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Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies
Open accessAbstract
Testing one SNP at a time does not fully realise the potential of genome-wide association studies to identify multiple causal variants, which is a plausible scenario for many complex diseases. We show that simultaneous analysis of the entire set of SNPs from a genome-wide study to identify the subset that best predicts disease outcome is now feasible, thanks to developments in stochastic search methods. We used a Bayesian-inspired penalised maximum likelihood approach in which every SNP can be considered for additive, dominant, and recessive contributions to disease risk. Posterior mode estimates were obtained for regression coefficients that were each assigned a prior with a sharp mode at zero. A non-zero coefficient estimate was interpreted as corresponding to a significant SNP. We investigated two prior distributions and show that the normal-exponential-gamma prior leads to improved SNP selection in comparison with single-SNP tests. We also derived an explicit approximation for type-I error that avoids the need to use permutation procedures. As well as genome-wide analyses, our method is well-suited to fine mapping with very dense SNP sets obtained from re-sequencing and/or imputation. It can accommodate quantitative as well as case-control phenotypes, covariate adjustment, and can be extended to search for interactions. Here, we demonstrate the power and empirical type-I error of our approach using simulated case-control data sets of up to 500 K SNPs, a real genome-wide data set of 300 K SNPs, and a sequence-based dataset, each of which can be analysed in a few hours on a desktop workstation.
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“Simulation studies and permutation testing that consider the entire set of tests performed (e.g., all windows and groups of variants across all genomic regions considered) to a get a global false positive rate are the most appropriate given their flexibility and sound theoretical bases, but will likely be very computationally intensive.75”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [75]
In the citing paper
“Newer regression techniques that make use of regularization and shrinkage parameters to control for collinearity and overfitting can be used to overcome these problems.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [75]
In the citing paper
“Two such techniques, ridge regression74 and the LASSO75,76 have been considered in genetic association analysis contexts, and other methods have also been proposed as well.77–80, 81”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [75]
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Metadata
- Authors
- Hoggart, C. J., Whittaker, J. C., De Iorio, M., & Balding, D. J.
- Affiliations
- Imperial College London (GB)
- London School of Hygiene & Tropical Medicine (GB)
- Corresponding author
- Hoggart, Clive J.
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-07-25
- Volume
- 4
- Issue
- 7
- Pages
- e1000130
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC2464715
- OpenAlex
- W2095741373
- Cited by
- 321
- References
- 31
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 11 · 2023: 7 · 2022: 8 · 2021: 9 · 2020: 9 · 2019: 20 · 2018: 20 · 2017: 15 · 2016: 16 · 2015: 21 · 2014: 30 · 2013: 29 · 2012: 42
- FWCI
- 14.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Medical Research Council
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- genome-wide association study, posterior mode estimation, fine mapping, complex diseases, quantitative phenotypes, gene-gene interaction, stochastic search
- MeSH terms
- Algorithms, Bayes Theorem, Chromosomes, Human, Computer Simulation, Diabetes Mellitus, Type 2, Feasibility Studies, Genotype, Haplotypes, Humans, Software, Linkage Disequilibrium, Genome, Human, Likelihood Functions, Case-Control Studies, Sequence Analysis, DNA, Genetic Predisposition to Disease, Physical Chromosome Mapping, Polymorphism, Single Nucleotide
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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