection in maritime pine

A two-generation maritime pine (Pinus pinaster Ait.) breeding population (n=661) was genotyped using 2500 SNP markers. The extent of linkage disequilibrium and utility of genomic selection for growth and stem straightness improvement were investigated. The overall intra-chromosomal linkage disequilibrium was r(2)=0.01. Linkage disequilibrium corrected for genomic relationships derived from markers was smaller (rV(2)=0.006). Genomic BLUP, Bayesian ridge regression and Bayesian LASSO regression statistical models were used to obtain genomic estimated breeding values. Two validation methods (random sampling 50% of the population and 10% of the progeny generation as validation sets) were used with 100 replications. The average predictive ability across statistical models and validation methods was about 0.49 for stem sweep, and 0.47 and 0.43 for total height and tree diameter, respectively. The sensitivity analysis suggested that prior densities (variance explained by markers) had little or no discernible effect on posterior means (residual variance) in Bayesian prediction models. Sampling from the progeny generation for model validation increased the predictive ability of markers for tree diameter and stem sweep but not for total height. The results are promising despite low linkage disequilibrium and low marker coverage of the genome (∼1.39 markers/cM).

High-density SNP assay development for genetic analysis in maritime pine (Pinus pinaster)

Maritime pine provides essential ecosystem services in the south-western Mediterranean basin, where it covers around 4 million ha. Its scattered distribution over a range of environmental conditions makes it an ideal forest tree species for studies of local adaptation and evolutionary responses to climatic change. Highly multiplexed single nucleotide polymorphism (SNP) genotyping arrays are increasingly used to study genetic variation in living organisms and for practical applications in plant and animal breeding and genetic resource conservation. We developed a 9k Illumina Infinium SNP array and genotyped maritime pine trees from (i) a three-generation inbred (F2) pedigree, (ii) the French breeding population and (iii) natural populations from Portugal and the French Atlantic coast. A large proportion of the exploitable SNPs (2052/8410, i.e. 24.4%) segregated in the mapping population and could be mapped, providing the densest ever gene-based linkage map for this species. Based on 5016 SNPs, natural and breeding populations from the French gene pool exhibited similar level of genetic diversity. Population genetics and structure analyses based on 3981 SNP markers common to the Portuguese and French gene pools revealed high levels of differentiation, leading to the identification of a set of highly differentiated SNPs that could be used for seed provenance certification. Finally, we discuss how the validated SNPs could facilitate the identification of ecologically and economically relevant genes in this species, improving our understanding of the demography and selective forces shaping its natural genetic diversity, and providing support for new breeding strategies.

Performance and carcass characteristics when sorting feedlot cattle on the basis of phenotype, and leptin genotype along with differential use of β-adrenergic agonists

Kononoff, P. J., Defoor, P. J., Engler, M. J., Swingle, R. S., James, S. Y., Deobald, H. M., Deobald, R. L., Woronuk, G. N. and Marquess, F. L. S. 2015. Performance and carcass characteristics when sorting feedlot cattle on the basis of phenotype, and leptin genotype along with differential use of β-adrenergic agonists. Can. J. Anim. Sci. 95: 455–463. Crossbred steers were used in a randomized complete block design to evaluate a feedlot cattle sorting system. The sorting system combined information on live body weight, ultrasound fat thickness, coat color, and genotype on the leptin R25C genotype. Using described sorting criteria, a total of four groups (Groups 1–4) of cattle were created along with a randomly selected unsorted control (Group 5), with Groups 1 through 5 representing experimental treatments, and placed in one of five pens within each block. Block was replicated 10 times thus n equaled 50. All cattle were administered zilpaterol hydrochloride except Group 3, which we hypothesized would have a greater proportion of cattle reaching a higher value Quality Grade, and as a result administered ractopamine hydrochloride. The initial body weight of cattle was similar (P=0.426) between the control Group and Groups 1–4. Initial fat, as measured by ultrasound, was also observed to be similar (P=0.256) between these two groups. Overall, when cattle included in Groups 1–4 were compared with Group 5, hot carcass weight was greater (P=0.040), while the proportion of excessively heavy weight carcasses was less (P=0.049). Additionally, hot carcass weight gain tended (P=0.096) to be higher for Groups 1–4 compared with Group 5. No differences (P≥0.129) were observed for calculated yield grade. On average, cattle included in the Sorting Groups 1–4 returned $23 more per head than those in Group 5.

