Application Of Genomic Selection Research Articles

Genome-based prediction of Bayesian linear and non-linear regression models for ordinal data.

Linear and non-linear models used in applications of genomic selection (GS) can fit different types of responses (e.g., continuous, ordinal, binary). In recent years, several genomic-enabled prediction models have been developed for predicting complex traits in genomic-assisted animal and plant breeding. These models include linear, non-linear and non-parametric models, mostly for continuous responses and less frequently for categorical responses. Several linear and non-linear models are special cases of a more general family of statistical models known as artificial neural networks, which provide better prediction ability than other models. In this paper, we propose a Bayesian Regularized Neural Network (BRNNO) for modelling ordinal data. The proposed model was fitted using a Bayesian framework; we used the data augmentation algorithm to facilitate computations. The proposed model was fitted using the Gibbs Maximum a Posteriori and Generalized EM algorithm implemented by combining code written in C and R programming languages. The new model was tested with two real maize datasets evaluated for Septoria and GLS diseases and was compared with the Bayesian Ordered Probit Model (BOPM). Results indicated that the BRNNO model performed better in terms of genomic-based prediction than the BOPM model.

Genetic mapping and genomic selection for maize stalk strength

BackgroundMaize is one of the most important staple crops and is widely grown throughout the world. Stalk lodging can cause enormous yield losses in maize production. However, rind penetrometer resistance (RPR), which is recognized as a reliable measurement to evaluate stalk strength, has been shown to be efficient and useful for improving stalk lodging-resistance. Linkage mapping is an acknowledged approach for exploring the genetic architecture of target traits. In addition, genomic selection (GS) using whole genome markers enhances selection efficiency for genetically complex traits. In the present study, two recombinant inbred line (RIL) populations were utilized to dissect the genetic basis of RPR, which was evaluated in seven growth stages.ResultsThe optimal stages to measure stalk strength are the silking phase and stages after silking. A total of 66 and 45 quantitative trait loci (QTL) were identified in each RIL population. Several potential candidate genes were predicted according to the maize gene annotation database and were closely associated with the biosynthesis of cell wall components. Moreover, analysis of gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway further indicated that genes related to cell wall formation were involved in the determination of RPR. In addition, a multivariate model of genomic selection efficiently improved the prediction accuracy relative to a univariate model and a model considering RPR-relevant loci as fixed effects.ConclusionsThe genetic architecture of RPR is highly genetically complex. Multiple minor effect QTL are jointly involved in controlling phenotypic variation in RPR. Several pleiotropic QTL identified in multiple stages may contain reliable genes and can be used to develop functional markers for improving the selection efficiency of stalk strength. The application of genomic selection to RPR may be a promising approach to accelerate breeding process for improving stalk strength and enhancing lodging-resistance.

Effect of genomic selection on rate of inbreeding and coancestry and effective population size of Holstein and Jersey cattle populations

