ArticleGenome biology2023
A fast non-parametric test of association for multiple traits.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Chemogenetic placental activation and proteomic extracellular vesicle signatures predict functional roles across pregnancy and postpartum.American journal of physiology. Endocrinology and metabolism · 2026Article
- Hidden ecological risks in reservoir sediments: grain-size dependent enrichment of toxic metals in the black sea basin.Environmental geochemistry and health · 2026Article
- Article
- Sustainable lentil intensification in rice-fallow systems through bioaugmentation with atypical rhizobia and endophytes modulates rhizosphere and nodule microbiome dynamics.BMC plant biology · 2025Article
- Potentially causal associations between placental DNA methylation and schizophrenia and other neuropsychiatric disorders.Nature communications · 2025Article
- Article
- Advanced computational tools, artificial intelligence and machine-learning approaches in gut microbiota and biomarker identification.Frontiers in medical technology · 2024Review
- A fast non-parametric test of association for multiple traits.Genome biology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing availability of multidimensional phenotypic data in large cohorts of genotyped individuals requires efficient methods to identify genetic effects on multiple traits. Permutational multivariate analysis of variance (PERMANOVA) offers a powerful non-parametric approach. However, it relies on permutations to assess significance, which hinders the analysis of large datasets. Here, we derive the limiting null distribution of the PERMANOVA test statistic, providing a framework for the fast computation of asymptotic p values. Our asymptotic test presents controlled type I error and high power, often outperforming parametric approaches. We illustrate its applicability in the context of QTL mapping and GWAS.
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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.