Power of Daughter and Granddaughter Designs for Determining Linkage Between Marker Loci and Quantitative Trait Loci in Dairy Cattle

Weller, J. I.; Kashi, Yechezkel; Soller, M.

doi:10.3168/jds.s0022-0302(90)78938-2

Cited by 401 publications

(306 citation statements)

References 36 publications

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“…In outbred populations, daughter and granddaughter designs are used to identify marker-QTL associations in large dairy half-sib families obtained by artificial insemination [50]. The Churra breed is one of the most important dairy sheep in Spain, and the Churra programme is aimed at mapping QTL affecting milk production and functional traits.…”

Section: Churra Breed Resource Populationmentioning

confidence: 99%

Mapping quantitative trait loci for milk production and genetic polymorphisms of milk proteins in dairy sheep

2005

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-In this paper, we present recent advances in the molecular dissection of complex traits in dairy sheep and discuss their possible impact on breeding schemes. In the first step, we review the literature data on genetic polymorphisms and the effects of sheep α s1 -casein and β-lactoglobulin loci. It is concluded that the results are rather inconsistent and cannot be used in dairy sheep selection. In a second step, we describe the strategy implemented in France, Italy and Spain taking advantage of the genetic maps for QTL detection. These studies were part of a European project, called "genesheepsafety", which investigated both milk production and functional traits. Preliminary QTL results are presented for production traits. dairy sheep / milk proteins / genetic polymorphisms / QTL / milk production

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Section: Churra Breed Resource Populationmentioning

confidence: 99%

Mapping quantitative trait loci for milk production and genetic polymorphisms of milk proteins in dairy sheep

2005

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“…Most studies to date that attempted to identify quantitative trait loci (QTLs) in dairy cattle have used specific study designs such as (grand-) daughter designs (Weller et al, 1990;Holmberg and Andersson-Eklund, 2006;Hö glund et al, 2009), and have therefore been generally limited to routinely available phenotypes. We are not aware of any genome-wide association study for traditional measures of fertility in dairy cattle that has been undertaken using actual cow phenotypic data.…”

Section: Introductionmentioning

confidence: 99%

Genome-wide associations for fertility traits in Holstein–Friesian dairy cows using data from experimental research herds in four European countries

Berry

Bastiaansen

Veerkamp

et al. 2012

Animal

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Genome-wide association studies for difficult-to-measure traits are generally limited by the sample population size with accurate phenotypic data. The objective of this study was to utilise data on primiparous Holstein-Friesian cows from experimental farms in Ireland, the United Kingdom, the Netherlands and Sweden to identify genomic regions associated with traditional measures of fertility, as well as a fertility phenotype derived from milk progesterone profiles. Traditional fertility measures investigated were days to first heat, days to first service, pregnancy rate to first service, number of services and calving interval (CI); post-partum interval to the commencement of luteal activity (CLA) was derived using routine milk progesterone assays. Phenotypic and genotypic data on 37 590 single nucleotide polymorphisms (SNPs) were available for up to 1570 primiparous cows. Genetic parameters were estimated using linear animal models, and univariate and bivariate genome-wide association analyses were undertaken using Bayesian stochastic search variable selection performed using Gibbs sampling. Heritability estimates of the traditional fertility traits varied from 0.03 to 0.16; the heritability for CLA was 0.13. The posterior quantitative trait locus (QTL) probabilities, across the genome, for the traditional fertility measures were all ,0.021. Posterior QTL probabilities of 0.060 and 0.045 were observed for CLA on SNPs each on chromosome 2 and chromosome 21, respectively, in the univariate analyses; these probabilities increased when CLA was included in the bivariate analyses with the traditional fertility traits. For example, in the bivariate analysis with CI, the posterior QTL probability of the two aforementioned SNPs were 0.662 and 0.123. Candidate genes in the vicinity of these SNPs are discussed. The results from this study suggest that the power of genome-wide association studies in cattle may be increased by sharing of data and also possibly by using physiological measures of the trait under investigation.Keywords: genome-wide association, fertility, cow, progesterone ImplicationsTraditional genetic selection for fertility is made difficult by the low heritability of traditional fertility phenotypes. This limitation can be minimised by increasing the heritability of the trait(s) or by using genomic information that explains a large proportion of the genetic variation in fertility. In the present study, both approaches were investigated. Heritability estimates greater than some traditional fertility traits were observed for the post-partum commencement of luteal activity quantified using milk progesterone concentration. Specific regions of the genome associated with fertility were also observed, albeit they only explained a small proportion of the total genetic variance in the traits.

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“…Several procedures have been described for mapping QTLs in experimental crosses [10,20,21] and in outbred populations [1,14,33]. In all these settings, hypothesis testing is one of the most delicate and controversial issues.…”

Section: Introductionmentioning

confidence: 99%

Bayes factors for detection of Quantitative Trait Loci

2001

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-A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor. Bayes factor / Quantitative Trait Loci / hypothesis testing / Markov Chain Monte Carlo

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Power of Daughter and Granddaughter Designs for Determining Linkage Between Marker Loci and Quantitative Trait Loci in Dairy Cattle

Cited by 401 publications

References 36 publications

Mapping quantitative trait loci for milk production and genetic polymorphisms of milk proteins in dairy sheep

Mapping quantitative trait loci for milk production and genetic polymorphisms of milk proteins in dairy sheep

Genome-wide associations for fertility traits in Holstein–Friesian dairy cows using data from experimental research herds in four European countries

Bayes factors for detection of Quantitative Trait Loci

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