Implementation Of Genomic Selection Research Articles

Predicting for disease resistance in aquaculture species using machine learning models

Predicting disease resistance is one of the most prominent applications of aquaculture selective breeding. Reductions in genotyping costs have allowed the implementation of genomic selection in an abundance of aquaculture species and their related diseases showing promising results. Machine learning (ML) models can be of value for prediction purposes, as suggested by several studies in both plants and livestock. The current study aimed to test the efficiency of various ML models in predicting disease resistance using both simulated and real datasets. More specifically, models like decision trees (DT), support vector machines (SVM), random forests (RF), adaptive boosting (Adaboost) and extreme gradient boosting (XGB) were benchmarked against genomic best linear unbiased prediction for threshold traits backend by Markov chain Monte Carlo (GBLUP-MCMC) both in terms of prediction efficiency and required computational time. Moreover, the model ranking was tested in datasets where the ratio between the two observed phenotypes (resistant vs non-resistant) was unbalanced. Across all tested datasets, XGB ranked first with a slight advantage over GBLUP-MCMC, ranging between 1–4 %. SVM and RF delivered predictions in tight proximity with the ones from XGB and GBLUP-MCMC. In addition, predictions 3–4 % lower compared to GBLUP-MCMC were obtained with Adaboost. On the other hand, the predictions from DT were consistently low (∼40 % lower compared to GBLUP-MCMC). All tested ML models had significantly reduced computational requirements than GBLUP-MCMC. In the case of XGB, the computational requirements were reduced more than 20-fold as opposed to GBLUP-MCMC under the settings of the current study. RF delivered both competitive predictions and was highly efficient in terms of the required computational time (∼3 min). Overall, the results of the current study suggest that ML models can be valuable tools in aquaculture breeding studies for disease resistance.

Genomic selection for resistance to Francisellosis in commercial Nile tilapia population: Genetic and genomic parameters, correlation with growth rate and predictive ability

Nile tilapia has become the most important species within freshwater aquaculture worldwide, the global tilapia market being expected to reach US$ 25 billion before the end of 2028. Since on an average 50% of the biomass is being lost during outbreaks of Francisellosis in tilapia farms, this study was undertaken to estimate the genetic parameters for resistance to this economically important disease of Nile tilapia, and calculate the genetic correlation with body weights at different ages. A further aim was to characterize the potential of, and to select the best genomic model for, the implementation of genomic selection for resistance to Francisellosis in the commercial Nile tilapia breeding program. The study revealed moderate to high heritabilities (0.37 ± 0.05 to 0.51 ± 0.06) for resistance to Francisellosis using both pedigree and genomic-based estimates. The genetic correlation between resistance to Francisellosis and five different growth traits at different ages ranged from −0.09 to −0.29. Compared to pedigree-based models, GBLUP was found to increase the predictive ability for resistance to Francisellosis by 57%, and the use of Bayesian models would increase this predictive ability by up to 217% in the practical breeding scenario. Thus, the study shows the potential to increase resistance to Francisellosis by genetic selection in commercial Nile tilapia breeding programs. The genetic correlation between resistance to Francisellosis and growth traits showed negative trends, although they were significantly not different from zero. A very large increase in predictive ability for resistance to Francisellosis can be achieved by using Bayesian models for genomic selection.

An efficient integrated approach for nonlethal DNA sampling and genome-wide genotyping in bivalve molluscs

Genomic selection (GS) currently represents the hot research area in aquaculture, but implementing GS in some aquatic animals is still hampered by the lacking of efficient nonlethal DNA sampling approach for genome-wide genotyping. This is particularly challenging for bivalve molluscs as they generally have small body size and are vulnerable to mass-tissue sampling methods. Here we describe an integrated approach for rapid, cost-effective nonlethal gill DNA sampling and genome-scale genotyping in scallop. The nonlethal DNA sampling procedure can be finished within 20 min and bypass many tedious and time-consuming steps in the traditional phenol-chloroform method and without using any commercial kit. Experimental optimization of nonlethal gill DNA sampling for genome-wide genotyping was achieved by taking advantage of orthogonal array testing for conquering the complexity of factor combinations. Based on the orthogonal array testing and preferred reduction of gill usage, the best configuration of tissue lysis for direct DNA use without any purification steps was determined, and its superior genotyping performance and high reproducibility were validated by using the high-throughput, cost-efficient 2b-RAD sequencing. We also demonstrated the great applicability of our method on other bivalves. Taken together, our study provides a highly efficient integrated approach of nonlethal DNA sampling and genome-wide genotyping for implementation of GS in shellfish aquaculture.

