A chickpea genetic variation map based on the sequencing of 3,366 genomes.

Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources1. So far, few chickpea (Cicer arietinum) germplasm accessions have been characterized at the genome sequence level2. Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic diversity across cultivated chickpea and its wild progenitor accessions. A divergence tree using genes present in around 80% of individuals in one species allowed us to estimate the divergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic diversity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic diversity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively.

Dissecting loci that underpin the genetic correlations between production, fertility, and urea traits in Australian Holstein cattle.

Unfavorable genetic correlations between milk production, fertility, and urea traits have been reported. However, knowledge of the genomic regions associated with these unfavorable correlations is limited. Here, we used the correlation scan method to identify and investigate the regions driving or antagonizing the genetic correlations between production vs. fertility, urea vs. fertility, and urea vs. production traits. Driving regions produce an estimate of correlation that is in the same direction as the global correlation. Antagonizing regions produce an estimate in the opposite direction of the global estimates. Our dataset comprised 6567, 4700, and 12,658 Holstein cattle with records of production traits (milk yield, fat yield, and protein yield), fertility (calving interval) and urea traits (milk urea nitrogen and blood urea nitrogen predicted using milk-mid-infrared spectroscopy), respectively. Several regions across the genome drive the correlations between production, fertility, and urea traits. Antagonizing regions were confined to certain parts of the genome and the genes within these regions were mostly involved in preventing metabolic dysregulation, liver reprogramming, metabolism remodeling, and lipid homeostasis. The driving regions were enriched for QTL related to puberty, milk, and health-related traits. Antagonizing regions were mostly related to muscle development, metabolic body weight, and milk traits. In conclusion, we have identified genomic regions of potential importance for dairy cattle breeding. Future studies could investigate the antagonizing regions as potential genomic regions to break the unfavorable correlations and improve milk production as well as fertility and urea traits.

Concurrently mapping quantitative trait loci associations from multiple subspecies within hybrid populations.

Many of the world's agriculturally important plant and animal populations consist of hybrids of subspecies. Cattle in tropical and sub-tropical regions for example, originate from two subspecies, Bos taurus indicus (Bos indicus) and Bos taurus taurus (Bos taurus). Methods to derive the underlying genetic architecture for these two subspecies are essential to develop accurate genomic predictions in these hybrid populations. We propose a novel method to achieve this. First, we use haplotypes to assign SNP alleles to ancestral subspecies of origin in a multi-breed and multi-subspecies population. Then we use a BayesR framework to allow SNP alleles originating from the different subspecies differing effects. Applying this method in a composite population of B. indicus and B. taurus hybrids, our results show that there are underlying genomic differences between the two subspecies, and these effects are not identified in multi-breed genomic evaluations that do not account for subspecies of origin effects. The method slightly improved the accuracy of genomic prediction. More significantly, by allocating SNP alleles to ancestral subspecies of origin, we were able to identify four SNP with high posterior probabilities of inclusion that have not been previously associated with cattle fertility and were close to genes associated with fertility in other species. These results show that haplotypes can be used to trace subspecies of origin through the genome of this hybrid population and, in conjunction with our novel Bayesian analysis, subspecies SNP allele allocation can be used to increase the accuracy of QTL association mapping in genetically diverse populations.

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

BACKGROUND: It has been challenging to implement genomic selection in multi-breed tropical beef cattle populations. If commercial (often crossbred) animals could be used in the reference population for these genomic evaluations, this could allow for very large reference populations. In tropical beef systems, such animals often have no pedigree information. Here we investigate potential models for such data, using marker heterozygosity (to model heterosis) and breed composition derived from genetic markers, as covariates in the model. Models treated breed effects as either fixed or random, and included genomic best linear unbiased prediction (GBLUP) and BayesR. A tropically-adapted beef cattle dataset of 29,391 purebred, crossbred and composite commercial animals was used to evaluate the models. RESULTS: Treating breed effects as random, in an approach analogous to genetic groups allowed partitioning of the genetic variance into within-breed and across breed-components (even with a large number of breeds), and estimation of within-breed and across-breed genomic estimated breeding values (GEBV). We demonstrate that moderately-accurate (0.30-0.43) GEBV can be calculated using these models. Treating breed effects as random gave more accurate GEBV than treating breed as fixed. A simple GBLUP model where no breed effects were fitted gave the same accuracy (and correlations of GEBV very close to 1) as a model where GEBV for within-breed and the GEBV for (random) across-breed effects were included. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy, with 3% accuracy improvement averaged across traits, especially when the validation population was less related to the reference population. Estimates of heterosis from our models were in line with previous estimates from beef cattle. A method for estimating the number of effective breed comparisons for each breed combination accumulated across contemporary groups is presented. CONCLUSIONS: When no pedigree is available, breed composition and heterosis for inclusion in multi-breed genomic evaluation can be estimated from genotypes. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy.

