Linear models for breeding values prediction in haplotype-assisted selection - an analysis of QTL-MAS Workshop 2011 Data

Mucha, Anna; Wierzbicki, Heliodor

doi:10.1186/1753-6561-6-s2-s11

Cited by 7 publications

(10 citation statements)

References 5 publications

(5 reference statements)

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“…A similar finding was reported by Mucha and Wierzbicki [ 87 ] when random models considered haplotypes. These authors suggested that the inclusion of haplotypes as random effects in models may increase the precision in the estimation of breeding values of animals with unknown phenotype (whose breeding value has been inferred after the phenotypic observations from ancestors and/or descendants) while it prevents the use of high-dimensional models as compared to when models include SNPs.…”

Section: Discussionsupporting

confidence: 89%

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Inostroza

González

Landi

et al. 2020

Animals

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Considering casein haplotype variants rather than SNPs may maximize the understanding of heritable mechanisms and their implication on the expression of functional traits related to milk production. Effects of casein complex haplotypes on milk yield, milk composition, and curve shape parameters were used using a Bayesian inference for ANOVA. We identified 48 single nucleotide polymorphisms (SNPs) present in the casein complex of 159 unrelated individuals of diverse ancestry, which were organized into 86 haplotypes. The Ali and Schaeffer model was chosen as the best fitting model for milk yield (Kg), protein, fat, dry matter, and lactose (%), while parabolic yield-density was chosen as the best fitting model for somatic cells count (SCC × 103 sc/mL). Peak and persistence for all traits were computed respectively. Statistically significant differences (p < 0.05) were found for milk yield and components. However, no significant difference was found for any curve shape parameter except for protein percentage peak. Those haplotypes for which higher milk yields were reported were the ones that had higher percentages for protein, fat, dry matter, and lactose, while the opposite trend was described by somatic cells counts. Conclusively, casein complex haplotypes can be considered in selection strategies for economically important traits in dairy goats.

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Section: Discussionsupporting

confidence: 89%

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Inostroza

González

Landi

et al. 2020

Animals

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“…The GBLUP performances were more variable with a very low correlation given by the Mucha et al [ 17 ] version based on haplotypes, and higher values for the Zeng et al [ 10 ] and Ogutu et al [ 9 ] proposals. Finally, the fixed effect linear model was far below all other methods.…”

Section: Resultsmentioning

confidence: 99%

“…This was particularly true for the Bayes group, where the ranking BayesCπ > BayesB > BayesA > BayesZ > BayesS was preserved, and even more for the Ogutu et al [ 9 ] selection variable, with a very low correlation observed for the adaptative Elastic Net. Notably the random model proposed by Mucha et al [ 17 ] fell in the worst positions with this criterion.…”

Section: Resultsmentioning

confidence: 99%

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Comparison of analyses of the XVth QTLMAS common dataset III: Genomic Estimations of Breeding Values

et al. 2012

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BackgroundThe QTLMAS XVth dataset consisted of pedigree, marker genotypes and quantitative trait performances of animals with a sib family structure. Pedigree and genotypes concerned 3,000 progenies among those 2,000 were phenotyped. The trait was regulated by 8 QTLs which displayed additive, imprinting or epistatic effects. The 1,000 unphenotyped progenies were considered as candidates to selection and their Genomic Estimated Breeding Values (GEBV) were evaluated by participants of the XVth QTLMAS workshop. This paper aims at comparing the GEBV estimation results obtained by seven participants to the workshop.MethodsFrom the known QTL genotypes of each candidate, two "true" genomic values (TV) were estimated by organizers: the genotypic value of the candidate (TGV) and the expectation of its progeny genotypic values (TBV). GEBV were computed by the participants following different statistical methods: random linear models (including BLUP and Ridge Regression), selection variable techniques (LASSO, Elastic Net) and Bayesian methods. Accuracy was evaluated by the correlation between TV (TGV or TBV) and GEBV presented by participants. Rank correlation of the best 10% of individuals and error in predictions were also evaluated. Bias was tested by regression of TV on GEBV.ResultsLarge differences between methods were found for all criteria and type of genetic values (TGV, TBV). In general, the criteria ranked consistently methods belonging to the same family.ConclusionsBayesian methods - A

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“…Boleckova et al ( 2012 ) drew a similar conclusion after having tested linear models with haplotypes as explanatory variables. A like tendency was described by Mucha and Wierzbicki ( 2012 ), who used simulated data in a haplotype-based breeding value prediction study. The correlations between EBV and DGV for production traits in the validation dataset were lower than the correlations presented by Solberg et al ( 2008 ).…”

Section: Discussionmentioning

confidence: 99%

High-frequency marker haplotypes in the genomic selection of dairy cattle

et al. 2019

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The aim of this study was to predict the genomic breeding value (DGV) of production, selected conformation and reproductive traits, and somatic cell score of dairy cattle in Poland using high-frequency marker haplotypes. The dataset consisted of phenotypic, genotypic, and pedigree data of 1216 Polish Holstein-Friesian bulls. The genotypic data consisted of 54,000 single-nucleotide polymorphisms (SNPs). The data were divided into two subsets: a test dataset ( n = 1064) and a validation dataset ( n = 152). Genotypic data were selected using three criteria: the percentage of missing genotypes, minor allele frequency, and linkage disequilibrium. The purpose of the data selection was to identify blocks of SNPs that were then used for the construction of haplotypes. Only haplotypes with a frequency higher than 25% were selected. DGV was predicted using four variants of a linear model with random haplotype effects and deregressed breeding values as the response variables. The accuracy of genomic prediction was checked by comparing DGVs with estimated breeding values (EBVs) using two methods: Pearson’s correlations and the regression of EBV on DGV. The use of high-frequency haplotypes showed a tendency to underestimate DGVs. None of the models tested was clearly superior with regard to the traits studied. DGVs of production and conformation traits as well as somatic cell score (medium or high heritability traits) were more accurate than those estimated for fertility traits (low heritability traits).

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Linear models for breeding values prediction in haplotype-assisted selection - an analysis of QTL-MAS Workshop 2011 Data

Cited by 7 publications

References 5 publications

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Comparison of analyses of the XVth QTLMAS common dataset III: Genomic Estimations of Breeding Values

High-frequency marker haplotypes in the genomic selection of dairy cattle

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