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Population-Based Association Studies

  • Xiaofeng ZhuEmail author
  • ShuangLin Zhang
Chapter

Abstract

Population-based association studies have been playing a major role in mapping genes affected complex diseases. The advantages of population based association studies include greater efficiency in sample recruitment and more power than family-based studies. However, population-based association mapping may lead to false positive findings if population stratification is not properly considered. In this chapter, we will review population-based association mapping methods that can control false positive rate due to population stratification. These methods include approaches. We will apply these methods to a simulated data set and illustrate the approaches. We will apply these methods to a simulated data set and illustrate the advantages and limitations of these methods.

Keywords

Markov Chain Monte Carlo Marker Locus Candidate Locus Admix Population Genomic Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

Acknowledgements

This work was supported by grant from National Human Genome Research Institute (HG003054).

References

  1. 1.
    Risch N, Merikangas K, (9–13–1996) The future of genetic studies of complex human diseases. Science 273:1516–1517Google Scholar
  2. 2.
    Risch NJ, (6–15–2000) Searching for genetic determinants in the new millennium. Nature 405:847–856Google Scholar
  3. 3.
    Clayton, (2007) Population association. In: Balding DJ, Bishop M and Cannings C (eds) Handbook of statistical genetics. pp. 939–959Google Scholar
  4. 4.
    Knowler WC, Williams RC, Pettitt DJ, Steinberg AG (1988) Gm3;5,13,14 and type 2 diabetes mellitus: an association in American Indians with genetic admixture. Am J Hum Genet 43: 520–526PubMedGoogle Scholar
  5. 5.
    Lander ES, Schork NJ, (9–30–1994) Genetic dissection of complex traits. Science 265: 2037–2048Google Scholar
  6. 6.
    Campbell CD, Ogburn EL, Lunetta KL, Lyon HN, Freedman ML, Groop LC, Altshuler D, Ardlie KG, Hirschhorn JN (2005) Demonstrating stratification in a European American population. Nat Genet 37:868–872CrossRefPubMedGoogle Scholar
  7. 7.
    Marchini J, Cardon LR, Phillips MS, Donnelly P (2004) The effects of human population structure on large genetic association studies. Nat Genet 36:512–517CrossRefPubMedGoogle Scholar
  8. 8.
    Spielman RS, McGinnis RE, Ewens WJ (1993) Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet 52:506–516PubMedGoogle Scholar
  9. 9.
    Spielman RS, Ewens WJ (1998) A sibship test for linkage in the presence of association: the sib transmission/disequilibrium test. Am J Hum Genet 62:450–458CrossRefPubMedGoogle Scholar
  10. 10.
    Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997–1004CrossRefPubMedGoogle Scholar
  11. 11.
    Pritchard JK, Rosenberg NA (1999) Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 65:220–228CrossRefPubMedGoogle Scholar
  12. 12.
    Pritchard JK, Stephens M, Rosenberg NA, Donnelly P (2000) Association mapping in structured populations. Am J Hum Genet 67:170–181CrossRefPubMedGoogle Scholar
  13. 13.
    Satten GA, Flanders WD, Yang Q (2001) Accounting for unmeasured population substructure in case-control studies of genetic association using a novel latent-class model. Am J Hum Genet 68:466–477CrossRefPubMedGoogle Scholar
  14. 14.
    Chen HS, Zhu X, Zhao H, Zhang S (2003) Qualitative semi-parametric test for genetic associations in case-control designs under structured populations. Ann Hum Genet 67:250–264CrossRefPubMedGoogle Scholar
  15. 15.
    Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909CrossRefPubMedGoogle Scholar
  16. 16.
    Zhang S, Zhu X, Zhao H (2003) On a semiparametric test to detect associations between quantitative traits and candidate genes using unrelated individuals. Genet Epidemiol 24:44–56CrossRefPubMedGoogle Scholar
