Population-based case-control association studies

Current Protocols in Human Genetics

Volumn , Issue SUPPL.74, 2012, Pages

  Hancock, Dana B ^a   Scott, William K ^b  

^a RTI INTERNATIONAL   (United States)

^b UNIVERSITY OF MIAMI   (United States)

Author keywords

Association; Candidate gene; Case control study; Confounding; Genome wide; Logistic regression; Meta analysis; Sequencing

Indexed keywords

APOLIPOPROTEIN E4; COMPLEMENT FACTOR H;

ALZHEIMER DISEASE; ARTICLE; BREAST CANCER; BREAST CARCINOGENESIS; CASE CONTROL STUDY; CAUSAL ATTRIBUTION; CELIAC DISEASE; COHORT ANALYSIS; CROSS-SECTIONAL STUDY; GENE LINKAGE DISEQUILIBRIUM; GENE LOCUS; GENE MUTATION; GENETIC ASSOCIATION; GENETIC PREDISPOSITION; GENETIC RISK; GENETIC TRAIT; GENETIC VARIABILITY; GENOTYPE ENVIRONMENT INTERACTION; HAPLOTYPE MAP; HIGH THROUGHPUT SEQUENCING; HUMAN; OBSERVATIONAL STUDY; ONCOGENE; POPULATION BASED CASE CONTROL STUDY; PRIORITY JOURNAL; RARE DISEASE; RETINA MACULA AGE RELATED DEGENERATION; SINGLE NUCLEOTIDE POLYMORPHISM; TUMOR SUPPRESSOR GENE; METHODOLOGY; EPIDEMIOLOGY; HAPLOTYPE; TECHNIQUE;

CASE-CONTROL STUDIES; COHORT STUDIES; CROSS-SECTIONAL STUDIES; GENETIC ASSOCIATION STUDIES; GENOME-WIDE ASSOCIATION STUDY; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; RESEARCH DESIGN; EPIDEMIOLOGY, MOLECULAR; GENETIC PREDISPOSITION TO DISEASE; HAPLOTYPES; METHODS;

EID: 44249086933     PISSN: 19348266     EISSN: 19348258     Source Type: Journal    
DOI: 10.1002/0471142905.hg0117s74     Document Type: Article

References (118)

1. 1000 Genomes Project Consortium. 2010.Amap of human genome variation from population-scale sequencing. Nature 467:1061-1073.
2. Allen, A.S. and Satten, G.A., 2009. A novel haplotype-sharing approach for genome-wide case-control association studies implicates the calpastatin gene in Parkinson's disease. Genet. Epidemiol. 33:657-667.
3. Aschard, H., Hancock, D.B., London, S.J., and Kraft, P. 2010. Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects. Hum. Hered. 70:292-300.
4. Aschengrau, A. and Seage, G.R.I. 2003. Essentials of Epidemiology in Public Health. Jones and Bartlett Publishers, Sudbury, Mass.
5. Barrett, J.C., Fry, B., Maller, J., and Daly, M.J. 2005. Haploview: Analysis and visualization of LD and haplotype maps. Bioinformatics 21:263-265.
6. Beaty, T.H., Ruczinski, I., Murray, J.C., Marazita, M.L., Munger, R.G., Hetmanski, J.B., Murray, T., Redett, R.J., Fallin, M.D., Liang, K.Y., Wu, T., Patel, P.J., Jin, S.C., Zhang, T.X., Schwender, H., Wu-Chou, Y.H., Chen, P.K., Chong, S.S., Cheah, F., Yeow, V., Ye, X., Wang, H., Huang, S., Jabs, E.W., Shi, B., Wilcox, A.J., Lie, R.T., Jee, S.H., Christensen, K., Doheny, K.F., Pugh, E.W., Ling, H., and Scott, A.F. 2011. Evidence for gene-environment interaction in a genome wide study of nonsyndromic cleft palate. Genet. Epidemiol. 35:469-478.
7. Charles, B.A., Shriner, D., Doumatey, A., Chen, G., Zhou, J., Huang, H., Herbert, A., Gerry, N.P., Christman, M.F., Adeyemo, A., and Rotimi, C.N. 2011. A genome-wide association study of serum uric acid in African Americans. BMC Med Genomics 4:17.
