Untangling the genetic architecture of grain yield (GY) and yield stability is an important determining factor to optimize genomics-assisted selection strategies in wheat. We conducted in-depth ...investigation on the above using a large set of advanced bread wheat lines (4,302), which were genotyped with genotyping-by-sequencing markers and phenotyped under contrasting (irrigated and stress) environments. Haplotypes-based genome-wide-association study (GWAS) identified 58 associations with GY and 15 with superiority index
(measure of stability). Sixteen associations with GY were "environment-specific" with two on chromosomes 3B and 6B with the large effects and 8 associations were consistent across environments and trials. For
, 8 associations were from chromosomes 4B and 7B, indicating 'hot spot' regions for stability. Epistatic interactions contributed to an additional 5-9% variation on average. We further explored whether integrating consistent and robust associations identified in GWAS as fixed effects in prediction models improves prediction accuracy. For GY, the model accounting for the haplotype-based GWAS loci as fixed effects led to up to 9-10% increase in prediction accuracy, whereas for
this approach did not provide any advantage. This is the first report of integrating genetic architecture of GY and yield stability into prediction models in wheat.
Spotted sea bass (Lateolabrax maculatus), widely distributed along the Chinese coasts, is an economically important aquaculture fish species. Recently, degeneration of genetic characteristics such as ...the decline in the growth rate severely hampers the development of its industry, and genetic improvement for this species is urgently required. In this study, the first genome-wide association study (GWAS) for growth traits (body weight, body height, total length and body length) were conducted and the potential performance of genomic selection (GS) were evaluated by genomic prediction of breeding values. Based on >4 million single-nucleotide polymorphisms (SNPs) genotyped by whole-genome resequencing for 514 individuals from Dongying (DY, 301 individuals) and Tangshan populations (TS, 213 individuals), GWAS detected a total of 66 growth-related SNPs located in multiple chromosomes but no major QTL, suggesting that growth traits were controlled by a polygenic genetic architecture. Candidate growth associated genes were identified to be involved in cytoskeleton reorganization, neuromodulation, angiogenesis and cell adhesion, and vascular endothelial growth factor (VEGF) and estrogen signaling pathways were considered to play important roles for growth. Predictive accuracies of the genomic estimated breeding value (GEBV) were compared among rrBLUP, BayesB, BayesC and BL models, and rrBLUP was determined as the optimal model for growth traits. Furthermore, the predictive performance based on different selection strategies of SNPs were compared, indicating using GWAS-informative SNPs was more efficient than random selected markers. These results highlighted the potential of GWAS to improve predictive accuracies of GS and reduce genotyping cost substantially. Our study laid the basis for further elucidate genetic mechanisms and demonstrated the application potential of GS approach for growth traits in spotted sea bass, which will facilitate future breeding of fast growth strains.
•66 growth-related SNPs were identified by GWAS.•Candidate growth-related genes were involved in cytoskeleton reorganization, neuromodulation, angiogenesis and cell adhesion.•VEGF and estrogen signaling pathways may play important roles for growth.•rrBLUP was determined as the optimal model for growth traits.•Using GWAS-informative SNPs was more efficient than random selected markers.
An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool ...that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.
•Genomic selection (GS) promises faster breeding through increased selection accuracy.•Design of TP and GS models play critical role in prediction accuracy.•Speed breeding and high throughput genotyping and phenotyping can enhance GS efficiency.