Molecular tools to support metabolic and immune function research in the Guinea Fowl (Numida meleagris)

BACKGROUND

Guinea fowl (Numidia meleagris) production as an alternative source of meat and poultry has shown potential for economic viability. However, there has been little progress in characterizing the transcriptome of the guinea fowl. In this study RNA-sequencing and de novo transcriptome assembly of several Guinea fowl tissues (pancreas, hypothalamus, liver, bone marrow and bursa) which play key roles in regulating feed intake, satiety, and immune function was performed using Illumina's Hi-Seq 2000.

RESULTS

74 million sequences were generated and assembled into 96,492 contigs using the Trinity software suite. Over 39,000 of these transcripts were found to have in silico translated protein sequences that are homologous to chicken protein sequences. Gene ontology analysis uncovered 416 transcripts with metabolic functions and 703 with immune function.

CONCLUSION

The transcriptome information presented here will support the development of molecular approaches to improve production efficiency of the guinea fowl and other avian species.

Genomics-assisted breeding for drought tolerance in chickpea

Terminal drought is one of the major constraints in chickpea (Cicer arietinum L.), causing more than 50% production losses. With the objective of accelerating genetic understanding and crop improvement through genomics-assisted breeding, a draft genome sequence has been assembled for the CDC Frontier variety. In this context, 544.73Mb of sequence data were assembled, capturing of 73.8% of the genome in scaffolds. In addition, large-scale genomic resources including several thousand simple sequence repeats and several million single nucleotide polymorphisms, high-density diversity array technology (15360 clones) and Illumina GoldenGate assay genotyping platforms, high-density genetic maps and transcriptome assemblies have been developed. In parallel, by using linkage mapping approach, one genomic region harbouring quantitative trait loci for several drought tolerance traits has been identified and successfully introgressed in three leading chickpea varieties (e.g. JG 11, Chefe, KAK 2) by using a marker-assisted backcrossing approach. A multilocation evaluation of these marker-assisted backcrossing lines provided several lines with 10-24% higher yield than the respective recurrent parents.Modern breeding approaches like marker-assisted recurrent selection and genomic selection are being deployed for enhancing drought tolerance in chickpea. Some novel mapping populations such as multiparent advanced generation intercross and nested association mapping populations are also being developed for trait mapping at higher resolution, as well as for enhancing the genetic base of chickpea. Such advances in genomics and genomics-assisted breeding will accelerate precision and efficiency in breeding for stress tolerance in chickpea.

