ArticleMethods in molecular biology (Clifton, N.J.)2015
Biological knowledge-driven analysis of epistasis in human GWAS with application to lipid traits.
Article in Methods in molecular biology (Clifton, N.J.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 13 citations in OpenAlex.
- Searching for gene-gene interactions through variance quantitative trait loci of 29 continuous Taiwan Biobank phenotypes.Frontiers in genetics · 2024Article
- A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction.Frontiers in bioinformatics · 2022Review
- Genomic approaches for the elucidation of genes and gene networks underlying cardiovascular traits.Biophysical reviews · 2018Review
- The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation.Annals of translational medicine · 2018Article
- Exploring and Harnessing Haplotype Diversity to Improve Yield Stability in Crops.Frontiers in plant science · 2017Review
- Reverse Pathway Genetic Approach Identifies Epistasis in Autism Spectrum Disorders.PLoS genetics · 2017Article
- A survey about methods dedicated to epistasis detection.Frontiers in genetics · 2015Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
While the importance of epistasis is well established, specific gene-gene interactions have rarely been identified in human genome-wide association studies (GWAS), mainly due to low power associated with such interaction tests. In this chapter, we integrate biological knowledge and human GWAS data to reveal epistatic interactions underlying quantitative lipid traits, which are major risk factors for coronary artery disease. To increase power to detect interactions, we only tested pairs of SNPs filtered by prior biological knowledge, including GWAS results, protein-protein interactions (PPIs), and pathway information. Using published GWAS and 9,713 European Americans (EA) from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and LIPC affecting high-density lipoprotein cholesterol (HDL-C) levels. We then validated this interaction in additional multiethnic cohorts from ARIC, the Framingham Heart Study, and the Multi-Ethnic Study of Atherosclerosis. Both HMGCR and LIPC are involved in the metabolism of lipids and lipoproteins, and LIPC itself has been marginally associated with HDL-C. Furthermore, no significant interaction was detected using PPI and pathway information, mainly due to the stringent significance level required after correcting for the large number of tests conducted. These results suggest the potential of biological knowledge-driven approaches to detect epistatic interactions in human GWAS, which may hold the key to exploring the role gene-gene interactions play in connecting genotypes and complex phenotypes in future GWAS.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.