A low-marker density implementation of genomic selection in aquaculture using within-family genomic breeding values

AbstractWhite spot syndrome virus (WSSV) causes major worldwide losses in shrimp aquaculture. The development of resistant shrimp populations is an attractive option for management of the disease. However, heritability for WSSV resistance is generally low and genetic improvement by conventional selection has been slow. This study was designed to determine the power and accuracy of genomic selection to improve WSSV resistance in Litopenaeus vannamei. Shrimp were experimentally challenged with WSSV and resistance was evaluated as dead or alive (DOA) 23 days after infestation. All shrimp in the challenge test were genotyped for 18,643 single nucleotide polymorphisms. Breeding candidates (G0) were ranked on genomic breeding values for WSSV resistance. Two G1 populations were produced, one from G0 breeders with high and the other with low estimated breeding values. A third population was produced from “random” mating of parent stock. The average survival was 25% in the low, 38% in the random and 51% in the high-genomic breeding value groups. Genomic heritability for DOA (0.41 in G1) was high for this type of trait. The realised genetic gain and high heritability clearly demonstrates large potential for further genetic improvement of WSSV resistance in the evaluated L. vannamei population using genomic selection.

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Designing dairy cattle breeding schemes under genomic selection: a review of international research

Animal Production Science ◽

10.1071/an11098 ◽

2012 ◽

Vol 52 (3) ◽

pp. 107 ◽

Cited By ~ 67

Author(s):

J. E. Pryce ◽

H. D. Daetwyler

Keyword(s):

Dairy Cattle ◽

Genomic Selection ◽

Genetic Gain ◽

Reproductive Technologies ◽

Progeny Testing ◽

Genomic Breeding ◽

Breeding Values ◽

Scheme Design ◽

Dairy Cattle Breeding ◽

Breeding Schemes

High rates of genetic gain can be achieved through (1) accurate predictions of breeding values (2) high intensities of selection and (3) shorter generation intervals. Reliabilities of ~60% are currently achievable using genomic selection in dairy cattle. This breakthrough means that selection of animals can happen at a very early age (i.e. as soon as a DNA sample is available) and has opened opportunities to radically redesign breeding schemes. Most research over the past decade has focussed on the feasibility of genomic selection, especially how to increase the accuracy of genomic breeding values. More recently, how to apply genomic technology to breeding schemes has generated a lot of interest. Some of this research remains the intellectual property of breeding companies, but there are examples in the public domain. Here we review published research into breeding scheme design using genomic selection and evaluate which designs appear to be promising (in terms of rates of genetic gain) and those that may have unfavourable side-effects (i.e. increasing the rate of inbreeding). The schemes range from fairly conservative designs where bulls are screened genomically to reduce numbers entering progeny testing, to schemes where very large numbers of bull calves are screened and used as sires as soon as they reach sexual maturity. More radical schemes that incorporate the use of reproductive technologies (in juveniles) and genomic selection in nucleus herds are also described. The models used are either deterministic and more recently tend to be stochastic, simulating populations of cattle. A key driver of the rate of genetic gain is the generation interval, which could range from being similar to that in conventional testing (~5 years), down to as little as 1.5 years. Generally, the rate of genetic gain is between 12% and 100% more than in conventional progeny testing, while the rate of inbreeding tends to be lower per generation than in progeny testing because Mendelian sampling terms can be estimated more accurately. However, short generation intervals can lead to higher rates of inbreeding per year in genomic breeding programs.