In it for the long run: perspectives on exploiting long-read sequencing in livestock for population scale studies of structural variants.

Studies have demonstrated that structural variants (SV) play a substantial role in the evolution of species and have an impact on Mendelian traits in the genome. However, unlike small variants (< 50 bp), it has been challenging to accurately identify and genotype SV at the population scale using short-read sequencing. Long-read sequencing technologies are becoming competitively priced and can address several of the disadvantages of short-read sequencing for the discovery and genotyping of SV. In livestock species, analysis of SV at the population scale still faces challenges due to the lack of resources, high costs, technological barriers, and computational limitations. In this review, we summarize recent progress in the characterization of SV in the major livestock species, the obstacles that still need to be overcome, as well as the future directions in this growing field. It seems timely that research communities pool resources to build global population-scale long-read sequencing consortiums for the major livestock species for which the application of genomic tools has become cost-effective.

Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock.

Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher's exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p = 0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p = 0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p = 0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits.

Although the genetic correlations between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated. Yet, we don't fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations (Brahman; BB, Tropical Composite; TC) to develop a novel framework termed correlation scan (CS). This framework was used to identify local regions associated with the genetic correlations between male and female fertility traits. Animals were genotyped with bovine high-density single nucleotide polymorphisms (SNPs) chip assay. The data used consisted of ~1000 individual records measured through frequent ovarian scanning for age at first corpus luteum (AGECL) and a laboratory assay for serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP effects in a 100-SNPs sliding window in each chromosome to identify local genomic regions that either drive or antagonize the genetic correlations between traits. We used Fisher's Z-statistics through a permutation method to confirm which regions of the genome harboured significant correlations. About 30% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two populations. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. For BB, the most important chromosome in terms of local regions is often located on bovine chromosome (BTA) 14. However, the important regions are spread across few different BTA's in TC. Quantitative trait loci (QTLs) and functional enrichment analysis revealed many significant windows co-localized with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to chromosome X. These results suggest regions of chromosome X for further investigation into the trade-offs between male and female fertility. We compared the CS with two other recently proposed methods that map local genomic correlations. Some genomic regions were significant across methods. Yet, many significant regions identified with the CS were overlooked by other methods.

Transcriptional changes in the peripheral blood leukocytes from Brangus cattle before and after tick challenge with Rhipicephalus australis.

BACKGROUND: Disease emergence and production loss caused by cattle tick infestations have focused attention on genetic selection strategies to breed beef cattle with increased tick resistance. However, the mechanisms behind host responses to tick infestation have not been fully characterised. Hence, this study examined gene expression profiles of peripheral blood leukocytes from tick-naive Brangus steers (Bos taurus x Bos indicus) at 0, 3, and 12 weeks following artificial tick challenge experiments with Rhipicephalus australis larvae. The aim of the study was to investigate the effect of tick infestation on host leukocyte response to explore genes associated with the expression of high and low host resistance to ticks. RESULTS: Animals with high (HR, n = 5) and low (LR, n = 5) host resistance were identified after repeated tick challenge. A total of 3644 unique differentially expressed genes (FDR < 0.05) were identified in the comparison of tick-exposed (both HR and LR) and tick-naive steers for the 3-week and 12-week infestation period. Enrichment analyses showed genes were involved in leukocyte chemotaxis, coagulation, and inflammatory response. The IL-17 signalling, and cytokine-cytokine interactions pathways appeared to be relevant in protection and immunopathology to tick challenge. Comparison of HR and LR phenotypes at timepoints of weeks 0, 3, and 12 showed there were 69, 8, and 4 differentially expressed genes, respectively. Most of these genes were related to immune, tissue remodelling, and angiogenesis functions, suggesting this is relevant in the development of resistance or susceptibility to tick challenge. CONCLUSIONS: This study showed the effect of tick infestation on Brangus cattle with variable phenotypes of host resistance to R. australis ticks. Steers responded to infestation by expressing leukocyte genes related to chemotaxis, cytokine secretion, and inflammatory response. The altered expression of genes from the bovine MHC complex in highly resistant animals at pre- and post- infestation stages also supports the relevance of this genomic region for disease resilience. Overall, this study offers a resource of leukocyte gene expression data on matched tick-naive and tick-infested steers relevant for the improvement of tick resistance in composite cattle.

