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Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping

Overview of attention for article published in BMC Genomics, August 2017
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

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6 tweeters

Citations

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3 Dimensions

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16 Mendeley
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Title
Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping
Published in
BMC Genomics, August 2017
DOI 10.1186/s12864-017-4030-x
Pubmed ID
Authors

Tingting Wang, Yi-Ping Phoebe Chen, Iona M. MacLeod, Jennie E. Pryce, Michael E. Goddard, Ben J. Hayes

Abstract

Using whole genome sequence data might improve genomic prediction accuracy, when compared with high-density SNP arrays, and could lead to identification of casual mutations affecting complex traits. For some traits, the most accurate genomic predictions are achieved with non-linear Bayesian methods. However, as the number of variants and the size of the reference population increase, the computational time required to implement these Bayesian methods (typically with Monte Carlo Markov Chain sampling) becomes unfeasibly long. Here, we applied a new method, HyB_BR (for Hybrid BayesR), which implements a mixture model of normal distributions and hybridizes an Expectation-Maximization (EM) algorithm followed by Markov Chain Monte Carlo (MCMC) sampling, to genomic prediction in a large dairy cattle population with imputed whole genome sequence data. The imputed whole genome sequence data included 994,019 variant genotypes of 16,214 Holstein and Jersey bulls and cows. Traits included fat yield, milk volume, protein kg, fat% and protein% in milk, as well as fertility and heat tolerance. HyB_BR achieved genomic prediction accuracies as high as the full MCMC implementation of BayesR, both for predicting a validation set of Holstein and Jersey bulls (multi-breed prediction) and a validation set of Australian Red bulls (across-breed prediction). HyB_BR had a ten fold reduction in compute time, compared with the MCMC implementation of BayesR (48 hours versus 594 hours). We also demonstrate that in many cases HyB_BR identified sequence variants with a high posterior probability of affecting the milk production or fertility traits that were similar to those identified in BayesR. For heat tolerance, both HyB_BR and BayesR found variants in or close to promising candidate genes associated with this trait and not detected by previous studies. The results demonstrate that HyB_BR is a feasible method for simultaneous genomic prediction and QTL mapping with whole genome sequence in large reference populations.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 3 19%
Researcher 3 19%
Student > Postgraduate 3 19%
Student > Ph. D. Student 2 13%
Other 2 13%
Other 3 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 69%
Unspecified 4 25%
Computer Science 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 21 August 2017.
All research outputs
#3,122,177
of 11,638,207 outputs
Outputs from BMC Genomics
#2,042
of 6,952 outputs
Outputs of similar age
#86,778
of 265,124 outputs
Outputs of similar age from BMC Genomics
#34
of 88 outputs
Altmetric has tracked 11,638,207 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 6,952 research outputs from this source. They receive a mean Attention Score of 4.2. 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 265,124 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 88 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.