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Accuracy of genomic selection using stochastic search variable selection in Australian Holstein Friesian dairy cattle

Genetics Research ◽

10.1017/s0016672309990243 ◽

2009 ◽

Vol 91 (5) ◽

pp. 307-311 ◽

Cited By ~ 93

Author(s):

KLARA L. VERBYLA ◽

BEN J. HAYES ◽

PHILIP J. BOWMAN ◽

MICHAEL E. GODDARD

Keyword(s):

Variable Selection ◽

Genomic Selection ◽

Critical Issue ◽

Stochastic Search ◽

Selection Strategy ◽

Genomic Breeding ◽

Breeding Values ◽

Stochastic Search Variable Selection ◽

Snp Data ◽

Search Variable

SummaryGenomic selection describes a selection strategy based on genomic breeding values predicted from dense single nucleotide polymorphism (SNP) data. Multiple methods have been proposed but the critical issue is how to decide whether an SNP should be included in the predictive set to estimate breeding values. One major disadvantage of the traditional Bayes B approach is its high computational demands caused by the changing dimensionality of the models. The use of stochastic search variable selection (SSVS) retains the same assumptions about the distribution of SNP effects as Bayes B, while maintaining constant dimensionality. When Bayesian SSVS was used to predict genomic breeding values for real dairy data over a range of traits it produced accuracies higher or equivalent to other genomic selection methods with significantly decreased computational and time demands than Bayes B.

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Genomic selection in crops, trees and forages: a review

Crop and Pasture Science ◽

10.1071/cp13363 ◽

2014 ◽

Vol 65 (11) ◽

pp. 1177 ◽

Cited By ~ 70

Author(s):

Z. Lin ◽

B. J. Hayes ◽

H. D. Daetwyler

Keyword(s):

Family Structure ◽

Genomic Selection ◽

Genetic Gain ◽

Population Level ◽

Early Age ◽

Marker Density ◽

Breeding Strategies ◽

Effective Population ◽

Genomic Breeding ◽

Factors Affecting

Genomic selection is now being used at an accelerating pace in many plant species. This review first discusses the factors affecting the accuracy of genomic selection, and then interprets results of existing plant genomic selection studies in light of these factors. Differences between genomic breeding strategies for self-pollinated and open-pollinated species, and between-population level v. within-family design, are highlighted. As expected, more training individuals, higher trait heritability and higher marker density generally lead to better accuracy of genomic breeding values in both self-pollinated and open-pollinated plants. Most published studies to date have artificially limited effective population size by using designs of bi-parental or within-family structure to increase accuracies. The capacity of genomic selection to reduce generation intervals by accurately evaluating traits at an early age makes it an effective tool to deliver more genetic gain from plant breeding in many cases.

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Simulation studies to optimize genomic selection in honey bees

Genetics Selection Evolution ◽

10.1186/s12711-021-00654-x ◽

2021 ◽

Vol 53 (1) ◽

Author(s):

Richard Bernstein ◽

Manuel Du ◽

Andreas Hoppe ◽

Kaspar Bienefeld

Keyword(s):

Genomic Selection ◽

Genetic Gain ◽

Honey Bees ◽

Reference Population ◽

Breeding Program ◽

Genomic Relationship Matrix ◽

Relationship Matrix ◽

Genomic Relationship ◽

Genomic Breeding ◽

Breeding Values

Abstract Background With the completion of a single nucleotide polymorphism (SNP) chip for honey bees, the technical basis of genomic selection is laid. However, for its application in practice, methods to estimate genomic breeding values need to be adapted to the specificities of the genetics and breeding infrastructure of this species. Drone-producing queens (DPQ) are used for mating control, and usually, they head non-phenotyped colonies that will be placed on mating stations. Breeding queens (BQ) head colonies that are intended to be phenotyped and used to produce new queens. Our aim was to evaluate different breeding program designs for the initiation of genomic selection in honey bees. Methods Stochastic simulations were conducted to evaluate the quality of the estimated breeding values. We developed a variation of the genomic relationship matrix to include genotypes of DPQ and tested different sizes of the reference population. The results were used to estimate genetic gain in the initial selection cycle of a genomic breeding program. This program was run over six years, and different numbers of genotyped queens per year were considered. Resources could be allocated to increase the reference population, or to perform genomic preselection of BQ and/or DPQ. Results Including the genotypes of 5000 phenotyped BQ increased the accuracy of predictions of breeding values by up to 173%, depending on the size of the reference population and the trait considered. To initiate a breeding program, genotyping a minimum number of 1000 queens per year is required. In this case, genetic gain was highest when genomic preselection of DPQ was coupled with the genotyping of 10–20% of the phenotyped BQ. For maximum genetic gain per used genotype, more than 2500 genotyped queens per year and preselection of all BQ and DPQ are required. Conclusions This study shows that the first priority in a breeding program is to genotype phenotyped BQ to obtain a sufficiently large reference population, which allows successful genomic preselection of queens. To maximize genetic gain, DPQ should be preselected, and their genotypes included in the genomic relationship matrix. We suggest, that the developed methods for genomic prediction are suitable for implementation in genomic honey bee breeding programs.

