Accuracy of Prediction of Gene Content in Large Animal Populations and its Use for Candidate Gene Detection and Genetic Evaluation

Gengler, Nicolas; Abras, S.; Verkenne, Catherine; Vanderick, Sylvie; Szydłowski, M.; Renaville, Robert

doi:10.3168/jds.2007-0231

Cited by 24 publications

(31 citation statements)

References 16 publications

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“…These results confirm the tendencies reported by Gengler et al (2008) and Waters et al (2011). Therefore, the tyrosine allele could be useful in selection to improve resistance to mastitis.…”

Section: Discussionsupporting

confidence: 91%

The F279Y polymorphism of the GHR gene and its relation to milk production and somatic cell score in German Holstein dairy cattle

et al. 2011

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The bovine growth hormone receptor (GHR) gene has been identified as a strong positional and functional candidate gene influencing milk production. A non-synonymous single nucleotide polymorphism (SNP) in exon 8 leads to a phenylalanine to tyrosine amino acid substitution (F279Y) in the receptor. The aim of the study was to estimate the effects of the F279Y mutation on milk yield, fat, protein, casein, and lactose yield and content, as well as somatic cell score (SCS), in a German Holstein dairy cattle population. The analysis of 1,370 dairy cows confirmed a strong association of the F279Y polymorphism with milk yield, as well as with fat, protein, and casein contents. Furthermore, increasing effects on lactose yield and content for the 279Y allele were found. Even though the tyrosine variant occurred as the minor allele (16.5%), its substitution effects were 320 kg (305 d), 0.02 kg per day, and 0.07 kg per day for milk, casein, and lactose yields, respectively. The same allele had negative effects on fat, protein, and casein contents. Finally, the high-milk-yield tyrosine allele was also associated with lower SCS (p < 0.05). The data support the high potential of the F279Y polymorphism as a marker for the improvement of milk traits in selection programs.

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Section: Discussionsupporting

confidence: 91%

The F279Y polymorphism of the GHR gene and its relation to milk production and somatic cell score in German Holstein dairy cattle

et al. 2011

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“…Gengler et al (2007) reported also very similar correlations between true and predicted gene contents using either the S-MM or SIP. Allele substitution effects were overestimated in agreement with Gengler et al (2008), but in disagreement with Baruch and Weller (2009), who found that allele substitution effects were underestimated. Clearly, the overestimation is partly due to genotyping only a selected group of sires in generations 1 to 4 (see Table 6), whereas Baruch and Weller (2009) genotyped all bulls in each generation.…”

Section: Methods and Resultsmentioning

confidence: 55%

The effect of missing marker genotypes on the accuracy of gene-assisted breeding value estimation: a comparison of methods

Mulder

Meuwissen

Calus

et al. 2010

Animal

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In livestock populations, missing genotypes on a large proportion of the animals is a major problem when implementing geneassisted breeding value estimation for genes with known effect. The objective of this study was to compare different methods to deal with missing genotypes on accuracy of gene-assisted breeding value estimation for identified bi-allelic genes using Monte Carlo simulation. A nested full-sib half-sib structure was simulated with a mixed inheritance model with one bi-allelic quantitative trait loci (QTL) and a polygenic effect due to infinite number of polygenes. The effect of the QTL was included in gene-assisted BLUP either by random regression on predicted gene content, i.e. the number of positive alleles, or including haplotype effects in the model with an inverse IBD matrix to account for identity-by-descent relationships between haplotypes using linkage analysis information (IBD-LA). The inverse IBD matrix was constructed using segregation indicator probabilities obtained from multiple marker iterative peeling. Gene contents for unknown genotypes were predicted using either multiple marker iterative peeling or mixed model methodology. For both methods, gene-assisted breeding value estimation increased accuracies of total estimated breeding value (EBV) with 0% to 22% for genotyped animals in comparison to conventional breeding value estimation. For animals that were not genotyped, the increase in accuracy was much lower (0% to 5%), but still substantial when the heritability was 0.1 and when the QTL explained at least 15% of the genetic variance. Regression on predicted gene content yielded higher accuracies than IBD-LA. Allele substitution effects were, however, overestimated, especially when only sires and males in the last generation were genotyped. For juveniles without phenotypic records and traits measured only on females, the superiority of regression on gene content over IBD-LA was larger than when all animals had phenotypes. Missing gene contents were predicted with higher accuracy using multiple-marker iterative peeling than with using mixed model methodology, but the difference in accuracy of total EBV was negligible and mixed model methodology was computationally much faster than multiple iterative peeling. For large livestock populations it can be concluded that gene-assisted breeding value estimation can be practically best performed by regression on gene contents, using mixed model methodology to predict missing marker genotypes, combining phenotypic information of genotyped and ungenotyped animals in one evaluation. This technique would be, in principle, also feasible for genomic selection. It is expected that genomic selection for ungenotyped animals using predicted single nucleotide polymorphism gene contents might be beneficial especially for low heritable traits.

