BLUP Selection Research Articles

Evaluation of the Mean Performance and Stability of Chickpea Genotypes by Integration AMMI and BLUP Models and Selection Based on Multi-Trait Stability Index (MTSI)

Evaluation of the Mean Performance and Stability of Chickpea Genotypes by Integration AMMI and BLUP Models and Selection Based on Multi-Trait Stability Index (MTSI)

Combined BLUP selection indexes with parents and F2 populations in soybean (Glycine max) breeding

Combined BLUP selection indexes with parents and F2 populations in soybean (Glycine max) breeding

Comparative Analysis of Pedigree-Based BLUP and Phenotypic Mass Selection for Developing Elite Inbred Lines, Based on Field and Simulated Data

Pedigree-based best linear unbiased prediction (pBLUP) is an effective method for genetic evaluation. The objective of this study was to assess the efficacy of pBLUP to develop superior inbred lines, using field and simulated data. This study was based on the data of 30,986 plants from six S0 generations and eight inbred progeny trials (S1–S4), performed over eight years. We measured the grain yield and the expansion volume (EV). We also simulated the EV and grain yield of 5000 plants (S0–S4) in two in silico populations, assuming 300 and 400 genes distributed across 10 chromosomes of 200 cM, respectively, and 10% of pleiotropic genes. We selected selfed plants based on the predicted additive value for EV by fitting the individual model in inbred population. The use of pBLUP provided total genetic gains in EV in the range of 1 to 45%, inversely proportional to the level of improvement of the population, and indirect changes in grain yield in the range of −17 to 3%. Only from the analysis of the in silico populations, assuming selection based on the true additive value and genetic gain computed from the genotypic values, there was evidence that pBLUP is superior to phenotypic mass selection.

Predicted genetic gain for carcass yield in rainbow trout from indirect and genomic selection

Carcass and fillet yields are traits of great economic importance in aquaculture species, including rainbow trout. Headless gutted carcass percentage (HC) is a convenient selection criterion to improve carcass yield given that it is highly genetically correlated with the latter and also with fillet yield. However, HC is a sib trait that cannot be recorded on selection candidates. Consequently, the within-family component of the genetic variance cannot be exploited with traditional pedigree-based BLUP selection. Two alternatives to exploit this component would be to select directly on an indicator trait genetically correlated with HC that can be recorded on live candidates or to apply genomic selection. The objective of this simulation study was to predict the phenotypic gains for HC in rainbow trout breeding programs when four alternative selection strategies are used: i) sib selection for HC, ii) indirect selection for a morphological indicator recorded in vivo; iii) genomic selection for HC; and iv) genomic selection for the indicator. Also, the four strategies were compared in a multitrait selection scenario where body weight was also included in the breeding objective. The different scenarios were compared at the same selection intensity and number of records (2000) for HC (on sibs) and IHC (on candidates). Two different heritabilities for HC (0.55 and 0.25) were considered. For the highest heritability, the phenotypic gain for HC was higher with sib than with indirect selection for both BLUP (1.03 versus 0.98) and genomic selection (1.22 versus 1.04). However, for the lowest heritability, the phenotypic gain for HC was lower with sib than with indirect selection for both BLUP (0.60 versus 0.64) and genomic selection (0.70 versus 0.71). In any case, the differences in phenotypic gains for HC between sib and indirect selection were not large. Therefore, given that sib selection implies extra costs associated with maintaining and genotyping sibs and indirect selection allows higher selection intensity for a given number of fish, indirect selection appears to be a more cost-effective option. The patterns found for single trait selection were maintained when the yield trait (HC or indicator) was selected simultaneously with body weight. We conclude that the optimum approach to improve carcass yield is a combination of genomic and indirect selection in both single and multitrait selection scenarios.

