ArticleBioinformatics (Oxford, England)2015
Genome-wide detection of intervals of genetic heterogeneity associated with complex traits.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- The benefits of permutation-based genome-wide association studies.Journal of experimental botany · 2024Article
- Genome-wide analysis to uncover how Pocillopora acuta survives the challenging intertidal environment.Scientific reports · 2024Article
- Population-aware permutation-based significance thresholds for genome-wide association studies.Bioinformatics advances · 2024Article
- DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.BMC bioinformatics · 2023Article
- Higher-order genetic interaction discovery with network-based biological priors.Bioinformatics (Oxford, England) · 2023Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions.Bioinformatics (Oxford, England) · 2022Article
- CALDERA: finding all significant de Bruijn subgraphs for bacterial GWAS.Bioinformatics (Oxford, England) · 2022Article
- Network-guided search for genetic heterogeneity between gene pairs.Bioinformatics (Oxford, England) · 2021Article
- AraPheno and the AraGWAS Catalog 2020: a major database update including RNA-Seq and knockout mutation data for Arabidopsis thaliana.Nucleic acids research · 2020Article
- HiSSI: high-order SNP-SNP interactions detection based on efficient significant pattern and differential evolution.BMC medical genomics · 2019Article
- CASMAP: detection of statistically significant combinations of SNPs in association mapping.Bioinformatics (Oxford, England) · 2019Article
- A systems genetics resource and analysis of sleep regulation in the mouse.PLoS biology · 2018Article
- Genome-wide genetic heterogeneity discovery with categorical covariates.Bioinformatics (Oxford, England) · 2017Article
- AraPheno: a public database for Arabidopsis thaliana phenotypes.Nucleic acids research · 2017Article
- easyGWAS: A Cloud-Based Platform for Comparing the Results of Genome-Wide Association Studies.The Plant cell · 2017Article
- Genetic architecture of nonadditive inheritance in Arabidopsis thaliana hybrids.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationGenetic heterogeneity, the fact that several sequence variants give rise to the same phenotype, is a phenomenon that is of the utmost interest in the analysis of complex phenotypes. Current approaches for finding regions in the genome that exhibit genetic heterogeneity suffer from at least one of two shortcomings: (i) they require the definition of an exact interval in the genome that is to be tested for genetic heterogeneity, potentially missing intervals of high relevance, or (ii) they suffer from an enormous multiple hypothesis testing problem due to the large number of potential candidate intervals being tested, which results in either many false positives or a lack of power to detect true intervals.
resultsHere, we present an approach that overcomes both problems: it allows one to automatically find all contiguous sequences of single nucleotide polymorphisms in the genome that are jointly associated with the phenotype. It also solves both the inherent computational efficiency problem and the statistical problem of multiple hypothesis testing, which are both caused by the huge number of candidate intervals. We demonstrate on Arabidopsis thaliana genome-wide association study data that our approach can discover regions that exhibit genetic heterogeneity and would be missed by single-locus mapping.
conclusionsOur novel approach can contribute to the genome-wide discovery of intervals that are involved in the genetic heterogeneity underlying complex phenotypes. AVAILABILITY AND IMPLEMENTATION: The code can be obtained at: http://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/sis.html.
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