Performance of genome prediction for morphological and growth-related traits in Yellow River carp

Common Carp (Cyprinus carpio) is the earliest truly domesticated fish, and genetic improvement of its economic traits is important for satisfying human protein demand.However, very little is known about the performance of Genomic selection (GS) in common carp by used SNP array data. Here we estimated the genetic parameters and evaluated the performance of GS for 24 morphological and growth-related traits in Yellow River carp. The result showed that the heritability of these traits ranged from medium to high. The predictive ability of most traits based on family cross-validation was higher than that of random cross-validation. Compared with best linear unbiased prediction (BLUP) method, genomic BLUP (GBLUP) method has more advantage on predictive ability of low heritability traits, with a maximum increase ratio of 37.5%. Gene annotation information is not helpful to improving predictive ability, but GBLUP based on haplotype information obtained highest accuracy on 6 of 24 traits, and compared with GBLUP, the average ratio of prediction abilities increased by 2%. This study shows that it is necessary to assess the effect of family structure on reference group construction before calculated the breeding value. In additon, biological information has a certain potential for improving prediction ability. Low density array genotyping, as a kind of method can decrease the cost of genotyping and facilitate the implementation of genomic selection in aquatic breeding.

Optimizing imputation of marker data from genotyping-by-sequencing (GBS) for genomic selection in non-model species: Rubber tree (Hevea brasiliensis) as a case study

Genotyping-by-sequencing (GBS) provides the marker density required for genomic predictions (GP). However, GBS gives a high proportion of missing SNP data which, for species without a chromosome-level genome assembly, must be imputed without knowing the SNP physical positions. Here, we compared GP accuracy with seven map-independent and two map-dependent imputation approaches, and when using all SNPs against the subset of genetically mapped SNPs. We used two rubber tree (Hevea brasiliensis) datasets with three traits. The results showed that the best imputation approaches were LinkImputeR, Beagle and FImpute. Using the genetically mapped SNPs increased GP accuracy by 4.3%. Using LinkImputeR on all the markers allowed avoiding genetic mapping, with a slight decrease in GP accuracy. LinkImputeR gave the highest level of correctly imputed genotypes and its performances were further improved by its ability to define a subset of SNPs imputed optimally. These results will contribute to the efficient implementation of genomic selection with GBS. For Hevea, GBS is promising for rubber yield improvement, with GP accuracies reaching 0.52.

Accuracy of Imputation of Microsatellite Markers from a 50K SNP Chip in Spanish Assaf Sheep.

Simple SummaryParentage misassignments directly affect genetic gain in traditional breeding programs. The use of genetic markers facilitates parentage verification. In sheep, microsatellite markers and single nucleotide polymorphism (SNP) markers have been proposed by the International Society of Animal Sciences (ISAG) for parentage testing. Since the implementation of genomic selection, the microsatellite information used for parental testing in previous generations is gradually being replaced by SNPs. However, parentage verifications should all be performed using the same technology. A strategy for transitioning from microsatellites to SNP markers, while avoiding extra genotyping costs, is the imputation of microsatellite alleles from SNP haplotypes. This study aims to identify the optimum approach, using a minimum number of SNPs to accurately impute microsatellite markers and developing a low-density SNP chip for parentage verification in the Assaf sheep breed. The imputation approach described here reached high accuracies using a low number of SNP markers, which supports the development of a low-density SNP chip that could avoid the problems of genotyping with both technologies, being a cost-effective method for parentage testing. This study will help sheep breeders to perform parentage verification when different genotyping platforms have been used across generations.Transitioning from traditional to new genotyping technologies requires the development of bridging methodologies to avoid extra genotyping costs. This study aims to identify the optimum number of single nucleotide polymorphisms (SNPs) necessary to accurately impute microsatellite markers to develop a low-density SNP chip for parentage verification in the Assaf sheep breed. The accuracy of microsatellite marker imputation was assessed with three metrics: genotype concordance (C), genotype dosage (length r2), and allelic dosage (allelic r2), for all imputation scenarios tested (0.5–10 Mb microsatellite flanking SNP windows). The imputation accuracy for the three metrics analyzed for all haplotype lengths tested was higher than 0.90 (C), 0.80 (length r2), and 0.75 (allelic r2), indicating strong genotype concordance. The window with 2 Mb length provides the best accuracy for the imputation procedure and the design of an affordable low-density SNP chip for parentage testing. We additionally evaluated imputation performance under two null models, naive (imputing the most common allele) and random (imputing by randomly selecting the allele), which in comparison showed weak genotype concordances (0.41 and 0.15, respectively). Therefore, we describe a precise methodology in the present article to impute multiallelic microsatellite genotypes from a low-density SNP chip in sheep and solve the problem of parentage verification when different genotyping platforms have been used across generations.