8. Cohen, J.C., Kiss, R.S., Pertsemlidis, A., Marcel, Y.L., McPherson, R., and Hobbs, H.H. 2004. Multiple rare alleles contribute to low plasma levels ofHDLcholesterol. Science 305:869-872.
9. Cohn, L.D. and Becker, B.J. 2003. How metaanalysis increases statistical power. Psychol. Methods 8:243-253.
10. Cordell, H.J. and Clayton, D.G., 2005. Genetic association studies. Lancet 366:1121-1131.
11. Corder, E.H., Saunders, A.M., Strittmatter, W.J., Schmechel, D.E., Gaskell, P.C., Small, G.W., Roses, A.D., Haines, J.L., and Pericak-Vance, M.A. 1993. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science 261:921-923.
12. Cornelis, M.C., Agrawal, A., Cole, J.W., Hansel, N.N., Barnes, K.C., Beaty, T.H., Bennett, S.N., Bierut, L.J., Boerwinkle, E., Doheny, K.F., Feenstra, B., Feingold, E., Fornage, M., Haiman, C.A., Harris, E.L., Hayes, M.G., Heit, J.A., Hu, F.B., Kang, J.H., Laurie, C.C., Ling, H., Manolio, T.A., Marazita, M.L., Mathias, R.A., Mirel, D.B., Paschall, J., Pasquale, L.R., Pugh, E.W., Rice, J.P., Udren, J., van Dam, R.M., Wang, X., Wiggs, J.L., Williams, K., Yu, K.; GENEVA Consortium. 2010. The Gene, Environment Association Studies consortium (GENEVA): Maximizing the knowledge obtained fromGWAS by collaboration across studies of multiple conditions. Genet. Epidemiol. 34:364-372.
13. Daly, M.J., Rioux, J.D., Schaffner, S.F., Hudson, T.J., and Lander, E.S. 2001. High-resolution haplotype structure in the human genome. Nat. Genet. 29:229-232.
14. Davies, H.T., Crombie, I.K., and Tavakoli, M. 1998. When can odds ratios mislead? BMJ 316:989-991.
15. Dawson, E., Abecasis, G.R., Bumpstead, S., Chen, Y., Hunt, S., Beare, D.M., Pabial, J., Dibling, T., Tinsley, E., Kirby, S., Carter, D., Papaspyridonos, M., Livingstone, S., Ganske, R., Lõhmussaar, E., Zernant, J., Tõnisson, N., Remm, M., Mägi, R., Puurand, T., Vilo, J., Kurg, A., Rice, K., Deloukas, P., Mott, R., Metspalu, A., Bentley, D.R., Cardon, L.R., and Dunham, I. 2002. A first-generation linkage disequilibrium map of human chromosome 22. Nature 418:544-548.
16. de Bakker, P.I., Ferreira, M.A., Jia, X., Neale, B.M., Raychaudhuri, S., and Voight, B.F. 2008. Practical aspects of imputation-driven meta-analysis of genome-wide association studies. Hum. Mol. Genet. 17:R122-R128
17. Dite, G.S., Jenkins, M.A., Southey, M.C., Hocking, J.S., Giles, G.G., McCredie, M.R., Venter, D.J., and Hopper, J.L. 2003. Familial risks, early-onset breast cancer, and BRCA1 and BRCA2 germline mutations. J. Natl. Cancer Inst. 95:448-457.
18. Drescher, K. and Schill, W. 1991. Attributable risk estimation from case-control data via logistic regression. Biometrics 47:1247-1256
19. Gabriel, S.B., Schaffner, S.F., Nguyen, H., Moore, J.M., Roy, J., Blumenstiel, B., Higgins, J., DeFelice, M., Lochner, A., Faggart, M., Liu-Cordero, S.N., Rotimi, C., Adeyemo, A., Cooper, R., Ward, R., Lander, E.S., Daly, M.J., and Altshuler, D. 2002. The structure of haplotype blocks in the human genome. Science 296:2225-2229.
20. Greenland, S. 1999. Relation of probability of causation to relative risk and doubling dose: A methodologic error that has become a social problem. Am. J. Public Health 89:1166-1169
21. Gusev, A., Kennym, E.E., Lowe, J.K., Salit, J., Saxena, R., Kathiresan, S., Altshuler, D.M., Friedman, J.M., Breslow, J.L., and Pe'er, I. 2011.DASH:Amethod for identical-by-descent haplotype mapping uncovers association with recent variation. Am. J. Hum. Genet. 88:706-717.
