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Australia has one of the oldest modern wheat breeding programs worldwide although the crop was first introduced to the country in 1788. Breeders selected wheat with high adaptation to different Australian climates, while ensuring satisfactory yield and quality. This artificial selection left distinct genomic signatures that can be used to retrospectively understand breeding targets, and to detect economically important alleles. To study the effect of artificial selection on modern cultivars and cultivars released in different Australian states, we genotyped 482 Australian cultivars representing the history of wheat breeding in Australia since 1840. Computer simulation showed that 86 genomic regions were significantly affected by artificial selection. Characterization of 18 major genes known to affect wheat adaptation, yield, and quality revealed that many were affected by artificial selection and contained within regions under selection. Similarly, many reported QTL and genes for yield, quality, and adaptation were also contained in regions affected by artificial selection. These included TaCwi‐A1, TaGw2‐6A, Sus‐2B, TaSus1‐7A, TaSAP1‐7A, Glu‐A1, Glu‐B1, Glu‐B3, PinA, PinB, Ppo‐D1, Psy‐A1, Psy‐A2, Rht‐A1, Rht‐B1, Ppd‐D1, Vrn‐A1, Vrn‐B1, and Cre8. Interestingly, 17 regions affected by artificial selection were in moderate‐to‐high linkage disequilibrium with each other with an average r2 value of 0.35 indicating strong simultaneous selection on specific alleles. These regions included Glu‐B1, TaGw2‐6A, Cre8, Ppd‐D1, Rht‐B1, Vrn‐B1, TaSus1‐7A, TaSAP1‐7A, and Psy‐A1 plus multiple QTL affecting wheat yield and yield components. These results highlighted the effects of the long‐term artificial selection on Australian wheat germplasm and identified putative regions underlying important traits in wheat. [ABSTRACT FROM AUTHOR]

In livestock, genotype x environment interaction (G x E) has been widely investigated, with genotype defined at the level of subspecies, breeds, individual animals within a breed (for example performance of offspring of elite sires across environments), and genotypes at single-nucleotide polymorphisms (SNPs). Environments can be described by category (e.g., tropical vs. temperate, high vs. low farm input levels, countries) and by continuous variables such as temperature. To predict breeding values of genotypes in environments described by categories, multitrait models with each category a different trait are used. The models are now being used to predict genomic estimated breeding values (GEBV) for different environments such as the value of a bull's genetics for his daughter's milk production in different countries. The multitrait genomic model has also been used to enable reference populations to be merged across environments and across countries, leading to more accurate GEBV. When the environment can be described by a continuous variable, random regression models have been used to predict response of genotypes to the environment. For example, these models have been used to determine if there are SNP genotypes associated with less sensitivity of milk production to increasing temperature. In both livestock and plant breeding, methods that use genomic information can better cope with a reduced degree of replication of individuals across environments, as it is actually the alleles that must be replicated across environments. More accurate estimates of G x E with the genomic approach may therefore be achievable than was possible in the past. [ABSTRACT FROM AUTHOR]

The proportion of genetic variation in complex traits explained by rare variants is a key question for genomic prediction, and for identifying the basis of “missing heritability”–the proportion of additive genetic variation not captured by common variants on SNP arrays. Sequence variants in transcript and regulatory regions from 429 sequenced animals were used to impute high density SNP genotypes of 3311 Holstein sires to sequence. There were 675,062 common variants (MAF>0.05), 102,549 uncommon variants (0.01

Doubled haploids are routinely created and phenotypically selected in plant breeding programs to accelerate the breeding cycle. Genomic selection, which makes use of both phenotypes and genotypes, has been shown to further improve genetic gain through prediction of performance before or without phenotypic characterization of novel germplasm. Additional opportunities exist to combine genomic prediction methods with the creation of doubled haploids. Here we propose an extension to genomic selection, optimal haploid value (OHV) selection, which predicts the best doubled haploid that can be produced from a segregating plant. This method focuses selection on the haplotype and optimizes the breeding program toward its end goal of generating an elite fixed line. We rigorously tested OHV selection breeding programs, using computer simulation, and show that it results in up to 0.6 standard deviations more genetic gain than genomic selection. At the same time, OHV selection preserved a substantially greater amount of genetic diversity in the population than genomic selection, which is important to achieve long-term genetic gain in breeding populations. [ABSTRACT FROM AUTHOR]

The 1000 bull genomes project supports the goal of accelerating the rates of genetic gain in domestic cattle while at the same time considering animal health and welfare by providing the annotated sequence variants and genotypes of key ancestor bulls. In the first phase of the 1000 bull genomes project, we sequenced the whole genomes of 234 cattle to an average of 8.3-fold coverage. This sequencing includes data for 129 individuals from the global Holstein-Friesian population, 43 individuals from the Fleckvieh breed and 15 individuals from the Jersey breed. We identified a total of 28.3 million variants, with an average of 1.44 heterozygous sites per kilobase for each individual. We demonstrate the use of this database in identifying a recessive mutation underlying embryonic death and a dominant mutation underlying lethal chrondrodysplasia. We also performed genome-wide association studies for milk production and curly coat, using imputed sequence variants, and identified variants associated with these traits in cattle. [ABSTRACT FROM AUTHOR]

Abstract: Previous association studies revealed several single nucleotide polymorphisms (SNPs) that explained the observed phenotypic variation for meat tenderness and long-chain omega-3 polyunsaturated fatty acid (PUFA) content of Australian lamb. To confirm the validity of these associated SNPs at predicting meat tenderness and omega-3 PUFA content, an independent validation study was designed. The OvineSNP50 genotypes of these animals were used to impute the 192 SNP Meat Quality Research (MQR) panel genotypes on nearly 6200 animals from the Cooperative Research Centre for Sheep Industry Innovation Information Nucleus Flock and Sheep Genomics Falkiner Memorial Field Station flock. Association analysis revealed numerous SNP from the 192 SNP MQR panel that were associated with carcass quality – fat depth at the C-site and eye muscle depth; shear force at day 1 and day 5 after slaughter (SF1 and SF5); and omega-3 PUFA content at P <0.01. However, 1 SNP was independently validated for SF5 (i.e. CAST_101781475). The magnitude of the effect of each significant SNP and the relative allele frequencies across Merino-, Maternal- and Terminal-sired progeny was determined. The independently validated SNP for SF5 and the associated SNP with omega-3 PUFA content will accelerate efforts to improve these phenotypic traits in Australian lamb. [Copyright &y& Elsevier]

The genomic prediction of phenotypes and breeding values in animals and plants has developed rapidly into its own research field. Results of genomic prediction studies are often difficult to compare because data simulation varies, real or simulated data are not fully described, and not all relevant results are reported. In addition, some new methods have been compared only in limited genetic architectures, leading to potentially misleading conclusions. In this article we review simulation procedures, discuss validation and reporting of results, and apply benchmark procedures for a variety of genomic prediction methods in simulated and real example data. Plant and animal breeding programs are being transformed by the use of genomic data, which are becoming widely available and cost-effective to predict genetic merit. A large number of genomic prediction studies have been published using both simulated and real data. The relative novelty of this area of research has made the development of scientific conventions dif cult with regard to description of the real data, simulation of genomes, validation and reporting of results, and forward in time methods. In this review article we discuss the generation of simulated genotype and phenotype data, using approaches such as the coalescent and forward in time simulation. We outline ways to validate simulated data and genomic prediction results, including cross-validation. The accuracy and bias of genomic prediction are highlighted as performance indicators that should be reported. We suggest that a measure of relatedness between the reference and validation individuals be reported, as its impact on the accuracy of genomic prediction is substantial. A large number of methods were compared in example simulated and real (pine and wheat) data sets, all of which are publicly available. In our limited simulations, most methods performed similarly in traits with a large number of quantitative trait loci (QTL), whereas in traits with fewer QTL variable selection did have some advantages. In the real data sets examined here all methods had very similar accuracies. We conclude that no single method can serve as a benchmark for genomic prediction. We recommend comparing accuracy and bias of new methods to results from genomic best linear prediction and a variable selection approach ( e.g., BayesB), because, together, these methods are appropriate for a range of e.g. genetic architectures. An accompanying article in this issue provides a comprehensive review of genomic prediction methods and discusses a selection of topics related to application of genomic prediction in plants and animals. [ABSTRACT FROM AUTHOR]