Evaluation the Mean Performance and Stability of Rice Genotypes by Combining Features of AMMI and BLUP Techniques and Selection Based on Multiple Traits

Evaluation the Mean Performance and Stability of Rice Genotypes by Combining Features of AMMI and BLUP Techniques and Selection Based on Multiple Traits

The efficiency of multi-generation selection on maternal traits, with implications for reindeer

Maternally affected traits, such as juvenile growth and survival, provide resilience in mammal species, in particular for reindeer living in extreme northern habitat. The genetic variation in such traits is caused by direct and maternal genetic effects (DGE and MGE, respectively). We used Willham’s variance-component approach and extended a family index with the focal individual and its full- and half-sibs to an approximated BLUP (pseudo-BLUP) by including the parents’ estimated breeding values. Most of the deviations of the predicted responses from the simulated ones were 4.1% for DGE and 5.3% for MGE. The benefits of index and BLUP selection are high in the case of negative correlation, large full-sib family and in particular, when maternal half-sibs are available. Higher economic value for MGE than for DGE is needed, since with equal heritabilities and economic weights for the effects the maternal response is 40 to 70% of the direct one. With negative correlation, records on collateral relatives beyond sibs are possibly needed. They would support also the prediction of MGE in uniparous reindeer lacking full-sib information.

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.

SeqBreed: a python tool to evaluate genomic prediction in complex scenarios

BackgroundGenomic prediction (GP) is a method whereby DNA polymorphism information is used to predict breeding values for complex traits. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help design optimum breeding programs and experiments, including genome-wide association studies and genomic selection experiments, we have developed SeqBreed, a generic and flexible forward simulator programmed in python3.ResultsSeqBreed accommodates sex and mitochondrion chromosomes as well as autopolyploidy. It can simulate any number of complex phenotypes that are determined by any number of causal loci. SeqBreed implements several GP methods, including genomic best linear unbiased prediction (GBLUP), single-step GBLUP, pedigree-based BLUP, and mass selection. We illustrate its functionality with Drosophila genome reference panel (DGRP) sequence data and with tetraploid potato genotype data.ConclusionsSeqBreed is a flexible and easy to use tool that can be used to optimize GP or genome-wide association studies. It incorporates some of the most popular GP methods and includes several visualization tools. Code is open and can be freely modified. Software, documentation, and examples are available at https://github.com/miguelperezenciso/SeqBreed.

Controlling bias in genomic breeding values for young genotyped bulls

The objectives of this study were to investigate bias in genomic predictions for dairy cattle and to find a practical approach to reduce the bias. The simulated data included phenotypes, pedigrees, and genotypes, mimicking a dairy cattle population (i.e., cows with phenotypes and bulls with no phenotypes) and assuming selection by breeding values or no selection. With the simulated data, genomic estimated breeding values (GEBV) were calculated with a single-step genomic BLUP and compared with true breeding values. Phenotypes and genotypes were simulated in 10 generations and in the last 4 generations, respectively. Phenotypes in the last generation were removed to predict breeding values for those individuals using only genomic and pedigree information. Complete pedigrees and incomplete pedigrees with 50% missing dams were created to construct the pedigree-based relationship matrix with and without inbreeding. With missing dams, unknown parent groups (UPG) were assigned in relationship matrices. Regression coefficients (b1) and coefficients of determination (R2) of true breeding values on (G)EBV were calculated to investigate inflation and accuracy in GEBV for genotyped animals, respectively. In addition to the simulation study, 18 linear type traits of US Holsteins were examined. For the 18 type traits, b1 and R2 of GEBV with full data sets on GEBV with partial data sets for young genotyped bulls were calculated. The results from the simulation study indicated inflation in GEBV for genotyped males that were evaluated with only pedigree and genomic information under BLUP selection. However, when UPG for only pedigree-based relationships were included, the inflation was reduced, accuracy was highest, and genetic trends had no bias. For the linear type traits, when UPG for only pedigree-based relationships were included, the results were generally in agreement with those from the simulation study, implying less bias in genetic trends. However, when including no UPG, UPG in pedigree-based relationships, or UPG in genomic relationships, inflation and accuracy in GEBV were similar. The results from the simulation and type traits suggest that UPG must be defined accurately to be estimable and inbreeding should be included in pedigree-based relationships. In dairy cattle, known pedigree information with inbreeding and estimable UPG plays an important role in improving compatibility between pedigree-based and genomic relationship matrices, resulting in more reliable genomic predictions.

