An approach to incorporate linkage disequilibrium structure into genomic association analysis

Fengyu Zhang; Diane Wagener

doi:10.1016/S1673-8527(08)60055-7

Volume 35 Issue 6

Jun. 2008

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Article Navigation > Journal of Genetics and Genomics > 2008 > 35(6): 381-385

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An approach to incorporate linkage disequilibrium structure into genomic association analysis

doi: 10.1016/S1673-8527(08)60055-7

Statistics and Epidemiology Unit, Research Triangle Institute, NC 27709, USA

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Corresponding author: E-mail address: zhangf28@gmail.com (Fengyu Zhang)
Received Date: 2007-11-15
Accepted Date: 2008-02-19
Rev Recd Date: 2008-02-18

Available Online: 2008-06-18

Publish Date: 2008-06-20

Abstract

Abstract

In this study, we propose to use the principal component analysis (PCA) and regression model to incorporate linkage disequilibrium (LD) in genomic association data analysis. To accommodate LD in genomic data and reduce multiple testing, we suggest performing PCA and extracting the PCA score to capture the variation of genomic data, after which regression analysis is used to assess the association of the disease with the principal component score. An empirical analysis result shows that both genotype-based correlation matrix and haplotype-based LD matrix can produce similar results for PCA. Principal component score seems to be more powerful in detecting genetic association because the principal component score is quantitatively measured and may be able to capture the effect of multiple loci.
- genetic association analysis,
- principal component analysis,
- linkage disequilibrium

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References(16)

References

[1]	Beckmann, L., Ziegler, A., Duggal, P. et al. Haplotypes and haplotype-tagging single-nucleotide polymorphism: Presentation Group 8 of Genetic Analysis Workshop 14 Genet. Epidemiol., 29 (2005),pp. S59-S71
[2]	Chapman, J.M., Cooper, J.D., Todd, J.A. et al. Detecting disease associations due to linkage disequilibrium using haplotype tags: A class of tests and the determinants of statistical power Hum. Hered., 56 (2003),pp. 18-31
[3]	Clayton, D., Chapman, J., Cooper, J. Use of unphased multilocus genotype data in indirect association studies Genet. Epidemiol., 27 (2004),pp. 415-428
[4]	Devlin, B., Risch, N. A comparison of linkage disequilibrium measures for fine-scale mapping Genomics, 29 (1995),pp. 311-322
[5]	He, J., Zelikovsky, A. MLR-tagging: Informative SNP selection for unphased genotypes based on multiple linear regression Bioinformatics (Oxford, England), 22 (2006),pp. 2558-2561
[6]	Horne, B.D., Camp, N.J. Principal component analysis for selection of optimal SNP-sets that capture intragenic genetic variation Genet. Epidemiol., 26 (2004),pp. 11-21
[7]	Liu, N., Sawyer, S.L., Mukherjee, N. et al. Haplotype block structures show significant variation among populations Genet. Epidemiol., 27 (2004),pp. 385-400
[8]	Niu, T. Algorithms for inferring haplotypes Genet. Epidemiol., 27 (2004),pp. 334-347
[9]	Rinaldo, A., Bacanu, S.A., Devlin, B. et al. Characterization of multilocus linkage disequilibrium Genet. Epidemiol., 28 (2005),pp. 193-206
[10]	Risch, N.J. Searching for genetic determinants in the new millennium Nature, 405 (2000),pp. 847-856
[11]	Roy, N.S., Farheen, S., Roy, N. et al. Portability of tag SNPs across isolated population groups: An example from India Ann. Hum. Genet., 72 (2007),pp. 82-89
[12]	Sidak, Z. On multivariate normal probabilities of rectangles: Their dependence on correlations Ann. Math. Statist., 39 (1968),pp. 1425-1434
[13]	Sidak, Z. On probabilities of rectangles in multivariate normal student distributions: Their dependence on correlations Ann. Math. Statist., 41 (1971),pp. 169-175
[14]	Wang, T., Elston, R.C. Improved power by use of a weighted score test for linkage disequilibrium mapping Am. J. Hum. Genet., 80 (2007),pp. 353-360
[15]	Zaykin, D.V., Westfall, P.H., Young, S.S. et al. Testing association of statistically inferred haplotypes with discrete and continuous traits in samples of unrelated individuals Hum. Hered., 53 (2002),pp. 79-91
[16]	Zhang, K., Calabrese, P., Nordborg, M. et al. Haplotype block structure and its applications to association studies: Power and study designs Am. J. Hum. Genet., 71 (2002),pp. 1386-1394