Estimation by simulation of the efficiency of the French marker-assisted selection program in dairy cattle<i>(Open Access publication)</i>

Guillaume, François; Fritz, Sébastien; Boichard, Didier; Druet, Tom

doi:10.1051/gse:2007036

Cited by 12 publications

(13 citation statements)

References 17 publications

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“…The benefits of high densities are seen more clearly when assuming very large populations involving several families, as illustrated by Guillaume et al (2008) in a dairy cattle dataset. They found a substantial gain in reliability, by replacing microsatellite markers by 10 SNPs within a 1-cM bracket around the QTL, from 43 to 79 % in comparison to classical selection.…”

Section: Level Of Variation For the Targeted Traitsmentioning

confidence: 91%

“…For a given number of QTLs, a scenario with even QTL effects led to lower MAS efficiency compared to QTL effects in geometric distributions, with differences being particularly noticeable within an h 2 range of 0.15~0.2. With a pedigree-based dairy cattle dataset, Guillaume et al (2008) found that MAS was more beneficial when there are fewer QTLs with large effects, with situations close to the infinitesimal model being less favorable for MAS. Geometrical or Lshape distributions of QTL effects are thought to reflect what is observed for many quantitative traits (Kearsey and Farquhar 1998;Bernardo 2008), in livestock (Silva et al 2011) and in crops (Truntzler et al 2010).…”

Section: Level Of Variation For the Targeted Traitsmentioning

confidence: 96%

See 1 more Smart Citation

Potential for marker-assisted selection for forest tree breeding: lessons from 20 years of MAS in crops

Muranty

Jorge²,

Bastien³

et al. 2014

Tree Genetics & Genomes

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For the most part, molecular markers and detection of quantitative trait loci have been developed for forest tree species in view to performing marker-assisted selection (MAS). However, MAS has not been applied to forest trees until now. In parallel, some success stories of MAS in crop breeding have been reported. Recently, genotyping techniques have undergone a tremendous increase in throughput, moving the trend from MAS to genomic selection. We analyzed 250 papers reporting the use of MAS in plant breeding and found that the most popular schemes used were gene pyramiding and marker-assisted backcross manipulating a single or very few genomic regions which have a major impact on crop value. We reviewed theoretical and simulation studies to identify the parametric space in which MAS is expected to bring about significant advantages over phenotypic selection. Then, we tried to explain why MAS has not been applied to forest trees and discuss the opportunities offered by recent advances in these species.

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Section: Level Of Variation For the Targeted Traitsmentioning

confidence: 91%

Section: Level Of Variation For the Targeted Traitsmentioning

confidence: 96%

Potential for marker-assisted selection for forest tree breeding: lessons from 20 years of MAS in crops

Muranty

Jorge²,

Bastien³

et al. 2014

Tree Genetics & Genomes

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“…These markers were selected previously in two different experimental designs: the French MAS program (e.g. Guillaume et al 2008) and a Normande-Holstein F-2 design implemented in an experimental farm (Larroque et al 2007). The mean number of markers per chromosome was 5.1, ranging from 2 to 10.…”

Section: Genotyping Datamentioning

confidence: 99%

Estimation of genetic parameters and genome scan for 15 semen characteristics traits of Holstein bulls

Druet¹,

Fritz²,

Sellem³

et al. 2009

J Animal Breeding Genetics

100

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A QTL detection experiment was performed in French dairy cattle to search for QTL related to male fertility. Ten families, involving a total of 515 bulls, were phenotyped for ejaculated volume and sperm concentration, number of spermatozoa, motility, velocity, percentage of motile spermatozoa after thawing and abnormal spermatozoa. A set of 148 microsatellite markers were used to realize a genome scan. First, genetic parameters were estimated for all traits. Semen production traits were found to have moderate heritabilities (from 0.15 to 0.30) while some of the semen quality traits such as motility had high heritabilities (close to 0.60). Genetic correlations among traits showed negative relationships between volume and concentration and between volume and most quality traits such as motility or abnormal sperm while correlations between concentration and these traits were rather favourable. Percentages of abnormal sperm were negatively related to quality traits, especially with motility and velocity of spermatozoa. Three QTL related to abnormal sperm frequencies were significant at p < 0.01. In total, 11 QTL (p < 0.05) were detected. However, the number of QTL detected was within the range of expected false positives. Because of the lack of power to find QTL in this design further analyses are required to confirm these QTL.

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“…The multiple trait derivative free restricted maximum likelihood (MTDFREML) [26] software has been modified to facilitate the prediction of breeding values using GRMs [27]. While GS is now being tested within commercial dairy cattle populations [14,28,29], traditional progeny testing schemes used to achieve high accuracies on the bulls released for widespread use are being modified to reflect the gains in selection response that are possible when bulls, at birth, have estimates of genetic merit with accuracies that are similar to those achieved, on average, with 11 daughter equivalents [14]. …”

Section: Introductionmentioning

confidence: 99%

Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle

et al. 2010

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BackgroundMolecular estimates of breeding value are expected to increase selection response due to improvements in the accuracy of selection and a reduction in generation interval, particularly for traits that are difficult or expensive to record or are measured late in life. Several statistical methods for incorporating molecular data into breeding value estimation have been proposed, however, most studies have utilized simulated data in which the generated linkage disequilibrium may not represent the targeted livestock population. A genomic relationship matrix was developed for 698 Angus steers and 1,707 Angus sires using 41,028 single nucleotide polymorphisms and breeding values were estimated using feed efficiency phenotypes (average daily feed intake, residual feed intake, and average daily gain) recorded on the steers. The number of SNPs needed to accurately estimate a genomic relationship matrix was evaluated in this population.ResultsResults were compared to estimates produced from pedigree-based mixed model analysis of 862 Angus steers with 34,864 identified paternal relatives but no female ancestors. Estimates of additive genetic variance and breeding value accuracies were similar for AFI and RFI using the numerator and genomic relationship matrices despite fewer animals in the genomic analysis. Bootstrap analyses indicated that 2,500-10,000 markers are required for robust estimation of genomic relationship matrices in cattle.ConclusionsThis research shows that breeding values and their accuracies may be estimated for commercially important sires for traits recorded in experimental populations without the need for pedigree data to establish identity by descent between members of the commercial and experimental populations when at least 2,500 SNPs are available for the generation of a genomic relationship matrix.

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Estimation by simulation of the efficiency of the French marker-assisted selection program in dairy cattle(Open Access publication)

Cited by 12 publications

References 17 publications

Potential for marker-assisted selection for forest tree breeding: lessons from 20 years of MAS in crops

Potential for marker-assisted selection for forest tree breeding: lessons from 20 years of MAS in crops

Estimation of genetic parameters and genome scan for 15 semen characteristics traits of Holstein bulls

Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle

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