ArticleGigaScience2014
Applying compressed sensing to genome-wide association studies.
Article in GigaScience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Biobank-scale methods and projections for sparse polygenic prediction from machine learning.Scientific reports · 2023Article
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- Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank.Genes · 2021Article
- Sibling validation of polygenic risk scores and complex trait prediction.Scientific reports · 2020Article
- Genetic architecture of complex traits and disease risk predictors.Scientific reports · 2020Article
- Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity.GigaScience · 2020Article
- Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer.Scientific reports · 2019Article
- Accurate Genomic Prediction of Human Height.Genetics · 2018Article
- Uncovering the Genetic Architectures of Quantitative Traits.Computational and structural biotechnology journal · 2016Review
- Article
- Article
- Applying compressed sensing to genome-wide association studies.GigaScience · 2014Article
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5 authors.
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Abstract
backgroundThe aim of a genome-wide association study (GWAS) is to isolate DNA markers for variants affecting phenotypes of interest. This is constrained by the fact that the number of markers often far exceeds the number of samples. Compressed sensing (CS) is a body of theory regarding signal recovery when the number of predictor variables (i.e., genotyped markers) exceeds the sample size. Its applicability to GWAS has not been investigated.
resultsUsing CS theory, we show that all markers with nonzero coefficients can be identified (selected) using an efficient algorithm, provided that they are sufficiently few in number (sparse) relative to sample size. For heritability equal to one (h (2) = 1), there is a sharp phase transition from poor performance to complete selection as the sample size is increased. For heritability below one, complete selection still occurs, but the transition is smoothed. We find for h (2) ∼ 0.5 that a sample size of approximately thirty times the number of markers with nonzero coefficients is sufficient for full selection. This boundary is only weakly dependent on the number of genotyped markers.
conclusionPractical measures of signal recovery are robust to linkage disequilibrium between a true causal variant and markers residing in the same genomic region. Given a limited sample size, it is possible to discover a phase transition by increasing the penalization; in this case a subset of the support may be recovered. Applying this approach to the GWAS analysis of height, we show that 70-100% of the selected markers are strongly correlated with height-associated markers identified by the GIANT Consortium.
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