Pedigree-based Selection Research Articles

Genotyping both live and dead animals to improve post-weaning survival of pigs in breeding programs

BackgroundIn this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS).MethodsStochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10). Pedigree-based selection was conducted as a point of reference. Variance components were estimated and then estimated breeding values (EBV) were obtained in each breeding scheme using a linear or a threshold model. Selection was for a single trait, i.e. PWS with a heritability of 0.02 on the observed scale. The trait was simulated on the underlying scale and was recorded as binary (0/1). Selection candidates were genotyped and phenotyped before selection, with only live candidates eligible for selection. Genotyping strategies differed in the proportion of live and dead animals genotyped, but the phenotypes of all animals were used for predicting EBV of the selection candidates.ResultsBased on a 0.01 rate of pedigree-based inbreeding, GSD realised 14 to 33% more genetic gain than GOS for all breeding schemes depending on PWS and litter size. GSD increased the prediction accuracy of EBV for PWS by at least 14% compared to GOS. The use of a linear versus a threshold model did not have an impact on genetic gain for PWS regardless of the genotyping strategy and the bias of the EBV did not differ significantly among genotyping strategies.ConclusionsGenotyping both dead and live animals was more informative than genotyping only live animals to predict the EBV for PWS of selection candidates, but with marginal increases in genetic gain when the proportion of dead animals genotyped was 60% or greater. Therefore, it would be worthwhile to use genomic information on both live and more than 20% dead animals to compute EBV for the genetic improvement of PWS under the assumption that dead animals reflect increased liability on the underlying scale.

Genetic parameters and genomic prediction of resistance to koi herpesvirus disease using a low-density SNP panel on two Amur mirror carp populations

Koi herpesvirus disease (KHVD), caused by Cyprinid herpesvirus-3 (CyHV-3), is one of the most serious threats to carp farming. In the present study, we investigated the efficiency of a low-density (LD) SNP panel for estimating genetic parameters and breeding values to KHVD resistance in the Amur mirror carp (AMC). Two populations (Pop 1 and Pop 2) of AMC generated from unrelated parents were created using a partial factorial design. One-year old fish (Pop 1 = 1500 individuals.; Pop 2 = 1200 individuals) were challenged with CyHV-3 and phenotyped to KHVD resistance. 218 SNPs originating from a medium genotyping platform previously applied to Pop 1 (15615 SNPs; denoted as MD panel) with the highest association to KHVD resistance were used as a LD panel to genotype individuals of Pop 2. Genetic parameters and estimated pedigree-based BLUP (EBV) and genomic-based GBLUP (GEBV_MD and GEBV_LD) breeding values were calculated and obtained for Pop 1 using either pedigree, MD or LD panel and for Pop 2 using either pedigree or the LD panel. The heritability estimates of KHVD resistance were very high for both populations ranging from 0.42 to 0.96. Selection for KHVD resistance in Pop 2 using the LD panel would have led to a relative increase of ∼7% in prediction accuracy compared to the pedigree-based selection. Pearson correlation coefficients between pedigree-based and genomic-based estimated breeding values (EBV vs. GEBV_MD; EBV vs. GEBV_LD; GEBV_MD vs. GEBV_LD) showed a strong association for both populations (0.79 – 0.91). In addition, the concordance rate of individuals selected by pedigree-based (EBV) and genomic-based breeding values (GEBV_MD and GEBV_LD) within selection pressures of 5%, 10% and 20% were not statistically different in most cases. In conclusion, the low-density SNP panel could be useful for a selection program focused on the genetic improvement of KHVD resistance.

Genome-wide association study and genomic prediction of tolerance to acute hypoxia in rainbow trout

