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Technological Issues and Experimental Design of Gene Association Studies

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Disease Gene Identification

Part of the book series: Methods in Molecular Biology ((MIMB,volume 700))

Abstract

Genome-wide association studies (GWAS), in which thousands of single-nucleotide polymorphisms (SNPs) spanning the genome are genotyped in individuals who are phenotypically well characterized, ­currently represent the most popular strategy for identifying gene regions associated with common ­diseases and related quantitative traits. Improvements in technology and throughput capability, development of powerful statistical tools, and more widespread acceptance of pooling-based genotyping approaches have led to greater utilization of GWAS in human genetics research. However, important considerations for optimal experimental design, including selection of the most appropriate genotyping platform, can enhance the utility of the approach even further. This chapter reviews experimental and technological issues that may affect the success of GWAS findings and proposes strategies for developing the most comprehensive, logical, and cost-effective approaches for genotyping given the population of interest.

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References

  1. Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P (2005) A haplotype map of the human genome. Nature 437:1299–1320

    Article  Google Scholar 

  2. Frazer KA, Ballinger DG, Cox DR, Hinds DA, Stuve LL, Gibbs RA et al (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449:851–861

    Article  PubMed  CAS  Google Scholar 

  3. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JP et al (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9:356–369

    Article  PubMed  CAS  Google Scholar 

  4. Manolio TA, Brooks LD, Collins FS (2008) A HapMap harvest of insights into the genetics of common disease. J Clin Invest 118:1590–1605

    Article  PubMed  CAS  Google Scholar 

  5. Reich DE, Lander ES (2001) On the allelic spectrum of human disease. Trends Genet 17:502–510

    Article  PubMed  CAS  Google Scholar 

  6. Bodmer W, Bonilla C (2008) Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 40:695–701

    Article  PubMed  CAS  Google Scholar 

  7. Pearson TA, Manolio TA (2008) How to interpret a genome-wide association study. JAMA 299:1335–1344

    Article  PubMed  CAS  Google Scholar 

  8. Barrett JC, Cardon LR (2006) Evaluating coverage of genome-wide association studies. Nat Genet 38:659–662

    Article  PubMed  CAS  Google Scholar 

  9. Clark AG, Li J (2007) Conjuring SNPs to detect associations. Nat Genet 39:815–816

    Article  PubMed  CAS  Google Scholar 

  10. Pe’er I, de Bakker PI, Maller J, Yelensky R, Altshuler D, Daly MJ (2006) Evaluating and improving power in whole-genome association studies using fixed marker sets. Nat Genet 38:663–667

    Article  PubMed  Google Scholar 

  11. Slatkin M (2008) Linkage disequilibrium – understanding the evolutionary past and mapping the medical future. Nat Rev Genet 9:477–485

    Article  PubMed  CAS  Google Scholar 

  12. Hsueh WC, Mitchell BD, Aburomia R, Pollin T, Sakul H, Gelder Ehm M et al (2000) Diabetes in the Old Order Amish: characterization and heritability analysis of the Amish Family Diabetes Study. Diabetes Care 23:595–601

    Article  PubMed  CAS  Google Scholar 

  13. Millis MP, Bowen D, Kingsley C, Watanabe RM, Wolford JK (2007) Variants in the plasmacytoma variant translocation gene (PVT1) are associated with end-stage renal disease attributed to type 1 diabetes. Diabetes 56:3027–3032

    Article  PubMed  CAS  Google Scholar 

  14. Siva N (2008) 1000 Genomes project. Nat Biotechnol 26:256

    PubMed  Google Scholar 

  15. Ragoussis J (2009) Genotyping technologies for genetic research. Annu Rev Genomics Hum Genet 10:117–133

    Article  PubMed  CAS  Google Scholar 

  16. Barnes C, Plagnol V, Fitzgerald T, Redon R, Marchini J, Clayton D et al (2008) A robust statistical method for case-control association testing with copy number variation. Nat Genet 40:1245–1252