Trait-specific long-term consequences of genomic selection in beef cattle.

Simulation studies allow addressing consequences of selection schemes, helping to identify effective strategies to enable genetic gain and maintain genetic diversity. The aim of this study was to evaluate the long-term impact of genomic selection (GS) in genetic progress and genetic diversity of beef cattle. Forward-in-time simulation generated a population with pattern of linkage disequilibrium close to that previously reported for real beef cattle populations. Different scenarios of GS and traditional pedigree-based BLUP (PBLUP) selection were simulated for 15 generations, mimicking selection for female reproduction and meat quality. For GS scenarios, an alternative selection criterion was simulated (wGBLUP), intended to enhance long-term gains by attributing more weight to favorable alleles with low frequency. GS allowed genetic progress up to 40% greater than PBLUP, for female reproduction and meat quality. The alternative criterion wGBLUP did not increase long-term response, although allowed reducing inbreeding rates and loss of favorable alleles. The results suggest that GS outperforms PBLUP when the selected trait is under less polygenic background and that attributing more weight to low-frequency favorable alleles can reduce inbreeding rates and loss of favorable alleles in GS.

Crossbreed evaluations in single-step genomic best linear unbiased predictor using adjusted realized relationship matrices1

Combining purebreed and crossbreed information is beneficial for genetic evaluation of some livestock species. Genetic evaluations can use relationships based on genomic information, relying on allele frequencies that are breed specific. Single-step genomic BLUP (ssGBLUP) does not account for different allele frequencies, which could limit the genetic gain in crossbreed evaluations. In this study, we tested the performance of different breed-specific genomic relationship matrices () in ssGBLUP for crossbreed evaluations; we also tested the importance of genotyping crossbred animals. Genotypes were available for purebreeds (AA and BB) and crossbreeds (F) in simulated and real pig populations. The number of genotyped animals was, on average, 4,315 for the simulated population and 15,798 for the real population. Cross-validation was performed on 1,200 and 3,117 F animals in the simulated and real populations, respectively. Simulated scenarios were under no artificial selection, mass selection, or BLUP selection. Two genomic relationship matrices were constructed based on breed-specific allele frequencies: 1) , a genomic relationship matrix centered by breed-specific allele frequencies, and 2) , a genomic relationship matrix centered and scaled by breed-specific allele frequencies. All (the across-breed genomic relationship matrix), , and were also tuned to account for selective genotyping. Using breed-specific allele frequencies reduced the number of negative relationships between 2 purebreeds, pulling the average closer to 0, as in the pedigree-based relationship matrix. For simulated populations that included mass selection, genomic EBV (GEBV) in F, when using and , were, on average, 10% more accurate than ; however, after tuning to account for selective genotyping, provided the same accuracy as for breed-specific genomic relationship matrices. For the real population, accuracies for litter size in F were 0.62 for , , and , and tuning had no impact on accuracy, except for , which was 1 percentage point less accurate. Accuracy of GEBV for number of stillborns in F1 was 0.5 for all tested genomic relationship matrices with no changes after tuning. We observed that genotyping F increased accuracies of GEBV for the same animals by up to 39% compared with having genotypes for only AA and BB. In crossbreed evaluations, accounting for breed-specific allele frequencies promoted changes in G that were not influential enough to improve accuracy of GEBV. Therefore, the best performance of ssGBLUP for crossbreed evaluations requires genotypes for pure- and crossbreeds and no breed-specific adjustments in the realized relationship matrix.

Optimizing selection with several constraints in poultry breeding.

