ArticleNature communications2016
A multi-marker association method for genome-wide association studies without the need for population structure correction.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Defining the serum proteomic signature of hepatic steatosis, inflammation, ballooning and fibrosis in non-alcoholic fatty liver disease.Journal of hepatology · 2023Trial
- Automated machine learning for genome wide association studies.Bioinformatics (Oxford, England) · 2023Article
- QT-GWAS: A novel method for unveiling biosynthetic loci affecting qualitative metabolic traits.Molecular plant · 2023Article
- Genome-wide association study and its applications in the non-model crop Sesamum indicum.BMC plant biology · 2021Review
- Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Review
- A fast wavelet-based functional association analysis replicates several susceptibility loci for birth weight in a Norwegian population.BMC genomics · 2021Article
- Genome Characterization ofAntibiotics (Basel, Switzerland) · 2021Article
- Status and prospects of genome-wide association studies in plants.The plant genome · 2021Review
- Extending greedy feature selection algorithms to multiple solutions.Data mining and knowledge discovery · 2021Article
- Genetic Variability ofFrontiers in plant science · 2021Article
- Article
- Multi-resolution localization of causal variants across the genome.Nature communications · 2020Article
- Genetic Architecture and Genomic Prediction of Cooking Time in Common Bean (Frontiers in plant science · 2020Article
- Natural variation in Arabidopsis shoot branching plasticity in response to nitrate supply affects fitness.PLoS genetics · 2019Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
All common genome-wide association (GWA) methods rely on population structure correction, to avoid false genotype-to-phenotype associations. However, population structure correction is a stringent penalization, which also impedes identification of real associations. Using recent statistical advances, we developed a new GWA method, called Quantitative Trait Cluster Association Test (QTCAT), enabling simultaneous multi-marker associations while considering correlations between markers. With this, QTCAT overcomes the need for population structure correction and also reflects the polygenic nature of complex traits better than single-marker methods. Using simulated data, we show that QTCAT clearly outperforms linear mixed model approaches. Moreover, using QTCAT to reanalyse public human, mouse and Arabidopsis GWA data revealed nearly all known and some previously undetected associations. Following up on the most significant novel association in the Arabidopsis data allowed us to identify a so far unknown component of root growth.
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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.