scholarly journals New cycle, same old mistakes? Overlapping vs. discrete generations in long-term recurrent selection

2021 ◽  
Author(s):  
Marlee R. Labroo ◽  
Jessica E. Rutkoski
Keyword(s):  
Genetic Gain ◽  
Overlapping Generations ◽  
Recurrent Selection ◽  
Phenotypic Selection ◽  
Breeding Value ◽  
Breeding Values ◽  
Truncation Selection ◽  
Breeding Cycles ◽  
Error Bias

Background: Recurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. In recurrent phenotypic selection, generations do not overlap, which means that breeding candidates are evaluated and considered for selection for only one cycle. With recurrent genomic selection, candidates can be evaluated based on genomic estimated breeding values indefinitely, therefore facilitating overlapping generations. Candidates with true high breeding values that were discarded in one cycle due to underestimation of breeding value could be identified and selected in subsequent cycles. The consequences of allowing generations to overlap in recurrent selection are unknown. We assessed whether maintaining overlapping and discrete generations led to differences in genetic gain for phenotypic, genomic truncation, and genomic optimum contribution recurrent selection by simulation of traits with various heritabilities and genetic architectures across fifty breeding cycles. We also assessed differences of overlapping and discrete generations in a conventional breeding scheme with multiple stages and cohorts. Results: With phenotypic selection, overlapping generations led to decreased genetic gain compared to discrete generations due to increased selection error bias. Selected individuals, which were in the upper tail of the distribution of phenotypic values, tended to also have high absolute error relative to their true breeding value compared to the overall population. Without repeated phenotyping, these erroneously outlying individuals were repeatedly selected across cycles, leading to decreased genetic gain. With genomic truncation selection, overlapping and discrete generations performed similarly as updating breeding values precluded repeatedly selecting individuals with inaccurately high estimates of breeding values in subsequent cycles. Overlapping generations did not outperform discrete generations in the presence of a positive genetic trend with genomic truncation selection, as past generations had lower mean genetic values than the current generation of selection candidates. With genomic optimum contribution selection, overlapping and discrete generations performed similarly, but overlapping generations slightly outperformed discrete generations in the long term if the targeted inbreeding rate was extremely low. Conclusions: Maintaining discrete generations in recurrent phenotypic mass selection leads to increased genetic gain, especially at low heritabilities, by preventing selection error bias. With genomic truncation selection and genomic optimum contribution selection, genetic gain does not differ between discrete and overlapping generations assuming non-genetic effects are not present. Overlapping generations may increase genetic gain in the long term with very low targeted rates of inbreeding in genomic optimum contribution selection.

scholarly journals Identification of superior parental lines for biparental crossing via genomic prediction

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243159
Author(s):  
Ping-Yuan Chung ◽  
Chen-Tuo Liao
Keyword(s):  
Genetic Gain ◽  
Genomic Prediction ◽  
Genomic Diversity ◽  
Breeding Value ◽  
Selection Strategy ◽  
Parental Selection ◽  
Breeding Values ◽  
Truncation Selection ◽  
Selection Approach ◽  
Parental Lines

A parental selection approach based on genomic prediction has been developed to help plant breeders identify a set of superior parental lines from a candidate population before conducting field trials. A classical parental selection approach based on genomic prediction usually involves truncation selection, i.e., selecting the top fraction of accessions on the basis of their genomic estimated breeding values (GEBVs). However, truncation selection inevitably results in the loss of genomic diversity during the breeding process. To preserve genomic diversity, the selection of closely related accessions should be avoided during parental selection. We thus propose a new index to quantify the genomic diversity for a set of candidate accessions, and analyze two real rice (Oryza sativa L.) genome datasets to compare several selection strategies. Our results showed that the pure truncation selection strategy produced the best starting breeding value but the least genomic diversity in the base population, leading to less genetic gain. On the other hand, strategies that considered only genomic diversity resulted in greater genomic diversity but less favorable starting breeding values, leading to more genetic gain but unsatisfactorily performing recombination inbred lines (RILs) in progeny populations. Among all strategies investigated in this study, compromised strategies, which considered both GEBVs and genomic diversity, produced the best or second-best performing RILs mainly because these strategies balance the starting breeding value with the maintenance of genomic diversity.