Genetic diversity in livestock populations is a significant contributor to the sustainability of animal production. Also, genetic diversity allows animal production to become more responsive to environmental changes and market demands. The loss of genetic diversity can result in a plateau in production and may also result in loss of fitness or viability in animal production. In this study, we investigated the rate of inbreeding (ΔF), rate of coancestry (Δf), and effective population size (Ne) as important quantitative indicators of genetic diversity and evaluated the effect of the recent implementation of genomic selection on the loss of genetic diversity in North American Holstein and Jersey dairy cattle. To estimate the rate of inbreeding and coancestry, inbreeding and coancestry coefficients were calculated using the traditional pedigree method and genomic methods estimated from segment- and marker-based approaches. Furthermore, we estimated Ne from the rate of inbreeding and coancestry and extent of linkage disequilibrium. A total of 205,755 and 89,238 pedigreed and genotyped animals born between 1990 and 2018 inclusively were available for Holsteins and Jerseys, respectively. The estimated average pedigree inbreeding coefficients were 7.74 and 7.20% for Holsteins and Jerseys, respectively. The corresponding values for the segment and marker-by-marker genomic inbreeding coefficients were 13.61, 15.64, and 31.40% for Holsteins and 21.16, 22.54, and 42.62% for Jerseys, respectively. The average coancestry coefficients were 8.33 and 15.84% for Holsteins and 9.23 and 23.46% for Jerseys with pedigree and genomic measures, respectively. Generation interval for the whole 29-yr time period averaged approximately 5 yr for all selection pathways combined. The ΔF per generation based on pedigree, segment, and marker-by-marker genomic measures for the entire 29-yr period was estimated to be 0.75, 1.10, 1.16, and 1.02% for Holstein animals and 0.67, 0.62, 0.63, and 0.59% for Jersey animals, respectively. The Δf was estimated to be 0.98 and 0.98% for Holsteins and 0.73 and 0.78% for Jerseys with pedigree and genomic measures, respectively. These ΔF and Δf translated to an Ne that ranged from 43 to 66 animals for Holsteins and 64 to 85 animals for Jerseys. In addition, the Ne based on linkage disequilibrium was 58 and 120 for Holsteins and Jerseys, respectively. The 10-yr period that involved the application of genomic selection resulted in an increased ΔF per generation with ranges from 1.19 to 2.06% for pedigree and genomic measures in Holsteins. Given the rate at which inbreeding is increasing after the implementation of genomic selection, there is a need to implement measures and means for controlling the rate of inbreeding per year, which will help to manage and maintain farm animal genetic resources.

Current status of genomic evaluation.

Early application of genomic selection relied on SNP estimation with phenotypes or de-regressed proofs (DRP). Chips of 50k SNP seemed sufficient for an accurate estimation of SNP effects. Genomic estimated breeding values (GEBV) were composed of an index with parent average, direct genomic value, and deduction of a parental index to eliminate double counting. Use of SNP selection or weighting increased accuracy with small data sets but had minimal to no impact with large data sets. Efforts to include potentially causative SNP derived from sequence data or high-density chips showed limited or no gain in accuracy. After the implementation of genomic selection, EBV by BLUP became biased because of genomic preselection and DRP computed based on EBV required adjustments, and the creation of DRP for females is hard and subject to double counting. Genomic selection was greatly simplified by single-step genomic BLUP (ssGBLUP). This method based on combining genomic and pedigree relationships automatically creates an index with all sources of information, can use any combination of male and female genotypes, and accounts for preselection. To avoid biases, especially under strong selection, ssGBLUP requires that pedigree and genomic relationships are compatible. Because the inversion of the genomic relationship matrix (G) becomes costly with more than 100k genotyped animals, large data computations in ssGBLUP were solved by exploiting limited dimensionality of genomic data due to limited effective population size. With such dimensionality ranging from 4k in chickens to about 15k in cattle, the inverse of G can be created directly (e.g., by the algorithm for proven and young) at a linear cost. Due to its simplicity and accuracy, ssGBLUP is routinely used for genomic selection by the major chicken, pig, and beef industries. Single step can be used to derive SNP effects for indirect prediction and for genome-wide association studies, including computations of the P-values. Alternative single-step formulations exist that use SNP effects for genotyped or for all animals. Although genomics is the new standard in breeding and genetics, there are still some problems that need to be solved. This involves new validation procedures that are unaffected by selection, parameter estimation that accounts for all the genomic data used in selection, and strategies to address reduction in genetic variances after genomic selection was implemented.

The application of genomic selection in pig cross breeding.

Genomic selection is a form of marker-assisted selection in which genetic markers covering the entire genome are used so that all quantitative trait loci are in linkage disequilibrium with at least one marker. Genomic selection improves the efficiency and accuracy of breeding and it is widely used in purebred breeding across many animal species. However, some studies indicate that the accuracy of genome selection in cross breeding needs to be improved,especially in cross population. As one of the most extensive breeding methods employed in the swine industry, cross breeding has significant, potential research and economic value to further improve its performance by combining with genomic selection. In this review, we summarize the application of genomic selection in pigs, and elucidate the genomic selection deficiencies in breeding hybrid pigs. This review will also provide valuable insights for the future application and improvement of genomic selection in pig cross breeding.

