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A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping

Overview of attention for article published in BMC Genomics, September 2016
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4 tweeters

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6 Dimensions

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Title
A hybrid expectation maximisation and MCMC sampling algorithm to implement Bayesian mixture model based genomic prediction and QTL mapping
Published in
BMC Genomics, September 2016
DOI 10.1186/s12864-016-3082-7
Pubmed ID
Authors

Tingting Wang, Yi-Ping Phoebe Chen, Phil J. Bowman, Michael E. Goddard, Ben J. Hayes

Abstract

Bayesian mixture models in which the effects of SNP are assumed to come from normal distributions with different variances are attractive for simultaneous genomic prediction and QTL mapping. These models are usually implemented with Monte Carlo Markov Chain (MCMC) sampling, which requires long compute times with large genomic data sets. Here, we present an efficient approach (termed HyB_BR), which is a hybrid of an Expectation-Maximisation algorithm, followed by a limited number of MCMC without the requirement for burn-in. To test prediction accuracy from HyB_BR, dairy cattle and human disease trait data were used. In the dairy cattle data, there were four quantitative traits (milk volume, protein kg, fat% in milk and fertility) measured in 16,214 cattle from two breeds genotyped for 632,002 SNPs. Validation of genomic predictions was in a subset of cattle either from the reference set or in animals from a third breeds that were not in the reference set. In all cases, HyB_BR gave almost identical accuracies to Bayesian mixture models implemented with full MCMC, however computational time was reduced by up to 1/17 of that required by full MCMC. The SNPs with high posterior probability of a non-zero effect were also very similar between full MCMC and HyB_BR, with several known genes affecting milk production in this category, as well as some novel genes. HyB_BR was also applied to seven human diseases with 4890 individuals genotyped for around 300 K SNPs in a case/control design, from the Welcome Trust Case Control Consortium (WTCCC). In this data set, the results demonstrated again that HyB_BR performed as well as Bayesian mixture models with full MCMC for genomic predictions and genetic architecture inference while reducing the computational time from 45 h with full MCMC to 3 h with HyB_BR. The results for quantitative traits in cattle and disease in humans demonstrate that HyB_BR can perform equally well as Bayesian mixture models implemented with full MCMC in terms of prediction accuracy, but with up to 17 times faster than the full MCMC implementations. The HyB_BR algorithm makes simultaneous genomic prediction, QTL mapping and inference of genetic architecture feasible in large genomic data sets.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 6%
Unknown 15 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 31%
Unspecified 4 25%
Researcher 3 19%
Student > Doctoral Student 2 13%
Other 1 6%
Other 1 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 56%
Unspecified 4 25%
Computer Science 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Social Sciences 1 6%
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 15 October 2016.
All research outputs
#6,737,684
of 11,293,566 outputs
Outputs from BMC Genomics
#4,004
of 6,784 outputs
Outputs of similar age
#133,750
of 260,223 outputs
Outputs of similar age from BMC Genomics
#145
of 303 outputs
Altmetric has tracked 11,293,566 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 6,784 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
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We're also able to compare this research output to 303 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.