  17. 17.
    Zhu X, Zhang S, Zhao H, Cooper RS (2002) Association mapping, using a mixture model for complex traits. Genet Epidemiol 23:181–196CrossRefPubMedGoogle Scholar
  18. 18.
    Smith MW, Patterson N, Lautenberger JA, Truelove AL, McDonald GJ, Waliszewska A, Kessing BD, Malasky MJ, Scafe C, Le E, De Jager PL, Mignault AA, Yi Z, De TG, Essex M, Sankale JL, Moore JH, Poku K, Phair JP, Goedert JJ, Vlahov D, Williams SM, Tishkoff SA, Winkler CA, De L, V, Woodage T, Sninsky JJ, Hafler DA, Altshuler D, Gilbert DA, O’Brien SJ, Reich D (2004) A high-density admixture map for disease gene discovery in african americans. Am J Hum Genet 74:1001–1013Google Scholar
  19. 19.
    Agresti A (2002) Categorical data analysis. Second edition, John Wiley & Son IGoogle Scholar
  20. 20.
    Chakravarti A, Buetow KH, Antonarakis SE, Waber PG, Boehm CD, Kazazian HH (1984) Nonuniform recombination within the human beta-globin gene cluster. Am J Hum Genet 36:1239–1258PubMedGoogle Scholar
  21. 21.
    Lewontin RC, Kojima KI (1960) The evolutionary dynamics of complex polymorphisms. Evolution 14:458–472CrossRefGoogle Scholar
  22. 22.
    Lewontin RC (1964) The interaction of selection and linkage. I. General Considerations; Heterotic Models. Genetics 49:49–67Google Scholar
  23. 23.
    Sasieni PD (1997) From genotypes to genes: doubling the sample size. Biometrics 53: 1253–1261CrossRefPubMedGoogle Scholar
  24. 24.
    Armitage P (1955) Test for linear trend in proportions and frequencies. Biometrics 11:375–386CrossRefGoogle Scholar
  25. 25.
    Bacanu SA, Devlin B, Roeder K (2000) The power of genomic control. Am J Hum Genet 66:1933–1944CrossRefPubMedGoogle Scholar
  26. 26.
    Devlin B, Roeder K, Wasserman L (2001) Genomic control, a new approach to genetic-based association studies. Theor Popul Biol 60:155–166CrossRefPubMedGoogle Scholar
  27. 27.
    Reich DE, Goldstein DB (2001) Detecting association in a case-control study while correcting for population stratification. Genet Epidemiol 20:4–16CrossRefPubMedGoogle Scholar
  28. 28.
    Zheng G, Freidlin B, Gastwirth JL (2006) Robust genomic control for association studies. Am J Hum Genet 78:350–356CrossRefPubMedGoogle Scholar
  29. 29.
    Zhang S, Zhao H (2001) Quantitative similarity-based association tests using population samples. Am J Hum Genet 69:601–614CrossRefPubMedGoogle Scholar
  30. 30.
    Hoggart CJ, Parra EJ, Shriver MD, Bonilla C, Kittles RA, Clayton DG, McKeigue PM (2003) Control of confounding of genetic associations in stratified populations. Am J Hum Genet 72:1492–1504CrossRefPubMedGoogle Scholar
  31. 31.
    Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587PubMedGoogle Scholar
  32. 32.
    Cavalli-Sforza LL, Menozzi P, Piazza A (1994) The history and geography of human genes. Princeton University PressGoogle Scholar
  33. 33.
    Celeux G, Govaert G (1995) Gaussian parsimonious clustering model. Pattern Recognition 28:781–793CrossRefGoogle Scholar
  34. 34.
    Severini TA, Wong W (1992) Profile likelihood and conditionally parametric models. Ann Stat 20:1768–1802CrossRefGoogle Scholar
  35. 35.
    Severini TA, Staniswalis JG (1994) Quasi-likelihood estimation in semiparametric models. J Amer Stat Assoc 89:501–511CrossRefGoogle Scholar
  36. 36.
    Simonoff JS (1996) Smoothing Methods in StatisticsGoogle Scholar
  37. 37.
    Zhu X, Li S, Cooper RS, Elston RC (2008) A unified association analysis approach for family and unrelated samples correcting for stratification. Am J Hum Genet In Press.Google Scholar
  38. 38.
    The Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678CrossRefGoogle Scholar
  39. 39.
    Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett AJ, Melzer D, Ferrucci L, Loos RJ, Barroso I, Wareham NJ, Karpe F, Owen KR, Cardon LR, Walker M, Hitman GA, Palmer CN, Doney AS, Morris AD, Smith GD, Hattersley AT, McCarthy MI (5–11–2007) A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316:889–894Google Scholar
  40. 40.
    Gudmundsson J, Sulem P, Manolescu A, Amundadottir LT, Gudbjartsson D, Helgason A, Rafnar T, Bergthorsson JT, Agnarsson BA, Baker A, Sigurdsson A, Benediktsdottir KR, Jakobsdottir M, Xu J, Blondal T, Kostic J, Sun J, Ghosh S, Stacey SN, Mouy M, Saemundsdottir J, Backman VM, Kristjansson K, Tres A, Partin AW, bers-Akkers MT, Godino-Ivan MJ, Walsh PC, Swinkels DW, Navarrete S, Isaacs SD, Aben KK, Graif T, Cashy J, Ruiz-Echarri M, Wiley KE, Suarez BK, Witjes JA, Frigge M, Ober C, Jonsson E, Einarsson GV, Mayordomo JI, Kiemeney LA, Isaacs WB, Catalona WJ, Barkardottir RB, Gulcher JR, Thorsteinsdottir U, Kong A, Stefansson K (2007) Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat Genet 39:631–637Google Scholar
  41. 41.
    Hafler DA, Compston A, Sawcer S, Lander ES, Daly MJ, De Jager PL, de Bakker PI, Gabriel SB, Mirel DB, Ivinson AJ, Pericak-Vance MA, Gregory SG, Rioux JD, McCauley JL, Haines JL, Barcellos LF, Cree B, Oksenberg JR, Hauser SL (8–30–2007) Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 357:851–862Google Scholar
  42. 42.
    Herbert A, Gerry NP, McQueen MB, Heid IM, Pfeufer A, Illig T, Wichmann HE, Meitinger T, Hunter D, Hu FB, Colditz G, Hinney A, Hebebrand J, Koberwitz K, Zhu X, Cooper R, Ardlie K, Lyon H, Hirschhorn JN, Laird NM, Lenburg ME, Lange C, Christman MF (4–14–2006) A common genetic variant is associated with adult and childhood obesity. Science 312:279–283Google Scholar
  43. 43.
    Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson BK, Isomaa B, Lettre G, Lindblad U, Lyon HN, Melander O, Newton-Cheh C, Nilsson P, Orho-Melander M, Rastam L, Speliotes EK, Taskinen MR, Tuomi T, Guiducci C, Berglund A, Carlson J, Gianniny L, Hackett R, Hall L, Holmkvist J, Laurila E, Sjogren M, Sterner M, Surti A, Svensson M, Svensson M, Tewhey R, Blumenstiel B, Parkin M, Defelice M, Barry R, Brodeur W, Camarata J, Chia N, Fava M, Gibbons J, Handsaker B, Healy C, Nguyen K, Gates C, Sougnez C, Gage D, Nizzari M, Gabriel SB, Chirn GW, Ma Q, Parikh H, Richardson D, Ricke D, Purcell S (6–1–2007) Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316:1331–1336Google Scholar
  44. 44.
    Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF, Bergman RN, Tuomilehto J, Collins FS, Boehnke M (6–1–2007) A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316:1341–1345Google Scholar
  45. 45.
    Smyth DJ, Cooper JD, Bailey R, Field S, Burren O, Smink LJ, Guja C, Ionescu-Tirgoviste C, Widmer B, Dunger DB, Savage DA, Walker NM, Clayton DG, Todd JA (2006) A genome-wide association study of nonsynonymous SNPs identifies a type 1 diabetes locus in the interferon-induced helicase (IFIH1) region. Nat Genet 38:617–619CrossRefPubMedGoogle Scholar
  46. 46.
    Zanke BW, Greenwood CM, Rangrej J, Kustra R, Tenesa A, Farrington SM, Prendergast J, Olschwang S, Chiang T, Crowdy E, Ferretti V, Laflamme P, Sundararajan S, Roumy S, Olivier JF, Robidoux F, Sladek R, Montpetit A, Campbell P, Bezieau S, O’Shea AM, Zogopoulos G, Cotterchio M, Newcomb P, McLaughlin J, Younghusband B, Green R, Green J, Porteous ME, Campbell H, Blanche H, Sahbatou M, Tubacher E, Bonaiti-Pellie C, Buecher B, Riboli E, Kury S, Chanock SJ, Potter J, Thomas G, Gallinger S, Hudson TJ, Dunlop MG (2007) Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24. Nat Genet 39:989–994CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Department of Epidemiology and BiostatisticsCase Western Reserve UniversityClevelandUSA
  2. 2.Department of Mathematical ScienceMichigan Technological UniversityHoughtonUSA

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