Accuracy of genomic selection for age at puberty in a multi‐breed population of tropically adapted beef cattle

Farah, Michel Marques; Swan, Andrew; Fortes, M. R. S.; Fonseca, Ricardo da; Moore, S. S.; Kelly, Matthew

doi:10.1111/age.12362

Cited by 17 publications

(9 citation statements)

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“…Benefits of multi-breed genomic predictions have also been reported in other studies [ 42 , 45 – 47 ]. Hozé et al [ 48 ] working with three dairy cattle breeds and HD SNP chip (777 K) also observed that multi-breed GS can contribute to increased genomic evaluation accuracy in small breeds (or populations).…”

Section: Discussionsupporting

confidence: 71%

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Prediction of genomic breeding values for growth, carcass and meat quality traits in a multi-breed sheep population using a HD SNP chip

et al. 2017

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BackgroundNew Zealand has some unique Terminal Sire composite sheep breeds, which were developed in the last three decades to meet commercial needs. These composite breeds were developed based on crossing various Terminal Sire and Maternal breeds and, therefore, present high genetic diversity compared to other sheep breeds. Their breeding programs are focused on improving carcass and meat quality traits. There is an interest from the industry to implement genomic selection in this population to increase the rates of genetic gain. Therefore, the main objectives of this study were to determine the accuracy of predicted genomic breeding values for various growth, carcass and meat quality traits using a HD SNP chip and to evaluate alternative genomic relationship matrices, validation designs and genomic prediction scenarios. A large multi-breed population (n = 14,845) was genotyped with the HD SNP chip (600 K) and phenotypes were collected for a variety of traits.ResultsThe average observed accuracies (± SD) for traits measured in the live animal, carcass, and, meat quality traits ranged from 0.18 ± 0.07 to 0.33 ± 0.10, 0.28 ± 0.09 to 0.55 ± 0.05 and 0.21 ± 0.07 to 0.36 ± 0.08, respectively, depending on the scenario/method used in the genomic predictions. When accounting for population stratification by adjusting for 2, 4 or 6 principal components (PCs) the observed accuracies of molecular breeding values (mBVs) decreased or kept constant for all traits. The mBVs observed accuracies when fitting both G and A matrices were similar to fitting only G matrix. The lowest accuracies were observed for k-means cross-validation and forward validation performed within each k-means cluster.ConclusionsThe accuracies observed in this study support the feasibility of genomic selection for growth, carcass and meat quality traits in New Zealand Terminal Sire breeds using the Ovine HD SNP chip. There was a clear advantage on using a mixed training population instead of performing analyzes per genomic clusters. In order to perform genomic predictions per breed group, genotyping more animals is recommended to increase the size of the training population within each group and the genetic relationship between training and validation populations. The different scenarios evaluated in this study will help geneticists and breeders to make wiser decisions in their breeding programs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-017-0476-8) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 71%

“…Dodds et al [ 18 ] reported K values ranging from 0.16 to 0.90. Slopes well different from 1 have been reported in other studies [ 28 , 45 , 52 , 53 ].…”

Section: Discussionsupporting

confidence: 67%

Prediction of genomic breeding values for growth, carcass and meat quality traits in a multi-breed sheep population using a HD SNP chip

et al. 2017

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“…G enomic selection (GS) is based on the use of genomic information to predict the genetic value of phenotyped or nonphenotyped individuals (Heffner et al, 2009). It has been effectively used in animal (de Campos et al, 2015; Farah et al, 2016) and plant breeding (Jarquin et al, 2016; Huang et al, 2016). This strategy seeks to exploit information from markers in linkage disequilibrium (LD) with chromosomal regions that control the traits under evaluation (Heslot et al, 2015).…”