Genome wide association studies (GWAS) have largely succeeded family-based linkage studies in livestock and human populations as the preferred method to map loci for complex or quantitative traits. However, the type of results produced by the two analyses contrast sharply due to differences in linkage disequilibrium (LD) imposed by the design of studies. In this paper, we demonstrate that association and linkage studies are in agreement provided that (i) the effects from both studies are estimated appropriately as random effects, (ii) all markers are fitted simultaneously and (iii) appropriate adjustments are made for the differences in LD between the study designs. We demonstrate with real data that linkage results can be predicted by the sum of association effects. Our association study captured most of the linkage information because we could predict the linkage results with moderate accuracy. We suggest that the ability of common single nucleotide polymorphism (SNP) to capture the genetic variance in a population will depend on the effective population size of the study organism. The results provide further evidence for many loci of small effect underlying complex traits. The analysis suggests a more informed method for GWAS is to fit statistical models where all SNPs are analysed simultaneously and as random effects. [ABSTRACT FROM AUTHOR]

Using a combination of whole-genome resequencing and high-density genotyping arrays, genome-wide haplotypes were reconstructed for two of the most important bulls in the history of the dairy cattle industry, Pawnee Farm Arlinda Chief ("Chief") and his son Walkway Chief Mark ("Mark"), each accounting for ∼7% of all current genomes. We aligned 20.5 Gbp (∼7.3× coverage) and 37.9 Gbp (∼13.5× coverage) of the Chief and Mark genomic sequences, respectively. More than 1.3 million high-quality SNPs were detected in Chief and Mark sequences. The genome-wide haplotypes inherited by Mark from Chief were reconstructed using ∼1 million informative SNPs. Comparison of a set of 15,826 SNPs that overlapped in the sequence-based and BovineSNP50 SNPs showed the accuracy of the sequence-based haplotype reconstruction to be as high as 97%. By using the BovineSNP50 genotypes, the frequencies of Chief alleles on his two haplotypes then were determined in 1,149 of his descendants, and the distribution was compared with the frequencies that would be expected assuming no selection. We identified 49 chromosomal segments in which Chief alleles showed strong evidence of selection. Candidate polymorphisms for traits that have been under selection in the dairy cattle population then were identified by referencing Chief's DNA sequence within these selected chromosome blocks. Eleven candidate genes were identified with functions related to milk-production, fertility, and disease-resistance traits. These data demonstrate that haplotype reconstruction of an ancestral proband by whole-genome resequencing in combination with high-density SNP genotyping of descendants can be used for rapid, genome-wide identification of the ancestor's alleles that have been subjected to artificial selection. [ABSTRACT FROM AUTHOR]

Related individuals share potentially long chromosome segments that trace to a common ancestor. We describe a phasing algorithm (ChromoPhase) that utilizes this characteristic of finite populations to phase large sections of a chromosome. In addition to phasing, our method imputes missing genotypes in individuals genotyped at lower marker density when more densely genotyped relatives are available. ChromoPhase uses a pedigree to collect an individual's (the proband) surrogate parents and offspring and uses genotypic similarity to identify its genomic surrogates. The algorithm then cycles through the relatives and genomic surrogates one at a time to find shared chromosome segments. Once a segment has been identified, any missing information in the proband is filled in with information from the relative. We tested ChromoPhase in a simulated population consisting of 400 individuals at a marker density of 1500/M, which is approximately equivalent to a 50K bovine single nucleotide polymorphism chip. In simulated data, 99.9% loci were correctly phased and, when imputing from 100 to 1500 markers, more than 87% of missing genotypes were correctly imputed. Performance increased when the number of generations available in the pedigree increased, but was reduced when the sparse genotype contained fewer loci. However, in simulated data, ChromoPhase correctly imputed at least 12% more genotypes than fastPHASE, depending on sparse marker density. We also tested the algorithm in a real Holstein cattle data set to impute 50K genotypes in animals with a sparse 3K genotype. In these data 92% of genotypes were correctly imputed in animals with a genotyped sire. We evaluated the accuracy of genomic predictions with the dense, sparse, and imputed simulated data sets and show that the reduction in genomic evaluation accuracy is modest even with imperfectly imputed genotype data. Our results demonstrate that imputation of missing genotypes, and potentially full genome sequence, using long-range phasing is feasible. [ABSTRACT FROM AUTHOR]

The rapid increase in high-throughput single-nucleotide polymorphism data has led to a great interest in applying genome-wide evaluation methods to identify an individual's genetic merit. Genome-wide evaluation combines statistical methods with genomic data to predict genetic values for complex traits. Considerable uncertainty currently exists in determining which genome-wide evaluation method is the most appropriate. We hypothesize that genome-wide methods deal differently with the genetic architecture of quantitative traits and genomes. A genomic linear method (GBLUP), and a genomic nonlinear Bayesian variable selection method (BayesB) are compared using stochastic simulation across three effective population sizes and a wide range of numbers of quantitative trait loci (NQTL). GBLUP had a constant accuracy, for a given heritability and sample size, regardless of NQTL. BayesB had a higher accuracy than GBLUP when NQTL was low, but this advantage diminished as NQTL increased and when NQTL became large, GBLUP slightly outperformed BayesB. In addition, deterministic equations are extended to predict the accuracy of both methods and to estimate the number of independent chromosome segments (Me) and NQTL. The predictions of accuracy and estimates of Me and NQTL were generally in good agreement with results from simulated data. We conclude that the relative accuracy of GBLUP and BayesB for a given number of records and heritability are highly dependent on Me, which is a property of the target genome, as well as the architecture of the trait (NQTL). [ABSTRACT FROM AUTHOR]

Background: The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy. Methodology/Principal Findings: We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability. Conclusions/Significance: This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk. [ABSTRACT FROM AUTHOR]

Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria's high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes. [ABSTRACT FROM AUTHOR]

Domestication and selective breeding has resulted in over 1000 extant cattle breeds. Many of these breeds do not excel in important traits but are adapted to local environments. These adaptations are a valuable source of genetic material for efforts to improve commercial breeds. As a step toward this goal we identified candidate regions to be under selection in genomes of nine Russian native cattle breeds adapted to survive in harsh climates. After comparing our data to other breeds of European and Asian origins we found known and novel candidate genes that could potentially be related to domestication, economically important traits and environmental adaptations in cattle. The Russian cattle breed genomes contained regions under putative selection with genes that may be related to adaptations to harsh environments (e.g., AQP5, RAD50, and RETREG1). We found genomic signatures of selective sweeps near key genes related to economically important traits, such as the milk production (e.g., DGAT1, ABCG2), growth (e.g., XKR4), and reproduction (e.g., CSF2). Our data point to candidate genes which should be included in future studies attempting to identify genes to improve the extant breeds and facilitate generation of commercial breeds that fit better into the environments of Russia and other countries with similar climates. [ABSTRACT FROM AUTHOR]