52 Genomic selection in the beef industry: Current achievements and future directions

Abstract In the past decade, genomic testing of beef cattle has evolved from applications in research to a routine practice for many beef cattle seedstock breeders. Testing for lethal genetic conditions or parentage was many breeders’ first experience with genomic testing. While the American Angus Association (AAA) began utilizing 384 SNP genotypes in genetic evaluations in 2009, the adoption of genotyping with higher density (~50,000 SNP) arrays by AAA in 2010 launched large-scale genotyping of Angus cattle for genetic evaluation. AAA transitioned from semi-annual to weekly genetic evaluations in 2010, and cost of genotyping decreased from $139 per animal in 2011, to $37 in 2017. In fiscal year 2018, AAA members genotyped over 160,000 animals for genetic evaluation, and as of April 2019, the AAA and Canadian Angus Association joint genetic evaluation includes over 635,000 genotyped animals. Now genotyping arrays with Angus-specific SNP content are used. The primary benefit to Angus breeders has been increased accuracy of genetic prediction for young animals, especially for traits with limited phenotypic information such as carcass traits, feed intake and mature cow size. Future benefits from genotyping include identification and selection against embryonic lethal alleles, better characterization of inbreeding, and selection tools for additional traits relevant to or measured in unique environments. Electronic sensors and other novel approaches may yield previously unmeasurable phenotypes for health and efficiency traits, which can be extended to wider populations for selection using genomics. New techniques such as DNA pooling and genotyping by sequencing may reduce costs enabling widespread testing in commercial cow-calf and cattle feeding enterprises. The application of genomic selection has clearly been a significant advancement in genetic selection in Angus cattle in the past ten years. This early adoption will expedite subsequent genomic tools at an increasing rate and will foster innovation.

49 Current status of genomic selection

Abstract Early application of genomic selection relied on SNP estimation with phenotypes or de-regressed proofs (DRP). Chips of 50k SNP seemed sufficient. Estimated breeding value was an index with parent average and deduction to eliminate double counting. Use of SNP selection or weighting increased accuracy with small data sets but less or none with large data sets. Use of DRP with female information required ad-hoc modifications. As BLUP is biased by genomic selection, use of DRP under genomic selection required adjustments. Efforts to include potentially causative SNP derived from sequence analysis showed limited or no gain. The genomic selection was greatly simplified using single-step GBLUP (ssGBLUP) because the procedure automatically creates the index, can use any combination of male and female genotypes, and accounts for preselection. ssGBLUP requires careful scaling for compatibility between pedigree and genomic relationships to avoid biases especially under strong selection. Large data computations in ssGBLUP were solved by exploiting limited dimensionality of SNP due to limited effective population size. With such dimensionality ranging from 4k in chicken to about 15k in Holsteins, the inverse of GRM can be created directly (e.g., by the APY algorithm) in linear cost. Due to its simplicity and accuracy ssGBLUP is routinely used for genomic selection by major companies in chicken, pigs and beef. ssGBLUP can be used to derive SNP effects for indirect prediction, and for GWAS, including computations of the P-values. An alternative single-step called ssBR exists that uses SNP effects instead of GRM. As BLUP is affected by pre-selection, there is need for new validation procedures unaffected by selection, and for parameter estimation that accounts for all the genomic data used in selection. Another issue are reduced variances due to the Bulmer effect.