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confidence: 99%

Genomic Prediction of Autogamous and Allogamous Plants by SNPs and Haplotypes

Matias¹,

Galli²,

Granato³

et al. 2017

Crop Science

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2951 RESEARCHG enomic selection (GS) is based on the use of genomic information to predict the genetic value of phenotyped or nonphenotyped individuals (Heffner et al., 2009). It has been effectively used in animal (de Campos et al., 2015;Farah et al., 2016) and plant breeding ( Jarquin et al., 2016;Huang et al., 2016). This strategy seeks to exploit information from markers in linkage disequilibrium (LD) with chromosomal regions that control the traits under evaluation (Heslot et al., 2015). To best explore this information, the development of genotyping technologies continues to generate significant quantities of markers. However, as marker densities increase, collinearity also increases. Additionally, evaluating a large number of individuals and complex experimental designs may lead to infeasibility of computational analysis.To reduce the number of genomic variables maintaining information, markers can be rearranged in LD blocks, known as haplotype blocks (Cuyabano et al., 2014). Capturing epistatic interactions of nearby single-nucleotide polymorphisms (SNPs), improving estimates of alleles identical by descent, and reducing the number of tests in association studies (which leads to the reduction in type I error rate) are some advantages of using haplotypes. Besides, it infers groups of correlated genes and alleles, ABSTRACTThe implementation of single-nucleotide polymorphism (SNP)-based genomic selection has demonstrated great predictive potential in plants. However, its application is sometimes limited to the biallelism of the marker. In this context, the use of haplotype blocks as multiallelic markers might improve genomic prediction. This study was performed to compare the predictive ability of Bayesian genomic prediction models using haplotypes (confidence interval and four-gamete), individual SNPs, and sets of SNPs selected according to haplotype construction. The use of haplotype matrices increased the predictive ability and selection coincidence with the phenotypic selection for the maize (Zea mays L.) breeding population. However, this was not observed for the rice (Oryza sativa L.) population, in which the use of the nonreduced SNP matrix was more efficient. Overall, the use of reduced SNP matrices did not lead to better predictive abilities. No difference was observed between the genomic prediction methods used. We found that the use of haplotypes has potential to increase predictive ability of genomic prediction in breeding populations of allogamous plants or plants with high multiallelism.

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“…Their accuracies were calculated as the correlation between GEBV and phenotypes adjusted for systematic effects divided by square root of heritability. More recently, Farah et al (2016) reported accuracies of 0.14 and 0.34 for the same 2 breeds using the GBLUP method. Using high-density SNP array and GBLUP, Kramer et al (2014) found an accuracy of 0.12 for days to first heat in Brown Swiss dairy cattle.…”

Section: Accuracy Of Genomic Predictionsmentioning

confidence: 99%

Genomic prediction of continuous and binary fertility traits of females in a composite beef cattle breed

Toghiani

Hay

Sumreddee

et al. 2017

Journal of Animal Science

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Reproduction efficiency is a major factor in the profitability of the beef cattle industry. Genomic selection (GS) is a promising tool that may improve the predictive accuracy and genetic gain of fertility traits. There is a wide range of traits used to measure fertility in dairy and beef cattle including continuous (days open), discrete (pregnancy status), and count (number of inseminations) responses. In this study, a joint analysis of age of puberty (AOP), age at first calving (AOC), and the heifer pregnancy status (HPS) was performed. Data used in this study consisted of records from 1,365 Composite Gene Combination (CGC; 50% Red Angus, 25% Charolais, 25% Tarentaise) first parity females born between 2002 and 2011. The pedigree file included 5,374 animals. A total of 3,902 animals were genotyped with different density SNP chips (3K to 50K SNP). Animals genotyped with low-density arrays were imputed to higher density (BovineSNP50 BeadChip) using FImpute. Data were analyzed using univariate and multivariate classical quantitative models (pedigree based) and univariate genomic approaches. For the latter, 3 different Bayesian methods (BayesA, BayesB, and BayesCπ) were implemented and compared. Estimates of heritabilities using univariate and multivariate analyses based on pedigree relationships ranged between 0.03 (for AOC) to 0.2 (AOP). Heritability of pregnancy status was 0.15 and 0.09 using the univariate and multivariate analyses, respectively. Genetic correlation between pregnancy status and the other 2 traits was low being 0.08 with age at puberty and -0.10 with age at first calving. Heritability estimates were slightly higher using genomic rather than average additive relationships. The accuracy of genomic prediction was similar across the 3 Bayesian methods with higher accuracies for age of puberty than the age at first calving likely due to the higher heritability of the former. The prediction of the binary pregnancy status measured using the area under the curve increased by 27% to 29% compared to a random classifier. Due to the small size of the data, all estimates have large posterior standard deviations and results should be interpreted with caution.

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Accuracy of genomic selection for age at puberty in a multi‐breed population of tropically adapted beef cattle

Cited by 17 publications

References 31 publications

Prediction of genomic breeding values for growth, carcass and meat quality traits in a multi-breed sheep population using a HD SNP chip

Prediction of genomic breeding values for growth, carcass and meat quality traits in a multi-breed sheep population using a HD SNP chip

Genomic Prediction of Autogamous and Allogamous Plants by SNPs and Haplotypes

Genomic prediction of continuous and binary fertility traits of females in a composite beef cattle breed

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