Use of field data in pig genomic selection schemes: a simulation study

Lillehammer, Marie; Sonesson, Anna K.; Meuwissen, Theo H. E.

doi:10.1017/s1751731115002669

Cited by 8 publications

(7 citation statements)

References 21 publications

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“…Moreover, some important traits, like disease resistance, cannot be measured in nucleus lines (Ib añez-Escriche et al 2009). By simulating a population structure organised in three tiers (nucleus, multiplier and production tiers), Lillehammer et al (2015) evaluated that the increase in the genetic gain of a trait not measured in the nucleus strongly depends on the economic weight assigned to it and that the most effective strategy, for the enlargement and update of the reference population, is the genotyping of animals of the production progeny of genotyped nucleus sires, other than the only genotyping nucleus sires. Finally, genomic prediction is calculated by using realised relationships instead of the expected genetic relationships as it happens in traditional models.…”

Section: Genomic Selection In Crossbred and Multi-breed Populationsmentioning

confidence: 99%

Genomic selection in pigs: state of the art and perspectives

Samoré

Fontanesi

2016

Italian Journal of Animal Science

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Genomic selection, that is based on the prediction of the breeding value (the genomic breeding value or GEBV) of the animals considering genomic information, is changing breeding strategies and approaches in dairy cattle and in several other livestock species. The starting points for the application of genomic selection in pigs were the development of a first commercial singlenucleotide polymorphism panel for high throughput genotyping, the sequencing of the pig genome and the application of statistical and methodological approaches first developed in dairy cattle and then adapted to the peculiarities of the pig breeding industry. In this review, we focused on the specific applications of the genomic selection in pigs, considering its limits, its advantages and its perspectives throughout an analysis of the simulation studies and field applications already available targeting many different traits (with high and low heritability). In addition, we presented an overview of the problems related to the implementation of genomic selection in crossbred and multi-breed pig populations and the potential solutions taking into account the economic aspects that should be faced in including genomic selection in pig breeding programmes. ARTICLE HISTORY

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Section: Genomic Selection In Crossbred and Multi-breed Populationsmentioning

confidence: 99%

Genomic selection in pigs: state of the art and perspectives

Samoré

Fontanesi

2016

Italian Journal of Animal Science

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“…A major concern in sustainable breeding and conservation programs is the preservation of genetic variance in the population [1–4]. To estimate the development of genetic variance under various conditions, Monte Carlo simulations have been widely applied in animal breeding and conservation genetics since computers were introduced [5, 6], and they remain a valuable tool [7, 8]. Currently, two main types of genetic models are used to investigate developments in genetic variance via simulations: Fisher’s infinitesimal model [9, 10] and the finite locus models [11, 12].…”

Section: Introductionmentioning

confidence: 99%

Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees

et al. 2019

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Stochastic simulation studies of animal breeding have mostly relied on either the infinitesimal genetic model or finite polygenic models. In this study, we investigated the long-term effects of the chosen model on honeybee breeding schemes. We implemented the infinitesimal model, as well as finite locus models, with 200 and 400 gene loci and simulated populations of 300 and 1000 colonies per year over the course of 100 years. The selection was of a directly and maternally influenced trait with maternal heritability of , direct heritability of , and a negative correlation between the effects of r md = − 0.18. Another set of simulations was run with parameters , , and r md = − 0.53. All models showed similar behavior for the first 20 years. Throughout the study, we observed a higher genetic gain in the direct than in the maternal effects and a smaller gain with a stronger negative covariance. In the long-term, however, only the infinitesimal model predicted sustainable linear genetic progress, while the finite locus models showed sublinear behavior and, after 100 years, only reached between 58% and 62% of the mean breeding values in the infinitesimal model. While the infinitesimal model suggested a reduction of genetic variance by 33% to 49% after 100 years, the finite locus models saw a more drastic loss of 76% to 92%. When designing sustainable breeding strategies, one should, therefore, not blindly trust the infinitesimal model as the predictions may be overly optimistic. Instead, the more conservative choice of the finite locus model should be favored.

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“…Some reports have shown that reference population size has a significant effect on the accuracy of GEBV ( 18 , 27 ). Five reference population sizes (500, 1,000, 1,500, 2,000, 3,000) and one validation population size 1,000 were selected to perform genomic selection in this study.…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, this study illustrated that the reference population including bulls that have more progeny can increase GEBV predicted accuracy ( 17 ). Lillehammer et al used simulated data to perform genomic selection of maternal traits in pigs, which illustrated that the genetic progress obtained by the population size of 1,000 was found to be 75% of the genetic progress of 5,000 ( 18 ). Anna Wolc et al used simulated data to perform genomic selection of laying chickens, and found that the generation interval was shortened by half ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Genomic Selection for Important Economic Traits of Cashmere and Meat Goats Assessed by Simulation Study

et al. 2022

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Genomic selection in plants and animals has become a standard tool for breeding because of the advantages of high accuracy and short generation intervals. Implementation of this technology is hindered by the high cost of genotyping and other factors. The aim of this study was to determine an optional marker density panel and reference population size for using genomic selection of goats, with speculation on the number of QTLs that affect the important economic traits of goats. In addition, the effect of buck population size in the reference population on the accuracy of genomic estimated breeding value (GEBV) was discussed. Based on the previous genetic evaluation results of Inner Mongolia White Cashmere Goats, live body weight (LBW, h2 = 0.11) and fiber diameter (FD, h2 = 0.34) were chosen to perform genomic selection in this study. Reasonable genome parameters and generation transmission processes were set, and phenotypic and genotype data of the two traits were simulated. Then, different sizes of the reference population and validation population were selected from progeny. The GEBVs were obtained by six methods, including GBLUP (Genomic Best Linear Unbiased Prediction), ssGBLUP (Single Step Genomic Best Linear Unbiased Prediction), BayesA, BayesB, Bayesian ridge regression, and Bayesian LASSO. The correlation coefficient between the predicted and realized phenotypes from simulation was calculated and used as a measure of the accuracy of GEBV in each trait. The results showed that the medium marker density Panel (45 K) could be used for genomic selection in goats, which can ensure the accuracy of the GEBV. The reference population size of 1,500 can achieve greater genetic progress in genomic selection for fiber diameter and live body weight in goats by comparing with the population size below this level. The accuracy of the GEBV for live body weight and fiber diameter was better when the number of QTLs was 100 and 50, respectively. Additionally, the accuracy of GEBV was discovered to be good when the buck population size was up to 200. Meanwhile, the accuracy of the GEBV for medium heritability traits (FDs) was found to be higher than the accuracy of the GEBV for low heritability traits (LBWs). These findings will provide theoretical guidance for genomic selection in goats by using real data.

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Use of field data in pig genomic selection schemes: a simulation study

Cited by 8 publications

References 21 publications

Genomic selection in pigs: state of the art and perspectives

Genomic selection in pigs: state of the art and perspectives

Comparison of infinitesimal and finite locus models for long-term breeding simulations with direct and maternal effects at the example of honeybees

Accuracy of Genomic Selection for Important Economic Traits of Cashmere and Meat Goats Assessed by Simulation Study

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