Genetics and genomics of reproductive performance in dairy and beef cattle

Excellent reproductive performance in both males and females is fundamental to profitable dairy and beef production systems. In this review we undertook a meta-analysis of genetic parameters for female reproductive performance across 55 dairy studies or populations and 12 beef studies or populations as well as across 28 different studies or populations for male reproductive performance. A plethora of reproductive phenotypes exist in dairy and beef cattle and a meta-analysis of the literature suggests that most of the female reproductive traits in dairy and beef cattle tend to be lowly heritable (0.02 to 0.04). Reproductive-related phenotypes in male animals (e.g. semen quality) tend to be more heritable than female reproductive phenotypes with mean heritability estimates of between 0.05 and 0.22 for semen-related traits with the exception of scrotal circumference (0.42) and field non-return rate (0.001). The low heritability of reproductive traits, in females in particular, does not however imply that genetic selection cannot alter phenotypic performance as evidenced by the decline until recently in dairy cow reproductive performance attributable in part to aggressive selection for increased milk production. Moreover, the antagonistic genetic correlations among reproductive traits and both milk (dairy cattle) and meat (beef cattle) yield is not unity thereby implying that simultaneous genetic selection for both increased (milk and meat) yield and reproductive performance is indeed possible. The required emphasis on reproductive traits within a breeding goal to halt deterioration will vary based on the underlying assumptions and is discussed using examples for Ireland, the United Kingdom and Australia as well as quantifying the impact on genetic gain for milk production. Advancements in genomic technologies can aid in increasing the accuracy of selection for especially reproductive traits and thus genetic gain. Elucidation of the underlying genomic mechanisms for reproduction could also aid in resolving genetic antagonisms. Past breeding programmes have contributed to the deterioration in reproductive performance of dairy and beef cattle. The tools now exist, however, to reverse the genetic trends in reproductive performance underlying the observed phenotypic trends.

Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population

Single-breed genomic selection (GS) based on medium single nucleotide polymorphism (SNP) density (~50,000; 50K) is now routinely implemented in several large cattle breeds. However, building large enough reference populations remains a challenge for many medium or small breeds. The high-density BovineHD BeadChip (HD chip; Illumina Inc., San Diego, CA) containing 777,609 SNP developed in 2010 is characterized by short-distance linkage disequilibrium expected to be maintained across breeds. Therefore, combining reference populations can be envisioned. A population of 1,869 influential ancestors from 3 dairy breeds (Holstein, Montbéliarde, and Normande) was genotyped with the HD chip. Using this sample, 50K genotypes were imputed within breed to high-density genotypes, leading to a large HD reference population. This population was used to develop a multi-breed genomic evaluation. The goal of this paper was to investigate the gain of multi-breed genomic evaluation for a small breed. The advantage of using a large breed (Normande in the present study) to mimic a small breed is the large potential validation population to compare alternative genomic selection approaches more reliably. In the Normande breed, 3 training sets were defined with 1,597, 404, and 198 bulls, and a unique validation set included the 394 youngest bulls. For each training set, estimated breeding values (EBV) were computed using pedigree-based BLUP, single-breed BayesC, or multi-breed BayesC for which the reference population was formed by any of the Normande training data sets and 4,989 Holstein and 1,788 Montbéliarde bulls. Phenotypes were standardized by within-breed genetic standard deviation, the proportion of polygenic variance was set to 30%, and the estimated number of SNP with a nonzero effect was about 7,000. The 2 genomic selection (GS) approaches were performed using either the 50K or HD genotypes. The correlations between EBV and observed daughter yield deviations (DYD) were computed for 6 traits and using the different prediction approaches. Compared with pedigree-based BLUP, the average gain in accuracy with GS in small populations was 0.057 for the single-breed and 0.086 for multi-breed approach. This gain was up to 0.193 and 0.209, respectively, with the large reference population. Improvement of EBV prediction due to the multi-breed evaluation was higher for animals not closely related to the reference population. In the case of a breed with a small reference population size, the increase in correlation due to multi-breed GS was 0.141 for bulls without their sire in reference population compared with 0.016 for bulls with their sire in reference population. These results demonstrate that multi-breed GS can contribute to increase genomic evaluation accuracy in small breeds.