Genomic prediction for disease resistance to Hepatopancreatic parvovirus and growth, carcass and quality traits in Banana shrimp Fenneropenaeus merguiensis

Conventional genetic improvement of disease resistance in aquatic animal species involves challenge tests or using qPCR to quantify viral load that is costly, time-consuming and causing biosecurity concerns. Recent developments in high throughput next generation genome sequencing platforms such as genotyping by sequencing (GBS) have opened new possibilities for improving disease traits based on DNA information. The principal aim of this study was thus to examine potential application of genomic selection to improve resistance to hepatopancreatic parvovirus (HPV) in banana shrimp Fenneropenaeus merguiensis. Specifically, we used a total of 9472 single nucleotide polymorphisms (SNPs) developed de novo from GBS platforms to assess accuracy of genomic prediction for HPV resistance and growth, carcass and quality-related traits in this white shrimp species. Our multi-locus mixed model analysis showed moderate heritabilities for HPV resistance (h2 = 0.46) and other traits studied (0.10 to 0.55). Genetic correlations of HPV titre with growth and carcass traits, estimated using SNPs markers, were negative (i.e., favourable), suggesting that selection for improved growth and carcass traits may have increased HPV resistance (i.e., reduced HPV titre). More importantly, our gBLUP model demonstrated that the accuracy of gBLUP prediction was moderate for HPV disease resistance (0.46). The genomic prediction accuracy was somewhat greater for growth and carcass related traits especially for body weight (0.76) and meat or tail weight (0.77). On the other hand, the prediction accuracy was from 0.25 to 0.41 for quality traits (raw and cooked colour and flesh streaks). Collectively, it is concluded that there are prospects to apply genomic selection in the genetic improvement for increased disease resistance, carcass and quality-related traits in this population of banana shrimp F. merguiensis.

Theoretical Evaluation of Multi-Breed Genomic Prediction in Chinese Indigenous Cattle.

Simple SummaryIn order to evaluate the potential application of genomic selection (GS) for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. We found the predictive accuracies to be low when the reference and validation populations were sampled from different breeds. When using multiple breeds for the reference population, the predictive accuracies were higher if the reference was comprised of breeds with close relationships. In addition, the accuracy increased in all scenarios when the heritability increased, and the genetic architecture of the QTL can affect genomic prediction. Our study suggested that the application of meta-populations can increase accuracy in scenarios with a reduced size of reference populations.Genomic selection (GS) has been widely considered as a valuable strategy for enhancing the rate of genetic gain in farm animals. However, the construction of a large reference population is a big challenge for small populations like indigenous cattle. In order to evaluate the potential application of GS for Chinese indigenous cattle, we assessed the influence of combining multiple populations on the reliability of genomic predictions for 10 indigenous breeds of Chinese cattle using simulated data. Also, we examined the effect of different genetic architecture on prediction accuracy. In this study, we simulated a set of genotype data by a resampling approach which can reflect the realistic linkage disequilibrium pattern for multiple populations. We found within-breed evaluations yielded the highest accuracies ranged from 0.64 to 0.68 for four different simulated genetic architectures. For scenarios using multiple breeds as reference, the predictive accuracies were higher when the reference was comprised of breeds with a close relationship, while the accuracies were low when prediction were carried out among breeds. In addition, the accuracy increased in all scenarios with the heritability increased. Our results suggested that using meta-population as reference can increase accuracy of genomic predictions for small populations. Moreover, multi-breed genomic selection was feasible for Chinese indigenous populations with genetic relationships.

Assessment of genetic diversity and population structure in cultured Australian Pacific oysters.

