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Research ArticleINVESTIGATION

Meta-analysis of Complex Diseases at Gene Level by Generalized Functional Linear Models

Ruzong Fan, Yifan Wang, Chi-yang Chiu, Wei Chen, Haobo Ren, Yun Li, Michael Boehnke, Christopher I. Amos, Jason H. Moore and Momiao Xiong
Genetics December 2015, genetics.115.180869; DOI: https://doi.org/10.1534/genetics.115.180869
Ruzong Fan
National Institutes of Health;
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  • For correspondence: fanr@mail.nih.gov
Yifan Wang
National Institutes of Health;
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Chi-yang Chiu
National Institutes of Health;
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Wei Chen
University of Pittsburgh;
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Haobo Ren
Regeneron Pharmaceuticals, Inc.;
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Yun Li
University of North Carolina;
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Michael Boehnke
University of Michigan;
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Christopher I. Amos
Dartmouth Medical School;
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Jason H. Moore
University of Pennsylvania;
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Momiao Xiong
University of Texas, Houston
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Supporting Information for Fan et al., 2016

Supporting Information

  • Supporting Information - File contains all supporting Figures and Tables. (.pdf, 112 KB)
  • Figure S1 - The empirical power of the heterogeneous Rao's efficient score test statistics (Het-Rao) of the models (1), (3), and (6) and MetaSKAT at α = 0:0001, when some causal variants are rare and some are common and the genetic effect is simulated as homogeneous. (.pdf, 36 KB)
  • Figure S2 - The empirical power of the heterogeneous Rao's efficient score test statistics (Het-Rao) of the models (1), (3), and (6) and MetaSKAT at α = 0:0001, when all causal variants are rare and the genetic effect is simulated as homogeneous. (.pdf, 36 KB)
  • Figure S3 - The empirical power of the heterogeneous Rao's efficient score test statistics (Het-Rao) of the models (1), (3), and (6) and MetaSKAT at α = 0:0001, when some causal variants are rare and some are common and the genetic effect is simulated as heterogeneous. (.pdf, 36 KB)
  • Figure S4 - The empirical power of the heterogeneous Rao's efficient score test statistics (Het-Rao) of the models (1), (3), and (6) and MetaSKAT at α = 0:0001, when all causal variants are rare and the genetic effect is simulated as heterogeneous. (.pdf, 36 kB)
  • Table S1 - Sample sizes of the cases and controls for each of the seven studies. (.pdf, 22 KB)
  • Table S2 - Summary of 22 genes and the number of genetic variants in each gene region by Mar. 2006 (NCBI36/hg18). (.pdf, 38 KB)
  • Table S3 - Association analysis of type 2 diabetes status in eight European cohorts by heterogeneous likelihood ratio test statistics (Het-LRT), Het-Meta-SKAT-O, and Het-Meta-SKAT. (.pdf, 48 KB)
  • Table S4 - Association analysis of type 2 diabetes status in eight European cohorts by homogeneous likelihood ratio test statistics (Hom-LRT), Hom-Meta-SKAT-O, and Hom-Meta-SKAT. (.pdf, 51 KB)
  • Table S5 - Association analysis of type 2 diabetes status in eight European cohorts by heterogeneous Rao's efficient score test statistics (Het-Rao), Het-Meta-SKAT-O, and Het-Meta-SKAT, Using Rare Variants (MAFs≤0.03). (.pdf, 43 KB)
  • Table S6 - Association analysis of type 2 diabetes status in eight European cohorts by heterogeneous Rao's efficient score test statistics (Het-Rao), Het-Meta-SKAT-O, and Het-Meta-SKAT, using common variants (MAFs>0.03). (.pdf, 44 KB)
  • Table S7 - Association Analysis of Type 2 Diabetes Status in Eight European Cohorts by Homogeneous Rao's Efficient Score Test Statistics (Hom-Rao), Hom-Meta-SKAT-O, and Hom-Meta-SKAT, Using Rare Variants (MAFs≤0.03). (.pdf, 43 KB)
  • Table S8 - Association Analysis of Type 2 Diabetes Status in Eight European Cohorts by Homogeneous Rao's Efficient Score Test Statistics (Hom-Rao), Hom-Meta-SKAT-O, and Hom-Meta-SKAT, using common variants (MAFs>0.03). (.pdf, 44 KB)

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