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Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism

Overview of attention for article published in BMC Genomics, August 2016
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Title
Genotype distribution-based inference of collective effects in genome-wide association studies: insights to age-related macular degeneration disease mechanism
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2871-3
Pubmed ID
Authors

Hyung Jun Woo, Chenggang Yu, Kamal Kumar, Bert Gold, Jaques Reifman

Abstract

Genome-wide association studies provide important insights to the genetic component of disease risks. However, an existing challenge is how to incorporate collective effects of interactions beyond the level of independent single nucleotide polymorphism (SNP) tests. While methods considering each SNP pair separately have provided insights, a large portion of expected heritability may reside in higher-order interaction effects. We describe an inference approach (discrete discriminant analysis; DDA) designed to probe collective interactions while treating both genotypes and phenotypes as random variables. The genotype distributions in case and control groups are modeled separately based on empirical allele frequency and covariance data, whose differences yield disease risk parameters. We compared pairwise tests and collective inference methods, the latter based both on DDA and logistic regression. Analyses using simulated data demonstrated that significantly higher sensitivity and specificity can be achieved with collective inference in comparison to pairwise tests, and with DDA in comparison to logistic regression. Using age-related macular degeneration (AMD) data, we demonstrated two possible applications of DDA. In the first application, a genome-wide SNP set is reduced into a small number (∼100) of variants via filtering and SNP pairs with significant interactions are identified. We found that interactions between SNPs with highest AMD association were epigenetically active in the liver, adipocytes, and mesenchymal stem cells. In the other application, multiple groups of SNPs were formed from the genome-wide data and their relative strengths of association were compared using cross-validation. This analysis allowed us to discover novel collections of loci for which interactions between SNPs play significant roles in their disease association. In particular, we considered pathway-based groups of SNPs containing up to ∼10, 000 variants in each group. In addition to pathways related to complement activation, our collective inference pointed to pathway groups involved in phospholipid synthesis, oxidative stress, and apoptosis, consistent with the AMD pathogenesis mechanism where the dysfunction of retinal pigment epithelium cells plays central roles. The simultaneous inference of collective interaction effects within a set of SNPs has the potential to reveal novel aspects of disease association.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 19%
Student > Master 5 16%
Student > Doctoral Student 4 13%
Student > Bachelor 3 10%
Other 3 10%
Other 6 19%
Unknown 4 13%
Readers by discipline Count As %
Medicine and Dentistry 10 32%
Biochemistry, Genetics and Molecular Biology 3 10%
Agricultural and Biological Sciences 3 10%
Mathematics 2 6%
Computer Science 1 3%
Other 5 16%
Unknown 7 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 27 April 2017.
All research outputs
#7,164,647
of 9,734,985 outputs
Outputs from BMC Genomics
#4,868
of 6,635 outputs
Outputs of similar age
#166,855
of 257,074 outputs
Outputs of similar age from BMC Genomics
#182
of 279 outputs
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