ArticleBMC bioinformatics2018
Learning the optimal scale for GWAS through hierarchical SNP aggregation.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Genetic risk of depression is different in subgroups of dietary ratio of tryptophan to large neutral amino acids.Scientific reports · 2023Article
- Genetic heterogeneity: Challenges, impacts, and methods through an associative lens.Genetic epidemiology · 2022Review
- A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping.Frontiers in genetics · 2022Article
- Genome-wide haplotype association study in imaging genetics using whole-brain sulcal openings of 16,304 UK Biobank subjects.European journal of human genetics : EJHG · 2021Article
- Accurate Imputation of Untyped Variants from Deep Sequencing Data.Methods in molecular biology (Clifton, N.J.) · 2021Review
- SNPeffect: identifying functional roles of SNPs using metabolic networks.The Plant journal : for cell and molecular biology · 2020Article
- Fast computation of genome-metagenome interaction effects.Algorithms for molecular biology : AMB · 2020Article
- TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies.Scientific reports · 2019Article
- Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics.Algorithms for molecular biology : AMB · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundGenome-Wide Association Studies (GWAS) seek to identify causal genomic variants associated with rare human diseases. The classical statistical approach for detecting these variants is based on univariate hypothesis testing, with healthy individuals being tested against affected individuals at each locus. Given that an individual's genotype is characterized by up to one million SNPs, this approach lacks precision, since it may yield a large number of false positives that can lead to erroneous conclusions about genetic associations with the disease. One way to improve the detection of true genetic associations is to reduce the number of hypotheses to be tested by grouping SNPs.
resultsWe propose a dimension-reduction approach which can be applied in the context of GWAS by making use of the haplotype structure of the human genome. We compare our method with standard univariate and group-based approaches on both synthetic and real GWAS data.
conclusionWe show that reducing the dimension of the predictor matrix by aggregating SNPs gives a greater precision in the detection of associations between the phenotype and genomic regions.
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