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM–WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM–WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM–WGP framework and compared it with CGM and WGP. The CGM–WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM–WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM–WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM–WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions. [ABSTRACT FROM AUTHOR]

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM–WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM–WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM–WGP model for heading and physiological maturity dates. However, the CGM–WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM–WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM–WGP modelling and suggest future efforts should focus on calibrating CGM–WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect. [ABSTRACT FROM AUTHOR]

Ascochyta Blight (AB) is a major disease of many cool-season legumes globally. In field pea, three fungal pathogens have been identified to be responsible for this disease in Australia, namely Peyronellaea pinodes, Peyronellaea pinodella and Phoma koolunga. Limited genomic resources for these pathogens have been generated, which has hampered the implementation of effective management strategies and breeding for resistant cultivars. Using Oxford Nanopore long-read sequencing, we report the first high-quality, fully annotated, near-chromosome-level nuclear and mitochondrial genome assemblies for 18 isolates from the Australian AB complex. Comparative genome analysis was performed to elucidate the differences and similarities between species and isolates using phylogenetic relationships and functional diversity. Our data indicated that P. pinodella and P. koolunga are heterothallic, while P. pinodes is homothallic. More homology and orthologous gene clusters are shared between P. pinodes and P. pinodella compared to P. koolunga. The analysis of the repetitive DNA content showed differences in the transposable repeat composition in the genomes and their expression in the transcriptomes. Significant repeat expansion in P. koolunga's genome was seen, with strong repeat-induced point mutation (RIP) activity being evident. Phylogenetic analysis revealed that genetic diversity can be exploited for species marker development. This study provided the much-needed genetic resources and characterization of the AB species to further drive research in key areas such as disease epidemiology and host–pathogen interactions. [ABSTRACT FROM AUTHOR]

Critically endangered breeds and populations are often crossed with more common breeds or subspecies. This results in genetic admixture that can be undesirable when it challenges the genetic integrity of wild and domestic populations, causing a loss in special characteristics or unique genetic material and ultimately extinction. Here, we present two genomic selection strategies, using genome-wide DNA markers, to recover the genomic content of the original endangered population from admixtures. Each strategy relies on the estimation of the proportion of nonintrogressed genome in individuals based on a different method: either genomic prediction or identification of breed-specific haplotypes. Then, breeding programs that remove introgressed genomic information can be designed. To test these strategies, we used empirical 50K SNP array data from two pure sheep breeds, Merino (used as target breed), Poll Dorset and an existing admixed population of both breeds. Sheep populations with varying degrees of introgression and admixture were simulated starting from these real genotypes. Both strategies were capable of identifying segment origin, and both removed up to the 100% of the Poll Dorset segments. While the selection process led to substantial inbreeding, we controlled it by imposing a minimum number of individuals contributing to the next generation. [ABSTRACT FROM AUTHOR]

Bayesian methods, such as BayesR, for predicting the genetic value or risk of individuals from their genotypes, such as Single Nucleotide Polymorphisms (SNP), are often implemented using a Markov Chain Monte Carlo (MCMC) process. However, the generation of Markov chains is computationally slow. We introduce a form of blocked Gibbs sampling for estimating SNP effects from Markov chains that greatly reduces computational time by sampling each SNP effect iteratively n-times from conditional block posteriors. Subsequent iteration over all blocks m-times produces chains of length m × n. We use this strategy to solve large-scale genomic prediction and fine-mapping problems using the Bayesian MCMC mixed-effects genetic model, BayesR3. We validate the method using simulated data, followed by analysis of empirical dairy cattle data using high dimension milk mid infra-red spectra data as an example of "omics" data and show its use to increase the precision of mapping variants affecting milk, fat, and protein yields relative to a univariate analysis of milk, fat, and protein. BayesR3 samples the polymorphisms affecting complex traits at reduced computational cost to predict the genetic value, breeding value, or individual risk of genotypes. [ABSTRACT FROM AUTHOR]

Key message: Genomic selection maximizes genetic gain by recycling parents to germplasm pool earlier and preserves genetic diversity by restricting the number of fixed alleles and the relationship in pulse breeding programs. Using a stochastic computer simulation, we investigated the benefit of optimization strategies in the context of genomic selection (GS) for pulse breeding programs. We simulated GS for moderately complex to highly complex traits such as disease resistance, grain weight and grain yield in multiple environments with a high level of genotype-by-environment interaction for grain yield. GS led to higher genetic gain per unit of time and higher genetic diversity loss than phenotypic selection by shortening the breeding cycle time. The genetic gain obtained from selecting the segregating parents early in the breeding cycle (at F1 or F2 stages) was substantially higher than selecting at later stages even though prediction accuracy was moderate. Increasing the number of F1 intercross (F1i) families and keeping the total number of progeny of F1i families constant, we observed a decrease in genetic gain and increase in genetic diversity, whereas increasing the number of progeny per F1i family while keeping a constant number of F1i families increased the rate of genetic gain and had higher genetic diversity loss per unit of time. Adding 50 F2 family phenotypes to the training population increased the accuracy of genomic breeding values (GEBVs) and genetic gain per year and decreased the rate of genetic diversity loss. Genetic diversity could be preserved by applying a strategy that restricted both the percentage of alleles fixed and the average relationship of the group of selected parents to preserve long-term genetic improvement in the pulse breeding program. [ABSTRACT FROM AUTHOR]

Maternal diversity based on a sub-region of mitochondrial genome or variants were commonly used to understand past demographic events in livestock. Additionally, there is growing evidence of direct association of mitochondrial genetic variants with a range of phenotypes. Therefore, this study used complete bovine mitogenomes from a large sequence database to explore the full spectrum of maternal diversity. Mitogenome diversity was evaluated among 1883 animals representing 156 globally important cattle breeds. Overall, the mitogenomes were diverse: presenting 11 major haplogroups, expanding to 1309 unique haplotypes, with nucleotide diversity 0.011 and haplotype diversity 0.999. A small proportion of African taurine (3.5%) and indicine (1.3%) haplogroups were found among the European taurine breeds and composites. The haplogrouping was largely consistent with the population structure derived from alternate clustering methods (e.g. PCA and hierarchical clustering). Further, we present evidence confirming a new indicine subgroup (I1a, 64 animals) mainly consisting of breeds originating from China and characterised by two private mutations within the I1 haplogroup. The total genetic variation was attributed mainly to within-breed variance (96.9%). The accuracy of the imputation of missing genotypes was high (99.8%) except for the relatively rare heteroplasmic genotypes, suggesting the potential for trait association studies within a breed. [ABSTRACT FROM AUTHOR]

Genome-wide association studies were conducted using a globally diverse safflower (Carthamus tinctorius L.) Genebank collection for grain yield (YP), days to flowering (DF), plant height (PH), 500 seed weight (SW), seed oil content (OL), and crude protein content (PR) in four environments (sites) that differed in water availability. Phenotypic variation was observed for all traits. YP exhibited low overall genetic correlations (rGoverall) across sites, while SW and OL had high rGoverall and high pairwise genetic correlations (rGij) across all pairwise sites. In total, 92 marker-trait associations (MTAs) were identified using three methods, single locus genome-wide association studies (GWAS) using a mixed linear model (MLM), the Bayesian multi-locus method (BayesR), and meta-GWAS. MTAs with large effects across all sites were detected for OL, SW, and PR, and MTAs specific for the different water stress sites were identified for all traits. Five MTAs were associated with multiple traits; 4 of 5 MTAs were variously associated with the three traits of SW, OL, and PR. This study provided insights into the phenotypic variability and genetic architecture of important safflower agronomic traits under different environments. [ABSTRACT FROM AUTHOR]