    Article  PubMed  CAS  Google Scholar 

  17. Li C, Li M, Long JR, Cai Q, Zheng W (2008) Evaluating cost efficiency of SNP chips in genome-wide association studies. Genet Epidemiol 32:387–395

    Article  PubMed  Google Scholar 

  18. Pritchard JK, Przeworski M (2001) Linkage disequilibrium in humans: models and data. Am J Hum Genet 69:1–14

    Article  PubMed  CAS  Google Scholar 

  19. Conrad DF, Jakobsson M, Coop G, Wen X, Wall JD, Rosenberg NA et al (2006) A worldwide survey of haplotype variation and linkage disequilibrium in the human genome. Nat Genet 38:1251–1260

    Article  PubMed  CAS  Google Scholar 

  20. 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 38:904–909

    Article  PubMed  CAS  Google Scholar 

  21. Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587

    PubMed  CAS  Google Scholar 

  22. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    PubMed  CAS  Google Scholar 

  23. Cooper GM, Zerr T, Kidd JM, Eichler EE, Nickerson DA (2008) Systematic assessment of copy number variant detection via genome-wide SNP genotyping. Nat Genet 40:1199–1203

    Article  PubMed  CAS  Google Scholar 

  24. Korn JM, Kuruvilla FG, McCarroll SA, Wysoker A, Nemesh J, Cawley S et al (2008) Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat Genet 40:1253–1260

    Article  PubMed  CAS  Google Scholar 

  25. McCarroll SA, Kuruvilla FG, Korn JM, Cawley S, Nemesh J, Wysoker A et al (2008) Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat Genet 40:1166–1174

    Article  PubMed  CAS  Google Scholar 

  26. Diskin SJ, Li M, Hou C, Yang S, Glessner J, Hakonarson H et al (2008) Adjustment of genomic waves in signal intensities from whole-genome SNP genotyping platforms. Nucleic Acids Res 36:e126

    Article  PubMed  Google Scholar 

  27. Spencer CC, Su Z, Donnelly P, Marchini J (2009) Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip. PLoS Genet 5:e1000477

    Article  PubMed  Google Scholar 

  28. Wang K, Chen Z, Tadesse MG, Glessner J, Grant SF, Hakonarson H et al (2008) Modeling genetic inheritance of copy number variations. Nucleic Acids Res 36:e138

    Article  PubMed  Google Scholar 

  29. Wang K, Li M, Hadley D, Liu R, Glessner J, Grant SF et al (2007) PennCNV: an integrated hidden Markov model designed for high-resolution copy number variation detection in whole-genome SNP genotyping data. Genome Res 17:1665–1674

    Article  PubMed  CAS  Google Scholar 

  30. Wang WY, Barratt BJ, Clayton DG, Todd JA (2005) Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 6:109–118

    Article  PubMed  CAS  Google Scholar 

  31. Hanson RL, Craig DW, Millis MP, Yeatts KA, Kobes S, Pearson JV et al (2007) Identification of PVT1 as a candidate gene for end-stage renal disease in type 2 diabetes using a pooling-based genome-wide single nucleotide polymorphism association study. Diabetes 56:975–983

    Article  PubMed  CAS  Google Scholar 

  32. Brohede J, Dunne R, McKay JD, Hannan GN (2005) PPC: an algorithm for accurate estimation of SNP allele frequencies in small equimolar pools of DNA using data from high density microarrays. Nucleic Acids Res 33:e142

    Article  PubMed  Google Scholar 

  33. Meaburn E, Butcher LM, Schalkwyk LC, Plomin R (2006) Genotyping pooled DNA using 100K SNP microarrays: a step towards genomewide association scans. Nucleic Acids Res 34:e27