Implementation of genomic selection in public-sector plant breeding programs: Current status and opportunities

The usefulness of genomic selection (GS) for improvement of complex traits has been demonstrated in plant and animal breeding. While fully adopted in the livestock sector and commercial plant breeding, the implementation of GS in public-sector plant breeding programs lags. The goal of this review is to discuss advancements in GS implementation and opportunities for near-term and long-term adoption in public-sector plant breeding programs. We also highlighted specific applications of GS for cultivar development ordered by what we believe to be their feasibility, where feasibility is defined by cost, disruption to current breeding practices, and risk.

Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle

There is a growing interest to improve feed efficiency (FE) traits in cattle. The genomic selection was proposed to improve these traits since they are difficult and expensive to measure. Up to date, there are scarce studies about the implementation of genomic selection for FE traits in indicine cattle under different scenarios of pseudo-phenotypes, models, and validation strategies on a commercial large scale. Thus, the aim was to evaluate the feasibility of genomic selection implementation for FE traits in Nelore cattle applying different models and pseudo-phenotypes under validation strategies. Phenotypic and genotypic information from 4 329 and 3 467 animals were used, respectively, which were tested for residual feed intake, DM intake, feed efficiency, feed conversion ratio, residual BW gain, and residual intake and BW gain. Six prediction methods were used: single-step genomic best linear unbiased prediction, Bayes A, Bayes B, Bayes Cπ, Bayesian least absolute shrinkage and selection operator (BLASSO), and Bayes R. Phenotypes adjusted for fixed effects (Y*), estimated breeding value (EBV), and EBV deregressed (DEBV) were used as pseudo-phenotypes. The validation approaches used were: (1) random: the data was randomly divided into ten subsets and the validation was done in each subset at a time; (2) age: the partition into training and testing sets was based on year of birth and testing animals were born after 2016; and (3) EBV accuracy: the data was split into two groups, being animals with accuracy above 0.45 the training set; and below 0.45 the validation set. In the analyses that used the Y* as pseudo-phenotype, prediction ability (PA) was obtained by dividing the correlation between pseudo-phenotype and genomic EBV (GEBV) by the square root of the heritability of the trait. When EBV and DEBV were used as the pseudo-phenotype, the simple correlation of this quantity with the GEBV was considered as PA. The prediction methods show similar results for PA and bias. The random cross-validation presented higher PA (0.17) than EBV accuracy (0.14) and age (0.13). The PA was higher for Y* than for EBV and DEBV (30.0 and 34.3%, respectively). Random validation presented the highest PA, being indicated for use in populations composed mainly of young animals and traits with few generations of data recording. For high heritability traits, the validation can be done by age, enabling the prediction of the next-generation genetic merit. These results would support breeders to identify genomic approaches that are more viable for genomic prediction for FE-related traits.

Adoption and Optimization of Genomic Selection To Sustain Breeding for Apricot Fruit Quality.

Genomic selection (GS) is a breeding approach which exploits genome-wide information and whose unprecedented success has shaped several animal and plant breeding schemes through delivering their genetic progress. This is the first study assessing the potential of GS in apricot (Prunus armeniaca) to enhance postharvest fruit quality attributes. Genomic predictions were based on a F1 pseudo-testcross population, comprising 153 individuals with contrasting fruit quality traits. They were phenotyped for physical and biochemical fruit metrics in contrasting climatic conditions over two years. Prediction accuracy (PA) varied from 0.31 for glucose content with the Bayesian LASSO (BL) to 0.78 for ethylene production with RR-BLUP, which yielded the most accurate predictions in comparison to Bayesian models and only 10% out of 61,030 SNPs were sufficient to reach accurate predictions. Useful insights were provided on the genetic architecture of apricot fruit quality whose integration in prediction models improved their performance, notably for traits governed by major QTL. Furthermore, multivariate modeling yielded promising outcomes in terms of PA within training partitions partially phenotyped for target traits. This provides a useful framework for the implementation of indirect selection based on easy-to-measure traits. Thus, we highlighted the main levers to take into account for the implementation of GS for fruit quality in apricot, but also to improve the genetic gain in perennial species.