Key message: Genomic selection enabled accurate prediction for the concentration of 13 nutritional element traits in wheat. Wheat biofortification is one of the most sustainable strategies to alleviate mineral deficiency in human diets. Here, we investigated the potential of genomic selection using BayesR and Bayesian ridge regression (BRR) models to predict grain yield (YLD) and the concentration of 13 nutritional elements in grains (B, Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P and Zn) using a population of 1470 spring wheat lines. The lines were grown in replicated field trials with two times of sowing (TOS) at 3 locations (Narrabri-NSW, all lines; Merredin-WA and Horsham-VIC, 200 core lines). Narrow-sense heritability across environments (locations/TOS) ranged from 0.09 to 0.45. Co, K, Na and Ca showed low to negative genetic correlations with other traits including YLD, while the remaining traits were negatively correlated with YLD. When all environments were included in the reference population, medium to high prediction accuracy was observed for the different traits across environments. BayesR had higher average prediction accuracy for mineral concentrations (r = 0.55) compared to BRR (r = 0.48) across all traits and environments but both methods had comparable accuracies for YLD. We also investigated the utility of one or two locations (reference locations) to predict the remaining location(s), as well as the ability of one TOS to predict the other. Under these scenarios, BayesR and BRR showed comparable performance but with lower prediction accuracy compared to the scenario of predicting reference environments for new lines. Our study demonstrates the potential of genomic selection for enriching wheat grain with nutritional elements in biofortification breeding. [ABSTRACT FROM AUTHOR]

Key message: Several stable QTL were detected using metaGWAS analysis for different agronomic and quality traits under 26 normal and heat stressed environments. Heat stress, exacerbated by global warming, has a negative influence on wheat production worldwide and climate resilient cultivars can help mitigate these impacts. Selection decisions should therefore depend on multi-environment experiments representing a range of temperatures at critical stages of development. Here, we applied a meta-genome wide association analysis (metaGWAS) approach to detect stable QTL with significant effects across multiple environments. The metaGWAS was applied to 11 traits scored in 26 trials that were sown at optimal or late times of sowing (TOS1 and TOS2, respectively) at five locations. A total of 2571 unique wheat genotypes (13,959 genotypes across all environments) were included and the analysis conducted on TOS1, TOS2 and both times of sowing combined (TOS1&2). The germplasm was genotyped using a 90 k Infinium chip and imputed to exome sequence level, resulting in 341,195 single nucleotide polymorphisms (SNPs). The average accuracy across all imputed SNPs was high (92.4%). The three metaGWAS analyses revealed 107 QTL for the 11 traits, of which 16 were detected in all three analyses and 23 were detected in TOS1&2 only. The remaining QTL were detected in either TOS1 or TOS2 with or without TOS1&2, reflecting the complex interactions between the environments and the detected QTL. Eight QTL were associated with grain yield and seven with multiple traits. The identified QTL provide an important resource for gene enrichment and fine mapping to further understand the mechanisms of gene × environment interaction under both heat stressed and unstressed conditions. [ABSTRACT FROM AUTHOR]

Due to the increase in the use of precision agriculture, field trials have increased in size to allow for genomic selection tool development by linking quantitative phenotypic traits to sequence variations in the DNA of various crops. Crop row detection is an important step to enable the development of an efficient downstream analysis pipeline for genomic selection. In this paper, an efficient crop row detection algorithm was proposed that detected crop rows in colour images without the use of templates and most other pre-information such as number of rows and spacing between rows. The method only requires input on field weed intensity. The algorithm was robust in challenging field trial conditions such as variable light, sudden shadows, poor illumination, presence of weeds and noise and irregular crop shape. The algorithm can be applied to crop images taken from the top and side views. The algorithm was tested on a public dataset with side view images of crop rows and on Genomic Sub-Selection dataset in which images were taken from the top view. Different analyses were performed to check the robustness of the algorithm and to the best of authors' knowledge, the Receiver Operating Characteristic graph has been applied for the first time in crop row detection algorithm testing. Lastly, comparing this algorithm with several state-of-the-art methods, it exhibited superior performance. [ABSTRACT FROM AUTHOR]

Feed efficiency and energy balance are important traits underpinning profitability and environmental sustainability in animal production. They are complex traits, and our understanding of their underlying biology is currently limited. One measure of feed efficiency is residual feed intake (RFI), which is the difference between actual and predicted intake. Variation in RFI among individuals is attributable to the metabolic efficiency of energy utilization. High RFI (H_RFI) animals require more energy per unit of weight gain or milk produced compared with low RFI (L_RFI) animals. Energy balance (EB) is a closely related trait calculated very similarly to RFI. Cellular energy metabolism in mitochondria involves mitochondrial protein (MiP) encoded by both nuclear (Nu MiP) and mitochondrial (Mt MiP) genomes. We hypothesized that MiP genes are differentially expressed (DE) between H_RFI and L_RFI animal groups and similarly between negative and positive EB groups. Our study aimed to characterize MiP gene expression in white blood cells of H_RFI and L_RFI cows using RNA sequencing to identify genes and biological pathways associated with feed efficiency in dairy cattle. We used the top and bottom 14 cows ranked for RFI and EB out of 109 animals as H_RFI and L_RFI, and positive and negative EB groups, respectively. The gene expression counts across all nuclear and mitochondrial genes for animals in each group were used for differential gene expression analyses, weighted gene correlation network analysis, functional enrichment, and identification of hub genes. Out of 244 DE genes between RFI groups, 38 were MiP genes. The DE genes were enriched for the oxidative phosphorylation (OXPHOS) and ribosome pathways. The DE MiP genes were underexpressed in L_RFI (and negative EB) compared with the H_RFI (and positive EB) groups, suggestive of reduced mitochondrial activity in the L_RFI group. None of the Mt MiP genes were among the DE MiP genes between the groups, which suggests a non-rate limiting role of Mt MiP genes in feed efficiency and warrants further investigation. The role of MiP, particularly the Nu MiP and OXPHOS pathways in RFI, was also supported by our gene correlation network analysis and the hub gene identification. We validated the findings in an independent data set. Overall, our study suggested that differences in feed efficiency in dairy cows may be linked to differences in cellular energy demand. This study broadens our knowledge of the biology of feed efficiency in dairy cattle. [ABSTRACT FROM AUTHOR]