22. Haines, J.L., Hauser, M.A., Schmidt, S., Scott, W.K., Olson, L.M., Gallins, P., Spencer, K.L., Kwan, S.Y., Noureddine, M., Gilbert, J.R., Schnetz-Boutaud, N., Agarwal, A., Postel, E.A., and Pericak-Vance, M.A. 2005. Complement factor H variant increases the risk of age-related macular degeneration. Science 308:419-421
23. Hamilton, C.M., Strader, L.C., Pratt, J.G., Maiese, D., Hendershot, T., Kwok, R.K., Hammond, J.A., Huggins, W., Jackman, D., Pan, H., Nettles, D.S., Beaty, T.H., Farrer, L.A., Kraft, P., Marazita, M.L., Ordovas, J.M., Pato, C.N., Spitz, M.R., Wagener, D., Williams, M., Junkins, H.A., Harlan, W.R., Ramos, E.M., and Haines, J. 2011. The PhenX Toolkit: Get the most from your measures. Am. J. Epidemiol. 174:253-260.
24. Hamza, T.H., Chen, H., Hill-Burns, E.M., Rhodes, S.L., Montimurro, J., Kay, D.M., Tenesa, A., Kusel, V.I., Sheehan, P., Eaaswarkhanth, M., Yearout, D., Samii, A., Roberts, J.W., Agarwal, P., Bordelon, Y., Park, Y., Wang, L., Gao, J., Vance, J.M., Kendler, K.S., Bacanu, S.A., Scott, W.K., Ritz, B., Nutt, J., Factor, S.A., Zabetian, C.P., and Payami, H. 2011. Genome-wide geneenvironment study identifies glutamate receptor gene GRIN2A as a Parkinson's disease modifier gene via interaction with coffee. PLoS Genet. 7:e1002237.
25. Hanley, J.A., Negassa, A., Edwardes, M.D., and Forrester, J.E. 2003. Statistical analysis of correlated data using generalized estimating equations: an orientation. Am. J. Epidemiol. 157:364-375.
26. Hardin, J.W. andHilbe, J.M. 2003. Generalized estimating equations. Chapman & Hall/CRC, Boca Raton, Fla.
27. Hattersley, A.T., and McCarthy, M.L. 2005. What makes a good genetic association study? Lancet 366:1315-1323.
28. Hauser, M.A., Li, Y.J., Takeuchi, S., Walters, R., Noureddine, M., Maready, M., Darden, T., Hulette, C., Martin, E., Hauser, E., Xu, H., Schmechel, D., Stenger, J.E., Dietrich, F., and Vance, J. 2003. Genomic convergence: Identifying candidate genes for Parkinson's disease by combining serial analysis of gene expression and genetic linkage. Hum. Mol. Genet. 12:671-677.
29. Hindorff, L.A., Sethupathy, P., Junkins, H.A., Ramos, E.M., Mehta, J.P., Collins, F.S., and Manolio, T.A. 2009. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. U.S.A. 106:9362-9367.
30. Hintsanen, P., Sevon, P., Onkamo, P., Eronen, L., and Toivonen, H. 2006. An empirical comparison of case-control and trio based study designs in high throughput association mapping. J.Med. Genet. 43:617-624.
31. Hirschhorn, J.N. and Daly, M.J., 2005. Genomewide association studies for common diseases and complex traits. Nat. Rev. Genet. 6:95-108
32. Ho, L.A. and Lange, E.M. 2010. Using public control genotype data to increase power and decrease cost of case-control genetic association studies. Hum. Genet. 128:597-608.
33. Hopper, J.L., Chenevix-Trench, G., Jolley, D.J., Dite, G.S., Jenkins, M.A., Venter, D.J., McCredie, M.R., and Giles, G.G. 1999. Design and analysis issues in a population-based, case-control-family study of the genetic epidemiology of breast cancer and the Co-operative Family Registry for Breast Cancer Studies (CFRBCS). J. Natl. Cancer Inst.Monogr. 26:95-100.