Hypoxia is one of the major threats to the aquaculture sector resulting in substantial economic losses to the fish farmers. Thus, tolerance to hypoxia is of high economic interest to be genetically improved by breeding programs. Rainbow trout (Oncorhynchus mykiss) is one of the most cultured salmonid species worldwide, with well-developed breeding programs. Still, studies of genetic potential to improve hypoxia tolerance in this species are rare. In the present study, 1320 individuals of rainbow trout were used for a genome-wide association study of acute hypoxia tolerance based on imputed high-density genotypes to explore the genetic architecture and related candidate genes affecting hypoxia response. Three significant (Omy31_1, Omy31_2, Omy20) and two putative (Omy15, Omy28) quantitative trait loci (QTLs) were detected, but each of them only explained between 0.2% and 0.8% of the genetic variance of acute hypoxia tolerance. However, heritability was estimated at a moderate value of 0.24–0.28, that suggests a solid potential to improve hypoxia tolerance in the studied rainbow trout population by genetic selection. Moreover, it was shown that genomic prediction for hypoxia tolerance would lead to a relative increase of ∼11% for genomic selection (GS) accuracy compared to the pedigree-based selection, considering a reference population of 1000 individuals. Finally, fifteen genes (ids, fmr1, arx, lonrf3, commd5, map4k4, smu1, b4galt1, re1, abca1, noa1, igfbp7, noxo1, bcl2a, mylk3) were proposed as potential functional candidates involved in hypoxia tolerance. Taking all proposed candidate genes (6 out of 15 genes) and high linkage disequilibrium (r2) values within the main QTL (Omy31_1), we may hypothesize that the complex response to acute hypoxia in rainbow trout, i.e., the interplay between behavioural, morphological, and physiological responses, is primarily encoded by a supergene. However, further functional validation of their effects may help to specify the biological mechanisms triggering a response to acute hypoxia in rainbow trout.

Genetic architecture and genomic selection of fatty acid composition predicted by Raman spectroscopy in rainbow trout

BackgroundIn response to major challenges regarding the supply and sustainability of marine ingredients in aquafeeds, the aquaculture industry has made a large-scale shift toward plant-based substitutions for fish oil and fish meal. But, this also led to lower levels of healthful n−3 long-chain polyunsaturated fatty acids (PUFAs)—especially eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids—in flesh. One potential solution is to select fish with better abilities to retain or synthesise PUFAs, to increase the efficiency of aquaculture and promote the production of healthier fish products. To this end, we aimed i) to estimate the genetic variability in fatty acid (FA) composition in visceral fat quantified by Raman spectroscopy, with respect to both individual FAs and groups under a feeding regime with limited n-3 PUFAs; ii) to study the genetic and phenotypic correlations between FAs and processing yields- and fat-related traits; iii) to detect QTLs associated with FA composition and identify candidate genes; and iv) to assess the efficiency of genomic selection compared to pedigree-based BLUP selection.ResultsProportions of the various FAs in fish were indirectly estimated using Raman scattering spectroscopy. Fish were genotyped using the 57 K SNP Axiom™ Trout Genotyping Array. Following quality control, the final analysis contained 29,652 SNPs from 1382 fish. Heritability estimates for traits ranged from 0.03 ± 0.03 (n-3 PUFAs) to 0.24 ± 0.05 (n-6 PUFAs), confirming the potential for genomic selection. n-3 PUFAs are positively correlated to a decrease in fat deposition in the fillet and in the viscera but negatively correlated to body weight. This highlights the potential interest to combine selection on FA and against fat deposition to improve nutritional merit of aquaculture products. Several QTLs were identified for FA composition, containing multiple candidate genes with indirect links to FA metabolism. In particular, one region on Omy1 was associated with n-6 PUFAs, monounsaturated FAs, linoleic acid, and EPA, while a region on Omy7 had effects on n-6 PUFAs, EPA, and linoleic acid. When we compared the effectiveness of breeding programmes based on genomic selection (using a reference population of 1000 individuals related to selection candidates) or on pedigree-based selection, we found that the former yielded increases in selection accuracy of 12 to 120% depending on the FA trait.ConclusionThis study reveals the polygenic genetic architecture for FA composition in rainbow trout and confirms that genomic selection has potential to improve EPA and DHA proportions in aquaculture species.

Comparison of BLUP and Bayesian methods for different sizes of training population in genomic selection

This study aims to compare the accuracy of pedigree-based and genomic-based breeding value prediction for different training population sizes. In this study, Bayes (A, B, C, Cpi) and GBLUP methods for genomic selection and BLUP method for pedigree-based selection were used. Genomic and pedigree-based breeding values were estimated for partial milk yield (158 days) of Holstein cows (400 individuals) from a private enterprise in the USA. For this aim, populations were created for indirect breeding value estimates as training (322–360) and test (78–40) populations. In animals genotyped with a 54k SNP, the marker file was encoded as –10, 0, and 10 for AA, AB, and BB marker genotypes, respectively. Bayes and GBLUP methods were performed using GenSel 4.55 software. A total of 50,000 iterations were used, with the first 5000 excluded as the burn-in. Pedigree-based breeding values were estimated by REML using MTDFREML software employing an animal model. Correlations between partial milk yield and estimated breeding values were used to assess the predictive ability for methods. Bayes B method gave the highest accuracy for the indirect estimate of breeding value.