Background: Mutations in the mitochondrial genome have been implicated in mitochondrial disease, often characterized by impaired cellular energy metabolism. Cellular energy metabolism in mitochondria involves mitochondrial proteins (MP) from both the nuclear (NuMP) and mitochondrial (MtMP) genomes. The expression of MP genes in tissues may be tissue specific to meet varying specific energy demands across the tissues. Currently, the characteristics of MP gene expression in tissues of dairy cattle are not well understood. In this study, we profile the expression of MP genes in 29 adult and six foetal tissues in dairy cattle using RNA sequencing and gene expression analyses: particularly differential gene expression and co-expression network analyses. Results: MP genes were differentially expressed (DE; over-expressed or under-expressed) across tissues in cattle. All 29 tissues showed DE NuMP genes in varying proportions of over-expression and under-expression. On the other hand, DE of MtMP genes was observed in < 50% of tissues and notably MtMP genes within a tissue was either all over-expressed or all under-expressed. A high proportion of NuMP (up to 60%) and MtMP (up to 100%) genes were over-expressed in tissues with expected high metabolic demand; heart, skeletal muscles and tongue, and under-expressed (up to 45% of NuMP, 77% of MtMP genes) in tissues with expected low metabolic rates; leukocytes, thymus, and lymph nodes. These tissues also invariably had the expression of all MtMP genes in the direction of dominant NuMP genes expression. The NuMP and MtMP genes were highly co-expressed across tissues and co-expression of genes in a cluster were non-random and functionally enriched for energy generation pathway. The differential gene expression and co-expression patterns were validated in independent cow and sheep datasets. Conclusions: The results of this study support the concept that there are biological interaction of MP genes from the mitochondrial and nuclear genomes given their over-expression in tissues with high energy demand and co-expression in tissues. This highlights the importance of considering MP genes from both genomes in future studies related to mitochondrial functions and traits related to energy metabolism. [ABSTRACT FROM AUTHOR]

There is evidence that some genotyping platforms might not work very well for Zebu cattle when compared with Taurine breeds. In addition, the availability of panels with low to moderate number of overlapping markers is a limitation for combining datasets for genomic evaluations, especially when animals are genotyped using different SNP panels. In the present study, we compared the performance of medium- and high-density (HD) commercially available panels and investigated the feasibility of developing an ultra-HD panel (SP) containing markers from an Illumina (HD_I) and an Affymetrix (HD_A) panels. The SP panel contained 1 123 442 SNPs. After performing SNP pruning on the basis of linkage disequilibrium, HD_A, HD_I and SP contained 429 624, 365 225 and 658 770 markers distributed across the whole genome. The overall mean proportion of markers pruned out per chromosome for HD_A, HD_I and SP was 15.17%, 43.18%, 38.63% respectively. The HD_I panel presented the highest mean number of runs-of-homozygosity segments per animal (45.48%, an increment of 5.11% compared with SP) and longer segments, on average (3057.95 kb per segment), than did both HD_A and SP. HD_I also showed the highest mean number of SNPs per run-of-homozygosity segment. Consequently, the majority of animals presented the highest genomic inbreeding levels when genotyped using HD_I. The visual examination of marker distribution along the genome illustrated uncovered regions among the different panels. Haplotype-block comparison among panels and the average haplotype size constructed on the basis of HD_A were smaller than those from HD_I. The average number of SNPs per haplotype was different between HD_A and HD_I. Both HD_A and HD_I panels achieved high imputation accuracies when used as the lower-density panels for imputing to SP. However, imputation accuracy from HD_A to SP was greater than was imputation from HD_I to SP. Imputation from one HD panel to the other is also feasible. Low- and medium-density panels, composed of markers that are subsets of both HD_A and HD_I panels, should be developed to achieve better imputation accuracies to both HD levels. Therefore, the genomic analyses performed in the present study showed significant differences among the SNP panels used. The present study provides an in-depth comparison of two high-density SNP arrays currently used to genotype Zebu cattle. This investigation has shed new information on the potential to design more efficient genotyping platforms for Nellore cattle. Furthermore, our findings are of great relevance when deciding which genotyping platform to use when investigating certain genomic regions or for specific research purposes. [ABSTRACT FROM AUTHOR]

In genome-wide association studies (GWAS), variants showing consistent effect directions across populations are considered as true discoveries. We model this information in an Effect Direction MEta-analysis (EDME) to quantify pleiotropy using GWAS of 34 Cholesky-decorrelated traits in 44,000+ cattle with sequence variants. The effect-direction agreement between independent bull and cow datasets was used to quantify the false discovery rate by effect direction (FDRed) and the number of affected traits for prioritised variants. Variants with multi-trait p < 1e–6 affected 1∼22 traits with an average of 10 traits. EDME assigns pleiotropic variants to each trait which informs the biology behind complex traits. New pleiotropic loci are identified, including signals from the cattle FTO locus mirroring its bystander effects on human obesity. When validated in the 1000-Bull Genome database, the prioritized pleiotropic variants consistently predicted expected phenotypic differences between dairy and beef cattle. EDME provides robust approaches to control GWAS FDR and quantify pleiotropy. Xiang et al. developed an Effect Direction Meta-analysis (EDME) approach to identify true pleiotropy. They used Cholesky-transformation to decorrelate the traits and identified many pleiotropic variants that consistently predicted phenotypic differences in cattle. [ABSTRACT FROM AUTHOR]

Summary: Sequence elimination is one of the main mechanisms that increases the divergence among homoeologous chromosomes after allopolyploidization to enhance the stability of recently established lineages, but it can cause a loss of some economically important genes. Synthetic hexaploid wheat (SHW) is an important source of genetic variation to the natural hexaploid wheat (NHW) genepool that has low genetic diversity. Here, we investigated the change between SHW and NHW genomes by utilizing a large germplasm set of primary synthetics and synthetic derivatives. Reproducible segment elimination (RSE) was declared if a large chromosomal chunk (>5 cM) produced no aligned reads in more than five SHWs. RSE in five genomic regions was the major source of variation between SHW and NHW. One RSE eliminated almost the complete short arm of chromosome 1B, which contains major genes for flour quality, disease resistance and different enzymes. The occurrence of RSE was highly dependent on the choice of diploid and tetraploid parental lines, their ancestral subpopulation and admixture, e.g. SHWs derived from Triticum dicoccon or from one of two Aegilops tauschii subpopulations were almost free of RSE, while highly admixed parents had higher RSE rates. The rate of RSE in synthetic derivatives was almost double that in primary synthetics. Genome‐wide association analysis detected four loci with minor effects on the occurrence of RSE, indicating that both parental lines and genetic factors were affecting the occurrence of RSE. Therefore, pre‐pre‐breeding strategies should be applied before introducing SHW into pre‐breeding programs to ensure genomic stability and avoid undesirable gene loss. Significance Statement: This study documented the occurrence of multiple reproducible sequence eliminations in synthetic hexaploid wheat compared with natural hexaploid wheat; a phenomenon affected by the choice of parental lines, their admixture and genetic factors. These sequence eliminations caused significant gene loss and could affect the fertility of the progeny of natural/synthetic hexaploid hybrids, therefor it is important to avoid these in practical breeding programs. [ABSTRACT FROM AUTHOR]

Key message: A multi-environment genomic prediction model incorporating environmental covariates increased the prediction accuracy of wheat grain protein content. The advantage of the haplotype-based model was dependent upon the trait of interest. The inclusion of environment covariates (EC) in genomic prediction models has the potential to precisely model environmental effects and genotype-by-environment interactions. Together with EC, a haplotype-based genomic prediction approach, which is capable of accommodating the interaction between local epistasis and environment, may increase the prediction accuracy. The main objectives of our study were to evaluate the potential of EC to portray the relationship between environments and the relevance of local epistasis modelled by haplotype-based approaches in multi-environment prediction. The results showed that among five traits: grain yield (GY), plant height, protein content, screenings percentage (SP) and thousand kernel weight, protein content exhibited a 2.1% increase in prediction accuracy when EC was used to model the environmental relationship compared to treatment of the environment as a regular random effect without a variance–covariance structure. The approach used a Gaussian kernel to characterise the relationship among environments that displayed no advantage in contrast to the use of a genomic relationship matrix. The prediction accuracies of haplotype-based approaches for SP were consistently higher than the genotype-based model when the numbers of single-nucleotide polymorphisms (SNP) in a haplotype were from three to ten. In contrast, for GY, haplotype-based models outperformed genotype-based methods when two to four SNPs were used to construct the haplotype. [ABSTRACT FROM AUTHOR]

Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (expression quantitative trait loci [eQTLs]) and concentration of metabolites (metabolic quantitative trait loci [mQTLs]) and under histone-modification marks in several tissues were discovered from multiomics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants, and for each set, we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r > 0.94) between bulls and cows. Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone-modification marks, and selection signatures. From these results,we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In additional 7,551 cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution, and trait heritability to rank variants, and the publicly available FAETH data provide a set of biological priors for cattle genomic selection worldwide. [ABSTRACT FROM AUTHOR]

Key message: Exploitation of data from a ryegrass breeding program has enabled rapid development and implementation of genomic selection for sward-based biomass yield with a twofold-to-threefold increase in genetic gain.Abstract: Genomic selection, which uses genome-wide sequence polymorphism data and quantitative genetics techniques to predict plant performance, has large potential for the improvement in pasture plants. Major factors influencing the accuracy of genomic selection include the size of reference populations, trait heritability values and the genetic diversity of breeding populations. Global diversity of the important forage species perennial ryegrass is high and so would require a large reference population in order to achieve moderate accuracies of genomic selection. However, diversity of germplasm within a breeding program is likely to be lower. In addition, de novo construction and characterisation of reference populations are a logistically complex process. Consequently, historical phenotypic records for seasonal biomass yield and heading date over a 18-year period within a commercial perennial ryegrass breeding program have been accessed, and target populations have been characterised with a high-density transcriptome-based genotyping-by-sequencing assay. Ability to predict observed phenotypic performance in each successive year was assessed by using all synthetic populations from previous years as a reference population. Moderate and high accuracies were achieved for the two traits, respectively, consistent with broad-sense heritability values. The present study represents the first demonstration and validation of genomic selection for seasonal biomass yield within a diverse commercial breeding program across multiple years. These results, supported by previous simulation studies, demonstrate the ability to predict sward-based phenotypic performance early in the process of individual plant selection, so shortening the breeding cycle, increasing the rate of genetic gain and allowing rapid adoption in ryegrass improvement programs. [ABSTRACT FROM AUTHOR]

Abstract: We have developed the first comprehensive simulator for polyploid genomes (PolySim) and demonstrated its value by performing large‐scale simulations to examine the effect of different population parameters on the evolution of polyploids. PolySim is unlimited in terms of ploidy, population size or number of simulated loci. Our process considered the evolution of polyploids from diploid ancestors, polysomic inheritance, inbreeding, recombination rate change in polyploids and gene flow from lower to higher ploidies. We compared the number of segregating single nucleotide polymorphisms, minor allele frequency, heterozygosity, R2 and average kinship relatedness between different simulated scenarios, and to real data from polyploid species. As expected, allotetraploid populations showed no difference from their ancestral diploids when population size remained constant and there was no gene flow or multivalent (MV) pairing between subgenomes. Autotetraploid populations showed significant differences from their ancestors for most parameters and diverged from their ancestral populations faster than allotetraploids. Autotetraploids can have significantly higher heterozygosity, relatedness and extended linkage disequilibrium compared with allotetraploids. Interestingly, autotetraploids were more sensitive to increasing selfing rate and decreasing population size. MV formation can homogenize allotetraploid subgenomes, but this homogenization requires a higher MV rate than previously proposed. Our results can be considered as the first building block to understand polyploid population evolutionary dynamics. PolySim can be used to simulate a wide variety of polyploid organisms that mimic empirical populations, which, in combination with quantitative genetics tools, can be used to investigate the power of genomewide association, genomic selection or breeding programme designs in these species. [ABSTRACT FROM AUTHOR]

Background: Mammalian phenotypes are shaped by numerous genome variants, many of which may regulate gene transcription or RNA splicing. To identify variants with regulatory functions in cattle, an important economic and model species, we used sequence variants to map a type of expression quantitative trait loci (expression QTLs) that are associated with variations in the RNA splicing, i.e., sQTLs. To further the understanding of regulatory variants, sQTLs were compare with other two types of expression QTLs, 1) variants associated with variations in gene expression, i.e., geQTLs and 2) variants associated with variations in exon expression, i.e., eeQTLs, in different tissues. Results: Using whole genome and RNA sequence data from four tissues of over 200 cattle, sQTLs identified using exon inclusion ratios were verified by matching their effects on adjacent intron excision ratios. sQTLs contained the highest percentage of variants that are within the intronic region of genes and contained the lowest percentage of variants that are within intergenic regions, compared to eeQTLs and geQTLs. Many geQTLs and sQTLs are also detected as eeQTLs. Many expression QTLs, including sQTLs, were significant in all four tissues and had a similar effect in each tissue. To verify such expression QTL sharing between tissues, variants surrounding (±1 Mb) the exon or gene were used to build local genomic relationship matrices (LGRM) and estimated genetic correlations between tissues. For many exons, the splicing and expression level was determined by the same cis additive genetic variance in different tissues. Thus, an effective but simple-to-implement meta-analysis combining information from three tissues is introduced to increase power to detect and validate sQTLs. sQTLs and eeQTLs together were more enriched for variants associated with cattle complex traits, compared to geQTLs. Several putative causal mutations were identified, including an sQTL at Chr6:87392580 within the 5th exon of kappa casein (CSN3) associated with milk production traits. Conclusions: Using novel analytical approaches, we report the first identification of numerous bovine sQTLs which are extensively shared between multiple tissue types. The significant overlaps between bovine sQTLs and complex traits QTL highlight the contribution of regulatory mutations to phenotypic variations. [ABSTRACT FROM AUTHOR]

Summary: The application of genomics in crops has the ability to significantly improve genetic gain for agriculture. Many marker‐dense tools have been developed, but few have seen broad adoption in plant genomics due to issues of significant variations of genome size, levels of ploidy, single nucleotide polymorphism (SNP) frequency and reproductive habit. When combined with limited breeding activities, small research communities and scant sequence resources, the suitability of popular systems is often suboptimal and routinely fails to effectively balance cost‐effectiveness and sample throughput. Genotyping‐by‐sequencing (GBS) encompasses a range of protocols including resequencing of the transcriptome. This study describes a skim GBS‐transcriptomics (GBS‐t) approach developed to be broadly applicable, cost‐effective and high‐throughput while still assaying a significant number of SNP loci. A range of crop species with differing levels of ploidy and degree of inbreeding/outbreeding were chosen, including perennial ryegrass, a diploid outbreeding forage grass; phalaris, a putative segmental allotetraploid outbreeding forage grass; lentil, a diploid inbreeding grain legume; and canola, an allotetraploid partially outbreeding oilseed. GBS‐t was validated as a simple and largely automated, cost‐effective method which generates sufficient SNPs (from 89 738 to 231 977) with acceptable levels of missing data and even genome coverage from c. 3 million sequence reads per sample. GBS‐t is therefore a broadly applicable system suitable for many crops, offering advantages over other systems. The correct choice of subsequent sequence analysis software is important, and the bioinformatics process should be iterative and tailored to the specific challenges posed by ploidy variation and extent of heterozygosity. [ABSTRACT FROM AUTHOR]

