1000 Genomes-based imputation identifies novel and refined associations for the Wellcome Trust Case Control Consortium phase 1 Data

European Journal of Human Genetics

Volumn 20, Issue 7, 2012, Pages 801-805

  Huang, Jie ^a   Ellinghaus, David ^b   Franke, Andre ^b   Howie, Bryan ^c   Li, Yun ^d  

^a WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^b UNIVERSITY OF KIEL   (Germany)

^c UNIVERSITY OF CHICAGO   (United States)

^d UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

Genome wide association study; imputation; the 1000 Genomes project

Indexed keywords

ARTICLE; CROHN DISEASE; CUX2 GENE; CYCLIN DEPENDENT KINASE INHIBITOR 2B GENE; GENE; GENETIC ANALYSIS; GENETIC ASSOCIATION; GENETIC IDENTIFICATION; GENETIC VARIABILITY; INSULIN DEPENDENT DIABETES MELLITUS; INTERLEUKIN 2 RECEPTOR ALPHA GENE; INTERLEUKIN 23 RECEPTOR GENE; NON INSULIN DEPENDENT DIABETES MELLITUS; PRIORITY JOURNAL; SINGLE NUCLEOTIDE POLYMORPHISM;

ALGORITHMS; CASE-CONTROL STUDIES; CHROMOSOME MAPPING; CROHN DISEASE; CYCLIN-DEPENDENT KINASE INHIBITOR P15; DIABETES MELLITUS, TYPE 1; DIABETES MELLITUS, TYPE 2; EXONS; GENETIC LINKAGE; GENETIC LOCI; GENETIC PREDISPOSITION TO DISEASE; GENETIC TESTING; GENOME, HUMAN; GENOME-WIDE ASSOCIATION STUDY; HAPLOTYPES; HUMANS; INTERLEUKIN-2 RECEPTOR ALPHA SUBUNIT; POLYMORPHISM, SINGLE NUCLEOTIDE; REFERENCE STANDARDS;

EID: 84863724842     PISSN: 10184813     EISSN: 14765438     Source Type: Journal    
DOI: 10.1038/ejhg.2012.3     Document Type: Article

References (33)

