Abstract
This chapter describes the main issues that genetic epidemiologists usually consider in the design of linkage and association studies. For linkage, we briefly consider the situation of rare highly penetrant alleles showing a disease pattern consistent with Mendelian inheritance investigated through parametric methods in large pedigrees, or with autozygosity mapping in inbred families, and we then turn our focus to the most common design, the affected sibling pair design that is of more relevance for common, complex diseases. Power and sample size calculations are provided as a function of the strength of the genetic effect being investigated. We also discuss the impact of other determinants of statistical power such as disease heterogeneity, pedigree and genotyping errors and the effect of the type and density of genetic markers. For association studies, we consider the popular case–control design for dichotomous phenotypes and we provide power and sample size calculations for one-stage and multistage designs. For candidate genes, guidelines are given on the prioritization of genetic variants, and for genome-wide association studies (GWAS) the issue of choosing an appropriate SNP array is discussed. A warning is issued regarding the danger of designing an underpowered replication study following an initial GWAS. The risk of finding spurious association due to population stratification, cryptic relatedness, and differential bias is underlined.
References
Lee-Kirsch MA, Gong ML, Schulz H, Ruschendorf F, Stein A, Pfeiffer C, Ballarini A, Gahr M, Hubner N, Linne M (2006) Familial chilblain lupus, a monogenic form of cutaneous lupus erythematosus, maps to chromosome 3p. Am J Hum Genet 79:731–737
Kruglyak L, Daly MJ, Reevedaly MP, Lander ES (1996) Parametric and nonparametric linkage analysis: a unified multipoint approach. Am J Hum Genet 58:1347–1363
Lander ES, Botstein D (1987) Homozygosity mapping–a way to map human recessive traits with the dna of inbred children. Science 236:1567–1570
Mueller RF, Bishop DT (1993) Autozygosity mapping, complex consanguinity, and autosomal recessive disorders. J Med Genet 30:798–799
Wang S, Haynes C, Barany F, Ott J (2009) Genome-wide autozygosity mapping in human populations. Genet Epidemiol 33:172–180
Boehnke M (1986) Estimating the power of a proposed linkage study–a practical computer-simulation approach. Am J Hum Genet 39:513–527
Ploughman LM, Boehnke M (1989) Estimating the power of a proposed linkage study for a complex genetic trait. Am J Hum Genet 44:543–551
Samani NJ, Burton P, Mangino M, Ball SG, Balmforth AJ, Barrett J, Bishop T, Hall A, Stribling J, De Souza P, Singh R, Ogleby J, Ridge C, Logtens E, Hopwood L, Faulkes J, Hall AS, Morrell C, Jackson BM, Barthorpe L, Burtonwood N, Dorsch M, Durham N, Forest C, Kelly N, Hall V, Lawrance R, Oldham J, Rennie E, Smith A, Thompson S, Adams S, Braund P, Clemitson JR, Bodycote C, Koekemoer A, Raleigh S, Maqbool A, Yuldasheva N, Ellis S, Mason S, Midgley L, Pleasants N, Cuthbert R, Tooze PF, Platts M, Fox J, Dixon R, Sheehan N, Scurrah K, Pickett S, Walters K, Nsengimana J, Group, The BHF Family Heart Study Research Group (2005) A genomewide linkage study of 1,933 families affected by premature coronary artery disease: British Heart Foundation (BHF) Family Heart Study. Am J Hum Genet 77:1011–1020
Whittemore AS, Tu IP (1998) Simple, robust linkage tests for affected sibs. Am J Hum Genet 62:1228–1242
Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273:1516–1517
Risch N (1990) Linkage strategies for genetically complex traits .2. The power of affected relative pairs. Am J Hum Genet 46:229–241
Lander E, Kruglyak L (1995) Genetic dissection of complex traits–guidelines for interpreting and reporting linkage results. Nat Genet 11:241–247
Bishop DT, Williamson JA (1990) The power of identity-by-state methods for linkage analysis. Am J Hum Genet 46:254–265
Risch NJ (2000) Searching for genetic determinants in the new millennium. Nature 405:847–856
Brown BD, Nsengimana J, Barrett JH, Lawrence RA, Steiner L, Cheng S, Bishop DT, Samani NJ, Ball SG, Balmforth AJ, Hall AS (2010) An evaluation of inflammatory gene polymorphisms in sibships discordant for premature coronary artery disease: the Grace-Immune study. BMC Med 8:5
Hodge SE, Vieland VJ, Greenberg DA (2002) Hlods remain powerful tools for detection of linkage in the presence of genetic heterogeneity. Am J Hum Genet 70:556–558
Whittemore AS, Halpern J (2001) Problems in the definition, interpretation, and evaluation of genetic heterogeneity. Am J Hum Genet 68:457–465
Altmuller J, Palmer LJ, Fischer G, Scherb H, Wjst M (2001) Genomewide scans of complex human diseases: true linkage is hard to find. Am J Hum Genet 69:936–950
Hauser ER, Watanabe RM, Duren WL, Bass MP, Langefeld CD, Boehnke M (2004) Ordered subset analysis in genetic linkage mapping of complex traits. Genet Epidemiol 27:53–63