A conventional potato breeding strategy uses targeted outcrossing, followed by phenotypic recurrent selection over a series of generations to identify improved cultivars. This paper reviews recent research in Australia aimed at improving the efficiency of such breeding. To develop marker-assisted selection (MAS) for traits of interest, our initial targets were qualitative disease resistances for potato cyst nematode (Globodera rostochiensis Ro1), Potato virus Y and Potato virus X. We undertook a cost analysis comparison between MAS and conventional screening, confirming that MAS would be cost-effective within a breeding programme. Then, as the majority of target traits are quantitative in nature, we also looked at methods to address these traits, including progeny testing and a quantitative genetic analysis technique to develop estimated breeding values (EBVs). We found the markers were useful for detecting the disease resistance characters, while the EBVs improved the analysis of the complex traits. Using a combination of MAS, EBVs and conventional screening methods, we then designed a breeding scheme for rapid selection of cultivars with multiple desirable traits, reducing the breeding cycle from over 10 to 4 years. We then explored the factors that will affect the application of genomic selection in potato and investigated strategies to incorporate genomic selection in potato breeding, as we found that it would accelerate genetic gain as the breeding cycle can be reduced to 1 year. Improvements in computational power are also flowing on to research capabilities such as sequencing, high-throughput phenotyping and data analysis, which will accelerate germplasm improvement and breeding. High-throughput phenotyping facilities are being developed that include automated glasshouse systems equipped with imaging sensors and in-field high-throughput phenotyping systems with sensors mounted on ground- or aerial-based vehicles. Using these technological improvements in phenotypic and genotypic analysis will reduce the breeding cycle in a cost-effective manner and means that we can now breed differently. [ABSTRACT FROM AUTHOR]

Background: Genotyping and whole-genome sequencing data have been generated for hundreds of thousands of cattle. International consortia used these data to compile imputation reference panels that facilitate the imputation of sequence variant genotypes for animals that have been genotyped using dense microarrays. Association studies with imputed sequence variant genotypes allow for the characterization of quantitative trait loci (QTL) at nucleotide resolution particularly when individuals from several breeds are included in the mapping populations. Results: We imputed genotypes for 28 million sequence variants in 17,229 cattle of the Braunvieh, Fleckvieh and Holstein breeds in order to compile large mapping populations that provide high power to identify QTL for milk production traits. Association tests between imputed sequence variant genotypes and fat and protein percentages in milk uncovered between six and thirteen QTL (P < 1e-8) per breed. Eight of the detected QTL were significant in more than one breed. We combined the results across breeds using meta-analysis and identified a total of 25 QTL including six that were not significant in the within-breed association studies. Two missense mutations in the ABCG2 (p.Y581S, rs43702337, P = 4.3e-34) and GHR (p.F279Y, rs385640152, P = 1.6e-74) genes were the top variants at QTL on chromosomes 6 and 20. Another known causal missense mutation in the DGAT1 gene (p.A232K, rs109326954, P = 8.4e-1436) was the second top variant at a QTL on chromosome 14 but its allelic substitution effects were inconsistent across breeds. It turned out that the conflicting allelic substitution effects resulted from flaws in the imputed genotypes due to the use of a multi-breed reference population for genotype imputation. Conclusions: Many QTL for milk production traits segregate across breeds and across-breed meta-analysis has greater power to detect such QTL than within-breed association testing. Association testing between imputed sequence variant genotypes and phenotypes of interest facilitates identifying causal mutations provided the accuracy of imputation is high. However, true causal mutations may remain undetected when the imputed sequence variant genotypes contain flaws. It is highly recommended to validate the effect of known causal variants in order to assess the ability to detect true causal mutations in association studies with imputed sequence variants. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) has been recognized as offering numerous potential benefits for ryegrass (Lolium perenne L.) breeding. While the theoretical benefits of GS in ryegrass breeding are clear, the best way to apply GS in practical breeding programs remains to be determined. The present study aimed to investigate genomic breeding options that best balance genetic gain, breeding costs, and the level of inbreeding using stochastic simulation. Nine GS scenarios were tested, including different numbers of selection candidates (10,000, 5000, and 2000 F1 seedlings) and three reference population sizes for GS composed of plots representing a sward-based trial (500, 200, and 100 plots). Low to moderate prediction accuracy was achieved for productivity traits across cycles (i.e., 0.1–0.45 for yield [h2 = 0.3]). Scenarios with larger reference populations (i.e., 500 plots) achieved higher prediction accuracy but, when considering field trial costs, were more expensive per unit of genetic gain. All nine GS scenarios delivered significantly higher genetic gain (up to fourfold) than the conventional breeding scenario over a 20-yr period. Scenarios with moderate selection intensity on F1 seedlings with fewer plots tested in field gave the most genetic gain per dollar invested (i.e., 2000 or 5000 F1 seedlings and 100 plots). However, all GS scenarios reduced genetic diversity in the breeding population more than phenotypic selection, highlighting the need to mitigate inbreeding when applying GS in perennial ryegrass. [ABSTRACT FROM AUTHOR]

Background: Genetic variation in both the composition and distribution of fat and muscle in the body is important to human health as well as the healthiness and value of meat from cattle and sheep. Here we use detailed phenotyping and a multi-trait approach to identify genes explaining variation in body composition traits. Results: A multi-trait genome wide association analysis of 56 carcass composition traits measured on 10,613 sheep with imputed and real genotypes on 510,174 SNPs was performed. We clustered 71 significant SNPs into five groups based on their pleiotropic effects across the 56 traits. Among these 71 significant SNPs, one group of 11 SNPs affected the fatty acid profile of themuscle and were close to 8 genes involved in fatty acid or triglyceride synthesis. Another group of 23 SNPs had an effect on mature size, based on their pattern of effects across traits, but the genes near this group of SNPs did not share any obvious function. Many of the likely candidate genes near SNPs with significant pleiotropic effects on the 56 traits are involved in intra-cellular signalling pathways. Among the significant SNPs were some with a convincing candidate gene due to the function of the gene (e.g. glycogen synthase affecting glycogen concentration) or because the same gene was associated with similar traits in other species. Conclusions: Using a multi-trait analysis increased the power to detect associations between SNP and body composition traits compared with the single trait analyses. Detailed phenotypic information helped to identify a convincing candidate in some cases as did information from other species. [ABSTRACT FROM AUTHOR]

Reference 1 Dorji J, Vander Jagt CJ, Garner JB. Expression of mitochondrial protein genes encoded by nuclear and mitochondrial genomes correlate with energy metabolism in dairy cattle. List of non-mitochondrial protein (Non-MP) genes clustering with the mitochondrial protein genes in cluster I (NuMP-MtMP cluster) in the Main Cows. [Extracted from the article]

Genomic-enabled prediction is becoming increasingly important in animal and plant breeding and is also receiving attention in human genetics. Deriving accurate predictions of complex traits requires implementing whole-genome regression (WGR) models where phenotypes are regressed on thousands of markers concurrently. Methods exist that allow implementing these large-p with regressions, and genome-enabled selection (GS) is being implemented in several plant and animal breeding programs. The small-n list of available methods is long, and the relationships between them have not been fully addressed. In this article we provide an overview of available methods for implementing parametric WGR models, discuss selected topics that emerge in applications, and present a general discussion of lessons learned from simulation and empirical data analysis in the last decade. [ABSTRACT FROM AUTHOR]