Poultry breeding schemes permanently face the need to control the evolution of coancestry and some critical traits, while selecting for a main breeding objective. The main aims of this article are first to present an efficient selection algorithm adapted to this situation and then to measure how the severity of constraints impacted on the degree of loss for the main trait, compared to BLUP selection on the main trait, without any constraint. Broiler dam and sire line schemes were mimicked by simulation over 10 generations and selection was carried out on the main trait under constraints for coancestry and for another trait, antagonistic with the main trait. The selection algorithm was a special simulated annealing (adaptative simulated annealing (ASA)). It was found to be rapid and able to meet constraints very accurately. A constraint on the second trait was found to induce an impact similar to or even greater than the impact of the constraint on coancestry. The family structure of selected poultry populations made it easy to control the evolution of coancestry at a reasonable cost but was not as useful for reducing the cost of controlling evolution of the antagonistic traits. Multiple constraints impacted almost additively on the genetic gain for the main trait. Adding constraints for several traits would therefore be justified in real life breeding schemes, possibly after evaluating their impact through simulated annealing.

Artificial selection with traditional or genomic relationships: consequences in coancestry and genetic diversity.

Estimated breeding values (EBVs) are traditionally obtained from pedigree information. However, EBVs from high-density genotypes can have higher accuracy than EBVs from pedigree information. At the same time, it has been shown that EBVs from genomic data lead to lower increases in inbreeding compared with traditional selection based on genealogies. Here we evaluate the performance with BLUP selection based on genealogical coancestry with three different genome-based coancestry estimates: (1) an estimate based on shared segments of homozygosity, (2) an approach based on SNP-by-SNP count corrected by allelic frequencies, and (3) the identity by state methodology. We evaluate the effect of different population sizes, different number of genomic markers, and several heritability values for a quantitative trait. The performance of the different measures of coancestry in BLUP is evaluated in the true breeding values after truncation selection and also in terms of coancestry and diversity maintained. Accordingly, cross-performances were also carried out, that is, how prediction based on genealogical records impacts the three other measures of coancestry and inbreeding, and viceversa. Our results show that the genetic gains are very similar for all four coancestries, but the genomic-based methods are superior to using genealogical coancestries in terms of maintaining diversity measured as observed heterozygosity. Furthermore, the measure of coancestry based on shared segments of the genome seems to provide slightly better results on some scenarios, and the increase in inbreeding and loss in diversity is only slightly larger than the other genomic selection methods in those scenarios. Our results shed light on genomic selection vs. traditional genealogical-based BLUP and make the case to manage the population variability using genomic information to preserve the future success of selection programmes.

Comparison of BLUE and BLUP/REML in the selection of clones and families of potato (Solanum tuberosum).

The use of best linear unbiased prediction/restricted maximum likelihood (BLUP/REML) in perennial crops and animal breeding enhances selection gain. However, its advantage with respect to annual crops is not clear. We compared the BLUP and best linear unbiased estimator selection efficiency in the breeding of various potato generations. This was done by simulating various selection intensities on clonal families (full sibs), and clones. The characters evaluated were tuber yield and tuber specific gravity. Two criteria were adopted for comparison: a) incidence of families or clones and b) selection gain. For tuber yield, BLUP/REML method was slightly more efficient for selecting families in the first clonal generation, if it were above 50%. Below this value, both methods were equivalent. However, they both presented equal behavior for family selection of tuber specific gravity. For clonal selection, BLUP/REML showed robust superiority from 10 to 90% selection intensities in both characters. Therefore, the adequate use of BLUP/REML in potato breeding can enhance the selection gain on the yield and specific gravity of tubers.