Genetic architecture and genomic selection of female reproduction traits in rainbow trout

BackgroundRainbow trout is a significant fish farming species under temperate climates. Female reproduction traits play an important role in the economy of breeding companies with the sale of fertilized eggs. The objectives of this study are threefold: to estimate the genetic parameters of female reproduction traits, to determine the genetic architecture of these traits by the identification of quantitative trait loci (QTL), and to assess the expected efficiency of a pedigree-based selection (BLUP) or genomic selection for these traits.ResultsA pedigreed population of 1343 trout were genotyped for 57,000 SNP markers and phenotyped for seven traits at 2 years of age: spawning date, female body weight before and after spawning, the spawn weight and the egg number of the spawn, the egg average weight and average diameter. Genetic parameters were estimated in multi-trait linear animal models. Heritability estimates were moderate, varying from 0.27 to 0.44. The female body weight was not genetically correlated to any of the reproduction traits. Spawn weight showed strong and favourable genetic correlation with the number of eggs in the spawn and individual egg size traits, but the egg number was uncorrelated to the egg size traits. The genome-wide association studies showed that all traits were very polygenic since less than 10% of the genetic variance was explained by the cumulative effects of the QTLs: for any trait, only 2 to 4 QTLs were detected that explained in-between 1 and 3% of the genetic variance. Genomic selection based on a reference population of only one thousand individuals related to candidates would improve the efficiency of BLUP selection from 16 to 37% depending on traits.ConclusionsOur genetic parameter estimates made unlikely the hypothesis that selection for growth could induce any indirect improvement for female reproduction traits. It is thus important to consider direct selection for spawn weight for improving egg production traits in rainbow trout breeding programs. Due to the low proportion of genetic variance explained by the few QTLs detected for each reproduction traits, marker assisted selection cannot be effective. However genomic selection would allow significant gains of accuracy compared to pedigree-based selection.

Development and Validation of an Open Access SNP Array for Nile Tilapia (Oreochromis niloticus).

Tilapia are among the most important farmed fish species worldwide, and are fundamental for the food security of many developing countries. Several genetically improved Nile tilapia (Oreochromis niloticus) strains exist, such as the iconic Genetically Improved Farmed Tilapia (GIFT), and breeding programs typically follow classical pedigree-based selection. The use of genome-wide single-nucleotide polymorphism (SNP) data can enable an understanding of the genetic architecture of economically important traits and the acceleration of genetic gain via genomic selection. Due to the global importance and diversity of Nile tilapia, an open access SNP array would be beneficial for aquaculture research and production. In the current study, a ∼65K SNP array was designed based on SNPs discovered from whole-genome sequence data from a GIFT breeding nucleus population and the overlap with SNP datasets from wild fish populations and several other farmed Nile tilapia strains. The SNP array was applied to clearly distinguish between different tilapia populations across Asia and Africa, with at least ∼30,000 SNPs segregating in each of the diverse population samples tested. It is anticipated that this SNP array will be an enabling tool for population genetics and tilapia breeding research, facilitating consistency and comparison of results across studies.

Genomic Breeding Programs Realize Larger Benefits by Cooperation in the Presence of Genotype × Environment Interaction Than Conventional Breeding Programs.

