Cited by 321Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsStatistical Methods and Inference

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Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies

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Hoggart, Clive J., Whittaker, John C., De Iorio, Maria, Balding, David J.·2008·PLoS Genetics

DOI 10.1371/journal.pgen.1000130PMC PMC2464715

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Abstract

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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Figure 1 Logarithms of NEG and DE densities. Fixed to have the same density at the origin.CC BY · PubMed Central

Figure 2 Main simulation study. Histograms of the number of selected SNPs tagging (at r 2>0.05) each causal SNP for (A) NEG and (B) ATT analyses.CC BY · PubMed Central

Figure 3 GWA simulation. (A) locations of the ten causal variants (vertical blue line) on the 20 Mb chromosome; also shown are the SNPs selected by NEG (red dots), and the SNPs with ATT p-value 5×10−7 (black dots) plotted against −log10 (p-value). (B) and (C) show zooms of two sub-intervals of (A).CC BY · PubMed Central

Figure 4 Re-sequencing simulation. Histograms of the maximum r 2 for each selected SNP with a causal variant for (A) NEG and (B) ATT analyses.CC BY · PubMed Central

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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.
ORCID: Hoggart, Clive J.
Whittaker, John C.
Balding, David 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
DOI: 10.1371/journal.pgen.1000130
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
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
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

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Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies

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