ReviewGenetic epidemiology2019
A review of kernel methods for genetic association studies.
Review in Genetic epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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.
The trial behind it
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Review of Genetic Variation as a Predictive Biomarker for Chronic Graft-Versus-Host-Disease After Allogeneic Stem Cell Transplantation.Frontiers in immunology · 2020Pooled it
- Archipelago Method for Variant Set Association Test Statistics.Genetic epidemiology · 2026Article
- Excalibur: A new ensemble method based on an optimal combination of aggregation tests for rare-variant association testing for sequencing data.PLoS computational biology · 2023Article
- Learning the kernel for rare variant genetic association test.Frontiers in genetics · 2023Article
- Genotype Value Decomposition: Simple Methods for the Computation of Kernel Statistics.Advanced genetics (Hoboken, N.J.) · 2022Article
- Brain DNA Methylation Patterns in CLDN5 Associated With Cognitive Decline.Biological psychiatry · 2022Article
- Recent advances and challenges of rare variant association analysis in the biobank sequencing era.Frontiers in genetics · 2022Review
- Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study.Frontiers in genetics · 2022Article
- MCKAT: a multi-dimensional copy number variant kernel association test.BMC bioinformatics · 2021Article
- PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.PLoS computational biology · 2021Article
- Variant-set association test for generalized linear mixed model.Genetic epidemiology · 2021Article
- A robust test for X-chromosome genetic association accounting for X-chromosome inactivation and imprinting.Genetics research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Evaluating the association of multiple genetic variants with a trait of interest by use of kernel-based methods has made a significant impact on how genetic association analyses are conducted. An advantage of kernel methods is that they tend to be robust when the genetic variants have effects that are a mixture of positive and negative effects, as well as when there is a small fraction of causal variants. Another advantage is that kernel methods fit within the framework of mixed models, providing flexible ways to adjust for additional covariates that influence traits. Herein, we review the basic ideas behind the use of kernel methods for genetic association analysis as well as recent methodological advancements for different types of traits, multivariate traits, pedigree data, and longitudinal data. Finally, we discuss opportunities for future research.
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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.