Genotype × environment interaction (G × E) is of increasing importance for dairy cattle breeders due to international multiple-environment selection of animals as well as the differentiation of production environments within countries. This theoretical simulation study tested the hypothesis that genomic selection (GS) breeding programs realize larger genetic benefits by cooperation in the presence of G × E than conventional pedigree-based selection (PS) breeding programs. We simulated two breeding programs each with their own cattle population and environment. Two populations had either equal or unequal population sizes. Selection of sires was done either across environments (cooperative) or within their own environment (independent). Four scenarios, (GS/PS) × (cooperative/independent), were performed. The genetic correlation (rg) between the single breeding goal trait expressed in two environments was varied between 0.5 and 0.9. We compared scenarios for genetic gain, rate of inbreeding, proportion of selected external sires, and the split-point rg that is the lowest value of rg for long-term cooperation. Between two equal-sized populations, cooperative GS breeding programs achieved a maximum increase of 19.3% in genetic gain and a maximum reduction of 24.4% in rate of inbreeding compared to independent GS breeding programs. The increase in genetic gain and the reduction in rate of inbreeding realized by GS breeding programs with cooperation were respectively at maximum 9.7% and 24.7% higher than those realized by PS breeding programs with cooperation. Secondly, cooperative GS breeding programs allowed a slightly lower split-point rg than cooperative PS breeding programs (0.85∼0.875 vs ≥ 0.9). Between two unequal-sized populations, cooperative GS breeding programs realized higher increase in genetic gain and showed greater probability for long-term cooperation than cooperative PS breeding programs. Secondly, cooperation using GS were more beneficial to the small population while also beneficial but much less to the large population. In summary, by cooperation in the presence of G × E, GS breeding programs realize larger improvements in terms of the genetic gain and rate of inbreeding, and have greater possibility of long-term cooperation than conventional PS breeding programs. Therefore, we recommend cooperative GS breeding programs in situations with mild to moderate G × E, depending on the sizes of two populations.

Impact of phenotypic information and composition of reference population on genomic prediction in fish under the presence of genotype by environment interaction

The difficulty or high cost to register pedigree and the presence of genotype by environment interaction (G×E) are the factors limiting conventional selective breeding in aquaculture. The limitation could be overcome by genomic selection. The objective of this study was to investigate the impact of phenotypic information and composition of reference population on genomic prediction in aquaculture under the presence of G×E, and the impact of genotyping strategy for selection candidates on genetic progress. The study was based on simulated data mimicking a rainbow trout population under G×E where genetic correlation between breeding environment and commercial production environment was 0.8, 0.5 or 0.2. Different scenarios of reference population were investigated, with regard to reference individuals from breeding population or production population, reference individuals were a random sample or a selected sample of top fish, and the phenotypic performance of the reference individuals were measured in breeding environment or in production environment. These scenarios were assessed on accuracy and unbiasedness of predicted breeding value for performance in commercial production environment. The results showed that genomic prediction greatly increased accuracy of selection, compared with conventional pedigree-based selection. Reference population comprising top individuals led to low accuracy and serious bias of genomic prediction. Accuracies of breeding values for performance in production environment predicted based on phenotypic information from breeding farm were low when genetic correlation between breeding and production environments was less than 0.8. With records of performance measured in production farm, reference population comprising individuals from breeding population gave higher prediction accuracy than the reference individuals from production population did. In addition, preselecting candidates from the breeding population for genotyping led to larger selection response than randomly genotyping candidates. These results indicate that genomic selection is feasible and promising in fish breeding under the presence of G×E. It is a good strategy to use a reference population comprising individuals randomly sampled from breeding population but reared in commercial production farm to get phenotypic records (RanBC in the context). Such a reference population is representative of the whole population, have a close relationship with candidates, and lead to a direct selection for performance in commercial production environment. In the situation of limited cost for genotyping selection candidates, genotyping top selection candidates is a good strategy since it can realize high selection intensity.

Different selection strategies for improvement of egg quality and hatching traits of Japanese quail

