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An evaluation of statistical approaches to rare variant analysis in genetic association studies
Open accessAbstract
Genome‐wide association (GWA) studies have proved to be extremely successful in identifying novel common polymorphisms contributing effects to the genetic component underlying complex traits. Nevertheless, one source of, as yet, undiscovered genetic determinants of complex traits are those mediated through the effects of rare variants. With the increasing availability of large‐scale re‐sequencing data for rare variant discovery, we have developed a novel statistical method for the detection of complex trait associations with these loci, based on searching for accumulations of minor alleles within the same functional unit. We have undertaken simulations to evaluate strategies for the identification of rare variant associations in population‐based genetic studies when data are available from re‐sequencing discovery efforts or from commercially available GWA chips. Our results demonstrate that methods based on accumulations of rare variants discovered through re‐sequencing offer substantially greater power than conventional analysis of GWA data, and thus provide an exciting opportunity for future discovery of genetic determinants of complex traits. Genet. Epidemiol. 34: 188–193, 2010. © 2009 Wiley‐Liss, Inc.
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In the citing paper
“This strategy should increase the fraction of variation explained by the variants as a whole and hence increase the power to detect their collective, rather than individual, effects.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
In the citing paper
“Recently, Morris and Zeggini33 assessed the power of simple regression methods for testing collapsed sets of rare variants for association with a quantitative trait and found that such approaches are indeed intuitive, flexible and powerful.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
In the citing paper
“However, they did not consider conditioning effects (Figure 1C) or problems associated with analyses involving many correlated predictor variables.33”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
In the citing paper
“Such methods provide a flexible framework for assessing the contribution of collections of rare variants to a phenotype.28, 33”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
In the citing paper
“However, their direct application via simple extensions of the methods described by Morris and Zeggini33 to the analysis of multiple individual rare variants or collapsed sets of variants may be problematic.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
In the citing paper
“They found that the use of a tally may be more powerful.33”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [33]
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Cites
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
Self-citationA haplotype map of the human genome
The International HapMap Consortium (2005) · Nature · doi:10.1038/nature04226 · Cited by 5,858
The Wellcome Trust Case Control Consortium, U.K.
Mossman, K. (2008) · Scientific American · doi:10.1038/scientificamerican0108-42a · Cited by 2,714
Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease
the NIDDK IBD Genetics Consortium, Barrett, J. C., the Belgian-French IBD Consortium, the Wellcome Trust Case Control Consortium, Hansoul, S., Nicolae, D. L., Cho, J. H., Duerr, R. H., Rioux, J. D., Brant, S. R., et al. (2008) · Nature Genetics · doi:10.1038/ng.175 · Cited by 2,593
Generating samples under a Wright–Fisher neutral model of genetic variation
Hudson, R. R. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.2.337 · Cited by 2,397
Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population.
(1995) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a040269 · Cited by 1,950
Wellcome Trust Case Control Consortium, Zeggini, E., Scott, L. J., Saxena, R., Voight, B. F., Marchini, J. L., Hu, T., de Bakker, P. I., Abecasis, G. R., Almgren, P., et al. (2008) · Nature Genetics · doi:10.1038/ng.120 · Cited by 1,864
Self-citationthe GIANT Consortium (2009) · Nature Genetics · doi:10.1038/ng.287 · Cited by 1,720
Self-citationLi, B., & Leal, S. M. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.06.024 · Cited by 1,527
Multiple Rare Alleles Contribute to Low Plasma Levels of HDL Cholesterol
Cohen, J. C., Kiss, R. S., Pertsemlidis, A., Marcel, Y. L., McPherson, R., & Hobbs, H. H. (2004) · Science · doi:10.1126/science.1099870 · Cited by 1,108
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Tachmazidou, I., & Zeggini, E. (2015) · Springer eBooks · doi:10.1007/978-1-4939-2824-8_15 · Cited by 1
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Li, Q., Pan, D., Yue, W., Gao, Y., & Yu, K. F. (2012) · Journal of Human Genetics · doi:10.1038/jhg.2012.33
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Byrnes, A. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/stk4-8r94
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Jackson, J., & Motsinger‐Reif, A. A. (2015) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-4666-6611-5.ch014
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Liu, D. J. (2012) · Rice Research Repository (Rice University)
Set-based Tests for Genetic Association and Gene-Environment Interaction.
He, Z. (2016) · Deep Blue (University of Michigan)
Gao, F. (2017) · eCommons (Cornell University) · doi:10.7298/x4fq9tkh
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Borgne, Y. L., Helaers, R., Lenaerts, T., Abramowicz, M. J., Smits, G., & Bontempi, G. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/168633
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Obolonkin, V., Zherelo, A., Krylov, G. G., Jorgensen, M., & Spelman, R. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.05177
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Liu, R., Lai, X., Xiao, X., Zhang, X., Zhu, X., & Wang, J. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983066
Using Rare Genetic Variation to Understand Human Demography and the Etiology of Complex Traits.
Reppell, M. (2014) · Deep Blue (University of Michigan)
Blumenthal, M. J. (2020) · Open University of Cape Town (University of Cape Town)
Xu, J. (2019) · TSpace (University of Toronto)
Emond, M., & West, T. E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.02.470949
Grid-based Gaussian process models for longitudinal genetic data
Chung, W. (2022) · Communications for Statistical Applications and Methods · doi:10.29220/csam.2022.29.1.745
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Metadata
- Authors
- Morris, A. P., & Zeggini, E.
- Affiliations
- Centre for Human Genetics (GB)
- Corresponding author
- Morris, Andrew P.
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2009-10-06
- Volume
- 34
- Issue
- 2
- Pages
- 188-193
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC2962811
- OpenAlex
- W2153899794
- Cited by
- 481
- References
- 22
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 8 · 2023: 14 · 2022: 11 · 2021: 14 · 2020: 13 · 2019: 30 · 2018: 20 · 2017: 32 · 2016: 42 · 2015: 47 · 2014: 53 · 2013: 48 · 2012: 59
- FWCI
- 27.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Genetic Epidemiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (081682, 064890, WT081682/Z/06/Z, WT088885/Z/09/Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- rare variants, genetic association studies, complex traits, statistical power, resequencing, functional units
- MeSH terms
- Chromosome Mapping, Computer Simulation, Genetics, Population, Genotype, Humans, Models, Genetic, Phenotype, Genetic Variation, Models, Statistical, Genome, Human, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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