scholarly journals Prediction of Genetic Contributions and Generation Intervals in Populations With Overlapping Generations Under Selection

Genetics ◽  
1999 ◽  
Vol 151 (3) ◽  
pp. 1197-1210 ◽  
Author(s):  
Piter Bijma ◽  
John A Woolliams
Keyword(s):  
Gene Flow ◽  
Overlapping Generations ◽  
Selective Advantage ◽  
Turnover Time ◽  
Breeding Value ◽  
Age Classes ◽  
Generation Interval ◽  
Young Parents ◽  
Genetic Contributions

Abstract A method to predict long-term genetic contributions of ancestors to future generations is studied in detail for a population with overlapping generations under mass or sib index selection. An existing method provides insight into the mechanisms determining the flow of genes through selected populations, and takes account of selection by modeling the long-term genetic contribution as a linear regression on breeding value. Total genetic contributions of age classes are modeled using a modified gene flow approach and long-term predictions are obtained assuming equilibrium genetic parameters. Generation interval was defined as the time in which genetic contributions sum to unity, which is equal to the turnover time of genes. Accurate predictions of long-term genetic contributions of individual animals, as well as total contributions of age classes were obtained. Due to selection, offspring of young parents had an above-average breeding value. Long-term genetic contributions of youngest age classes were therefore higher than expected from the age class distribution of parents, and generation interval was shorter than the average age of parents at birth of their offspring. Due to an increased selective advantage of offspring of young parents, generation interval decreased with increasing heritability and selection intensity. The method was compared to conventional gene flow and showed more accurate predictions of long-term genetic contributions.

scholarly journals Expected Genetic Contributions and Their Impact on Gene Flow and Genetic Gain

Genetics ◽  
1999 ◽  
Vol 153 (2) ◽  
pp. 1009-1020 ◽  
Author(s):  
J A Woolliams ◽  
P Bijma ◽  
B Villanueva
Keyword(s):  
Gene Flow ◽  
Genetic Gain ◽  
Overlapping Generations ◽  
Predictive Accuracy ◽  
Index Theory ◽  
Previous Theory ◽  
Selection Indices ◽  
Breeding Groups ◽  
Genetic Contributions

Abstract Long-term genetic contributions (ri) measure lasting gene flow from an individual i. By accounting for linkage disequilibrium generated by selection both within and between breeding groups (categories), assuming the infinitesimal model, a general formula was derived for the expected contribution of ancestor i in category q (μi(q)), given its selective advantages (si(q)). Results were applied to overlapping generations and to a variety of modes of inheritance and selection indices. Genetic gain was related to the covariance between ri and the Mendelian sampling deviation (ai), thereby linking gain to pedigree development. When si(q) includes ai, gain was related to E[μi(q)ai], decomposing it into components attributable to within and between families, within each category, for each element of si(q). The formula for μi(q) was consistent with previous index theory for predicting gain in discrete generations. For overlapping generations, accurate predictions of gene flow were obtained among and within categories in contrast to previous theory that gave qualitative errors among categories and no predictions within. The generation interval was defined as the period for which μi(q), summed over all ancestors born in that period, equaled 1. Predictive accuracy was supported by simulation results for gain and contributions with sib-indices, BLUP selection, and selection with imprinted variation.

scholarly journals Mendelian sampling terms as a selective advantage in optimum breeding schemes with restrictions on the rate of inbreeding