The extraction of automated plant phenomics from digital images has advanced in recent years. However, the accuracy of extracted phenomics, especially for individual plants in a field environment, requires improvement. In this paper, a new and efficient method of extracting individual plant areas and their mean normalized difference vegetation index from high-resolution digital images is proposed. The algorithm was applied on perennial ryegrass row field data multispectral images taken from the top view. First, the center points of individual plants from digital images were located to exclude plant positions without plants. Second, the accurate area of each plant was extracted using its center point and radius. Third, the accurate mean normalized difference vegetation index of each plant was extracted and adjusted for overlapping plants. The correlation between the extracted individual plant phenomics and fresh weight ranged between 0.63 and 0.75 across four time points. The methods proposed are applicable to other crops where individual plant phenotypes are of interest. [ABSTRACT FROM AUTHOR]

Simple Summary: Structural variants are large changes to the DNA sequences that differ from individual to individual. We discovered and quality-controlled a set of 24,908 structural variants and used a technique called imputation to infer them into 35,588 Holstein and Jersey cattle. We then investigated whether the structural variants affected key dairy cattle traits such as milk production, fertility and overall conformation. Structural variants explained generally less than 10 percent of the phenotypic variation in these traits. Four of the structural variants were significantly associated with dairy cattle production traits. However, the inclusion of the structural variants in the genomic prediction model did not increase genomic prediction accuracy. Structural variations (SVs) are large DNA segments of deletions, duplications, copy number variations, inversions and translocations in a re-sequenced genome compared to a reference genome. They have been found to be associated with several complex traits in dairy cattle and could potentially help to improve genomic prediction accuracy of dairy traits. Imputation of SVs was performed in individuals genotyped with single-nucleotide polymorphism (SNP) panels without the expense of sequencing them. In this study, we generated 24,908 high-quality SVs in a total of 478 whole-genome sequenced Holstein and Jersey cattle. We imputed 4489 SVs with R2 > 0.5 into 35,568 Holstein and Jersey dairy cattle with 578,999 SNPs with two pipelines, FImpute and Eagle2.3-Minimac3. Genome-wide association studies for production, fertility and overall type with these 4489 SVs revealed four significant SVs, of which two were highly linked to significant SNP. We also estimated the variance components for SNP and SV models for these traits using genomic best linear unbiased prediction (GBLUP). Furthermore, we assessed the effect on genomic prediction accuracy of adding SVs to GBLUP models. The estimated percentage of genetic variance captured by SVs for production traits was up to 4.57% for milk yield in bulls and 3.53% for protein yield in cows. Finally, no consistent increase in genomic prediction accuracy was observed when including SVs in GBLUP. [ABSTRACT FROM AUTHOR]

Perennial ryegrass biomass yield is an important driver of profitability for Australian dairy farmers, making it a primary goal for plant breeders. However, measuring and selecting cultivars for higher biomass yield is a major bottleneck in breeding, requiring conventional methods that may be imprecise, laborious, and/or destructive. For forage breeding programs to adopt phenomic technologies for biomass estimation, there exists the need to develop, integrate, and validate sensor-based data collection that is aligned with the growth characteristics of plants, plot design and size, and repeated measurements across the growing season to reduce the time and cost associated with the labor involved in data collection. A fully automated phenotyping platform (DairyBioBot) utilizing an unmanned ground vehicle (UGV) equipped with a ground-based Light Detection and Ranging (LiDAR) sensor and Real-Time Kinematic (RTK) positioning system was developed for the accurate and efficient measurement of plant volume as a proxy for biomass in large-scale perennial ryegrass field trials. The field data were collected from a perennial ryegrass row trial of 18 experimental varieties in 160 plots (three rows per plot). DairyBioBot utilized mission planning software to autonomously capture high-resolution LiDAR data and Global Positioning System (GPS) recordings. A custom developed data processing pipeline was used to generate a plant volume estimate from LiDAR data connected to GPS coordinates. A high correlation between LiDAR plant volume and biomass on a Fresh Mass (FM) basis was observed with the coefficient of determination of R2 = 0.71 at the row level and R2 = 0.73 at the plot level. This indicated that LiDAR plant volume is strongly correlated with biomass and therefore the DairyBioBot demonstrates the utility of an autonomous platform to estimate in-field biomass for perennial ryegrass. It is likely that no single platform will be optimal to measure plant biomass from landscape to plant scales; the development and application of autonomous ground-based platforms is of greatest benefit to forage breeding programs. [ABSTRACT FROM AUTHOR]

Genomic selection can increase the rate of genetic gain in crops through accumulation of positive alleles and reduce phenotyping costs by shortening the breeding cycle time. We performed genomic prediction for resistance to wheat rusts in tetraploid wheat accessions using three cross-validation with the objective of predicting: (1) rust resistance when individuals are not tested in all environments/locations, (2) the performance of lines across years, and (3) adult plant resistance (APR) of lines with bivariate models. The rationale for the latter is that seedling assays are faster and could increase prediction accuracy for APR. Predictions were derived from adult plant and seedling responses for leaf rust (Lr), stem rust (Sr) and stripe rust (Yr) in a panel of 391 accessions grown across multiple years and locations and genotyped using 16,483 single nucleotide polymorphisms. Different Bayesian models and genomic best linear unbiased prediction yielded similar accuracies for all traits. Site and year prediction accuracies for Lr and Yr ranged between 0.56–0.71 for Lr and 0.51–0.56 for Yr. While prediction accuracy for Sr was variable across different sites, accuracies for Yr were similar across different years and sites. The changes in accuracies can reflect higher genotype × environment (G × E) interactions due to climate or pathogenic variation. The use of seedling assays in genomic prediction was underscored by significant positive genetic correlations between all stage resistance (ASR) and APR (Lr: 0.45, Sr: 0.65, Yr: 0.50). Incorporating seedling phenotypes in the bivariate genomic approach increased prediction accuracy for all three rust diseases. Our work suggests that the underlying plant-host response to pathogens in the field and greenhouse screens is genetically correlated, but likely highly polygenic and therefore difficult to detect at the individual gene level. Overall, genomic prediction accuracies were in the range suitable for selection in early generations of the breeding cycle. [ABSTRACT FROM AUTHOR]

Background: DNA methylation has been shown to be involved in many biological processes, including X chromosome inactivation in females, paternal genomic imprinting, and others. Results: Based on the correlation patterns of methylation levels of neighboring CpG sites among 28 sperm whole genome bisulfite sequencing (WGBS) data (486 × coverage), we obtained 31,272 methylation haplotype blocks (MHBs). Among them, we defined conserved methylated regions (CMRs), variably methylated regions (VMRs) and highly variably methylated regions (HVMRs) among individuals, and showed that HVMRs might play roles in transcriptional regulation and function in complex traits variation and adaptive evolution by integrating evidence from traditional and molecular quantitative trait loci (QTL), and selection signatures. Using a weighted correlation network analysis (WGCNA), we also detected a co-regulated module of HVMRs that was significantly associated with reproduction traits, and enriched for glycosyltransferase genes, which play critical roles in spermatogenesis and fertilization. Additionally, we identified 46 VMRs significantly associated with reproduction traits, nine of which were regulated by cis-SNPs, implying the possible intrinsic relationships among genomic variations, DNA methylation, and phenotypes. These significant VMRs were co-localized (± 10 kb) with genes related to sperm motility and reproduction, including ZFP36L1, CRISP2 and HGF. We provided further evidence that rs109326022 within a predominant QTL on BTA18 might influence the reproduction traits through regulating the methylation level of nearby genes JOSD2 and ASPDH in sperm. Conclusion: In summary, our results demonstrated associations of sperm DNA methylation with reproduction traits, highlighting the potential of epigenomic information in genomic improvement programs for cattle. [ABSTRACT FROM AUTHOR]