ArticlePloS one2014
The challenges of genome-wide interaction studies: lessons to learn from the analysis of HDL blood levels.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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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Who cites it
5 citing papers in PubMed, 15 citations in OpenAlex.
- Integrating the environmental and genetic architectures of aging and mortality.Nature medicine · 2025Article
- Genetic Predisposition to Coronary Artery Disease in Type 2 Diabetes Mellitus.Circulation. Genomic and precision medicine · 2020Article
- Combinations of Genetic Variants Occurring Exclusively in Patients.Computational and structural biotechnology journal · 2017Review
- Article
- Association Study of a Proliferation-inducing Ligand, Spermatogenesis Associated 8, Platelet-derived Growth Factor Receptor-alpha, and POLB Polymorphisms with Systemic Lupus Erythematosus in Chinese Han Population.Chinese medical journal · 2016Article
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Authors and funding
28 authors at 12 institutions in 6 countries.
Funding
Abstract
Genome-wide association studies (GWAS) have revealed 74 single nucleotide polymorphisms (SNPs) associated with high-density lipoprotein cholesterol (HDL) blood levels. This study is, to our knowledge, the first genome-wide interaction study (GWIS) to identify SNP×SNP interactions associated with HDL levels. We performed a GWIS in the Rotterdam Study (RS) cohort I (RS-I) using the GLIDE tool which leverages the massively parallel computing power of Graphics Processing Units (GPUs) to perform linear regression on all genome-wide pairs of SNPs. By performing a meta-analysis together with Rotterdam Study cohorts II and III (RS-II and RS-III), we were able to filter 181 interaction terms with a p-value<1 · 10-8 that replicated in the two independent cohorts. We were not able to replicate any of these interaction term in the AGES, ARIC, CHS, ERF, FHS and NFBC-66 cohorts (Ntotal = 30,011) when adjusting for multiple testing. Our GWIS resulted in the consistent finding of a possible interaction between rs774801 in ARMC8 (ENSG00000114098) and rs12442098 in SPATA8 (ENSG00000185594) being associated with HDL levels. However, p-values do not reach the preset Bonferroni correction of the p-values. Our study suggest that even for highly genetically determined traits such as HDL the sample sizes needed to detect SNP×SNP interactions are large and the 2-step filtering approaches do not yield a solution. Here we present our analysis plan and our reservations concerning GWIS.
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Registered trials
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