Prowler: a novel trimming algorithm for Oxford Nanopore sequence data.

MOTIVATION: Trimming and filtering tools are useful in DNA sequencing analysis because they increase the accuracy of sequence alignments and thus the reliability of results. Oxford nanopore technologies (ONT) trimming and filtering tools are currently rudimentary, generally only filtering reads based on whole read average quality. This results in discarding reads that contain regions of high-quality sequence. Here, we propose Prowler, a trimmer that uses a window-based approach inspired by algorithms used to trim short read data. Importantly, we retain the phase and read length information by optionally replacing trimmed sections with Ns. RESULTS: Prowler was applied to mammalian and bacterial datasets, to assess its effect on alignment and assembly, respectively. Compared to data filtered with Nanofilt, alignments of data trimmed with Prowler had lower error rates and more mapped reads. Assemblies of Prowler trimmed data had a lower error rate than those filtered with Nanofilt; however, this came at some cost to assembly contiguity. AVAILABILITY AND IMPLEMENTATION: Prowler is implemented in Python and is available at https://github.com/ProwlerForNanopore/ProwlerTrimmer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Application of infrared thermal technology to assess the level of heat stress and milk yield reduction of cows in tropical smallholder dairy farms.

Panting score (PS) is a common research tool used to assess the physiological state of cows exposed to heat stress, but it is subjective. Infrared temperature (IRT), measured by either infrared thermometers or cameras, may be a more objective and reliable alternative. Very few studies thus far have evaluated the associations between PS, IRT, and milk production. We investigated the applicability of IRT compared with PS as a means of assessing heat stress and milk yield reduction in dairy cows in tropical smallholder dairy farms (SDF). In autumn 2017, SDF located across 4 typical dairy regions of Vietnam were each visited once to collect farm (n = 32) and individual cow data (n = 344). For each SDF, heat load index (HLI) inside the cowsheds, an indicator of environmental heat load calculated from ambient temperature, humidity, and wind speed, was measured. For each cow, PS (0 indicates a cow breathing normally, not panting; 4.5 indicates an extremely heat-stressed cow with excessive panting, tongue fully extended, and excessive drooling), IRT of the cow's body, single-day energy-corrected milk yield (ECM), body weight, and body condition score were measured. Cow genotype, age, lactation number, and days in milk were recorded. The IRT of the cows' inner vulval lip (IVuT) were measured with an infrared thermometer; and the IRT of the cows' vulval surface (OVuT), inner tail base surface (ITBT), ocular area, muzzle, armpit area, paralumbar fossa area, fore udder, rear udder, fore hoof, and hind hoof were also measured with an infrared camera. Multivariate mixed-effects models were used to assess the associations between HLI with PS and IRT, and associations between PS and IRT with ECM while accounting for the effects of other cow variables. All IRT correlated positively with PS (Pearson correlation, r = 0.23-0.50). Each unit increase in HLI was associated with increases of 0.07 units in PS and 0.09 to 0.23°C in IRT. Each degree (°C) increase in IVuT, OVuT, and ITBT was associated with decreases of 0.75, 0.87, and 0.70 kg/cow per day in ECM, respectively, whereas PS and other IRT were not significantly associated with ECM. Thus, all IRT showed potential to assess the heat stress level of cows; and IVuT, OVuT, and ITBT, but not PS and other IRT, showed potential to predict ECM reduction in cows during heat stress. First cross (F1) Holstein Brown Swiss and F1 Holstein Jersey showed lower PS and yielded higher ECM than the third backcross (B3) Holstein Zebu (7/8 Holstein + 1/8 Zebu) and pure Holstein. Thus, F1 Holstein Brown Swiss and F1 Holstein Jersey could be more suitable for tropical SDF than B3 Holstein Zebu and pure Holstein.

Genomic diversity and breed composition of Vietnamese smallholder dairy cows.