Abstract The present study evaluated the effect of different selection strategy (SS) on egg quality and hatching traits in four closed-bred flocks (CBF) of Japanese quail, already selected for higher 4-week body weight, and at three parental ages (PA). A total of 1620 eggs obtained from 540 quail breeders from the four CBF and three PA were used and the effects of three SS were investigated in relation to egg quality traits. For evaluation of SS on hatching traits, a total of 3240 quail hatching eggs were subjected to break out analysis. A completely randomised design in a factorial arrangement of 36 treatments with 9 replicates was applied. Treatments consisted of three selection strategies [pedigree based (PB), mass selected (MS) and random bred control (RBC)], four closed-bred flocks [Major (M), Kaleem (K), Saadat (S) and Zahid (Z)] and three parental age groups [10 (Y), 12 (P) and 14 (O) weeks]. Egg characteristics (egg weight, yolk index, Haugh unit score and shell thickness) and hatching traits (hatchability, infertile eggs, early embryonic mortality, late embryonic mortality and chick weight) were evaluated. Pedigree based selection significantly influenced hatchability, fertility, infertile eggs, early embryonic mortality, late embryonic mortality and chick weight. However, the results of different closed-bred flocks and age groups did not differ significantly. Regarding egg quality traits, significant effects of pedigree selection and closed-bred flocks were observed on yolk index and Haugh unit score. Age had significant effect on egg weight, yolk index, Haugh unit and shell thickness. It was concluded that pedigree-based selection among different age groups and closed-bred flocks had pronounced effects on egg quality and hatching traits in Japanese quail.

Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments.

Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information as prediction inputs in two different validation schemes. All models included main effects, but some considered interactions between the different types of pedigree and genomic covariates via Hadamard products of similarity kernels. Pedigree models always gave better prediction of new lines in observed environments than genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, genomes, and environments were included. When new lines were predicted in unobserved environments, in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design and prediction of the outcome of future breeding programs.

Genomic Prediction of Autotetraploids; Influence of Relationship Matrices, Allele Dosage, and Continuous Genotyping Calls in Phenotype Prediction.

Estimation of allele dosage, using genomic data, in autopolyploids is challenging and current methods often result in the misclassification of genotypes. Some progress has been made when using SNP arrays, but the major challenge is when using next generation sequencing data. Here we compare the use of read depth as continuous parameterization with ploidy parameterizations in the context of genomic selection (GS). Additionally, different sources of information to build relationship matrices were compared. A real breeding population of the autotetraploid species blueberry (Vaccinium corybosum), composed of 1,847 individuals was phenotyped for eight yield and fruit quality traits over two years. Continuous genotypic based models performed as well as the best models. This approach also reduces the computational time and avoids problems associated with misclassification of genotypic classes when assigning dosage in polyploid species. This approach could be very valuable for species with higher ploidy levels or for emerging crops where ploidy is not well understood. To our knowledge, this work constitutes the first study of genomic selection in blueberry. Accuracies are encouraging for application of GS for blueberry breeding. GS could reduce the time for cultivar release by three years, increasing the genetic gain per cycle by 86% on average when compared to phenotypic selection, and 32% when compared with pedigree-based selection. Finally, the genotypic and phenotypic data used in this study are made available for comparative analysis of dosage calling and genomic selection prediction models in the context of autopolyploids.

Accuracy of genomic selection for growth and wood quality traits in two control-pollinated progeny trials using exome capture as the genotyping platform in Norway spruce

BackgroundGenomic selection (GS) can increase genetic gain by reducing the length of breeding cycle in forest trees. Here we genotyped 1370 control-pollinated progeny trees from 128 full-sib families in Norway spruce (Picea abies (L.) Karst.), using exome capture as genotyping platform. We used 116,765 high-quality SNPs to develop genomic prediction models for tree height and wood quality traits. We assessed the impact of different genomic prediction methods, genotype-by-environment interaction (G × E), genetic composition, size of the training and validation set, relatedness, and number of SNPs on accuracy and predictive ability (PA) of GS.ResultsUsing G matrix slightly altered heritability estimates relative to pedigree-based method. GS accuracies were about 11–14% lower than those based on pedigree-based selection. The efficiency of GS per year varied from 1.71 to 1.78, compared to that of the pedigree-based model if breeding cycle length was halved using GS. Height GS accuracy decreased to more than 30% while using one site as training for GS prediction and using this model to predict the second site, indicating that G × E for tree height should be accommodated in model fitting. Using a half-sib family structure instead of full-sib structure led to a significant reduction in GS accuracy and PA. The full-sib family structure needed only 750 markers to reach similar accuracy and PA, as compared to 100,000 markers required for the half-sib family, indicating that maintaining the high relatedness in the model improves accuracy and PA. Using 4000–8000 markers in full-sib family structure was sufficient to obtain GS model accuracy and PA for tree height and wood quality traits, almost equivalent to that obtained with all markers.ConclusionsThe study indicates that GS would be efficient in reducing generation time of breeding cycle in conifer tree breeding program that requires long-term progeny testing. The sufficient number of trees within-family (16 for growth and 12 for wood quality traits) and number of SNPs (8000) are required for GS with full-sib family relationship. GS methods had little impact on GS efficiency for growth and wood quality traits. GS model should incorporate G × E effect when a strong G × E is detected.