34. Hopper, J.L., Bishop, D.T., and Easton, D.F. 2005. Population-based family studies in genetic epidemiology. Lancet 366:1397-1406
35. Horton, N.J. and Lipsitz, S.R. 1999. Review of software to fit generalized estimating equation regression models. Am. Stat. 53:160-169
36. Howie, B.N., Donnelly, P., and Marchini, J. 2009. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5:e1000529
37. Huang, L., Li, Y., Singleton, A.B., Hardy, J.A., Abecasis, G., Rosenberg, N.A., and Scheet, P. 2009. Genotype-imputation accuracy across worldwide human populations. Am. J. Hum. Genet. 84:235-250.
38. Hubbard, T., Barker, D., Birney, E., Cameron, G., Chen, Y., Clark, L., Cox, T., Cuff, J., Curwen, V., Down, T., Durbin, R., Eyras, E., Gilbert, J., Hammond, M., Huminiecki, L., Kasprzyk, A., Lehvaslaiho, H., Lijnzaad, P., Melsopp, C., Mongin, E., Pettett, R., Pocock, M., Potter, S., Rust, A., Schmidt, E., Searle, S., Slater, G., Smith, J., Spooner, W., Stabenau, A., Stalker, J., Stupka, E., Ureta-Vidal, A., Vastrik, I., and Clamp, M. 2002. The Ensembl genome database project. Nucleic Acids Res. 30:38-41.
39. International HapMap Consortium. 2003. The International HapMap Project. Nature 426:789-796.
40. International HapMap Consortium. 2005. A haplotype map of the human genome. Nature 437:1299-1320.
41. International HapMap Consortium. 2007. A second generation human haplotype map of over 3.1 million SNPs. Nature 449:851-861.
42. International HapMap 3 Consortium, Altshuler, D.M., Gibbs, R.A., Peltonen, L., Altshuler, D.M., Gibbs, R.A., Peltonen, L., Dermitzakis, E., Schaffner, S.F., Yu, F., Peltonen, L., Dermitzakis, E., Bonnen, P.E., Altshuler, D.M., Gibbs, R.A., de Bakker, P.I., Deloukas, P., Gabriel, S.B., Gwilliam, R., Hunt, S., Inouye, M., Jia, X., Palotie, A., Parkin, M., Whittaker, P., Yu, F., Chang, K., Hawes, A., Lewis, L.R., Ren, Y., Wheeler, D., Gibbs, R.A., Muzny, D.M., Barnes, C., Darvishi, K., Hurles, M., Korn, J.M., Kristiansson, K., Lee, C., McCarrol, S.A., Nemesh, J., Dermitzakis, E., Keinan, A., Montgomery, S.B., Pollack, S., Price, A.L., Soranzo, N., Bonnen, P.E., Gibbs, R.A., Gonzaga-Jauregui, C., Keinan, A., Price, A.L., Yu, F., Anttila, V., Brodeur, W., Daly, M.J., Leslie, S., McVean, G., Moutsianas, L., Nguyen, H., Schaffner, S.F., Zhang, Q., Ghori, M.J., McGinnis, R., McLaren, W., Pollack, S., Price, A.L., Schaffner, S.F., Takeuchi, F., Grossman, S.R., Shlyakhter, I., Hostetter, E.B., Sabeti, P.C., Adebamowo, C.A., Foster, M.W., Gordon, D.R., Licinio, J., Manca, M.C., Marshall, P.A., Matsuda, I., Ngare, D., Wang, V.O., Reddy, D., Rotimi, C.N., Royal, C.D., Sharp, R.R., Zeng, C., Brooks, L.D., and McEwen, J.E. 2010. Integrating common and rare genetic variation in diverse human populations. Nature 467:52-58.
43. Ioannidis, J.P. 2009. Prediction of cardiovascular disease outcomes and established cardiovascular risk factors by genome-wide association markers. Circ Cardiovasc Genet 2:7-15.
44. Johnson, A.D., Handsaker, R.E., Pulit, S.L., Nizzari, M.M., O'Donnell, C.J., and de Bakker, P.I. 2008. SNAP: A web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 24:2938-2939.
45. Ke, X., Miretti, M.M., Broxholme, J., Hunt, S., Beck, S., Bentley, D.R., Deloukas, P., and Cardon, L.R. 2005. A comparison of tagging methods and their tagging space. Hum. Mol. Genet. 14:2757-2767.