Vietnamese smallholder dairy cows (VDC) are the result of crossbreeding between different zebu (ZEB) and taurine dairy breeds through many undefined generations. Thus, the predominant breed composition of VDC is currently unknown. This study aimed to evaluate the level of genetic diversity and breed composition of VDC. The SNP data of 344 animals from 32 farms located across four dairy regions of Vietnam were collected and merged with genomic reference data, which included three ZEB breeds: Red Sindhi, Sahiwal and Brahman, three taurine breeds: Holstein (HOL), Jersey (JER) and Brown Swiss (BSW), and a composite breed: Chinese Yellow cattle. Diversity and admixture analyses were applied to the merged data set. The VDC were not excessively inbred, as indicated by very low inbreeding coefficients (Wright's FIS ranged from -0.017 to 0.003). The genetic fractions in the test herds suggested that the VDC are primarily composed of HOL (85.0%); however, JER (6.0%), BSW 5.3%) and ZEB (4.5%) had also contributed. Furthermore, major genotype groupings in the test herds were pure HOL (48%), B3:15/16HOL_1/16ZEB (22%) and B2:7/8HOL_1/8ZEB (12%). The genetic makeup of the VDC is mainly components of various dairy breeds but also has a small percentage of ZEB; thus, the VDC could be a good genetic base for selecting high milk-producing cows with some degree of adaptation to tropical conditions.

Multivariate analysis identifying the main factors associated with cow productivity and welfare in tropical smallholder dairy farms in Vietnam.

This study aimed to rank potential drivers of cow productivity and welfare in tropical smallholder dairy farms (SDFs) in Vietnam. Forty-one variables were collected from 32 SDFs located in four geographically diverse dairy regions, with eight SDFs per region. Twelve variables, including milk yield (MILK), percentages of milk fat (mFA), protein (mPR), dry matter (mDM), energy-corrected milk yield (ECM), heart girth (HG), body weight (BW), ECM per 100 kg BW (ECMbw), body condition score (BCS), panting score (PS), inseminations per conception (tAI), and milk electrical resistance (mRE) of cows, were fitted as outcome variables in the models. Twenty-one other variables describing farm altitude, housing condition, and diet for the cows, cow genotypes, and cow physiological stage were fitted as explanatory variables. Increased farm altitude was associated with increases in ECM and mRE and with decreases in PS and tAI (P < 0.05). Increases in roof heights and percentage of shed side open were associated with increases in ECM, mFA, and mDM (P < 0.05). Increased dry matter intake and dietary densities of dry matter and fat were associated with increased MILK, ECM, and ECMbw and decreased tAI (P < 0.05). Increased dietary lignin density was associated with increased PS. Increased genetic proportion of Brown Swiss in the herd was associated with increased MILK, ECM, and ECMbw (P < 0.05). Thus, to improve cow productivity and welfare in Vietnamese SDFs, the following interventions were identified for testing in future cause-effect experiments: increasing floor area per cow, roof heights, shed sides open, dry matter intake, dietary fat density, and the genetic proportion of Brown Swiss and decreasing dietary lignin density.

A linkage disequilibrium-based approach to position unmapped SNPs in crop species.

BACKGROUND: High-density SNP arrays are now available for a wide range of crop species. Despite the development of many tools for generating genetic maps, the genome position of many SNPs from these arrays is unknown. Here we propose a linkage disequilibrium (LD)-based algorithm to allocate unassigned SNPs to chromosome regions from sparse genetic maps. This algorithm was tested on sugarcane, wheat, and barley data sets. We calculated the algorithm's efficiency by masking SNPs with known locations, then assigning their position to the map with the algorithm, and finally comparing the assigned and true positions. RESULTS: In the 20-fold cross-validation, the mean proportion of masked mapped SNPs that were placed by the algorithm to a chromosome was 89.53, 94.25, and 97.23% for sugarcane, wheat, and barley, respectively. Of the markers that were placed in the genome, 98.73, 96.45 and 98.53% of the SNPs were positioned on the correct chromosome. The mean correlations between known and new estimated SNP positions were 0.97, 0.98, and 0.97 for sugarcane, wheat, and barley. The LD-based algorithm was used to assign 5920 out of 21,251 unpositioned markers to the current Q208 sugarcane genetic map, representing the highest density genetic map for this species to date. CONCLUSIONS: Our LD-based approach can be used to accurately assign unpositioned SNPs to existing genetic maps, improving genome-wide association studies and genomic prediction in crop species with fragmented and incomplete genome assemblies. This approach will facilitate genomic-assisted breeding for many orphan crops that lack genetic and genomic resources.