30 Inbreeding in American Angus cattle before and after the implementation of genomic selection

Abstract The aim of this study was to characterize the American Angus cattle population in terms of changes to the inbreeding rate (ΔF) and effective population size (Ne) before and after the implementation of genomic selection (GS). Genomic information (89,206 SNPs) was obtained for 25,960 bulls and 134,962 cows born between the years 2000 and 2017. Bulls and cows were independently grouped into two groups based on year of birth, pre-GS (2000–2009), and post-GS (2010–2017). Genomic inbreeding (FGRM) was calculated assuming fixed allele frequencies (0.5). Inbreeding based on runs of homozygosity (FROH) was calculated using software SNP1101 (Sargolzaei, 2014). The yearly ΔF for each group was estimated by regressing the inbreeding coefficients on year of birth. The generation intervals (L) were calculated for each of the four pathways of selection at both time periods (pre-GS and post-GS), where the mean of the sires of sires and dams of sires pathways was taken to be the generation interval for the bulls and the mean of the sires of dams and dams of dams pathways was taken to be the generation interval for the cows. The L and ΔF of the three inbreeding coefficients were used to estimate the Ne. Estimates of ΔF and Ne for both sexes at the two time periods can be found in table 1. In both sexes, ΔFROH decreased and NeROH increased from pre-GS to post-GS. For bulls, ΔFGRM and NeGRM did not change, and for cows, ΔFGRM decreased and NeGRM increased from pre-GS to post-GS. These results suggest that the implementation of genomic selection in Angus cattle has not caused the increased inbreeding rates and reduced effective population sizes seen in other species, but instead has been beneficial for the preservation of genetic diversity.

Favorable Conditions for Genomic Evaluation to Outperform Classical Pedigree Evaluation Highlighted by a Proof-of-Concept Study in Poplar.

Forest trees like poplar are particular in many ways compared to other domesticated species. They have long juvenile phases, ongoing crop-wild gene flow, extensive outcrossing, and slow growth. All these particularities tend to make the conduction of breeding programs and evaluation stages costly both in time and resources. Perennials like trees are therefore good candidates for the implementation of genomic selection (GS) which is a good way to accelerate the breeding process, by unchaining selection from phenotypic evaluation without affecting precision. In this study, we tried to compare GS to pedigree-based traditional evaluation, and evaluated under which conditions genomic evaluation outperforms classical pedigree evaluation. Several conditions were evaluated as the constitution of the training population by cross-validation, the implementation of multi-trait, single trait, additive and non-additive models with different estimation methods (G-BLUP or weighted G-BLUP). Finally, the impact of the marker densification was tested through four marker density sets. The population under study corresponds to a pedigree of 24 parents and 1,011 offspring, structured into 35 full-sib families. Four evaluation batches were planted in the same location and seven traits were evaluated on 1 and 2 years old trees. The quality of prediction was reported by the accuracy, the Spearman rank correlation and prediction bias and tested with a cross-validation and an independent individual test set. Our results show that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions. Genomic evaluation appeared to be advantageous when using an independent test set and a set of less precise phenotypes. Genome-based methods showed advantages over pedigree counterparts when ranking candidates at the within-family levels, for most of the families. Our study also showed that looking at ranking criteria as Spearman rank correlation can reveal benefits to genomic selection hidden by biased predictions.

Multi-Year Dynamics of Single-Step Genomic Prediction in an Applied Wheat Breeding Program

The availability of cost-efficient genotyping technologies has facilitated the implementation of genomic selection into numerous breeding programs. However, some studies reported a superiority of pedigree over genomic selection in line breeding, and as, aside from systematic record keeping, no additional costs are incurring in pedigree-based prediction, the question about the actual benefit of fingerprinting several hundred lines each year might suggest itself. This study aimed thus on shedding some light on this question by comparing pedigree, genomic, and single-step prediction models using phenotypic and genotypic data that has been collected during a time period of ten years in an applied wheat breeding program. The mentioned models were for this purpose empirically tested in a multi-year forward prediction as well as a supporting simulation study. Given the availability of deep pedigree records, pedigree prediction performed similar to genomic prediction for some of the investigated traits if preexisting information of the selection candidates was available. Notwithstanding, blending both information sources increased the prediction accuracy and thus the selection gain substantially, especially for low heritable traits. Nevertheless, the largest advantage of genomic predictions can be seen for breeding scenarios where such preexisting information is not systemically available or difficult and costly to obtain.

Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding.

SummaryTransgressive segregation and heterosis are the reasons that plant breeding works. Molecular explanations for both phenomena have been suggested and play a contributing role. However, it is often overlooked by molecular genetic researchers that transgressive segregation and heterosis are most simply explained by dispersion of favorable alleles. Therefore, advances in molecular biology will deliver the most impact on plant breeding when integrated with sources of heritable trait variation – and this will be best achieved within a quantitative genetics framework. An example of the power of quantitative approaches is the implementation of genomic selection, which has recently revolutionized animal breeding. Genomic selection is now being applied to both hybrid and inbred crops and is likely to be the major source of improvement in plant breeding practice over the next decade. Breeders’ ability to efficiently apply genomic selection methodologies is due to recent technology advances in genotyping and sequencing. Furthermore, targeted integration of additional molecular data (such as gene expression, gene copy number and methylation status) into genomic prediction models may increase their performance. In this review, we discuss and contextualize a suite of established quantitative genetics themes relating to hybrid vigour, transgressive segregation and their central relevance to plant breeding, with the aim of informing crop researchers outside of the quantitative genetics discipline of their relevance and importance to crop improvement. Better understanding between molecular and quantitative disciplines will increase the potential for further improvements in plant breeding methodologies and so help underpin future food security.

Maximizing efficiency of genomic selection in CIMMYT\u2019s tropical maize breeding program

Key messageHistorical data from breeding programs can be efficiently used to improve genomic selection accuracy, especially when the training set is optimized to subset individuals most informative of the target testing set.The current strategy for large-scale implementation of genomic selection (GS) at the International Maize and Wheat Improvement Center (CIMMYT) global maize breeding program has been to train models using information from full-sibs in a “test-half-predict-half approach.” Although effective, this approach has limitations, as it requires large full-sib populations and limits the ability to shorten variety testing and breeding cycle times. The primary objective of this study was to identify optimal experimental and training set designs to maximize prediction accuracy of GS in CIMMYT’s maize breeding programs. Training set (TS) design strategies were evaluated to determine the most efficient use of phenotypic data collected on relatives for genomic prediction (GP) using datasets containing 849 (DS1) and 1389 (DS2) DH-lines evaluated as testcrosses in 2017 and 2018, respectively. Our results show there is merit in the use of multiple bi-parental populations as TS when selected using algorithms to maximize relatedness between the training and prediction sets. In a breeding program where relevant past breeding information is not readily available, the phenotyping expenditure can be spread across connected bi-parental populations by phenotyping only a small number of lines from each population. This significantly improves prediction accuracy compared to within-population prediction, especially when the TS for within full-sib prediction is small. Finally, we demonstrate that prediction accuracy in either sparse testing or “test-half-predict-half” can further be improved by optimizing which lines are planted for phenotyping and which lines are to be only genotyped for advancement based on GP.

Development of a SNP linkage map and genome-wide association study for resistance to Aeromonas hydrophila in pacu (Piaractus mesopotamicus)

BackgroundPacu (Piaractus mesopotamicus) is one of the most important Neotropical aquaculture species from South America. Disease outbreaks caused by Aeromonas hydrophila infection have been considered significant contributors to the declining levels of pacu production. The current implementation of genomic selection for disease resistance has been adopted as a powerful strategy for improvement in fish species. This study aimed to investigate the genetic architecture of resistance to A. hydrophila in pacu via Genome-Wide Association Study (GWAS), the identification of suggestive Quantitative Trait Loci (QTLs) and putative genes associated with this trait. The genetic data were obtained from 381 juvenile individuals belonging to 14 full-sibling families. An experimental challenge was performed to gain access to the levels of genetic variation for resistance against the bacteria using the following trait definitions: binary test survival (TS) and time of death (TD).ResultsThe analyses of genetic parameters estimated moderate heritability (h2) for both resistance traits: 0.20 (± 0.09) for TS and 0.35 (± 0.15) for TD. A linkage map for pacu was developed to enable the GWAS, resulting in 27 linkage groups (LGs) with 17,453 mapped Single Nucleotide Polymorphisms (SNPs). The length of the LGs varied from 79.95 (LG14) to 137.01 (LG1) cM, with a total map length of 2755.60 cM. GWAS identified 22 putative QTLs associated to A. hydrophila resistance. They were distributed into 17 LGs, and were considered suggestive genomic regions explaining > 1% of the additive genetic variance (AGV) for the trait. Several candidate genes related to immune response were located close to the suggestive QTLs, such as tbk1, trim16, Il12rb2 and lyz2.ConclusionThis study describes the development of the first medium density linkage map for pacu, which will be used as a framework to study relevant traits to the production of this species. In addition, the resistance to A. hydrophila was found to be moderately heritable but with a polygenic architecture suggesting that genomic selection, instead of marker assisted selection, might be useful for efficiently improving resistance to one of the most problematic diseases that affects the South American aquaculture.