2004 ◽  
Vol 83 (1) ◽  
pp. 55-64 ◽  
Author(s):  
S. AVENDAÑO ◽  
J. A. WOOLLIAMS ◽  
B. VILLANUEVA
Keyword(s):  
Genetic Variation ◽  
Genetic Gain ◽  
Selective Advantage ◽  
Breeding Values ◽  
Truncation Selection ◽  
Joint Management ◽  
Breeding Schemes ◽  
Estimated Breeding Values ◽  
Available Information ◽  
Mendelian Sampling

Quadratic indices are a general approach for the joint management of genetic gain and inbreeding in artificial selection programmes. They provide the optimal contributions that selection candidates should have to obtain the maximum gain when the rate of inbreeding is constrained to a predefined value. This study shows that, when using quadratic indices, the selective advantage is a function of the Mendelian sampling terms. That is, at all times, contributions of selected candidates are allocated according to the best available information about their Mendelian sampling terms (i.e. about their superiority over their parental average) and not on their breeding values. By contrast, under standard truncation selection, both estimated breeding values and Mendelian sampling terms play a major role in determining contributions. A measure of the effectiveness of using genetic variation to achieve genetic gain is presented and benchmark values of 0·92 for quadratic optimisation and 0·5 for truncation selection are found for a rate of inbreeding of 0·01 and a heritability of 0·25.

scholarly journals Genetic Trends Estimation in IRRIs Rice Drought Breeding Program and Identification of High Yielding Drought-Tolerant Lines

2021 ◽  
Author(s):  
Apurva Khanna ◽  
Mahender Anumalla ◽  
Margaret Catolos ◽  
Jérôme Bartholomé ◽  
Roberto Fritsche-Neto ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genetic Gain ◽  
Recurrent Selection ◽  
Historical Data ◽  
Breeding Program ◽  
Positive Trend ◽  
Breeding Values ◽  
Genetic Gains ◽  
Rate Of Increase ◽  
Genetic Trends

Abstract BackgroundEstimation of genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI’s rice drought breeding program was used to estimate the genetic trends and assess the success of the breeding program. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes.ResultsA two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20-0.94 under the drought trials, and 0.43-0.83 under non-stress trials. Under non-stress conditions the genetic gain of 0.44% (21.20 kg/ha/year) for genotypes and 0.17 % (7.90 kg/ha/year) for checks was observed. The genetic trend under the drought conditions exhibited a positive trend with the genetic gain of 0.11% (1.98kg/ha/year) for genotypes and 0.55% (9.52kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.39% (12.13 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of >0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha).ConclusionsA positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI’s drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.

scholarly journals Simulation of pedigree vs. fully-informative marker based relationships matrices in a loblolly pine breeding population

2021 ◽  
Author(s):  
Adam R Festa ◽  
Ross Whetten
Keyword(s):  
Inbreeding Depression ◽  
Loblolly Pine ◽  
Genetic Gain ◽  
Field Experiments ◽  
Breeding Value ◽  
Genomic Relationship Matrix ◽  
Relationship Matrix ◽  
Genomic Relationship ◽  
Deleterious Alleles

Computer simulations of breeding strategies are an essential resource for tree breeders because they allow exploratory analyses into potential long-term impacts on genetic gain and inbreeding consequences without bearing the cost, time, or resource requirements of field experiments. Previous work has modeled the potential long-term implications on inbreeding and genetic gain using random mating and phenotypic selection. Reduction in sequencing costs has enabled the use of DNA marker-based relationship matrices in addition to or in place of pedigree-based allele sharing estimates; this has been shown to provide a significant increase in the accuracy of progeny breeding value prediction. A potential pitfall of genomic selection using genetic relationship matrices is increased coancestry among selections, leading to the accumulation of deleterious alleles and inbreeding depression. We used simulation to compare the relative genetic gain and risk of inbreeding depression within a breeding program similar to loblolly pine, utilizing pedigree-based or marker-based relationships over ten generations. We saw a faster rate of purging deleterious alleles when using a genomic relationship matrix based on markers that track identity-by-descent of segments of the genome. Additionally, we observed an increase in the rate of genetic gain when using a genomic relationship matrix instead of a pedigree-based relationship matrix. While the genetic variance of populations decreased more rapidly when using genomic-based relationship matrices as opposed to pedigree-based, there appeared to be no long-term consequences on the accumulation of deleterious alleles within the simulated breeding strategy.