Genomic selection and genetic gain for nut yield in an Australian macadamia breeding population.

BACKGROUND: Improving yield prediction and selection efficiency is critical for tree breeding. This is vital for macadamia trees with the time from crossing to production of new cultivars being almost a quarter of a century. Genomic selection (GS) is a useful tool in plant breeding, particularly with perennial trees, contributing to an increased rate of genetic gain and reducing the length of the breeding cycle. We investigated the potential of using GS methods to increase genetic gain and accelerate selection efficiency in the Australian macadamia breeding program with comparison to traditional breeding methods. This study evaluated the prediction accuracy of GS in a macadamia breeding population of 295 full-sib progeny from 32 families (29 parents, reciprocals combined), along with a subset of parents. Historical yield data for tree ages 5 to 8 years were used in the study, along with a set of 4113 SNP markers. The traits of focus were average nut yield from tree ages 5 to 8 years and yield stability, measured as the standard deviation of yield over these 4 years. GBLUP GS models were used to obtain genomic estimated breeding values for each genotype, with a five-fold cross-validation method and two techniques: prediction across related populations and prediction across unrelated populations. RESULTS: Narrow-sense heritability of yield and yield stability was low (h2 = 0.30 and 0.04, respectively). Prediction accuracy for yield was 0.57 for predictions across related populations and 0.14 when predicted across unrelated populations. Accuracy of prediction of yield stability was high (r = 0.79) for predictions across related populations. Predicted genetic gain of yield using GS in related populations was 474 g/year, more than double that of traditional breeding methods (226 g/year), due to the halving of generation length from 8 to 4 years. CONCLUSIONS: The results of this study indicate that the incorporation of GS for yield into the Australian macadamia breeding program may accelerate genetic gain due to reduction in generation length, though the cost of genotyping appears to be a constraint at present.

Strategies and considerations for implementing genomic selection to improve traits with additive and non-additive genetic architectures in sugarcane breeding.

KEY MESSAGE: Simulations highlight the potential of genomic selection to substantially increase genetic gain for complex traits in sugarcane. The success rate depends on the trait genetic architecture and the implementation strategy. Genomic selection (GS) has the potential to increase the rate of genetic gain in sugarcane beyond the levels achieved by conventional phenotypic selection (PS). To assess different implementation strategies, we simulated two different GS-based breeding strategies and compared genetic gain and genetic variance over five breeding cycles to standard PS. GS scheme 1 followed similar routines like conventional PS but included three rapid recurrent genomic selection (RRGS) steps. GS scheme 2 also included three RRGS steps but did not include a progeny assessment stage and therefore differed more fundamentally from PS. Under an additive trait model, both simulated GS schemes achieved annual genetic gains of 2.6-2.7% which were 1.9 times higher compared to standard phenotypic selection (1.4%). For a complex non-additive trait model, the expected annual rates of genetic gain were lower for all breeding schemes; however, the rates for the GS schemes (1.5-1.6%) were still greater than PS (1.1%). Investigating cost-benefit ratios with regard to numbers of genotyped clones showed that substantial benefits could be achieved when only 1500 clones were genotyped per 10-year breeding cycle for the additive genetic model. Our results show that under a complex non-additive genetic model, the success rate of GS depends on the implementation strategy, the number of genotyped clones and the stage of the breeding program, likely reflecting how changes in QTL allele frequencies change additive genetic variance and therefore the efficiency of selection. These results are encouraging and motivate further work to facilitate the adoption of GS in sugarcane breeding.

Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects.

KEY MESSAGE: Non-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance. In the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.

Accuracy of genomic prediction of complex traits in sugarcane.

