ArticleGenetic epidemiology2020
Convex combination sequence kernel association test for rare-variant studies.
Article in Genetic epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessment of the functionality and usability of open-source rare variant analysis pipelines.Briefings in bioinformatics · 2025Pooled it
- The sequence kernel association test for the proportional odds model.Bioinformatics (Oxford, England) · 2025Article
- Learning the kernel for rare variant genetic association test.Frontiers in genetics · 2023Article
- sumSTAAR: A flexible framework for gene-based association studies using GWAS summary statistics.PLoS computational biology · 2022Article
- Rare variants: data types and analysis strategies.Annals of translational medicine · 2021Article
- Rare variant association testing in the non-coding genome.Human genetics · 2020Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
We propose a novel variant set test for rare-variant association studies, which leverages multiple single-nucleotide variant (SNV) annotations. Our approach optimizes a convex combination of different sequence kernel association test (SKAT) statistics, where each statistic is constructed from a different annotation and combination weights are optimized through a multiple kernel learning algorithm. The combination test statistic is evaluated empirically through data splitting. In simulations, we find our method preserves type I error at
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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.