↓ 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 (75th 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
228 Dimensions

Readers on

mendeley
279 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

Ben J. Hayes, Jennie Pryce, Amanda J. Chamberlain, Phil J. Bowman, Mike E. Goddard

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 279 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 6 2%
Unknown 251 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 76 27%
Researcher 71 25%
Student > Master 28 10%
Student > Doctoral Student 24 9%
Unspecified 16 6%
Other 64 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 214 77%
Unspecified 20 7%
Biochemistry, Genetics and Molecular Biology 16 6%
Computer Science 7 3%
Veterinary Science and Veterinary Medicine 6 2%
Other 16 6%

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
#3,138,702
of 13,205,256 outputs
Outputs from PLoS Genetics
#2,905
of 6,589 outputs
Outputs of similar age
#3,029,892
of 12,578,470 outputs
Outputs of similar age from PLoS Genetics
#2,665
of 6,102 outputs
Altmetric has tracked 13,205,256 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,589 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.3. This one has gotten more attention than average, scoring higher than 55% 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 12,578,470 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 75% of its contemporaries.
We're also able to compare this research output to 6,102 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.