Coupling day length data and genomic prediction tools for predicting time-related traits under complex scenarios

Genomic selection (GS) has proven to be an efficient tool for predicting crop-rank performance of untested genotypes; however, when the traits have intermediate optima (phenology stages), this implementation might not be the most convenient. GS might deliver high-rank correlations but incurring in serious bias. Days to heading (DTH) is a crucial development stage in rice for regional adaptability with a significant impact on yield potential. The objective of this research consisted in develop a novel method that accurately predicts time-related traits such as DTH in unobserved environments. For this, we propose an implementation that incorporates day length information (DL) in the prediction process for two relevant scenarios: CV0, predicting tested genotypes in unobserved environments (C method); and CV00, predicting untested genotypes in unobserved environments (CB method). The use of DL has advantages over weather data since it can be determined in advance just by knowing the location and planting date. The proposed methods showed that DL information significantly helps to improve the predictive ability of DTH in unobserved environments. Under CV0, the C method returned a root-mean-square error (RMSE) of 3.9 days, a Pearson correlation (PC) of 0.98 and the differences between the predicted and observed environmental means (EMD) ranged between -4.95 and 4.67 days. For CV00, the CB method returned an RMSE of 7.3 days, a PC of 0.93 and the EMD ranged between -6.4 and 4.1 days while the conventional GS implementation produced an RMSE of 18.1 days, a PC of 0.41 and the EMD ranged between -31.5 and 28.7 days.

Expected benefits of genomic selection for growth and wood quality traits in Eucalyptus grandis

Genomic selection (GS) can substantially reduce breeding cycle times in forest trees compared to traditional breeding cycles. Practical implementation of GS in tree breeding requires an assessment of significant drivers of genetic gains over time, which may differ among species and breeding objectives. We present results of a GS study of growth and wood quality traits in an operational Eucalyptus grandis breeding program in South Africa. The training population consisted of 1575 full and half-sib individuals, genotyped with the Eucalyptus (EUChip60K) SNP chip resulting in 15,040 informative SNP markers. The accuracy of the GS models ranged from 0.47 (diameter) to 0.67 (fibre width). We compared a 4-year GS breeding cycle equivalent to half of a traditional 8-year E. grandis breeding cycle and obtained GS efficiencies ranging from 1.20 (wood density) to 1.62 (fibre length). Simulated over 17 years, the ratio of the accumulated genetic gains between three GS cycles and two traditional breeding cycles ranged from 1.53 (diameter) to 3.35 (wood density). To realise these genetic gains per unit time in E. grandis breeding, we show that significant adjustments have to be made to integrate GS into operational breeding steps.

Genetic mechanisms underlying feed utilization and implementation of genomic selection for improved feed efficiency in dairy cattle

The economic importance of genetically improving feed efficiency has been recognized by cattle producers worldwide. It has the potential to considerably reduce costs, minimize environmental impact, optimize land and resource use efficiency, and improve the overall cattle industry’s profitability. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. The main objective of this review paper is to present an overview of the main genetic and physiological mechanisms underlying feed utilization in ruminants and the process towards implementation of genomic selection for feed efficiency in dairy cattle. In summary, feed efficiency can be improved via numerous metabolic pathways and biological mechanisms through genetic selection. Various studies have indicated that feed efficiency is heritable, and genomic selection can be successfully implemented in dairy cattle with a large enough training population. In this context, some organizations have worked collaboratively to do research and develop training populations for successful implementation of joint international genomic evaluations. The integration of “-omics” technologies, further investments in high-throughput phenotyping, and identification of novel indicator traits will also be paramount in maximizing the rates of genetic progress for feed efficiency in dairy cattle worldwide.

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.