KEY MESSAGE: Complex traits in sugarcane can be accurately predicted using genome-wide DNA markers. Genomic single-step prediction is an attractive method for genomic selection in commercial breeding programs. Sugarcane breeding programs have achieved up to 1% genetic gain in key traits such as tonnes of cane per hectare (TCH), commercial cane sugar (CCS) and Fibre content over the past decades. Here, we assess the potential of genomic selection to increase the rate of genetic gain for these traits by deriving genomic estimated breeding values (GEBVs) from a reference population of 3984 clones genotyped for 26 K SNP. We evaluated the three different genomic prediction approaches GBLUP, genomic single step (GenomicSS), and BayesR. GenomicSS combining pedigree and SNP information from historic and recent breeding programs achieved the most accurate predictions for most traits (0.3-0.44). This method is attractive for routine genetic evaluation because it requires relatively little modification to the existing evaluation and results in breeding value estimates for all individuals, not only those genotyped. Adding information from early-stage trials added up to 5% accuracy for CCS and Fibre, but 0% for TCH, reflecting the importance of competition effects for TCH. These GEBV accuracies are sufficiently high that, combined with the right breeding strategy, a doubling of the rate of genetic gain could be achieved. We also assessed the flowering traits days to flowering, gender and pollen viability and found high heritabilities of 0.57, 0.78 and 0.72, respectively. The GEBV accuracies indicated that genomic selection could be used to improve these traits. This could open new avenues for breeders to manage their breeding programs, for example, by synchronising flowering time and selecting males with high pollen viability.

Imputation accuracy to whole-genome sequence in Nellore cattle.

BACKGROUND: A cost-effective strategy to explore the complete DNA sequence in animals for genetic evaluation purposes is to sequence key ancestors of a population, followed by imputation mechanisms to infer marker genotypes that were not originally reported in a target population of animals genotyped with single nucleotide polymorphism (SNP) panels. The feasibility of this process relies on the accuracy of the genotype imputation in that population, particularly for potential causal mutations which may be at low frequency and either within genes or regulatory regions. The objective of the present study was to investigate the imputation accuracy to the sequence level in a Nellore beef cattle population, including that for variants in annotation classes which are more likely to be functional. METHODS: Information of 151 key sequenced Nellore sires were used to assess the imputation accuracy from bovine HD BeadChip SNP (~ 777 k) to whole-genome sequence. The choice of the sires aimed at optimizing the imputation accuracy of a genotypic database, comprised of about 10,000 genotyped Nellore animals. Genotype imputation was performed using two computational approaches: FImpute3 and Minimac4 (after using Eagle for phasing). The accuracy of the imputation was evaluated using a fivefold cross-validation scheme and measured by the squared correlation between observed and imputed genotypes, calculated by individual and by SNP. SNPs were classified into a range of annotations, and the accuracy of imputation within each annotation classification was also evaluated. RESULTS: High average imputation accuracies per animal were achieved using both FImpute3 (0.94) and Minimac4 (0.95). On average, common variants (minor allele frequency (MAF) > 0.03) were more accurately imputed by Minimac4 and low-frequency variants (MAF ≤ 0.03) were more accurately imputed by FImpute3. The inherent Minimac4 Rsq imputation quality statistic appears to be a good indicator of the empirical Minimac4 imputation accuracy. Both software provided high average SNP-wise imputation accuracy for all classes of biological annotations. CONCLUSIONS: Our results indicate that imputation to whole-genome sequence is feasible in Nellore beef cattle since high imputation accuracies per individual are expected. SNP-wise imputation accuracy is software-dependent, especially for rare variants. The accuracy of imputation appears to be relatively independent of annotation classification.

Multi-environment analysis of sorghum breeding trials using additive and dominance genomic relationships.

KEY MESSAGE: Multi-environment models using marker-based kinship information for both additive and dominance effects can accurately predict hybrid performance in different environments. Sorghum is an important hybrid crop that is grown extensively in many subtropical and tropical regions including Northern NSW and Queensland in Australia. The highly varying weather patterns in the Australian summer months mean that sorghum hybrids exhibit a great deal of variation in yield between locations. To ultimately enable prediction of the outcome of crossing parental lines, both additive effects on yield performance and dominance interaction effects need to be characterised. This paper demonstrates that fitting a linear mixed model that includes both types of effects calculated using genetic markers in relationship matrices improves predictions. Genotype by environment interactions was investigated by comparing FA1 (single-factor analytic) and FA2 (two-factor analytic) structures. The G×E causes a change in hybrid rankings between trials with a difference of up to 25% of the hybrids in the top 10% of each trial. The prediction accuracies increased with the addition of the dominance term (over and above that achieved with an additive effect alone) by an average of 15% and a maximum of 60%. The percentage of dominance of the total genetic variance varied between trials with the trials with higher broad-sense heritability having the greater percentage of dominance. The inclusion of dominance in the factor analytic models improves the accuracy of the additive effects. Breeders selecting high yielding parents for crossing need to be aware of effects due to environment and dominance.