    Article  PubMed  Google Scholar 

  34. Meaburn E, Butcher LM, Liu L, Fernandes C, Hansen V, Al-Chalabi A et al (2005) Genotyping DNA pools on microarrays: tackling the QTL problem of large samples and large numbers of SNPs. BMC Genomics 6:52

    Article  PubMed  Google Scholar 

  35. Craig I, Meaburn E, Butcher L, Hill L, Plomin R (2005) Single-nucleotide polymorphism genotyping in DNA pools. Methods Mol Biol 311:147–164

    PubMed  CAS  Google Scholar 

  36. Kirov G, Nikolov I, Georgieva L, Moskvina V, Owen MJ, O’Donovan MC (2006) Pooled DNA genotyping on Affymetrix SNP genotyping arrays. BMC Genomics 7:27

    Article  PubMed  Google Scholar 

  37. Craig I, Plomin R (2006) Quantitative trait loci for IQ and other complex traits: single-nucleotide polymorphism genotyping using pooled DNA and microarrays. Genes Brain Behav 5(Suppl 1):32–37

    PubMed  CAS  Google Scholar 

  38. Liu QR, Drgon T, Walther D, Johnson C, Poleskaya O, Hess J et al (2005) Pooled association genome scanning: validation and use to identify addiction vulnerability loci in two samples. Proc Natl Acad Sci USA 102:11864–11869

    Article  PubMed  CAS  Google Scholar 

  39. Butcher LM, Meaburn E, Dale PS, Sham P, Schalkwyk LC, Craig IW et al (2005) Association analysis of mild mental impairment using DNA pooling to screen 432 brain-expressed single-nucleotide polymorphisms. Mol Psychiatry 10:384–392

    Article  PubMed  CAS  Google Scholar 

  40. Butcher LM, Meaburn E, Knight J, Sham PC, Schalkwyk LC, Craig IW et al (2005) SNPs, microarrays and pooled DNA: identification of four loci associated with mild mental impairment in a sample of 6000 children. Hum Mol Genet 14:1315–1325

    Article  PubMed  CAS  Google Scholar 

  41. Brown KM, Macgregor S, Montgomery GW, Craig DW, Zhao ZZ, Iyadurai K et al (2008) Common sequence variants on 20q11.22 confer melanoma susceptibility. Nat Genet 40:838–840

    Article  PubMed  CAS  Google Scholar 

  42. Pearson JV, Huentelman MJ, Halperin RF, Tembe WD, Melquist S, Homer N et al (2007) Identification of the genetic basis for complex disorders by use of pooling-based genomewide single-nucleotide-polymorphism association studies. Am J Hum Genet 80:126–139

    Article  PubMed  CAS  Google Scholar 

  43. de Bakker PI, Yelensky R, Pe’er I, Gabriel SB, Daly MJ, Altshuler D (2005) Efficiency and power in genetic association studies. Nat Genet 37:1217–1223

    Article  PubMed  Google Scholar 

  44. Carlson CS, Eberle MA, Rieder MJ, Yi Q, Kruglyak L, Nickerson DA (2004) Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. Am J Hum Genet 74:106–120

    Article  PubMed  CAS  Google Scholar 

  45. Howie BN, Carlson CS, Rieder MJ, Nickerson DA (2006) Efficient selection of tagging single-nucleotide polymorphisms in multiple populations. Hum Genet 120:58–68

    Article  PubMed  Google Scholar 

  46. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B et al (2002) The structure of haplotype blocks in the human genome. Science 296:2225–2229

    Article  PubMed  CAS  Google Scholar 

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Correspondence to Johanna K. DiStefano .

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DiStefano, J.K., Taverna, D.M. (2011). Technological Issues and Experimental Design of Gene Association Studies. In: DiStefano, J. (eds) Disease Gene Identification. Methods in Molecular Biology, vol 700. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61737-954-3_1

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  • DOI: https://doi.org/10.1007/978-1-61737-954-3_1

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-61737-953-6

  • Online ISBN: 978-1-61737-954-3

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