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A multi-trait Bayesian method for mapping QTL and genomic prediction.

Overview of attention for article published in Genetics Selection Evolution, March 2018
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Title
A multi-trait Bayesian method for mapping QTL and genomic prediction.
Published in
Genetics Selection Evolution, March 2018
DOI 10.1186/s12711-018-0377-y
Pubmed ID
Authors

Kemper, Kathryn E, Bowman, Philip J, Hayes, Benjamin J, Visscher, Peter M, Goddard, Michael E

Abstract

Genomic prediction and quantitative trait loci (QTL) mapping typically analyze one trait at a time but this may ignore the possibility that one polymorphism affects multiple traits. The aim of this study was to develop a multivariate Bayesian approach that could be used for simultaneously elucidating genetic architecture, QTL mapping, and genomic prediction. Our approach uses information from multiple traits to divide markers into 'unassociated' (no association with any trait) and 'associated' (associated with one or more traits). The effect of associated markers is estimated independently for each trait to avoid the assumption that QTL effects follow a multi-variate normal distribution. Using simulated data, our multivariate method (BayesMV) detected a larger number of true QTL (with a posterior probability > 0.9) and increased the accuracy of genomic prediction compared to an equivalent univariate method (BayesR). With real data, accuracies of genomic prediction in validation sets for milk yield traits with high-density genotypes were approximately equal to those from equivalent single-trait methods. BayesMV tended to select a similar number of single nucleotide polymorphisms (SNPs) per trait for genomic prediction compared to BayesR (i.e. those with non-zero effects), but BayesR selected different sets of SNPs for each trait, whereas BayesMV selected a common set of SNPs across traits. Despite these two dramatically different estimates of genetic architecture (i.e. different SNPs affecting each trait vs. pleiotropic SNPs), both models indicated that 3000 to 4000 SNPs are associated with a trait. The BayesMV approach may be advantageous when the aim is to develop a low-density SNP chip that works well for a number of traits. SNPs for milk yield traits identified by BayesMV and BayesR were also found to be associated with detailed milk composition. The BayesMV method simultaneously estimates the proportion of SNPs that are associated with a combination of traits. When applied to milk production traits, most of the identified SNPs were associated with all three traits (milk, fat and protein yield). BayesMV aims at exploiting pleiotropic QTL and selects a small number of SNPs that could be used to predict multiple traits.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 23%
Student > Ph. D. Student 4 18%
Student > Master 3 14%
Other 2 9%
Student > Doctoral Student 2 9%
Other 6 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 73%
Unspecified 2 9%
Medicine and Dentistry 2 9%
Social Sciences 1 5%
Biochemistry, Genetics and Molecular Biology 1 5%
Other 0 0%

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 02 October 2018.
All research outputs
#7,347,346
of 12,363,980 outputs
Outputs from Genetics Selection Evolution
#234
of 428 outputs
Outputs of similar age
#148,550
of 276,820 outputs
Outputs of similar age from Genetics Selection Evolution
#6
of 11 outputs
Altmetric has tracked 12,363,980 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 428 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.