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An Adaptive Weighted Sum Test for Family-Based Multi-Marker Association Studies

DOI: 10.4236/ojgen.2016.64007, PP. 61-73

Keywords: Family Data, Genetic Association Study, Population Stratification

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Abstract:

Backgrounds: Although many disease-associated common variants have been discovered through genome-wide association studies, much of the genetic effects of complex diseases have not been explained. Population-based association studies are vulnerable to population stratification. A possible solution is to use family-based tests. However, if tests only estimate the genetic effect from the within-family variation to avoid population stratification, they may ignore the useful genetic information from between-family variation and lose power. Methods: We have developed an adaptive weighted sum test for family-based association studies. The new test uses data driven weights to combine two test statistics, and the weights measure the strength of population stratification. When population stratification is strong, the proposed test will automatically put more weight on one statistic derived from within-family variation to maintain robustness against spurious positives. On the other hand, when the effect of population stratification is relatively weak, the proposed test will automatically put more weight on the other statistic derived from both within-family and between-family variation to make use of both sources of genetic variation; and at the same time, the degrees of freedom of the test will be reduced and power of the test will be increased. Results: In our study, the proposed method achieves a higher power in most scenarios of linkage disequilibrium structure as well as Hap Map data from different genes under different population structures while still keeping its robustness against population stratification.