Different Selection Strategies for the Improvement of the Growth Performance and Carcass Traits of Japanese Quails

ABSTRACT The present study evaluated different selection strategies to improve the growth performance and carcass traits of Japanese quails. To this end, 540 Japanese quails previously selected for high body weight at three ages (10, 12 and 14 weeks) from four close-bred flocks (Major, Kaleem, Saadat and Zahid) were subjected to three selection strategies: pedigree-based, mass selection or random-bred (controls). For pedigree-based selection, 108 birds (4 close-bred flocks × 3 age groups × 9 replicates × 1 bird each) with full pedigree and selected for high body weight were used, whereas in mass selection 324 birds (4 close-bred flocks × 3 age groups × 9 replicates × 3 birds each) with high body weight were selected to be the parents of next generation. Random-bred controls included 108 birds (4 close-bred flocks × 3 age groups × 9 replicates × 1bird each) which were not previously submitted to selection. The effects of selection strategies of Japanese quail parents from four close-bred flocks (CBF) at three ages on the performance growth and carcass traits of their progenies were measured. The parent flocks were selected at three ages (10, 12 and 14 weeks) and reared for 20 weeks, and their progenies were reared for four weeks. The progeny of pedigree-based selected parents presented better feed intake (g), body weight gain (g), feed conversion ratio, live and carcass weights (g), and carcass yield. The progeny of 10- and 14-week-old parents showed better growth performance and carcass traits, respectively. Moreover, there was no influence of CBF on growth and carcass traits. It was concluded that pedigree-based selection had pronounced effect on the growth performance and carcass traits of the progeny compared with mass selection and random breeding. It is suggested that parental ages of 10 and 14 weeks of age promote better progeny growth performance and carcass traits.

A new SNP-based vision of the genetics of sex determination in European sea bass (Dicentrarchus labrax)

BackgroundEuropean sea bass (Dicentrarchus labrax) is one of the most important farmed species in Mediterranean aquaculture. The observed sexual growth and maturity dimorphism in favour of females adds value towards deciphering the sex determination system of this species. Current knowledge indicates the existence of a polygenic sex determining determination system that interacts with temperature. This was explored by restriction-site associated DNA (RAD) marker analysis in a test panel of 175 offspring that originated from a factorial cross between two dams and four sires from a single full-sib family.ResultsThe first high-density single nucleotide polymorphism (SNP) based linkage map for sea bass was constructed, consisting of 6706 SNPs on 24 linkage groups. Indications for putative sex-determining QTL (quantitative trait loci) that were significant at the genome-wide threshold were detected on linkage groups 6, 11 and 18 to 21, although a genome-wide association study (GWAS) did not identify individual significant SNPs at a genome-wide threshold. A preliminary genomic prediction approach that tested the efficiency of SNP-based selection for female sea bass showed a slight advantage compared to traditional pedigree-based selection. However, when the same models were tested on the same animals for selection for greater length, a clear advantage of the SNP-based selection was observed.ConclusionsOverall, the results of this study provide additional support to the polygenic sex determination hypothesis in sea bass. In addition, identification of sex-ratio QTL may provide new opportunities for sex-ratio control in sea bass.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-015-0148-y) contains supplementary material, which is available to authorized users.

Genomic prediction of traits related to canine hip dysplasia.