scholarly journals A review of how dairy farmers can use and profit from genomic technologies

2012 ◽  
Vol 52 (3) ◽  
pp. 180 ◽  
Author(s):  
Jennie Pryce ◽  
Ben Hayes
Keyword(s):  
Energy Balance ◽  
Genomic Selection ◽  
Genetic Gain ◽  
Genetic Diseases ◽  
Breeding Value ◽  
Feed Conversion ◽  
Breeding Values ◽  
Genomic Technologies ◽  
Net Profit ◽  
Estimated Breeding Value

New genomic technologies can help farmers to (1) achieve higher annual rates of genetic gain through using genomically tested bulls in their herds, (2) select for ‘difficult’ to measure traits, such as feed conversion efficiency, methane emissions and energy balance, (3) select the best heifers to become herd replacements, (4) sell pedigree heifers at a premium, (5) use mating plans to optimise rates of genetic gain while controlling inbreeding, (6) achieve certainty in parentage of individual cows and (7) avoid genetic defects that could arise from mating cows to bulls that are known carriers of genetic diseases that are the result of a single lethal mutation. The first use does not require genotyping females and could approximately double the net income per cow that arises due to genetic improvement, mainly through a reduction in generation interval. On the basis of current rates of genetic gain, the net profit from using genotyped bulls could be worth AU$20/cow per year and is permanent and cumulative. One of the most powerful uses of genomic selection is to select for economically important, yet difficult- or expensive-to-measure traits, such as residual feed intake or energy balance. Provided the accuracy of genomic breeding values is high enough (i.e. correlation between the true and estimated breeding values), these traits lend themselves well to genomic selection. For selecting replacement heifers, if genotyping costs are AU$50/cow, the net profit of genotyping 40 heifers to select the top 20 as replacements (per 100 cows) would be worth approximately AU$41 per cow. However, using parent average estimated breeding-value information is free and can already be used to select replacement heifers. So, genotyping costs would need to be very low to be more profitable than selecting on parent average estimated breeding value. However, extra value from genotyping can also be captured by using other strategies. For example, mating plans that use genomic relationships rather than pedigree relationships to capture inbreeding are superior in terms of reducing progeny inbreeding at a desired level of genetic gain, although pedigree does an adequate job. So, again, the benefits of genotyping are small (<AU$10). Ascertainment of pedigree is an additional use of genotyping and is potentially worth ~AU$30 per cow. Avoidance of genetic diseases and selling of pedigree heifers have a value that should be estimated case-by-case. Because genotyping costs continue to fall, it may become increasingly popular to capture the extra value from genotyping females.

Breeding Value Estimation for Pigs in Closed Nucleus Herds

1988 ◽  
Vol 1988 ◽  
pp. 12-12 ◽  
Author(s):  
Naomi R. Wray
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Phenotypic Selection ◽  
Breeding Value ◽  
Simultaneous Estimation ◽  
Genetic Trend ◽  
Traditional Methods ◽  
Breeding Values ◽  
Value Estimation ◽  
The Uk

Best Linear Unbiased Prediction (BLUP) is now the method of choice for the estimation of breeding values in dairy and beef populations. The advantages of this mixed model methodology over traditional methods are well documented and include the simultaneous estimation of fixed effects and prediction of random effects and the utilization of records from all relatives to predict an individuals breeding value. In addition, account is taken of genetic trend and of reduction in genetic variance due to selection. In Canada, BLUP is now used for breeding value estimation of pigs but the structure of the Canadian pig industry is one of many herds practising selection with the herds linked by a widespread use of artificial insemination. The advantages of BLUP have not been investigated for the situation of the UK pig industry where most selection is performed within closed nucleus herds.The objectives of this study were to use computer simulation to determine rates of response, accuracy of prediction and accummulation of inbreeding for pigs in closed nucleus herds when selection decisions were based on estimated breeding values (EBVs) derived from BLUP compared to more traditional methods of phenotypic selection and index selection.