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“…Several methods exist to impute missing genotypes that, apart from the genotypic information at the locus of interest, use only pedigree data (Gengler et al, 2007(Gengler et al, , 2008, only surrounding markers (EastPHASE; Scheet and Stephens, 2006), or both (Li and Jiang, 2003;Kong et al, 2008;Meuwissen and Coddard, 2010;Mulder et al, 2010b). The mixed model (BLUP) method presented by Gengler et al (2007) uses the BLUP framework to derive missing gene content conditional on genotypic information of relatives.…”

Section: Introductionmentioning

confidence: 99%

Imputation of missing single nucleotide polymorphism genotypes using a multivariate mixed model framework1

Calus

Veerkamp

Mulder

2011

Journal of Animal Science

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The objective of this paper was to investigate, for various scenarios at low and high marker density, the accuracy of imputing genotypes when using a multivariate mixed model framework using information from 2, 4, or 10 surrounding markers. This model predicts genotypes at a locus, using genotypes at nearby loci as correlated traits, and the additive genetic relationship matrix to use information from genotyped relatives. For 2 scenarios this method was compared with the population-based imputation algorithms FastPHASE and Beagle. Accuracies of imputation were obtained with Monte Carlo simulation and predicted with selection index theory, using input from the simulated data. Five different scenarios of missing genotypes were considered: 1) genotypes of some loci are missing due to genotyping errors, 2) juvenile selection candidates are genotyped using a smaller SNP panel, 3) some animals in the pedigree of a breeding population are not genotyped, 4) juvenile selection candidates are not genotyped, and 5) 1 generation of animals in the top of the pedigree are not genotyped. Surrounding marker information did not improve accuracy of imputation when animals whose genotypes were imputed were not genotyped for those surrounding markers. When those animals were genotyped for surrounding markers, results indicated a limited gain when linkage disequilibrium (LD) between SNP was low, but a substantial increase in accuracy when LD between SNP was high. For scenario 1, using 1 vs. 11 SNP, accuracy was respectively 0.75 and 0.81 at low, and 0.75 and 0.93 at high density. For scenario 2, using 1 vs. 11 SNP, accuracy was, respectively, 0.70 and 0.73 at low, and 0.71 and 0.84 at high density. Beagle outperformed the other methods at high SNP density, whereas the multivariate mixed model was clearly superior when SNP density was low and animals where genotyped with a reduced SNP panel. The results showed that extending the univariate gene content method to a multivariate BLUP model with inclusion of surrounding marker information only yields greater imputation accuracy when the animals with imputed loci are at least genotyped for some SNP that are in LD with the SNP to be imputed. The equation derived from selection index theory accurately predicted the accuracy of imputation using the multivariate mixed model framework.

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Accuracy of Prediction of Gene Content in Large Animal Populations and its Use for Candidate Gene Detection and Genetic Evaluation

Cited by 24 publications

References 16 publications

The F279Y polymorphism of the GHR gene and its relation to milk production and somatic cell score in German Holstein dairy cattle

The F279Y polymorphism of the GHR gene and its relation to milk production and somatic cell score in German Holstein dairy cattle

The effect of missing marker genotypes on the accuracy of gene-assisted breeding value estimation: a comparison of methods

Imputation of missing single nucleotide polymorphism genotypes using a multivariate mixed model framework1

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