ArticleFrontiers in genetics2021
Testing Gene-Gene Interactions Based on a Neighborhood Perspective in Genome-wide Association Studies.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Cumulative Genetic Scores Interact with Maternal and Paternal Parenting in Predicting Parent-Adolescent Cohesion and Conflict.Journal of youth and adolescence · 2024Article
- A gene-based association test of interactions for maternal-fetal genotypes identifies genes associated with nonsyndromic congenital heart defects.Genetic epidemiology · 2023Article
- Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies.G3 (Bethesda, Md.) · 2023Article
- Maximal Information Coefficient-Based Testing to Identify Epistasis in Case-Control Association Studies.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unexplained genetic variation that causes complex diseases is often induced by gene-gene interactions (GGIs). Gene-based methods are one of the current statistical methodologies for discovering GGIs in case-control genome-wide association studies that are not only powerful statistically, but also interpretable biologically. However, most approaches include assumptions about the form of GGIs, which results in poor statistical performance. As a result, we propose gene-based testing based on the maximal neighborhood coefficient (MNC) called gene-based gene-gene interaction through a maximal neighborhood coefficient (GBMNC). MNC is a metric for capturing a wide range of relationships between two random vectors with arbitrary, but not necessarily equal, dimensions. We established a statistic that leverages the difference in MNC in case and in control samples as an indication of the existence of GGIs, based on the assumption that the joint distribution of two genes in cases and controls should not be substantially different if there is no interaction between them. We then used a permutation-based statistical test to evaluate this statistic and calculate a statistical
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Registered trials
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