Increased concern for the welfare of pedigree dogs has led to development of selection programs against inherited diseases. An example is canine hip dysplasia (CHD), which has a moderate heritability and a high prevalence in some large-sized breeds. To date, selection using phenotypes has led to only modest improvement, and alternative strategies such as genomic selection (GS) may prove more effective. The primary aims of this study were to compare the performance of pedigree- and genomic-based breeding against CHD in the UK Labrador retriever population and to evaluate the performance of different GS methods. A sample of 1179 Labrador Retrievers evaluated for CHD according to the UK scoring method (hip score, HS) was genotyped with the Illumina CanineHD BeadChip. Twelve functions of HS and its component traits were analyzed using different statistical methods (GBLUP, Bayes C and Single-Step methods), and results were compared with a pedigree-based approach (BLUP) using cross-validation. Genomic methods resulted in similar or higher accuracies than pedigree-based methods with training sets of 944 individuals for all but the untransformed HS, suggesting that GS is an effective strategy. GBLUP and Bayes C gave similar prediction accuracies for HS and related traits, indicating a polygenic architecture. This conclusion was also supported by the low accuracies obtained in additional GBLUP analyses performed using only the SNPs with highest test statistics, also indicating that marker-assisted selection (MAS) would not be as effective as GS. A Single-Step method that combines genomic and pedigree information also showed higher accuracy than GBLUP and Bayes C for the log-transformed HS, which is currently used for pedigree based evaluations in UK. In conclusion, GS is a promising alternative to pedigree-based selection against CHD, requiring more phenotypes with genomic data to improve further the accuracy of prediction.

Identity-by-descent genomic selection using selective and sparse genotyping for binary traits.

BackgroundGenomic selection (GS) allows estimation of the breeding value of individuals, even for non-phenotyped animals. The aim of the study was to examine the potential of identity-by-descent genomic selection (IBD-GS) in genomic selection for a binary, sib-evaluated trait, using different strategies of selective genotyping. This low-cost GS approach is based on linkage analysis of sparse genome-wide marker loci.FindingsLowly to highly heritable (h2 = 0.15, 0.30 or 0.60) binary traits with varying incidences (10 to 90%) were simulated for an aquaculture-like population. Genotyping was restricted to the 30% best families according to phenotype, using three genotyping strategies for training sibs. IBD-GS increased genetic gain compared to classical pedigree-based selection; the differences were largest at incidences of 10 to 50% of the desired category (i.e. a relative increase in genetic gain greater than 20%). Furthermore, the relative advantage of IBD-GS increased as the heritability of the trait increased. Differences were small between genotyping strategies, and most of the improvement was achieved by restricting genotyping to sibs with the least common binary phenotype. Genetic gains of IBD-GS relative to pedigree-based models were highest at low to moderate (10 to 50%) incidences of the category selected for, but decreased substantially at higher incidences (80 to 90%).ConclusionsThe IBD-GS approach, combined with sparse and selective genotyping, is well suited for genetic evaluation of binary traits. Genetic gain increased considerably compared with classical pedigree-based selection. Most of the improvement was achieved by selective genotyping of the sibs with the least common (minor) binary category phenotype. Furthermore, IBD-GS had greater advantage over classical pedigree-based models at low to moderate incidences of the category selected for.

Genomic selection accuracies within and between environments and small breeding groups in white spruce.

BackgroundGenomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of Ne ≈ 20. Marker subsets were also tested.ResultsModel fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71–0.79) and moderately high for growth (r = 0.52–0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies.ConclusionsGiven the need for high relatedness between CV sets to obtain good prediction accuracies, we recommend to build GS models for prediction within the same breeding population only. Breeding groups could be merged to build genomic prediction models as long as the total effective population size does not exceed 50 individuals in order to obtain high prediction accuracy such as that obtained in the present study. A number of markers limited to a few hundred would not negatively impact prediction accuracies, but these could decrease more rapidly over generations. The most promising short-term approach for genomic selection would likely be the selection of superior individuals within large full-sib families vegetatively propagated to implement multiclonal forestry.

Consequences for diversity when prioritizing animals for conservation with pedigree or genomic information

Up to now, prioritization of animals for conservation has been mainly based on pedigree information; however, genomic information may improve prioritization. In this study, we used two Holstein populations to investigate the consequences for genetic diversity when animals are prioritized with optimal contributions based on pedigree or genomic data and whether consequences are different at the chromosomal level. Selection with genomic kinships resulted in a higher conserved diversity, but differences were small. Largest differences were found when few animals were prioritized and when pedigree errors were present. We found more differences at the chromosomal level, where selection based on genomic kinships resulted in a higher conserved diversity for most chromosomes, but for some chromosomes, pedigree-based selection resulted in a higher conserved diversity. To optimize conservation strategies, genomic information can help to improve the selection of animals for conservation in those situations where pedigree information is unreliable or absent or when we want to conserve diversity at specific genome regions.