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Accuracy of breeding values when using and ignoring the polygenic effect in genomic breeding value estimation with a marker density of one SNP per cM

Journal of Animal Breeding and Genetics ◽

10.1111/j.1439-0388.2007.00691.x ◽

2007 ◽

Vol 124 (6) ◽

pp. 362-368 ◽

Cited By ~ 88

Author(s):

M.P.L. Calus ◽

R.F. Veerkamp

Keyword(s):

Breeding Value ◽

Polygenic Effect ◽

Marker Density ◽

Genomic Breeding ◽

Breeding Values ◽

Value Estimation ◽

Genomic Breeding Value ◽

Genomic Breeding Value Estimation

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Computation of genomic breeding values and genomic selection.

Linear models for the prediction of animal breeding values ◽

10.1079/9781780643915.0177 ◽

2015 ◽

pp. 177-203

Author(s):

R. Mrode

Keyword(s):

Genomic Selection ◽

Genomic Breeding ◽

Breeding Values

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Accuracy of predicting genomic breeding values for residual feed intake in Angus and Charolais beef cattle

Journal of Animal Science ◽

10.2527/jas.2012-5715 ◽

2013 ◽

Author(s):

L. Chen ◽

F. Schenkel ◽

M. Vinsky ◽

D. H. Crews ◽

C. Li

Keyword(s):

Beef Cattle ◽

Feed Intake ◽

Residual Feed Intake ◽

Genomic Breeding ◽

Breeding Values

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GPS Coordinates for Modelling Correlated Herd Effects in Genomic Prediction Models Applied to Hanwoo Beef Cattle

Animals ◽

10.3390/ani11072050 ◽

2021 ◽

Vol 11 (7) ◽

pp. 2050

Author(s):

Beatriz Castro Dias Cuyabano ◽

Gabriel Rovere ◽

Dajeong Lim ◽

Tae Hun Kim ◽

Hak Kyo Lee ◽

...

Keyword(s):

Environmental Factors ◽

Genomic Prediction ◽

Prediction Models ◽

Phenotypic Expression ◽

Genetic Evaluation ◽

Genomic Breeding ◽

Breeding Values ◽

Korean Cattle ◽

Evaluation Programs ◽

The Impact

It is widely known that the environment influences phenotypic expression and that its effects must be accounted for in genetic evaluation programs. The most used method to account for environmental effects is to add herd and contemporary group to the model. Although generally informative, the herd effect treats different farms as independent units. However, if two farms are located physically close to each other, they potentially share correlated environmental factors. We introduce a method to model herd effects that uses the physical distances between farms based on the Global Positioning System (GPS) coordinates as a proxy for the correlation matrix of these effects that aims to account for similarities and differences between farms due to environmental factors. A population of Hanwoo Korean cattle was used to evaluate the impact of modelling herd effects as correlated, in comparison to assuming the farms as completely independent units, on the variance components and genomic prediction. The main result was an increase in the reliabilities of the predicted genomic breeding values compared to reliabilities obtained with traditional models (across four traits evaluated, reliabilities of prediction presented increases that ranged from 0.05 ± 0.01 to 0.33 ± 0.03), suggesting that these models may overestimate heritabilities. Although little to no significant gain was obtained in phenotypic prediction, the increased reliability of the predicted genomic breeding values is of practical relevance for genetic evaluation programs.

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