↓ Skip to main content

Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits.

Overview of attention for article published in PLoS Genetics, September 2010
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
1 tweeter
wikipedia
1 Wikipedia page
f1000
1 research highlight platform

Citations

dimensions_citation
210 Dimensions

Readers on

mendeley
268 Mendeley
citeulike
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits.
Published in
PLoS Genetics, September 2010
DOI 10.1371/journal.pgen.1001139
Pubmed ID
Authors

Hayes BJ, Pryce J, Chamberlain AJ, Bowman PJ, Goddard ME, Ben J. Hayes, Jennie Pryce, Amanda J. Chamberlain, Phil J. Bowman, Mike E. Goddard, Michel Georges

Abstract

Prediction of genetic merit using dense SNP genotypes can be used for estimation of breeding values for selection of livestock, crops, and forage species; for prediction of disease risk; and for forensics. The accuracy of these genomic predictions depends in part on the genetic architecture of the trait, in particular number of loci affecting the trait and distribution of their effects. Here we investigate the difference among three traits in distribution of effects and the consequences for the accuracy of genomic predictions. Proportion of black coat colour in Holstein cattle was used as one model complex trait. Three loci, KIT, MITF, and a locus on chromosome 8, together explain 24% of the variation of proportion of black. However, a surprisingly large number of loci of small effect are necessary to capture the remaining variation. A second trait, fat concentration in milk, had one locus of large effect and a host of loci with very small effects. Both these distributions of effects were in contrast to that for a third trait, an index of scores for a number of aspects of cow confirmation ("overall type"), which had only loci of small effect. The differences in distribution of effects among the three traits were quantified by estimating the distribution of variance explained by chromosome segments containing 50 SNPs. This approach was taken to account for the imperfect linkage disequilibrium between the SNPs and the QTL affecting the traits. We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects (proportion black and fat percentage) than for a trait with no loci of large effect (overall type), provided the method of analysis takes advantage of the distribution of loci effects.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 268 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 2%
United Kingdom 4 1%
Brazil 4 1%
Switzerland 2 <1%
Poland 2 <1%
Austria 2 <1%
Sweden 1 <1%
Belgium 1 <1%
Netherlands 1 <1%
Other 7 3%
Unknown 239 89%

Demographic breakdown

Readers by professional status Count As %
Researcher 72 27%
Student > Ph. D. Student 72 27%
Student > Master 27 10%
Student > Doctoral Student 25 9%
Professor > Associate Professor 16 6%
Other 56 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 208 78%
Biochemistry, Genetics and Molecular Biology 15 6%
Unspecified 13 5%
Computer Science 7 3%
Veterinary Science and Veterinary Medicine 7 3%
Other 18 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 July 2018.
All research outputs
#2,859,147
of 12,372,723 outputs
Outputs from PLoS Genetics
#2,719
of 6,281 outputs
Outputs of similar age
#2,769,982
of 11,786,761 outputs
Outputs of similar age from PLoS Genetics
#2,642
of 6,077 outputs
Altmetric has tracked 12,372,723 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,281 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.8. This one has gotten more attention than average, scoring higher than 56% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 11,786,761 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 6,077 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.