Optimizing selection strategies of genomic selection in swine breeding program based on a dataset simulated

The conventional BLUP uses the phenotype and pedigree information to predict the estimated breeding values (EBV) of individuals in genetic evaluation. However, to obtain the phenotypes of interest in swine breeding program requires relatively a long time until the completion of performance testing, which produces an extra breeding cost for the culled pigs in swine industry. An alternative solution is to pre-select a predefined proportion of young replacement piglets for future performance testing through genomic selection, which could reduce the number of testing animals entering into performance testing program and hence reduce the breeding costs associated with the tests. In this study, four strategies of genomic selection applications in swine breeding program were compared through simulation to investigate the potential benefits in the different strategies in swine breeding program. For comparison purpose, the conventional BLUP selection was simulated as strategy 1 to form the benchmark basis for comparisons. Strategy 2 was an extreme case for applying genomic selection, where newborn piglets were selected directly based on their genomic enhanced breeding value (GEBV) only without further performance testing. Strategy 3 was to pre-select piglets, based on their GEBV, for entry to performance testing in early stage, and then the breeding stock were selected ultimately based on their EBVs predicted by BLUP method when the phenotypic records were available. Similar to strategy 3, strategies 4 and 5 also used the GEBVs to pre-select replacement piglets in an early stage; however, the breeding stocks in strategies 4 and 5 were selected based on the breeding values obtained using the bi-variable model and the conventional index method to combine GEBV and EBV information, respectively, when individuals had both GEBV and phenotypes available after the performance testing. Comparing these strategies, strategy 4 resulted in the highest accuracy in first three generations and achieved the best cumulative selection response in the last generation, followed by strategies 1, 3, 5, and 2. The proportion of pre-selection of boars and sows in the early stage affected the efficiency of genomic selection substantially.

Persistency of accuracy of genomic breeding values for different simulated pig breeding programs in developing countries

Genetic improvement of pigs in tropical developing countries has focused on imported exotic populations which have been subjected to intensive selection with attendant high population-wide linkage disequilibrium (LD). Presently, indigenous pig population with limited selection and low LD are being considered for improvement. Given that the infrastructure for genetic improvement using the conventional BLUP selection methods are lacking, a genome-wide selection (GS) program was proposed for developing countries. A simulation study was conducted to evaluate the option of using 60 K SNP panel and observed amount of LD in the exotic and indigenous pig populations. Several scenarios were evaluated including different size and structure of training and validation populations, different selection methods and long-term accuracy of GS in different population/breeding structures and traits. The training set included previously selected exotic population, unselected indigenous population and their crossbreds. Traits studied included number born alive (NBA), average daily gain (ADG) and back fat thickness (BFT). The ridge regression method was used to train the prediction model. The results showed that accuracies of genomic breeding values (GBVs) in the range of 0.30 (NBA) to 0.86 (BFT) in the validation population are expected if high density marker panels are utilized. The GS method improved accuracy of breeding values better than pedigree-based approach for traits with low heritability and in young animals with no performance data. Crossbred training population performed better than purebreds when validation was in populations with similar or a different structure as in the training set. Genome-wide selection holds promise for genetic improvement of pigs in the tropics.

Opportunities for genome-wide selection for pig breeding in developing countries1

Genetic improvement of exotic and indigenous pigs in tropical developing countries is desired. Implementations of traditional selection methods on tropical pig populations are limited by lack of data recording and analysis infrastructure. Genome-wide selection (GS) provides an approach for achieving faster genetic progress without developing a pedigree recording system. The implications of GS on long-term gain and inbreeding should be studied before actual implementation, especially where low linkage disequilibrium (LD) is anticipated in the target population. A simulation case study of this option was performed on the basis of the available 60,000 SNP panel for porcine genome. Computer simulation was used to explore the effects of various selection methods, trait heritability, and different breeding programs when applying GS. Genomic predictions were based on the ridge regression method. Genome-wide selection performed better than BLUP and phenotypic selection methods by increasing genetic gain and maintaining genetic variation while lowering inbreeding, especially for traits with low heritability. Indigenous pig populations with low LD can be improved by using GS if high-density marker panels are available. The combination of GS with repeated backcrossing of crossbreds to exotic pigs in developing countries promises to rapidly improve the genetic merit of the commercial population. Application of this novel method on a real population will need to be performed to validate these results.