46. Kent, W.J., Sugnet, C.W., Furey, T.S., Roskin, K.M., Pringle, T.H., Zahler, A.M., and Haussler, D. 2002. The human genome browser at UCSC. Genome Res. 12:996-1006.
47. Khoury, M.J., Beaty, T.H., and Cohen, B.H., 1993. Fundamentals of genetic epidemiology. Oxford University Press, New York.
48. Kinnamon, D.D., Hershberger, R.E., and Martin, E.R. 2012. Reconsidering association testing methods using single-variant statistics as alternatives to pooling tests for sequence data with rare variants. PLoS One 7:e30238.
49. Kitsios, G.D. and Zintzaras, E., 2009. Genomic convergence of genome-wide investigations for complex traits. Ann. Hum. Genet. 73:514-519.
50. Klein, R.J., Zeiss, C., Chew, E.Y., Tsai, J.Y., Sackler, R.S., Haynes, C., Henning, A.K., SanGiovanni, J.P., Mane, S.M., Mayne, S.T., Bracken, M.B., Ferris, F.L., Ott, J., Barnstable, C., and Hoh, J. 2005. Complement factorHpolymorphism in age-related macular degeneration. Science 308:385-389.
51. Kraft, P., Yen, Y.C., Stram, D.O., Morrison, J., and Gauderman, W.J. 2007. Exploiting geneenvironment interaction to detect genetic associations. Hum. Hered. 63:111-119.
52. Kruglyak, L. 1999. Prospects for whole-genome linkage disequilibrium mapping of common disease genes. Nat. Genet. 22:139-144.
53. Laird, N.M. and Lange, C. 2006. Family-based designs in the age of large-scale gene-association studies. Nat. Rev. Genet. 7:385-394.
54. Laurie, C.C., Doheny, K.F., Mirel, D.B., Pugh, E.W., Bierut, L.J., Bhangale, T., Boehm, F., Caporaso, N.E., Cornelis, M.C., Edenberg, H.J., Gabriel, S.B., Harris, E.L., Hu, F.B., Jacobs, K.B., Kraft, P., Landi, M.T., Lumley, T., Manolio, T.A., McHugh, C., Painter, I., Paschall, J., Rice, J.P., Rice, K.M., Zheng, X., Weir, B.S. GENEVA Investigators. 2010. Quality control and quality assurance in genotypic data for genome-wide association studies. Genet. Epidemiol. 34:591-602.
55. Le-Niculescu, H., Patel, S.D., Bhat, M., Kuczenski, R., Faraone, S.V., Tsuang, M.T., McMahon, F.J., Schork, N.J., Nurnberger, J.I. Jr., and Niculescu, A.B. 3rd. 2009. Convergent functional genomics of genome-wide association data for bipolar disorder: Comprehensive identification of candidate genes, pathways andmechanisms. Am. J. Med. Genet. B Neuropsychiatr. Genet. 150B:155-181.
56. Lettre, G., Palmer, C.D., Young, T., et al., 2011. Genome-wide association study of coronary heart disease and its risk factors in 8,090 African Americans: The NHLBI CARe Project. PLoS Genet 7:e1001300.
57. Li, B. and Leal, S.M. 2008. Methods for detecting associations with rare variants for common diseases: Application to analysis of sequence data. Am. J. Hum. Genet. 83:311-321.
58. Li, M.X., Gui, H.S., Kwan, J.S. and Sham, P.C. 2011.GATES:Arapid and powerful gene-based association test using extended Simes procedure. Am. J. Hum. Genet. 88:283-293.
59. 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. Genet. Epidemiol. 34:816-834.
60. Liang, X., Slifer, M., Martin, E.R., Schnetz-Boutaud, N., Bartlett, J., Anderson, B., Züchner, S., Gwirtsman, H., Gilbert, J.R., Pericak-Vance, M.A., and Haines, J.L. 2009. Genomic convergence to identify candidate genes for Alzheimer disease on chromosome 10. Hum. Mutat. 30:463-471.
61. Lin, D.Y. andTang, Z.Z. 2011.Ageneral framework for detecting disease associations with rare variants in sequencing studies. Am. J. Hum. Genet. 89:354-367.
62. Lin, D.Y. and Zeng, D. 2010. Meta-analysis of genome-wide association studies: No efficiency gain in using individual participant data. Genet. Epidemiol. 34:60-66.