The recent development of Pacific oyster (Crassostrea gigas) SNP genotyping arrays has allowed detailed characterisation of genetic diversity and population structure within and between oyster populations. It also raises the potential of harnessing genomic selection for genetic improvement in oyster breeding programmes. The aim of this study was to characterise a breeding population of Australian oysters through genotyping and analysis of 18027 SNPs, followed by comparison with genotypes of oyster sampled from Europe and Asia. This revealed that the Australian populations had similar population diversity (HE ) to oysters from New Zealand, the British Isles, France and Japan. Population divergence was assessed using PCA of genetic distance and revealed that Australian oysters were distinct from all other populations tested. Australian Pacific oysters originate from planned introductions sourced from three Japanese populations. Approximately 95% of these introductions were from geographically, and potentially genetically, distinct populations from the Nagasaki oysters assessed in this study. Finally, in preparation for the application of genomic selection in oyster breeding programmes, the strength of LD was evaluated and subsets of loci were tested for their ability to accurately infer relationships. Weak LD was observed on average; however, SNP subsets were shown to accurately reconstitute a genomic relationship matrix constructed using all loci. This suggests that low-density SNP panels may have utility in the Australian population tested, and the findings represent an important first step towards the design and implementation of genomic approaches for applied breeding in Pacific oysters.

Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle.

SummaryGenomic prediction has been widely utilized to estimate genomic breeding values (GEBVs) in farm animals. In this study, we conducted genomic prediction for 20 economically important traits including growth, carcass and meat quality traits in Chinese Simmental beef cattle. Five approaches (GBLUP, BayesA, BayesB, BayesCπ and BayesR) were used to estimate the genomic breeding values. The predictive accuracies ranged from 0.159 (lean meat percentage estimated by BayesCπ) to 0.518 (striploin weight estimated by BayesR). Moreover, we found that the average predictive accuracies across 20 traits were 0.361, 0.361, 0.367, 0.367 and 0.378, and the averaged regression coefficients were 0.89, 0.86, 0.89, 0.94 and 0.95 for GBLUP, BayesA, BayesB, BayesCπ and BayesR respectively. The genomic prediction accuracies were mostly moderate and high for growth and carcass traits, whereas meat quality traits showed relatively low accuracies. We concluded that Bayesian regression approaches, especially for BayesR and BayesCπ, were slightly superior to GBLUP for most traits. Increasing with the sizes of reference population, these two approaches are feasible for future application of genomic selection in Chinese beef cattle.

Genomics-assisted breeding for ear rot resistances and reduced mycotoxin contamination in maize: methods, advances and prospects.

Genetic mapping, genomic profiling and bioinformatic approaches were used to identify putative resistance genes for ear rots and low mycotoxin contamination in maize. Genomic selection seems to have good perspectives. Maize is globally an indispensable crop for humans and livestock. About 30% of yield is lost by fungal diseases with Gibberella, Fusarium and Aspergillus ear rots (ERs) having a high economic impact in most maize-growing regions of the world. They reduce not only yield, but also contaminate grains with mycotoxins like deoxynivalenol, zearalenone, fumonisins and aflatoxins, respectively. These mycotoxins pose serious health problems to humans and animals. A number of studies have been conducted to dissect the genetic architecture of resistance to these three major ear rots over the past decade. The review concentrates on studies carried out to locate quantitative trait loci (QTL) and candidate genes (CG) on the maize genome as well as the application of genomic selection in maize for resistance against Fusarium graminearum, Fusarium verticillioides and Aspergillus flavus. QTL studies by linkage or genome-wide association mapping, omic technologies (genomics, proteomics, transcriptomics and metabolomics) and bioinformatics are the methods used in the current studies to propose resistance genes against ear rot pathogens. Though a number of QTL and CG are reported, only a few specific genes were found to directly confer ER resistance in maize. A combination of two or more gene identification methods would provide a more powerful and reliable tool. Genomic selection seems to be promising for ER resistance breeding, but there are only a limited number of studies in this area. A strategy that can accurately validate and predict genotypes with major effect QTL and CG for selection will be worthwhile for practical breeding against ERs and mycotoxin contamination in maize.