Effect of non-random mating on genomic and BLUP selection schemes.

BackgroundThe risk of long-term unequal contribution of mating pairs to the gene pool is that deleterious recessive genes can be expressed. Such consequences could be alleviated by appropriately designing and optimizing breeding schemes i.e. by improving selection and mating procedures.MethodsWe studied the effect of mating designs, random, minimum coancestry and minimum covariance of ancestral contributions on rate of inbreeding and genetic gain for schemes with different information sources, i.e. sib test or own performance records, different genetic evaluation methods, i.e. BLUP or genomic selection, and different family structures, i.e. factorial or pair-wise.ResultsResults showed that substantial differences in rates of inbreeding due to mating design were present under schemes with a pair-wise family structure, for which minimum coancestry turned out to be more effective to generate lower rates of inbreeding. Specifically, substantial reductions in rates of inbreeding were observed in schemes using sib test records and BLUP evaluation. However, with a factorial family structure, differences in rates of inbreeding due mating designs were minor. Moreover, non-random mating had only a small effect in breeding schemes that used genomic evaluation, regardless of the information source.ConclusionsIt was concluded that minimum coancestry remains an efficient mating design when BLUP is used for genetic evaluation or when the size of the population is small, whereas the effect of non-random mating is smaller in schemes using genomic evaluation.

Strategies in identifying individuals in a segregant population of common bean and implications of genotype x environment interaction in the success of selection

The objective of this study was to compare the BLUP selection method with different selection strategies in F(2:4) and assess the efficiency of this method on the early choice of the best common bean (Phaseolus vulgaris) lines. Fifty-one F(2:4) progenies were produced from a cross between the CVIII8511 x RP-26 lines. A randomized block design was used with 20 replications and one-plant field plots. Character data on plant architecture and grain yield were obtained and then the sum of the standardized variables was estimated for simultaneous selection of both traits. Analysis was carried out by mixed models (BLUP) and the least squares method to compare different selection strategies, like mass selection, stratified mass selection and between and within progeny selection. The progenies selected by BLUP were assessed in advanced generations, always selecting the greatest and smallest sum of the standardized variables. Analyses by the least squares method and BLUP procedure ranked the progenies in the same way. The coincidence of the individuals identified by BLUP and between and within progeny selection was high and of the greatest magnitude when BLUP was compared with mass selection. Although BLUP is the best estimator of genotypic value, its efficiency in the response to long term selection is not different from any of the other methods, because it is also unable to predict the future effect of the progenies x environments interaction. It was inferred that selection success will always depend on the most accurate possible progeny assessment and using alternatives to reduce the progenies x environments interaction effect.

QTL detection and marker-assisted composite line development

Efficiency of marker-assisted selection (MAS) in a composite line from a cross between inbred lines was evaluated for a trait with heritability 0.1 or 0.25 using simulations. Only significant marker effects identified in a QTL scan in the initial cross were used for MAS. Three selection criteria were investigated: BM=sum of BLUE of marker effects+BLUP of polygenic BV; MR=sum of BLUE of marker effects; and M=number of favorable marker alleles. Reponses to selection over 10 generations, starting from the F2, were compared with standard BLUP selection on EBV derived from phenotype. All MAS strategies outperformed BLUP selection in initial generations by rapidly increasing the frequency of detected QTL. Criterion BM outperformed other criteria even when phenotypes were available only in the F2. With phenotypes available in all generations, MR and M tended to be comparable. When phenotypes and marker data were available only in the F2, MR gave the lowest genetic gain. The efficiency of MAS depended on the stringency of the threshold used for QTL detection. With phenotype and marker data in all generations, responses increased when the threshold became less stringent (α=0.05 versus 0.01) but differences in response were limited. Benefits of MAS over standard BLUP were lower with high heritability and when a greater proportion of favorable QTL alleles originated from one of the lines for models that did not include polygenic effects and large marker intervals. Responses decreased initially and increased in later generations when marker intervals were small.