Human Heredity
Original Paper
An Optimum Projection and Noise Reduction Approach for Detecting Rare and Common Variants Associated with Complex DiseasesTurkmen A.a, b · Lin S.aaDepartment of Statistics, The Ohio State University, Columbus, Ohio, and bThe Ohio State University at Newark, Newark, Ohio, USA
Asuman Turkmen, PhD The Ohio State University at Newark 1179 University Drive Newark, OH 43055 (USA) E-Mail turkmen@stat.osu.edu |
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Abstract
Background: Despite the thrilling advances in identifying gene variants that influence common diseases, most of the heritable risk for many common diseases still remains unidentified. One of the possible reasons for this missing heritability is that the genome-wide association study (GWAS) approaches have been focusing on common rather than rare single nucleotide variants (SNVs). Consequently, there is currently a great deal of interest in developing methods that can interrogate rare variants for association with diseases. Methods: We propose a two-step method (termed rPLS) to reveal possible genetic effects related to rare as well as common variants. The procedure starts with removing irrelevant variants using penalized regression (regularization) which is followed by partial least squares (PLS) on the surviving SNVs to find an optimal linear combination of rare and common SNVs within a genomic region that is tested for its association with the trait of interest. Results: Simulation settings based on the 1000 Genomes sequencing data and reflecting real situations demonstrated that rPLS performs well compared to existing methods especially when there are a large number of noncausal variants (both rare and common) present in the gene and when causal SNVs have different effect sizes and directions.
© 2012 S. Karger AG, Basel
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References
- Reich DE, Lander ES: On the allelic spectrum of human disease. Trends Genet 2001;17:502–510.
- Iyengar SK, Elston RC: The genetic basis of complex traits: rare variants or ‘common gene, common disease’? Methods Mol Biol 2007;376:71–84.
- Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Wittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TFC, McCarroll SA, Visscher PM: Finding the missing heritability of complex diseases. Nature 2009;461:747–753.
- Ji W, Foo JN, O’Roak BJ, Zhao H, Larson MG, Simon DB, Newton-Cheh C, State MW, Levy D, Lifton RP: Rare independent mutations in renal salt handling genes contribute to blood pressure variation. Nat Genet 2008;40:592–599.
- Cohen JC, Kiss RS, Pertsemlidis A, Marcel YL, McPherson R, Hobbs HH: Multiple rare alleles contribute to low plasma levels of HDL cholesterol. Science 2004;305:869–872.
-
Asimit J, Zeggini E: Rare variant association analysis methods for complex traits. Annu Rev Genet 2010;44:293–308.
External Resources
-
Morgenthaler S, Thilly WG: A strategy to discover genes that carry multiallelic or mono-allelic risk for common diseases: a cohort allelic sums test (cast). Mutat Res 2007;615:2856.
-
Li B, Leal SM: Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 2008;83:31121.
External Resources
- Madsen BE, Browning SR: A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 2009;5:e1000384.
-
Chapman JM, Whittaker J: Analysis of multiple SNPs in a candidate gene or region. Genet Epidemiol 2008;32:560566.
External Resources
-
Pan W: Asymptotic tests of association with multiple SNPs in linkage disequilibrium. Genet Epidemiol 2009;33:497507.
External Resources
- Wang T, Elston RC: Improved power by use of a weighted score test for linkage disequilibrium mapping. Am J Hum Genet 2007;80:353–360.
-
Goeman JJ, van de Geer S, van Houwelingen HC: Testing against a high dimensional alternative. J R Stat Soc B 2006;68:477493.
External Resources
-
Basu S, Pan W: Comparison of statistical tests for disease association with rare variants. Genet Epidemiol 2011;35:606–619.
External Resources
-
Almasy LA, Dyer TD, Peralta JM, Kent JW Jr, Charlesworth JC, Curran JE, Blangero J: Genetic Analysis Workshop 17 mini-exome simulation. BMC Proc 2011;5(suppl 9):S2.
External Resources
-
Tibshirani R: Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B 1996;58:267288.
-
Hoerl AE, Kennard RW: Ridge regression: biased estimation for nonorthogonal problems. Technometrics 1970;12:5567.
-
Zou H, Hastie T: Regularization and variable selection via the elastic net. J R Stat Soc Ser B 2005;67:301301.
-
Wold H: Estimation of principal components and related models by iterative least squares. Multivariate Analysis, Academic Press, New York, 1966, pp 391–420.
-
Boulesteix AL: PLS dimension reduction for classification with high-dimensional microarray data. Stat Appl Genet Mol Biol 2004;3:33.
-
Bhatia G, Bansal V, Harismendy O, Schork NJ, Topol EJ, Frazer K: A covering method for detecting genetic associations between rare variants and common phenotypes. PLoS Comput Biol 2010;6:e1000954.
External Resources
-
Han F, Pan W: A data-adaptive sum test for disease association with multiple common or rare variants. Hum Hered 2010a;70:4254.
External Resources
- Hoffmann TJ, Marini NJ, Witte JS: Comprehensive approach to analyzing rare genetic variants. PLoS One 2010;5:e13584.
-
De Jong S: SIMPLS: an alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems 1993;18:251–263.
External Resources
- Nguyen DV, Rocke DM: Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 2002a;18:39–50.
- Nguyen DV, Rocke DM: Multi-class cancer classification via partial least squares using gene expression profiles. Bioinformatics 2002b;18:1216–1226.
-
Basu S, Pan W, Shen X, Oetting WS: Multilocus association testing with penalized regression. Genet Epidemiol 2011;35:755–765.
External Resources
Article / Publication Details
Received: April 30, 2012
Accepted: September 30, 2012
Published online: November 13, 2012
Issue release date: November 2012
Number of Print Pages: 10
Number of Figures: 4
Number of Tables: 4
ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)
For additional information: https://www.karger.com/HHE
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