ArticleBioinformatics (Oxford, England)2017
Genome-wide genetic heterogeneity discovery with categorical covariates.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Higher-order genetic interaction discovery with network-based biological priors.Bioinformatics (Oxford, England) · 2023Article
- 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
- GWAS: Fast-forwarding gene identification and characterization in temperate Cereals: lessons from Barley - A review.Journal of advanced research · 2020Review
- Genetic Advances in Chronic Obstructive Pulmonary Disease. Insights from COPDGene.American journal of respiratory and critical care medicine · 2019Review
- CASMAP: detection of statistically significant combinations of SNPs in association mapping.Bioinformatics (Oxford, England) · 2019Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationGenetic heterogeneity is the phenomenon that distinct genetic variants may give rise to the same phenotype. The recently introduced algorithm Fast Automatic Interval Search ( FAIS ) enables the genome-wide search of candidate regions for genetic heterogeneity in the form of any contiguous sequence of variants, and achieves high computational efficiency and statistical power. Although FAIS can test all possible genomic regions for association with a phenotype, a key limitation is its inability to correct for confounders such as gender or population structure, which may lead to numerous false-positive associations.
resultsWe propose FastCMH , a method that overcomes this problem by properly accounting for categorical confounders, while still retaining statistical power and computational efficiency. Experiments comparing FastCMH with FAIS and multiple kinds of burden tests on simulated data, as well as on human and Arabidopsis samples, demonstrate that FastCMH can drastically reduce genomic inflation and discover associations that are missed by standard burden tests. AVAILABILITY AND IMPLEMENTATION: An R package fastcmh is available on CRAN and the source code can be found at: https://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/fastcmh.html. CONTACT: felipe.llinares@bsse.ethz.ch. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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