63. Liu, J.Z., McRae, A.F., Nyholt, D.R., Medland, S.E., Wray, N.R., Brown, K.M., AMFS Investigators, Hayward, N.K., Montgomery, G.W., Visscher, P.M., Martin, N.G., andMacgregor, S. 2010. A versatile gene-based test for genomewide association studies. Am. J. Hum. Genet. 87:139-145.
64. Madsen, B.E. and Browning, S.R. 2009. A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet. 5:e1000384.
65. Manning, A.K., LaValley, M., Liu, C.T., Rice, K., An, P., Liu, Y., Miljkovic, I., Rasmussen-Torvik, L., Harris, T.B., Province, M.A., Borecki, I.B., Florez, J.C., Meigs, J.B., Cupples, L.A., and Dupuis, J. 2011. Meta-analysis of geneenvironment interaction: Joint estimation of SNP and SNP x environment regression coefficients. Genet. Epidemiol. 35:11-18.
66. Manolio, T.A. Collins, F.S., Cox, N.J., Goldstein, D.B., Hindorff, L.A., Hunter, D.J., McCarthy, M.I., Ramos, E.M., Cardon, L.R., Chakravarti, A., Cho, J.H., Guttmacher, A.E., Kong, A., Kruglyak, L., Mardis, E., Rotimi, C.N., Slatkin, M., Valle, D., Whittemore, A.S., Boehnke, M., Clark, A.G., Eichler, E.E., Gibson, G., Haines, J.L., Mackay, T.F., McCarroll, S.A., and Visscher, P.M. 2009. Finding the missing heritability of complex diseases. Nature 461:747-753.
67. Marchini, J. and Howie, B., 2010. Genotype imputation for genome-wide association studies. Nat. Rev. Genet. 11:499-511.
68. Marchini, J., Cutler, D., Patterson, N., Stephens, M., Eskin, E., Halperin, E., Lin, S., Qin, Z.S., Munro, H.M., Abecasis, G.R., Donnelly, P. International HapMap Consortium. 2006. A comparison of phasing algorithms for trios and unrelated individuals. Am. J. Hum. Genet. 78:437-450.
69. McCarty, C.A., Chisholm, R.L., Chute, C.G., Kullo, I.J., Jarvik, G.P., Larson, E.B., Li, R., Masys, D.R., Ritchie, M.D., Roden, D.M., Struewing, J.P., Wolf, W.A. eMERGE Team. 2011. The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Genomics 4:13.
70. Misawa, K. and Kamatani, N. 2011. ParaHaplo 3.0: A program package for imputation and a haplotype-based whole-genome association study using hybrid parallel computing. Source Code Biol. Med. 6:10.
71. Morris, A.P. and Zeggini, E., 2010. An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet. Epidemiol. 34:188-193.
72. Moses, E.K., Fitzpatrick, E., Freed, K.A., Dyer, T.D., Forrest, S., Elliott, K., Johnson, M.P., Blangero, J., and Brennecke, S.P. 2006. Objective prioritization of positional candidate genes at a quantitative trait locus for pre-eclampsia on 2q22. Mol. Hum. Reprod. 12:505-512.
73. Moskvina, V., Craddock, N., Holmans, P., Owen, M.J., and O'Donovan, M.C., 2006. Effects of differential genotyping error rate on the type I error probability of case-control studies. Hum. Hered. 61:55-64.
74. Mudge, J., Miller, N.A., Khrebtukova, I., Lindquist, I.E., May, G.D., Huntley, J.J., Luo, S., Zhang, L., van Velkinburgh, J.C., Farmer, A.D., Lewis, S., Beavis, W.D., Schilkey, F.D., Virk, S.M., Black, C.F., Myers, M.K., Mader, L.C., Langley, R.J., Utsey, J.P., Kim, R.W., Roberts, R.C., Khalsa, S.K., Garcia, M., Ambriz-Griffith, V., Harlan, R., Czika, W., Martin, S., Wolfinger, R.D., Perrone-Bizzozero, N.I., Schroth, G.P., and Kingsmore, S.F. 2008. Genomic convergence analysis of schizophrenia: mRNA sequencing reveals altered synaptic vesicular transport in post-mortem cerebellum. PLoS One 3:e3625.
