Multilocus Association Testing of Quantitative Traits Based on Partial Least-squares Analysis

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2011 Feb 10

PMID 21304821

Citations 8

Authors

Feng Zhang

Xiong Guo

Hong-Wen Deng

Affiliations

Soon will be listed here.

Abstract

Because of combining the genetic information of multiple loci, multilocus association studies (MLAS) are expected to be more powerful than single locus association studies (SLAS) in disease genes mapping. However, some researchers found that MLAS had similar or reduced power relative to SLAS, which was partly attributed to the increased degrees of freedom (dfs) in MLAS. Based on partial least-squares (PLS) analysis, we develop a MLAS approach, while avoiding large dfs in MLAS. In this approach, genotypes are first decomposed into the PLS components that not only capture majority of the genetic information of multiple loci, but also are relevant for target traits. The extracted PLS components are then regressed on target traits to detect association under multilinear regression. Simulation study based on real data from the HapMap project were used to assess the performance of our PLS-based MLAS as well as other popular multilinear regression-based MLAS approaches under various scenarios, considering genetic effects and linkage disequilibrium structure of candidate genetic regions. Using PLS-based MLAS approach, we conducted a genome-wide MLAS of lean body mass, and compared it with our previous genome-wide SLAS of lean body mass. Simulations and real data analyses results support the improved power of our PLS-based MLAS in disease genes mapping relative to other three MLAS approaches investigated in this study. We aim to provide an effective and powerful MLAS approach, which may help to overcome the limitations of SLAS in disease genes mapping.

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References

Roeder K, Bacanu S, Sonpar V, Zhang X, Devlin B . Analysis of single-locus tests to detect gene/disease associations. Genet Epidemiol. 2005; 28(3):207-19. DOI: 10.1002/gepi.20050. View

Wang K, Abbott D . A principal components regression approach to multilocus genetic association studies. Genet Epidemiol. 2007; 32(2):108-18. DOI: 10.1002/gepi.20266. View

Wang T, Ho G, Ye K, Strickler H, Elston R . A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped. Genet Epidemiol. 2008; 33(1):6-15. PMC: 2700837. DOI: 10.1002/gepi.20351. View

Miles R, Sluka J, Halladay D, Santerre R, Hale L, Bloem L . ADAMTS-1: A cellular disintegrin and metalloprotease with thrombospondin motifs is a target for parathyroid hormone in bone. Endocrinology. 2000; 141(12):4533-42. DOI: 10.1210/endo.141.12.7817. View

Wessel J, Schork N . Generalized genomic distance-based regression methodology for multilocus association analysis. Am J Hum Genet. 2006; 79(5):792-806. PMC: 1698575. DOI: 10.1086/508346. View

Marttinen P, Corander J . Efficient Bayesian approach for multilocus association mapping including gene-gene interactions. BMC Bioinformatics. 2010; 11:443. PMC: 2942856. DOI: 10.1186/1471-2105-11-443. View

Liu Y, Wilson S, Wang L, Liu X, Guo Y, Li J . Identification of PLCL1 gene for hip bone size variation in females in a genome-wide association study. PLoS One. 2008; 3(9):e3160. PMC: 2522269. DOI: 10.1371/journal.pone.0003160. View

Pan W . Asymptotic tests of association with multiple SNPs in linkage disequilibrium. Genet Epidemiol. 2009; 33(6):497-507. PMC: 2732754. DOI: 10.1002/gepi.20402. View

Rosenberg P, Che A, Chen B . Multiple hypothesis testing strategies for genetic case-control association studies. Stat Med. 2005; 25(18):3134-49. DOI: 10.1002/sim.2407. View

10.

Zhang L, Guo Y, Liu Y, Liu Y, Xiong D, Liu X . Pathway-based genome-wide association analysis identified the importance of regulation-of-autophagy pathway for ultradistal radius BMD. J Bone Miner Res. 2010; 25(7):1572-80. PMC: 3153999. DOI: 10.1002/jbmr.36. View

11.

Choi J, Song J, Pai S . Associations of serum TRAIL concentrations, anthropometric variables, and serum lipid parameters in healthy adults. Ann Clin Lab Sci. 2005; 34(4):400-4. View

12.

Marchini J, Howie B, Myers S, McVean G, Donnelly P . A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet. 2007; 39(7):906-13. DOI: 10.1038/ng2088. View

13.

Zhang K, Sun F . Assessing the power of tag SNPs in the mapping of quantitative trait loci (QTL) with extremal and random samples. BMC Genet. 2005; 6:51. PMC: 1274312. DOI: 10.1186/1471-2156-6-51. View

14.

Li J, Ji L . Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (Edinb). 2005; 95(3):221-7. DOI: 10.1038/sj.hdy.6800717. View

15.

Nicolas P, Sun F, Li L . A model-based approach to selection of tag SNPs. BMC Bioinformatics. 2006; 7:303. PMC: 1525207. DOI: 10.1186/1471-2105-7-303. View

16.

Liu Y, Guo Y, Zhang L, Pei Y, Yu N, Yu P . Biological pathway-based genome-wide association analysis identified the vasoactive intestinal peptide (VIP) pathway important for obesity. Obesity (Silver Spring). 2010; 18(12):2339-46. PMC: 2980805. DOI: 10.1038/oby.2010.83. View

17.

Schork N . Genetics of complex disease: approaches, problems, and solutions. Am J Respir Crit Care Med. 1997; 156(4 Pt 2):S103-9. DOI: 10.1164/ajrccm.156.4.12-tac-5. View

18.

Gauderman W, Murcray C, Gilliland F, Conti D . Testing association between disease and multiple SNPs in a candidate gene. Genet Epidemiol. 2007; 31(5):383-95. DOI: 10.1002/gepi.20219. View

19.

Gunther W, Skaftnesmo K, Arnold H, Bjerkvig R, Terzis A . Distribution patterns of the anti-angiogenic protein ADAMTS-1 during rat development. Acta Histochem. 2005; 107(2):121-31. DOI: 10.1016/j.acthis.2004.07.009. View

20.

Hakonarson H, Grant S, Bradfield J, Marchand L, Kim C, Glessner J . A genome-wide association study identifies KIAA0350 as a type 1 diabetes gene. Nature. 2007; 448(7153):591-4. DOI: 10.1038/nature06010. View