Genomic selection: Status in different species and challenges for breeding

Technical advances and development in the market for genomic tools have facilitated access to whole-genome data across species. Building-up on the acquired knowledge of the genome sequences, large-scale genotyping has been optimized for broad use, so genotype information can be routinely used to predict genetic merit. Genomic selection (GS) refers to the use of aggregates of estimated marker effects as predictors which allow improved individual differentiation at young age. Realizable benefits of GS are influenced by several factors and vary in quantity and quality between species. General characteristics and challenges of GS in implementation and routine application are described, followed by an overview over the current status of its use, prospects and challenges in important animal species. Genetic gain for a particular trait can be enhanced by shortening of the generation interval, increased selection accuracy and increased selection intensity, with species- and breed-specific relevance of the determinants. Reliable predictions based on genetic marker effects require assembly of a reference for linking of phenotype and genotype data to allow estimation and regular re-estimation. Experiences from dairy breeding have shown that international collaboration can set the course for fast and successful implementation of innovative selection tools, so genomics may significantly impact the structures of future breeding and breeding programmes. Traits of great and increasing importance, which were difficult to improve in the conventional systems, could be emphasized, if continuous availability of high-quality phenotype data can be assured. Equally elaborate strategies for genotyping and phenotyping will allow tailored approaches to balance efficient animal production, sustainability, animal health and welfare in future.

Molecular value predictions: associations with beef quality, carcass, production, behavior, and efficiency phenotypes in Brahman cattle

Data from 2 previously published experiments, New South Wales (NSW; n = 161) and Western Australia (WA; n = 135), were used to test molecular value predictions (MVP), generated from commercially available gene markers, on economically important traits of Bos indicus (Brahman) cattle. Favorable tenderness MVP scores were associated with reduced shear force values of strip loin (LM) steaks aged 7 d from Achilles-hung carcasses (P ≤ 0.06), as well as steaks aged 1 (P ≤ 0.08) or 7 d (P ≤ 0.07) from carcasses hung from the pelvis (tenderstretch). Favorable tenderness MVP scores were also associated with improved consumer tenderness ratings for strip loin steaks aged 7 d and either Achilles hung (P ≤ 0.006) or tenderstretched (P ≤ 0.07). Similar results were observed in NSW for rump (top butt; gluteus medius) steaks, with favorable tenderness MVP scores associated with more tender (P = 0.006) and acceptable (P = 0.008) beef. Favorable marbling MVP scores were associated with improved (P ≤ 0.021) marbling scores and intramuscular fat (IMF) content in the NSW experiment, despite low variation in marbling in the Brahman cattle. For the WA experiment, however, there were no (P ≥ 0.71) relationships between marbling MVP and marbling scores or IMF content. Although residual (net) feed intake (RFI) was not associated (P = 0.63) with the RFI (feed efficiency) MVP, the RFI MVP was adversely associated with LM tenderness and acceptability of 7-d-aged Achilles-hung carcasses in NSW (P ≤ 0.031) and WA (P ≤ 0.037). Some other relationships and trends were noted between the MVP and the other traits, but few reached statistical significance, and none were evident in both experiments. Results from this study provide evidence to support the use of the tenderness MVP. The value of the marbling MVP, which was associated with marbling in only 1 herd, warrants further evaluation; however, there appears to be no evidence to support use of the RFI MVP in Brahman cattle.

Fast genomic predictions via Bayesian G-BLUP and multilocus models of threshold traits including censored Gaussian data

Because of the increased availability of genome-wide sets of molecular markers along with reduced cost of genotyping large samples of individuals, genomic estimated breeding values have become an essential resource in plant and animal breeding. Bayesian methods for breeding value estimation have proven to be accurate and efficient; however, the ever-increasing data sets are placing heavy demands on the parameter estimation algorithms. Although a commendable number of fast estimation algorithms are available for Bayesian models of continuous Gaussian traits, there is a shortage for corresponding models of discrete or censored phenotypes. In this work, we consider a threshold approach of binary, ordinal, and censored Gaussian observations for Bayesian multilocus association models and Bayesian genomic best linear unbiased prediction and present a high-speed generalized expectation maximization algorithm for parameter estimation under these models. We demonstrate our method with simulated and real data. Our example analyses suggest that the use of the extra information present in an ordered categorical or censored Gaussian data set, instead of dichotomizing the data into case-control observations, increases the accuracy of genomic breeding values predicted by Bayesian multilocus association models or by Bayesian genomic best linear unbiased prediction. Furthermore, the example analyses indicate that the correct threshold model is more accurate than the directly used Gaussian model with a censored Gaussian data, while with a binary or an ordinal data the superiority of the threshold model could not be confirmed.