75. Mukherjee, S., Simon, J., Bayuga, S., Ludwig, E., Yoo, S., Orlow, I., Viale, A., Offit, K., Kurtz, R.C., Olson, S.H., and Klein, R.J. 2011. Including additional controls from public databases improves the power of a genome-wide association study. Hum. Hered. 72:21-34.
76. Naj, A.C., Jun, G., Beecham, G.W., Wang, L.S., Vardarajan, B.N., Buros, J., Gallins, P.J. et al. 2011. Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease. Nat. Genet. 43:436-441.
77. Neale, B.M. and Purcell, S., 2008. The positives, protocols, and perils of genome-wide association. Am. J. Med. Genet. B Neuropsychiatr. Genet. 147B:1288-1294.
78. Newton-Cheh, C. and Hirschhorn, J.N. 2005. Genetic association studies of complex traits: Design and analysis issues. Mutat. Res. 573:54-69.
79. Nielsen, D.M., Ehm, M.G., and Weir, B.S., 1998. Detecting marker-disease association by testing for Hardy-Weinberg disequilibrium at a marker locus. Am. J. Hum. Genet. 63:1531-1540.
80. Nothnagel, M., Ellinghaus, D., Schreiber, S., Krawczak, M., and Franke, A. 2009. A comprehensive evaluation of SNP genotype imputation. Hum. Genet. 125:163-171.
81. Olkin, I. and Sampson, A. 1998. Comparison of meta-analysis versus analysis of variance of individual patient data. Biometrics 54:317-322.
82. Patel, S.D., Le-Niculescu, H., Koller, D.L., Green, S.D., Lahiri, D.K., McMahon, F.J., Nurnberger, J.I. Jr., and Niculescu, A.B. 3rd. 2010. Coming to grips with complex disorders: Genetic risk prediction in bipolar disorder using panels of genes identified through convergent functional genomics. Am. J.Med. Genet. B Neuropsychiatr. Genet. 153B:850-877.
83. Patil, N. et al., 2001. Blocks of limited haplotype diversity revealed by high-resolution scanning of human chromosome 21. Science 294:1719-1723.
84. Pe'er, I., Yelensky, R., Altshuler, D., and Daly, M.J., 2008. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32:381-385.
85. Peto, J., Collins, N., Barfoot, R., Seal, S., Warren, W., Rahman, N., Easton, D.F., Evans, C., Deacon, J., and Stratton, M.R. 1999. Prevalence of BRCA1 and BRCA2 gene mutations in patients with early-onset breast cancer. J. Natl. Cancer Inst. 91:943-949.
86. Pluzhnikov, A., Below., J.E., Konkashbaev, A., Tikhomirov, A., Kistner-Griffin, E., Roe, C.A., Nicolae, D.L., and Cox, N.J. 2010. Spoiling the whole bunch: Quality control aimed at preserving the integrity of high-throughput genotyping. Am. J. Hum. Genet. 87:123-128.
87. Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N.A., and Reich, D. 2006. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38:904-909.
88. Price, A.L. Kryukov, G.V., de Bakker, P.I., Purcell, S.M., Staples, J., Wei, L.J., and Sunyaev, S.R. 2010. Pooled association tests for rare variants in exon-resequencing studies. Am. J. Hum. Genet. 86:832-838.
89. Pritchard, J.K., Stephens, M., and Donnelly, P. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945-959.
90. Psaty, B.M., O'Donnell, C.J., Gudnason, V., Lunetta, K.L., Folsom, A.R., Rotter, J.I., Uitterlinden, A.G., Harris, T.B., Witteman, J.C., Boerwinkle, E.; CHARGE Consortium. 2009. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospectivemeta-analyses of genomewide association studies from 5 cohorts. Circ. Cardiovasc. Genet. 2:73-80.
91. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., Sham, P.C. 2007. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81:559-575.
92. Reich, D.E. and Lander, E.S. 2001. On the allelic spectrum of human disease. Trends Genet. 17:502-510.
93. Risch, N.J. 2000. Searching for genetic determinants in the new millennium. Nature 405:847-856.
94. Risch, N. and Merikangas, K. 1996. The future of genetic studies of complex human diseases. Science 273:1516-1517.
95. Risch, N. and Teng, J. 1998. The relative power of family-based and case-control designs for linkage disequilibrium studies of complex human diseases I. DNA pooling. Genome Res. 8:1273-1288.