Genetic and economic evaluation of alternative breeding scenarios for community based productivity improvements of three indigenous goat breeds in Ethiopia

On station small ruminant researches in Ethiopia were ineffective due to various factors. As alternative, community based breeding program (CBBP) has emerged. In the current CBBPs, sires’ side selection only (SN1) was practiced. The objective of the present work was to compare SN1 by simulating alternative breeding scenarios for Abergelle (AB), Centeral Highland (CH) and Woyto-Guji (WG) goat breeds in Ethiopia. Three scenarios including selection on dam’s side (SN2) in addition to SN1, application of genomic selection (SN3) on SN1 and use of sires from SN1 for mating in additional flocks (SN4) were simulated and compared with SN1 based on the predicted annual genetic gain (PAGG) and discounted profits. The breeding objective traits or selection criteria were six month weight (6mw, kg) for all breeds, average daily milk yield (ADM, kg) and survival to six month of age (SURV) for AB, litter size at birth (LSB) for CH and WG, litter size at weaning (LSW) for CH and kidding interval (KI, days) for WG. ZPLAN + software was used for the simulation. The PAGG for 6mw ranged from 0.086 kg (SN4) to 0.249 kg (SN3) for AB where it ranged from 0.471 kg (SN4) to 0.970 kg (SN3) for ADM for same breed. PAGG ranging from 0.130 kg (SN4) to 0.467 kg (SN3) and from 0.204 kg (SN4) to 0.311 kg (SN3) for CH Gonder and Ambo sites, respectively were simulated whereas this parameter ranged from 0.103 kg (SN4) to 0.271 kg (SN3) for WG. The alternative breeding scenarios to SN1 resulted in better PAGGs, especially for 6mw in three of the goat breeds and for ADM in AB. Based on the PAGGs and profitability we recommend SN2 over SN1. However, SN4 could also be applied, compared to SN1, in view of better profitability and suitability of addressing emerging demands.

Validation of Genotyping by Sequencing Using Transcriptomics for Diversity and Application of Genomic Selection in Tetraploid Potato.

Potato is an important food crop due to its increasing consumption, and as a result, there is demand for varieties with improved production. However, the current status of breeding for improved varieties is a long process which relies heavily on phenotypic evaluation and dated molecular techniques and has little emphasis on modern genotyping approaches. Evaluation and selection before a cultivar is commercialized typically takes 10–15 years. Molecular markers have been developed for disease and pest resistance, resulting in initial marker-assisted selection in breeding. This study has evaluated and implemented a high-throughput transcriptome sequencing method for dense marker discovery in potato for the application of genomic selection. An Australian relevant collection of commercial cultivars was selected, and identification and distribution of high quality SNPs were examined using standard bioinformatic pipelines and a custom approach for the prediction of allelic dosage. As a result, a large number of SNP markers were identified and filtered to generate a high-quality subset that was then combined with historic phenotypic data to assess the approach for genomic selection. Genomic selection potential was predicted for highly heritable traits and the approach demonstrated advantages over the previously used technologies in terms of markers identified as well as costs incurred. The high-quality SNP list also provided acceptable genome coverage which demonstrates its applicability for much larger future studies. This SNP list was also annotated to provide an indication of function and will serve as a resource for the community in future studies. Genome wide marker tools will provide significant benefits for potato breeding efforts and the application of genomic selection will greatly enhance genetic progress.

Phenomics and genomics of finger millet: current status and future prospects.

Diverse gene pool, advanced plant phenomics and genomics methods enhanced genetic gain and understanding of important agronomic, adaptation and nutritional traits in finger millet. Finger millet (Eleusine coracana L. Gaertn) is an important minor millet for food and nutritional security in semi-arid regions of the world. The crop has wide adaptability and can be grown right from high hills in Himalayan region to coastal plains. It provides food grain as well as palatable straw for cattle, and is fairly climate resilient. The crop has large gene pool with distinct features of both Indian and African germplasm types. Interspecific hybridization between Indian and African germplasm has resulted in greater yield enhancement and disease resistance. The crop has shown numerous advantages over major cereals in terms of stress adaptation, nutritional quality and health benefits. It has indispensable repository of novel genes for the benefits of mankind. Although rapid strides have been made in allele mining in model crops and major cereals, the progress in finger millet genomics is lacking. Comparative genomics have paved the way for the marker-assisted selection, where resistance gene homologues of rice for blast and sequence variants for nutritional traits from other cereals have been invariably used. Transcriptomics studies have provided preliminary understanding of the nutritional variation, drought and salinity tolerance. However, the genetics of many important traits in finger millet is poorly understood and need systematic efforts from biologists across disciplines. Recently, deciphered finger millet genome will enable identification of candidate genes for agronomically and nutritionally important traits. Further, improvement in genome assembly and application of genomic selection as well as genome editing in near future will provide plethora of information and opportunity to understand the genetics of complex traits.