Heritability and Bayesian genome-wide association study of first service conception and pregnancy in Brangus heifers

Brangus [3/8 Brahman (Bos indicus) × 5/8 Angus (Bos taurus); n ≈ 800] heifers from 67 sires were used to estimate heritability and conduct a genome-wide association study (GWAS) for 2 binary fertility traits: first service conception (FSC) and heifer pregnancy (HPG). Genotypes were from 53,692 loci on the BovineSNP50 (Infinium Bead Chips, Illumina, San Diego, CA). Yearling heifers were estrous synchronized, bred by AI, and then exposed to natural service breeding. Reproductive ultrasound and DNA-based parentage testing were used to determine if the heifer conceived by AI or natural service, and code for FSC and HPG traits. Success rates for FSC and HPG were 53.3% and 78.0% ± 0.01%, and corresponding heritability estimates were 0.18 ± 0.07 and 0.10 ± 0.06, respectively. The models used in obtaining these heritability estimates and GWAS included fixed effects of year (i.e., 2005 to 2007), birth location, calving season, age of dam, and contemporary group. In GWAS, simultaneous associations of 1 Mb SNP windows with phenotype were undertaken with Bayes C analyses using GenSel software. The 1 Mb windows contained 21.3 ± 1.1 SNP. Analyses fitted a mixture model that treated SNP effects as random, with an assumed fraction pi = 0.9995 having no effect on phenotype. The windows that accounted for 1.0% of genetic variance were considered as QTL associated with FSC or HPG. Eighteen QTL existed on 15 chromosomes for the 2 traits. On average, each QTL accounted for 2.43% ± 0.2% of the genetic variance. Chromosome 8 harbored 2 QTL for FSC and 1 for HPG; however, these regions did not overlap. Chromosomes 3, 15, 16, 19, 24, 26, 27, 29, and X included QTL only for FSC, whereas chromosomes 2, 4, 10, 13, and 20 contained QTL only for HPG. The multitude of QTL detected for FSC and HPG in this GWAS involving Brangus heifers exemplifies the complex regulation of variation in heifer fertility traits of low heritability.

A novel analytical method, Birth Date Selection Mapping, detects response of the Angus (Bos taurus) genome to selection on complex traits

BACKGROUND

Several methods have recently been developed to identify regions of the genome that have been exposed to strong selection. However, recent theoretical and empirical work suggests that polygenic models are required to identify the genomic regions that are more moderately responding to ongoing selection on complex traits. We examine the effects of multi-trait selection on the genome of a population of US registered Angus beef cattle born over a 50-year period representing approximately 10 generations of selection. We present results from the application of a quantitative genetic model, called Birth Date Selection Mapping, to identify signatures of recent ongoing selection.

RESULTS

We show that US Angus cattle have been systematically selected to alter their mean additive genetic merit for most of the 16 production traits routinely recorded by breeders. Using Birth Date Selection Mapping, we estimate the time-dependency of allele frequency for 44,817 SNP loci using genomic best linear unbiased prediction, generalized least squares, and BayesCπ analyses. Finally, we reconstruct the primary phenotypes that have historically been exposed to selection from a genome-wide analysis of the 16 production traits and gene ontology enrichment analysis.

CONCLUSIONS

We demonstrate that Birth Date Selection Mapping utilizing mixed models corrects for time-dependent pedigree sampling effects that lead to spurious SNP associations and reveals genomic signatures of ongoing selection on complex traits. Because multiple traits have historically been selected in concert and most quantitative trait loci have small effects, selection has incrementally altered allele frequencies throughout the genome. Two quantitative trait loci of large effect were not the most strongly selected of the loci due to their antagonistic pleiotropic effects on strongly selected phenotypes. Birth Date Selection Mapping may readily be extended to temporally-stratified human or model organism populations.