96. Rockhill, B., Newman, B., and Weinberg, C. 1998. Use and misuse of population attributable fractions. Am. J. Public Health 88:15-19.
97. Roeder, K. and Luca, D. 2009. Searching for disease susceptibility variants in structured populations. Genomics 93:1-4.
98. Rothman, K.J. and Greenland, S. 1998. Modern Epidemiology. Lippincott-Raven, Philadelphia.
99. Scheet, P. and Stephens, M. 2006. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78:629-644.
100. Schoenbach, V.J. and Rosamond, W.D. 2000. Understanding the fundamentals of epidemiologyan evolving text. University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
101. Smith, D.J. and Lusis, A.J. 2002. The allelic structure of common disease. Hum. Mol. Genet. 11:2455-2461.
102. Stenger, J.E., Xu, H., Haynes, C., Hauser, E.R., Pericak-Vance, M., Goldschmidt-Clermont, P.J., and Vance, J.M. 2005. Statistical Viewer: A tool to upload and integrate linkage and association data as plots displayed within the Ensembl genome browser. BMC Bioinformatics 6: 95.
103. Stephens, M., Smith, N.J., and Donnelly, P. 2001. A new statistical method for haplotype reconstruction from population data. Am. J. Hum. Genet. 68:978-989.
104. Stram, D.O. 2005. Software for tag single nucleotide polymorphism selection. Hum. Genomics 2:144-151.
105. Tang, H., Quertermous, T., Rodriguez, B., Kardia, S.L., Zhu, X., Brown, A., Pankow, J.S., Province, M.A., Hunt, S.C., Boerwinkle, E., Schork, N.J., and Risch, N.J. 2005. Genetic structure, self-identified race/ethnicity, and confounding in case-control association studies. Am. J. Hum. Genet. 76:268-275.
106. Tatusova, T.A., Karsch-Mizrachi, I., and Ostell, J.A. 1999. Complete genomes inWWWEntrez: Data representation and analysis. Bioinformatics 15:536-543.
107. Torgerson, D.G. et al., 2011. Meta-analysis of genome-wide association studies of asthma in ethnically diverse North American populations. Nat Genet, 43:887-892.
108. Trynka, G., Ampleford, E.J., Chiu, G.Y., et al. 2011. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat. Genet. 43:1193-1201.
109. Walter, S.D. 1976. The estimation and interpretation of attributable risk in health research. Biometrics 32:829-849.
110. Walter, S.D. 2000. Choice of effect measure for epidemiological data. J. Clin. Epidemiol. 53:931-939.
111. Wang, W.Y., Barratt, B.J., Clayton, D.G., and Todd, J.A. 2005. Genome-wide association studies: Theoretical and practical concerns. Nat. Rev. Genet. 6:109-118.
112. Wheeler, H.E., Metter, E.J., Tanaka, T., Absher, D., Higgins, J., Zahn, J.M., Wilhelmy, J., Davis, R.W., Singleton, A., Myers, R.M., Ferrucci, L., and Kim, S.K. 2009. Sequential use of transcriptional profiling, expression quantitative trait mapping, and gene association implicates MMP20 in human kidney aging. PLoS Genet 5:e1000685.
113. Willer, C.J., Li, Y., and Abecasis, G.R. 2010. METAL: Fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26:2190-2191.
114. Witte, J.S., Gauderman, W.J., and Thomas, D.C. 1999. Asymptotic bias and efficiency in case-control studies of candidate genes and gene-environment interactions: Basic family designs. Am. J. Epidemiol. 149:693-705.
115. Yi, N., Liu, N., Zhi, D., and Li, J. 2011. Hierarchical generalized linear models for multiple groups of rare and common variants: Jointly estimating group and individual-variant effects. PLoS Genet. 7:e1002382.
116. Zeger, S.L. and Liang, K.Y. 1986. Longitudinal data analysis for discrete and continuous outcomes. Biometrics 42:121-130.
117. Zhai, W., Todd, M.J., and Nielsen, R. 2004. Is haplotype block identification useful for association mapping studies? Genet. Epidemiol. 27:80-83.
118. Zhang, J. and Yu, K.F., 1998. What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA 280:1690-1691.