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Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions

Overview of attention for article published in Journal of Animal Science and Biotechnology, February 2016
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Title
Mixture models detect large effect QTL better than GBLUP and result in more accurate and persistent predictions
Published in
Journal of Animal Science and Biotechnology, February 2016
DOI 10.1186/s40104-016-0066-z
Pubmed ID
Authors

Anna Wolc, Jesus Arango, Petek Settar, Janet E. Fulton, Neil P. O’Sullivan, Jack C. M. Dekkers, Rohan Fernando, Dorian J. Garrick

Abstract

Accurate evaluation of SNP effects is important for genome wide association studies and for genomic prediction. The genetic architecture of quantitative traits differs widely, with some traits exhibiting few if any quantitative trait loci (QTL) with large effects, while other traits have one or several easily detectable QTL with large effects. Body weight in broilers and egg weight in layers are two examples of traits that have QTL of large effect. A commonly used method for genome wide association studies is to fit a mixture model such as BayesB that assumes some known proportion of SNP effects are zero. In contrast, the most commonly used method for genomic prediction is known as GBLUP, which involves fitting an animal model to phenotypic data with the variance-covariance or genomic relationship matrix among the animals being determined by genome wide SNP genotypes. Genotypes at each SNP are typically weighted equally in determining the genomic relationship matrix for GBLUP. We used the equivalent marker effects model formulation of GBLUP for this study. We compare these two classes of models using egg weight data collected over 8 generations from 2,324 animals genotyped with a 42 K SNP panel. Using data from the first 7 generations, both BayesB and GBLUP found the largest QTL in a similar well-recognized QTL region, but this QTL was estimated to account for 24 % of genetic variation with BayesB and less than 1 % with GBLUP. When predicting phenotypes in generation 8 BayesB accounted for 36 % of the phenotypic variation and GBLUP for 25 %. When using only data from any one generation, the same QTL was identified with BayesB in all but one generation but never with GBLUP. Predictions of phenotypes in generations 2 to 7 based on only 295 animals from generation 1 accounted for 10 % phenotypic variation with BayesB but only 6 % with GBLUP. Predicting phenotype using only the marker effects in the 1 Mb region that accounted for the largest effect on egg weight from generation 1 data alone accounted for almost 8 % variation using BayesB but had no predictive power with GBLUP. In conclusion, In the presence of large effect QTL, BayesB did a better job of QTL detection and its genomic predictions were more accurate and persistent than those from GBLUP.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 3%
Unknown 37 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 21%
Researcher 8 21%
Student > Master 6 16%
Professor 4 11%
Student > Doctoral Student 3 8%
Other 6 16%
Unknown 3 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 66%
Veterinary Science and Veterinary Medicine 2 5%
Biochemistry, Genetics and Molecular Biology 2 5%
Medicine and Dentistry 2 5%
Social Sciences 1 3%
Other 1 3%
Unknown 5 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 13 February 2016.
All research outputs
#15,169,949
of 25,374,917 outputs
Outputs from Journal of Animal Science and Biotechnology
#240
of 903 outputs
Outputs of similar age
#208,432
of 409,980 outputs
Outputs of similar age from Journal of Animal Science and Biotechnology
#5
of 13 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 903 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 70% 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 409,980 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 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 61% of its contemporaries.