1. WTCCC: Genome-wide association study of 14000 cases of seven common diseases and 3000 shared controls. Nature 2007; 447: 661-678.
2. Orho-Melander M, Melander O, Guiducci C et al: Common missense variant in the glucokinase regulatory protein gene is associated with increased plasma triglyceride and C-reactive protein but lower fasting glucose concentrations. Diabetes 2008; 57: 3112-3121.
3. de Bakker PI, Ferreira MA, Jia X, Neale BM, Raychaudhuri S, Voight BF: Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum Mol Genet 2008; 17: R122-R128.
4. Li Y, Willer C, Sanna S, Abecasis G: Genotype imputation. Annu Rev Genomics Hum Genet 2009; 10: 387-406.
5. Marchini J, Howie B: Genotype imputation for genome-wide association studies. Nat Rev Genet 2010; 11: 499-511.
6. Thorisson GA, Smith AV, Krishnan L, Stein LD: The International HapMap Project Web site. Genome Res 2005; 15: 1592-1593. (Pubitemid 41545013)
7. Durbin RM, Abecasis GR, Altshuler DL et al: A map of human genome variation from population-scale sequencing. Nature 2010; 467: 1061-1073.
8. Liu JZ, Tozzi F, Waterworth DM et al: Meta-analysis and imputation refines the association of 15q25 with smoking quantity. Nat Genet 2010; 42: 436-440.
9. Sanna S, Pitzalis M, Zoledziewska M et al: Variants within the immunoregulatory CBLB gene are associated with multiple sclerosis. Nat Genet 2010; 42: 495-497.
10. Purcell S, Neale B, Todd-Brown K et al: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 2007; 81: 559-575. (Pubitemid 47330214)
11. Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR: MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010; 34: 816-834.
12. Adzhubei IA, Schmidt S, Peshkin L et al: A method and server for predicting damaging missense mutations. Nat Methods 2010; 7: 248-249.
13. Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA: Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am J Hum Genet 2004; 74: 106-120. (Pubitemid 38085242)
14. Browning BL, Browning SR: A unified approach to genotype imputation and haplotypephase inference for large data sets of trios and unrelated individuals. Am J Hum Genet 2009; 84: 210-223.
15. Devlin B, Roeder K: Genomic control for association studies. Biometrics 1999; 55: 997-1004.
16. The International HapMap Consortium: A second generation human haplotype map of over 3.1 million SNPs. Nature 2007; 449: 851-861.
17. Pe'er I, Yelensky R, Altshuler D, Daly MJ: Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol 2008; 32: 381-385. (Pubitemid 351656309)
18. Barrett JC, Clayton DG, Concannon P et al: Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat Genet 2009; 41: 703-707.
19. Saxena R, Voight BF, Lyssenko V et al: Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 2007; 316: 1331-1336. (Pubitemid 46871653)
20. Shea J, Agarwala V, Philippakis AA et al: Comparing strategies to fine-map the association of common SNPs at chromosome 9p21 with type 2 diabetes and myocardial infarction. Nat Genet 2011; 43: 801-805.
21. Iulianella A, Sharma M, Durnin M, Vanden Heuvel GB, Trainor PA: Cux2 (Cutl2) integrates neural progenitor development with cell-cycle progression during spinal cord neurogenesis. Development 2008; 135: 729-741. (Pubitemid 351405026)
22. Barrett JC, Hansoul S, Nicolae DL et al: Genome-wide association defines more than 30 distinct susceptibility loci for Crohn¢s disease. Nat Genet 2008; 40: 955-962.
23. Nothnagel M, Ellinghaus D, Schreiber S, Krawczak M, Franke A: A comprehensive evaluation of SNP genotype imputation. Hum Genet 2009; 125: 163-171.
24. Pei YF, Li J, Zhang L, Papasian CJ, Deng HW: Analyses and comparison of accuracy of different genotype imputation methods. PLoS One 2008; 3: e3551.
25. Zheng J, Li Y, Abecasis GR, Scheet P: A comparison of approaches to account for uncertainty in analysis of imputed genotypes. Genet Epidemiol 2011; 35: 102-110.
26. Pei YF, Zhang L, Li J, Deng HW: Analyses and comparison of imputation-based association methods. PLoS One 2010; 5: e10827.
27. Nyholt DR: A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet 2004; 74: 765-769. (Pubitemid 38420107)
28. Conneely KN, Boehnke M: So many correlated tests, so little time! Rapid adjustment of p values for multiple correlated tests. Am J Hum Genet 2007; 81: 1158-1168. (Pubitemid 350211447)
29. Gao XY: Multiple testing corrections for imputed SNPs. Genet. Epidemiol 2011; 35: 154-158.
30. Wen SH, Lu ZS: Factors affecting the effective number of tests in genetic association studies: a comparative study of three PCA-based methods. J Hum Genet 2011; 56: 428-435.
31. Kullo IJ, de Andrade M, Boerwinkle E, McConnell JP, Kardia SL, Turner ST: Pleiotropic genetic effects contribute to the correlation between HDL cholesterol, triglycerides, and LDL particle size in hypertensive sibships. Am J Hypertens 2005; 18: 99-103. (Pubitemid 40187355)
32. Avery CL, He Q, North KE et al: A phenomics-based strategy identifies loci on APOC1, BRAP, and PLCG1 associated with metabolic syndrome phenotype domains. PLoS Genet 2011; 7: e1002322.
33. Zawistowski M, Gopalakrishnan S, Ding J, Li Y, Grimm S, Zollner S: Extending rare-variant testing strategies: analysis of noncoding sequence and imputed genotypes. Am J Hum Genet 2010; 87: 604-617.