The use of Karyomapping for genomic evaluation and PGT-A of preimplantation cattle embryos: the first live-born calves

Objectives Karyomapping was developed as a means of screening for monogenic and chromosomal disorders simultaneously in human IVF embryos using SNP chips. SNP chips in cattle breeding on the other hand are used for genomic selection i.e. to establish estimated breeding values (e.g. for food conversion efficiencies, milk production etc.). This has led to significantly improved rates of genetic gain through the screening and selection of animals at birth, but with the inherent drawback of having to wait for calf to be born before the result can be obtained and the fact that improvements within a herd breed are limited by the number of animals born. Application of genomic selection to in vitro produced (IVP) embryos however could bring further benefits thanks to the ability to test a greater number of individuals in a shorter time, thereby increasing selection speed and intensity. Aneuploidy is common in IVP embryos and could lead to developmental arrest. Karyomapping can therefore allow simultaneous genomic selection and aneuploidy detection, offering the power to maximise pregnancy rates of high quality offspring. Moreover, Karyomapping can be used to characterise the frequency and parental origin of aneuploidy. The objective of this study therefore was to apply Karyomapping to cattle breeding with a view to establishing the first live births by this approach. Method We used Karyomapping to characterise the frequency and parental origin of aneuploidy in IVP bovine embryos in order to establish an estimate of total aneuploidy rates in each parental germline. Furthermore, estimations of genome wide recombination rates in cattle were obtained and we performed the first embryo transfers. Results Following Karyomapping of trophectoderm biopsies, 68.8% of the embryos (n=42/61) appeared euploid while the rest displayed one or more abnormalities. Trisomies were detected with a frequency of 2.10% (n=68/3248 chromosomes) while monosomies with a frequency of just 0.31% (n=10/3248 chromosomes) resulting in a statistically significant difference. Monosomies appeared with a similar frequency of 0.24% (n=4/1653) and 0.36% (n=6/1653) in chromosomes of paternal and maternal origin, respectively. However, trisomies appeared with a frequency of 2.60% (n=43/1653) in chromosomes of maternal origin while the frequency for paternal chromosomes was 1.51% (n=25/1653) resulting in a significant difference. The average genome-wide recombination distance per Mb was calculated as 1.67 cM/Mb, which is higher than previous studies. Moreover, the maternal X chromosome showed a much-reduced number of crossovers than other chromosomes of similar size. Following transfer of euploid embryos, 4 live births are reported at time of writing. Conclusions We demonstrate, for the first time, a proof of principle for the application of Karyomapping to cattle breeding, with the birth of four calves after analysis in this way. This combined genomic selection and PGT-A approach was highly reliable, with calves showing 98% concordance with their respective embryo biopsies for SNP typing and 100% concordance with their respective biopsies for aneuploidy screening. This approach permits a significant increase in the rate of genetic gain and is applicable to basic research to investigate meiosis and aneuploidy in cattle.