scholarly journals Uses of genomics in livestock agriculture

2012 ◽  
Vol 52 (3) ◽  
pp. 73 ◽  
Author(s):  
M. E. Goddard
Keyword(s):  
Genomic Selection ◽  
Genetic Gain ◽  
Additive Genetic Variance ◽  
Reference Population ◽  
Biological Information ◽  
Breeding Value ◽  
Biological Efficiency ◽  
Minimum Age ◽  
Livestock Products

World demand for livestock products is likely to increase in coming decades but the cost of production could escalate faster than the price due to competition for land, water, grain and fertiliser and the effects of climate change and its mitigation. To remain competitive for these resources, livestock agriculture has to dramatically increase in efficiency of production. Genetic gain is one mechanism to achieve increased efficiency and there is the opportunity to utilise the scientific advances in genomics. Three ways in which genomics can be used are in additive genetic improvement, exploitation of non-additive genetic variance and management which exploits genotype by environment interactions to optimise management. Genomic selection is already being widely implemented in dairy cattle and beef cattle and sheep will follow in the future once the accuracy of genomic selection is high enough. The accuracy of equations that predict breeding value from DNA genotypes can be increased by increasing the size of the reference population from which the equations are estimated, increasing the density of markers, using genome sequences instead of markers, using more appropriate statistical procedures and incorporating biological information into the prediction. In the long term, genomic selection combined with reproductive technology that reduces the minimum age at breeding will greatly increase the rate of genetic gain. This will allow long-term increases in biological efficiency and short-term tailoring of livestock to meet the demands of particular markets and opportunities.

scholarly journals Reduction of genotyping marker density for genomic selection is not an affordable approach to long-term breeding in cross-pollinated crops

2021 ◽  
Author(s):  
Júlio César DoVale ◽  
Humberto Fanelli Carvalho ◽  
Felipe Sabadin ◽  
Roberto Fritsche-Neto
Keyword(s):  
Genomic Selection ◽  
Recurrent Selection ◽  
Predictive Ability ◽  
Computational Time ◽  
Marker Density ◽  
Additive Variance ◽  
Selection Scheme ◽  
Performance Accuracy ◽  
Breeding Cycles

ABSTRACTThe selection of informative markers has been studied massively as an alternative to reduce genotyping costs for the genomic selection (GS) application. Low-density marker panels are attractive for GS because they decrease computational time-consuming and multicollinearity beyond more individuals can be genotyped with the same cost. Nevertheless, these inferences are usually made empirically using “static” training sets and populations, which are adequate only to predict a breeding program’s initial cycles but might not for long-term cycles. Moreover, to the best of our knowledge, none of these inferences considered the inclusion of dominance into the GS models, which is particularly important to predict cross-pollinated crops. Therefore, that reveals an important and unexplored topic for allogamous long-term breeding. To achieve this goal, we employed two approaches: the former used empirical maize datasets, and the latter simulations of long-term breeding cycles of phenotypic and genomic recurrent selection (intrapopulation and reciprocal). Then, we observed the reducing marker density effect on populations (mean, the best genotypes performance, accuracy, additive variance) over cycles and models (additive, additive-dominance, specific combining ability (SCA)). Our results indicate that the markers reduction based on different linkage disequili brium (LD) levels is viable only within a cycle and brings a significant decrease in predictive ability over generations. Furthermore, in the long-term, regardless of the selection scheme adopted, the more makers, the better because they buffer LD losses caused by recombination over breeding cycles. Finally, regarding the accuracy, the additive-dominant models tend to outperform the additive ones and perform similar to the SCA.

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