Due to lack of acquired immune system, the oysters cultured along coasts are subject to frequent pathogen threats, which leads to severe disease outbreaks around the world. It's well recognized that ...the selection breeding of aquatic animals can be accelerated via the harnessing of genomic tools to increase genetic gain and shorten the breeding time. In this work, we carried out genomic selection breeding in the Pacific oysters (Crassostrea gigas) for genetic improvement of resistance to Vibriosis. The genome-wide variations were genotyped by ddRAD-seq from 295 oysters with contrasted resistance to Vibrio infection. Based on genome-wide SNPs, we performed an estimation of genomic heritability and prediction accuracy for resistance to Vibrio alginolyticus in C. gigas. The genomic heritability of resistance to V. alginolyticus was low to moderate, ranging from 0.1405 to 0.2730. Four genomic selection models including rrBLUP, Bayes A, Bayes B and Bayesian Lasso were evaluated, of which Bayes A showed superior prediction accuracy and computational speed. The genomic estimated breeding value (GEBV) calculated by genomic selection model can effectively distinguish the resistance or susceptibility of oysters to Vibriosis. Selection of individuals with high GEBV as broodstock greatly improved the resistance to Vibriosis of their progeny, resulting in 18.42% increase in relative survival rate and 12.73% increase in relative survival time compared to the control population. For the first time, this work reported the efficiency of genomic selection breeding for genetic improvement for resistance trait to Vibriosis in the C. gigas, which would greatly accelerate the cultivation of Vibriosis resistant oyster strains to support the healthy and sustainable development of aquaculture.
Flowchart for implementation of genomic selection for resistance to Vibrio in the Pacific oyster, Crassostrea gigas. Display omitted
•Low to moderate genomic heritability of resistance to Vibriosis was estimated by genome-wide SNPs in Crassostrea gigas.•The feasibility of using genomics tools to accelerate resistance breeding of C. gigas was evaluated for the first time.•The progeny bred from broodstocks with high GEBV showed higher level of resistance to Vibriosis in C. gigas.•A C. gigas strain with Vibrio resistance has been cultivated based on genomic selection.
Genomic selection (GS) has resulted in rapid rates of genetic gains especially in dairy cattle in developed countries resulting in a higher proportion of genomically proven young bulls being used in ...breeding. This success has been undergirded by well-established conventional genetic evaluation systems. Here, the status of GS in terms of the structure of the reference and validation populations, response variables, genomic prediction models, validation methods, and imputation efficiency in breeding programs of developing countries, where smallholder systems predominate and the basic components for conventional breeding are mostly lacking is examined. Also, the application of genomic tools and identification of genome-wide signatures of selection is reviewed. The studies on genomic prediction in developing countries are mostly in dairy and beef cattle usually with small reference populations (500-3,000 animals) and are mostly cows. The input variables tended to be pre-corrected phenotypic records and the small reference populations has made implementation of various Bayesian methods feasible in addition to GBLUP. Multi-trait single-step has been used to incorporate genomic information from foreign bulls, thus GS in developing countries would benefit from collaborations with developed countries, as many dairy sires used are from developed countries where they may have been genotyped and phenotyped. Cross validation approaches have been implemented in most studies resulting in accuracies of 0.20-0.60. Genotyping animals with a mixture of HD and LD chips, followed by imputation to the HD have been implemented with imputation accuracies of 0.74-0.99 reported. This increases the prospects of reducing genotyping costs and hence the cost-effectiveness of GS. Next-generation sequencing and associated technologies have allowed the determination of breed composition, parent verification, genome diversity, and genome-wide selection sweeps. This information can be incorporated into breeding programs aiming to utilize GS. Cost-effective GS in beef cattle in developing countries may involve usage of reproductive technologies (AI and
fertilization) to efficiently propagate superior genetics from the genomics pipeline. For dairy cattle, sexed semen of genomically proven young bulls could substantially improve profitability thus increase prospects of small holder farmers buying-in into genomic breeding programs.
Pacific oysters are a key aquaculture species globally, and genetic improvement via selective breeding is a major target. Genomic selection has the potential to expedite genetic gain for key target ...traits of a breeding program, but has not yet been evaluated in oyster. The recent development of SNP arrays for Pacific oyster (
) raises the opportunity to test genomic selection strategies for polygenic traits. In this study, a population of 820 oysters (comprising 23 full-sibling families) were genotyped using a medium density SNP array (23 K informative SNPs), and the genetic architecture of growth-related traits shell height (SH), shell length (SL), and wet weight (WW) was evaluated. Heritability was estimated to be moderate for the three traits (0.26 ± 0.06 for SH, 0.23 ± 0.06 for SL and 0.35 ± 0.05 for WW), and results of a GWAS indicated that the underlying genetic architecture was polygenic. Genomic prediction approaches were used to estimate breeding values for growth, and compared to pedigree based approaches. The accuracy of the genomic prediction models (GBLUP) outperformed the traditional pedigree approach (PBLUP) by ∼25% for SL and WW, and ∼30% for SH. Further, reduction in SNP marker density had little impact on prediction accuracy, even when density was reduced to a few hundred SNPs. These results suggest that the use of genomic selection in oyster breeding could offer benefits for the selection of breeding candidates to improve complex economic traits at relatively modest cost.