References

[1]  Lee, S., Abecasis, G.R., Boehnke, M. and Lin, X. (2014) Rare-Variant Association Study Designs and Statistical Tests. American Journal of Human Genetics, 95, 5-23.
http://dx.doi.org/10.1016/j.ajhg.2014.06.009
[2]  He, Z., O’Roak, B., Smith, J.D., Wang, G., Hooker, S., Santos-Cortez, R.L.P., Li, B., Kan, M., Krumm, N., Nickerson, D.A., Shendure, J., Eichler, E.E. and Leal, S.M. (2014) Rare-Variant Extensions of the Transmission Disequilibrium Test: Application to Autism Exome Sequence Data. American Journal of Human Genetics, 94, 33-46.
http://dx.doi.org/10.1016/j.ajhg.2013.11.021
[3]  Jiang, Y., Satten, G.A., Han, Y., Epstein, M.P., Heinzen, E.L., Goldstein, D.B. and Allen, A.S. (2014) Utilizing Population Controls in Rare-Variant Case-Parent Association Tests. American Journal of Human Genetics, 94, 845-853. http://dx.doi.org/10.1016/j.ajhg.2014.04.014
[4]  Wang, X., Lee, S., Zhu, X., Redline, S. and Lin, X. (2013) GEE-Based SNP Set Association Test for Continuous and Discrete Traits in Family-Based Association Studies. Genetic Epidemiology, 37, 778-786.
http://dx.doi.org/10.1002/gepi.21763
[5]  Cheung, C.Y., Thompson, E.A. and Wijsman, E.M. (2013) GIGI: An Approach to Effective Imputation of Dense Genotypes on Large Pedigrees. American Journal of Human Genetics, 92, 504-516.
http://dx.doi.org/10.1016/j.ajhg.2013.02.011
[6]  Saad, M. and Wijsman, E. (2013) Power of Family-Based Association Designs to Detect Rare Variants in Large Pedigrees Using Imputed Genotypes. Genetic Epidemiology, 38, 1-9.
http://dx.doi.org/10.1002/gepi.21776
[7]  Cobat, A., Abel, L., Alcais, A. and Schurr, E. (2014) A General Efficient and Flexible Approach for Genome-Wide Association Analyses of Imputed Genotypes in Family-Based Designs. Genetic Epidemiology, 38, 560-571.
http://dx.doi.org/10.1002/gepi.21842
[8]  Yu, Z. (2012) Family-Based Association Tests Using Genotype Data with Uncertainty. Biostatiistics, 13, 228-240.
http://dx.doi.org/10.1093/biostatistics/kxr045
[9]  Laird, N.M., Horvath, S. and Xu, X. (2000) Implementing a Unified Approach to Family-Based Tests of Association. Genetic Epidemiology, 19, S36-S42.
http://dx.doi.org/10.1002/1098-2272(2000)19:1+<::AID-GEPI6>3.0.CO;2-M
[10]  Rakovski, C.S., Xu, X., Lazarus, R., Blacker, D. and Laird, N.M. (2007) A New Multimarker Test for Family-Based Association Studies. Genetic Epidemiology, 31, 9-17.
http://dx.doi.org/10.1002/gepi.20186
[11]  Xu, X., Rakovski, C., Xu, X.P. and Laird, N. (2006) An Efficient Family-Based Association Test Using Multiple Markers. Genetic Epidemiology, 30, 620-626.
http://dx.doi.org/10.1002/gepi.20174
[12]  Lange, C., De Meo, D., Silverman, E.K., Weiss, S.T. and Laird, N.M. (2003) Using the Noninformative Families in Family-Based Association Tests: A Powerful New Testing Strategy. American Journal of Human Genetics, 73, 801-811. http://dx.doi.org/10.1086/378591
[13]  Pan, W. (2009) Asymptotic Tests of Association with Multiple SNPs in Linkage Disequilibrium. Genetic Epidemiology, 33, 497-507.
http://dx.doi.org/10.1002/gepi.20402
[14]  Li, Y., Willer, C.J., Ding, J., Scheet, P. and Abecasis, G.R. (2010) MaCH: Using Sequence and Genotype Data to Estimate Haplotypes and Unobserved Genotypes. Genetic Epidemiology, 34, 816-834.
http://dx.doi.org/10.1002/gepi.20533
[15]  Browning, B.I. and Browning, S.R. (2009) A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals. American Journal of Human Genetics, 84, 210-223. http://dx.doi.org/10.1016/j.ajhg.2009.01.005
[16]  Montgomery, S.B., Goode, D.L., Kvikstad, E., Albers, C.A., Zhang, Z.D., Mu, X.J., Ananda, G., Howie, B., Karczewski, K.J., Smith, K.S., et al. (2013) 1000 Genomes Project Consortium. The Origin, Evolution, and Functional Impact of Short Insertion-Deletion Variants Identified in 179 Human Genomes. Genome Research, 23, 749-761.
http://dx.doi.org/10.1101/gr.148718.112
[17]  Wang, Y., Lu, J., Yu, J., Gibbs, R.A. and Yu, F. (2013) An Integrative Variant Analysis Pipeline for Accurate Geno-type/Haplotype Inference in Population NGS Data. Genome Research, 23, 833-842.
http://dx.doi.org/10.1101/gr.146084.112
[18]  Delaneau, O., Howie, B., Cox, A.J., Zagury, J.F. and Marchini, J. (2013) Haplotype Estimation Using Sequencing Reads. Genetic Epidemiology, 93, 687-696.
http://dx.doi.org/10.1016/j.ajhg.2013.09.002
[19]  Chapman, J. and Whittaker, J. (2008) Analysis of Multiple SNPs in a Candidate Gene or Region. Genetic Epidemiology, 32, 560-566.
http://dx.doi.org/10.1002/gepi.20330
[20]  Jiang, R.F., Dong, J.P. and Dai, Y.L. (2009) Improving Power in Genetic-Association Studies via Wavelet Transformation. BMC Genetics, 10, 53.
http://dx.doi.org/10.1186/1471-2156-10-53
[21]  Wang, K. and Abbott, D. (2008) A Principal Components Regression Approach to Multilocus Genetic Association Studies. Genetic Epidemiology, 32, 108-118.
http://dx.doi.org/10.1002/gepi.20266
[22]  Wang, T. and Elston, R.C. (2007) Improved Power by Use of a Weighted Score Test for Linkage Disequilibrium Mapping. American Journal of Human Genetics, 80, 353-360.
http://dx.doi.org/10.1086/511312
[23]  Abecasis, G.R., Cardon, L.R. and Cookson, W.O.C. (2000) A General Test of Association for Quantitative Traits in Nuclear Families. American Journal of Human Genetics, 42, 279-292.
http://dx.doi.org/10.1086/302698

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