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

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

Application of genomic selection in agriculture

Traditional breeding technique for genetic improvement of crops are based on, information on phenotypes and pedigrees to predict breeding values, has been found very successful. But, genetic gain through this technique is found to be very slow, time consuming. However, Due to availability of latest DNA sequencing technologies, now it is possible to estimate breeding values more accurately by using information on variation in DNA sequence. Lots of research has been done in direction of marker assisted selection, still it has some limitation on its implementation. Genomic selection (GS) is proposed to overcome such limitation. GS is a form of marker-assisted selection in which genetic markers covering the whole genome are used. GS predicts breeding value using information available on phenotype and high density marker. Several techniques has been developed for selection and prediction of genotype, these techniques are based on analysis of genotypic and phenotypic data.

Strategies for within-litter selection of piglets using ultra-low density SNP panels

Genotyping costs and the large number of selection candidates are major factors that inhibit the application of genomic selection in the swine industry and other small-sized livestock species. In order to reduce genotyping costs and increase the uptake of genomic selection, a possible strategy is to genotype animals with an affordable low-density (LD) SNP panel and, then accurately impute the LD panel to a high-density (HD) SNP panel. For within-litter piglet selection, genotyping all piglets from all farrows using the commercially available SNP chips is still cost prohibitive. Consequently, genomic evaluation is limited in this stage and genotypic and phenotypic data from all piglets in a litter are rarely available. This study investigates the feasibility of implementing genomic selection for within-litter piglet selection, using a total of nine simulated LD panels: from the “ultra” low (300–3000 SNP markers) to moderately low (6000–10, 000 SNP markers). For each LD panel, the performance of the genomic predictions according to the accuracy of genotype imputation, the accuracy of the genomic estimated breeding values (GEBV) based on the imputed data, and distribution of the correctly selected animals within litter was evaluated and compared to using the simulated HD panel (60,000 SNP) and True Breeding Values (TBVs). In this simulation study, we considered three economically important traits: back fat thickness (BF), growth rate of age to 100 Kg (GR), and litter size (LS). For the LD panel sizes ranging from 300 to 10,000, the accuracy of imputation (measured as concordance rate) ranged from 73.20 to 99.81%; and the mean proportion of the correctly selected top rank animals within litter ranged from 55 to 98%. Based on the trade-off between panel size and genomic selection accuracy, the use of a LD panel containing 1500 SNPs might be recommended, as this panel yielded more than 85% correctly selected animals within-litter based on all three traits.

Expected benefit of genomic selection over forward selection in conifer breeding and deployment.

Genomic selection is a proven technology in animal and plant breeding to accelerate genetic gain, but as yet is to be fully realised in forest tree breeding. This paper examines, through stochastic simulation, the potential benefits of genomic selection (GS) over forward selection (FS) in a typical conifer breeding program. Methods of speeding the deployment of selected material were also considered, including top-grafting onto mature seed orchard ortets, using additional replicates of clones in archives for crossing, and embryogenesis and clonal propagation. Genetic gain per generation was found to increase considerably when the size of the training population was larger (800 c.f. 3000 clones), or when the heritability was higher (0.2 c.f. 0.5). The largest genetic gain, of 24% was achieved where large training populations (3000 clones) and high heritability traits (0.5) were combined. The accuracy of genomic breeding values (GEBVs) increased with the increase in the number of clones in the training population, the heritability of the trait and the density of the SNP markers. Calculated accuracies of simulated GEBVs and genetic gain per unit of time suggested that 2000 clones in the training population is the minimum size for effective genomic selection for conifers. Compared with forward selection, genomic selection with 2000 clones in the training population, and a 60K SNP panel, an increase of 1.58 mm per year in diameter-at-breast-height (DBH) and 2.44 kg/m3 per year for wood density can be expected. After one generation (9-years), this would be equivalent to 14.23 mm and 21.97 kg/m3 for DBH and wood density respectively. Deploying clones of the selected individuals always resulted in higher additional genetic gain than deploying progeny/seedlings. Deploying genetic material selected from genomic selection with top-grafting for early coning appeared to be the best option. Application of genomic selection to conifer breeding programs, combined with deployment tools such as top-grafting and embryogenesis are powerful tools to speed the delivery of genetic gain to the forest.