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Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases

Overview of attention for article published in BioData Mining, May 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#27 of 288)
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

news
1 news outlet
twitter
19 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
33 Mendeley
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Title
Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases
Published in
BioData Mining, May 2017
DOI 10.1186/s13040-017-0139-3
Pubmed ID
Authors

Jason H. Moore, Peter C. Andrews, Randal S. Olson, Sarah E. Carlson, Curt R. Larock, Mario J. Bulhoes, James P. O’Connor, Ellen M. Greytak, Steven L. Armentrout

Abstract

Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway. We show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer's disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer's disease. We discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.

Twitter Demographics

The data shown below were collected from the profiles of 19 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 33 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 39%
Student > Master 5 15%
Researcher 3 9%
Professor > Associate Professor 2 6%
Other 1 3%
Other 3 9%
Unknown 6 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 18%
Computer Science 5 15%
Biochemistry, Genetics and Molecular Biology 4 12%
Medicine and Dentistry 3 9%
Neuroscience 3 9%
Other 4 12%
Unknown 8 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 20 December 2019.
All research outputs
#1,397,990
of 19,762,584 outputs
Outputs from BioData Mining
#27
of 288 outputs
Outputs of similar age
#31,042
of 284,879 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 19,762,584 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 288 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one has done particularly well, scoring higher than 90% 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 284,879 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 89% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them