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
Altshuler D, Brooks LD, Chakravarti A, Collins FS, Daly MJ, Donnelly P (2005) A haplotype map of the human genome. Nature 437:1299–1320
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
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
Manolio TA, Brooks LD, Collins FS (2008) A HapMap harvest of insights into the genetics of common disease. J Clin Invest 118:1590–1605
Reich DE, Lander ES (2001) On the allelic spectrum of human disease. Trends Genet 17:502–510
Bodmer W, Bonilla C (2008) Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 40:695–701
Pearson TA, Manolio TA (2008) How to interpret a genome-wide association study. JAMA 299:1335–1344
Barrett JC, Cardon LR (2006) Evaluating coverage of genome-wide association studies. Nat Genet 38:659–662
Clark AG, Li J (2007) Conjuring SNPs to detect associations. Nat Genet 39:815–816
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
Slatkin M (2008) Linkage disequilibrium – understanding the evolutionary past and mapping the medical future. Nat Rev Genet 9:477–485
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
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
Siva N (2008) 1000 Genomes project. Nat Biotechnol 26:256
Ragoussis J (2009) Genotyping technologies for genetic research. Annu Rev Genomics Hum Genet 10:117–133
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
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
Pritchard JK, Przeworski M (2001) Linkage disequilibrium in humans: models and data. Am J Hum Genet 69:1–14
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
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
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
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959
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
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
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
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
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
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
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
Wang WY, Barratt BJ, Clayton DG, Todd JA (2005) Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 6:109–118
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
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
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
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
Craig I, Meaburn E, Butcher L, Hill L, Plomin R (2005) Single-nucleotide polymorphism genotyping in DNA pools. Methods Mol Biol 311:147–164
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
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
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
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
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
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
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
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
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
Howie BN, Carlson CS, Rieder MJ, Nickerson DA (2006) Efficient selection of tagging single-nucleotide polymorphisms in multiple populations. Hum Genet 120:58–68
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
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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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