Genomic selection has been commonly used for selection for over a decade. In this time, the rate of genetic gain has more than doubled in some countries, while inbreeding per year has also increased. ...Inbreeding can result in a loss of genetic diversity, decreased long-term response to selection, reduced animal performance and ultimately, decreased farm profitability. We quantified and compared changes in genetic gain and diversity resulting from genomic selection in Australian Holstein and Jersey cattle populations. To increase the accuracy of genomic selection, Australia has had a female genomic reference population since 2013, specifically designed to be representative of commercial populations and thus including both Holstein and Jersey cows. Herds that kept excellent health and fertility data were invited to join this population and most their animals were genotyped. In both breeds, the rate of genetic gain and inbreeding was greatest in bulls, and then the female genomic reference population, and finally the wider national herd. When comparing pre- and postgenomic selection, the rates of genetic gain for the national economic index has increased by ~160% in Holstein females and ~100% in Jersey females. This has been accompanied by doubling of the rates of inbreeding in female populations, and the rate of inbreeding has increased several fold in Holstein bulls since the widespread use of genomic selection. Where cow genotype data were available to perform a more accurate genomic analysis, greater rates of pedigree and genomic inbreeding were observed, indicating actual inbreeding levels could be underestimated in the national population due to gaps in pedigrees. Based on current rates of genetic gain, the female reference population is progressing ahead of the national herd and could be used to infer and track the future inbreeding and genetic trends of the national herds.
Infectious and parasitic diseases generate large economic losses in salmon farming. A feasible and sustainable alternative to prevent disease outbreaks may be represented by genetic improvement for ...disease resistance. To include disease resistance into the breeding goal, prior knowledge of the levels of genetic variation for these traits is required. Furthermore, the information from the genetic architecture and molecular factors involved in resistance against diseases may be used to accelerate the genetic progress for these traits. In this regard, marker assisted selection and genomic selection are approaches which incorporate molecular information to increase the accuracy when predicting the genetic merit of selection candidates. In this article we review and discuss key aspects related to disease resistance in salmonid species, from both a genetic and genomic perspective, with emphasis in the applicability of disease resistance traits into breeding programs in salmonids.
The use of breeding programs for the Pacific white shrimp (Penaeus (Litopenaeus) vannamei) based on mixed linear models with pedigreed data are described. The application of these classic breeding ...methods yielded continuous progress of great value to increase the profitability of the shrimp industry in several countries. Recent advances in such areas as genomics in shrimp will allow for the development of new breeding programs in the near future that will increase genetic progress. In particular, these novel techniques may help increase disease resistance to specific emerging diseases, which is today a very important component of shrimp breeding programs. Thanks to increased selection accuracy, simulated genetic advance using genomic selection for survival to a disease challenge was up to 2.6 times that of phenotypic sib selection.
The principle of genomic selection (GS) entails estimating breeding values (BVs) by summing all the SNP polygenic effects. The VIS/NIRS wavelength and abundance values can directly reflect the ...concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all “polygenic effects” associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359 Duroc×Landrace×Yorkshire pigs from Guangxi, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed four breeding strategies applied to different scenarios: I, only spectral and genotypic data exist for the target population; II, only spectral data exist for the target population; III, only spectral and genotypic data but with different prediction processes exist for the target population; and IV, only spectral and phenotypic data exist for the target population. The four scenarios were used to evaluate the GEBV accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies II, III, and IV was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2%, 40.8% and 15.5% respectively on average. Among them, the prediction accuracy of Strategy II for Fat (%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy I was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the four strategies was lower than that of traditional GS methods, with Strategy IV being the lowest as it ’did not require genotyping. Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.
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