Nsengimana J, Samani NJ, Hall AS, Balmforth AJ, Mangino M, Yuldasheva N, Maqbool A, Braund P, Burton P, Bishop DT, Ball SG, Barrett JH, Group, T. B. F. H. S. R (2007) Enhanced linkage of a locus on chromosome 2 to premature coronary artery disease in the absence of hypercholesterolemia. Eur J Hum Genet 15:313–319
Abecasis GR, Cherny SS, Cardon LR (2001) The impact of genotyping error on family-based analysis of quantitative traits. Eur J Hum Genet 9:130–134
Abecasis GR, Cherny SS, Cookson WOC, Cardon LR (2001) GRR: graphical representation of relationship errors. Bioinformatics 17:742–743
Pompanon F, Bonin A, Bellemain E, Taberlet P (2005) Genotyping errors: causes, consequences and solutions. Nat Rev Genet 6:847–859
Chang YPC, Kim JDO, Schwander K, Rao DC, Miller MB, Weder AB, Cooper RS, Schork NJ, Province MA, Morrison AC, Kardia SL, Quertermous T, Chakravarti A (2006) The impact of data quality on the identification of complex disease genes: experience from the family blood pressure program. Eur J Hum Genet 14:469–477
Goring HHH, Ott J (1997) Relationship estimation in affected rib pair analysis of late-onset diseases. Eur J Hum Genet 5:69–77
Boehnke M, Cox NJ (1997) Accurate inference of relationships in sib-pair linkage studies. Am J Hum Genet 61:423–429
Douglas JA, Boehnke M, Lange K (2000) A multipoint method for detecting genotyping errors and mutations in sibling-pair linkage data. Am J Hum Genet 66:1287–1297
Sun L, Wilder K, Mcpeek MS (2002) Enhanced pedigree error detection. Hum Hered 54:99–110
Sobel E, Papp JC, Lange K (2002) Detection and integration of genotyping errors in statistical genetics. Am J Hum Genet 70:496–508
Ray A, Weeks DE (2008) Relationship uncertainty linkage statistics (RULS): affected relative pair statistics that model relationship uncertainty. Genet Epidemiol 32:313–324
Hauser ER, Boehnke M, Guo SW, Risch N (1996) Affected-sib-pair interval mapping and exclusion for complex genetic traits: sampling considerations. Genet Epidemiol 13:117–137
Sawcer SJ, Maranian M, Singlehurst S, Yeo TW, Compston A, Daly MJ, De Jager PL, Gabriel S, Hafler DA, Ivinson AJ, Lander ES, Rioux JD, Walsh E, Gregory SG, Schmidt S, Pericak-Vance MA, Barcellos L, Hauser SL, Oksenberg JR, Kenealy SJ, Haines JL, Int Multiple Sclerosis Genetics, C (2004) Enhancing linkage analysis of complex disorders: an evaluation of high-density genotyping. Hum Mol Genet 13:1943–1949
Evans DM, Cardon LR (2004) Guidelines for genotyping in genomewide linkage studies: single-nucleotide-polymorphism maps versus microsatellite maps. Am J Hum Genet 75:687–692
Guo XQ, Elston RC (2000) Two-stage global search designs for linkage analysis II: including discordant relative pairs in the study. Genet Epidemiol 18:111–127
Huang QQ, Shete S, Amos CI (2004) Ignoring linkage disequilibrium among tightly linked markers induces false-positive evidence of linkage for affected sib pair analysis. Am J Hum Genet 75:1106–1112
Schaid DJ, Guenther JC, Christensen GB, Hebbring S, Rosenow C, Hilker CA, Mcdonnell SK, Cunningham JM, Slager SL, Blute ML, Thibodeau SN (2004) Comparison of microsatellites versus single-nucleotide polymorphisms in a genome linkage screen for prostate cancer-susceptibility loci. Am J Hum Genet 75:948–965
Nsengimana J, Renard H, Goldgar D (2005) Linkage analysis of complex diseases using microsatellites and single-nucleotide polymorphisms: application to alcoholism. BMC Genet 6:S10
Wilcox MA, Pugh EW, Zhang HP, Zhong XY, Levinson DE, Kennedys GC, Wijsman EM (2005) Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the coga and simulated data sets for genetic analysis workshop 14: presentation groups 1, 2, and 3. Genet Epidemiol 29:S7–S28
Boyles AL, Scott WK, Martin ER, Schmidt S, Li YJ, Ashley-Koch A, Bass MP, Schmidt M, Pericak-Vance MA, Speer MC, Hauser ER (2005) Linkage disequilibrium inflates type I error rates in multipoint linkage analysis when parental genotypes are missing. Hum Hered 59:220–227
Abecasis GR, Wigginton JE (2005) Handling marker-marker linkage disequilibrium: pedigree analysis with clustered markers. Am J Hum Genet 77:754–767
Kurbasic A, Hossjer O (2008) A general method for linkage disequilibrium correction for multipoint linkage and association. Genet Epidemiol 32:647–657
Webb EL, Sellick GS, Houlston RS (2005) Snplink: multipoint linkage analysis of densely distributed Snp data incorporating automated linkage disequilibrium removal. Bioinformatics 21:3060–3061
Fukuda Y, Nakahara Y, Date H, Takahashi Y, Goto J, Miyashita A, Kuwano R, Adachi H, Nakamura E, Tsuji S (2009) Snp hitlink: a high-throughput linkage analysis system employing dense Snp data. Bmc Bioinformatics 10:121
Selmer KK, Brandal K, Olstad OK, Birkenes B, Undlien DE, Egeland T (2009) Genome-wide linkage analysis with clustered Snp markers. J Biomol Screen 14:92–96
Fischer A, Nothnagel M, Schürmann M, Müller-Quernheim J, Schreiber S, Hofmann S (2010) A genome-wide linkage analysis in 181 German sarcoidosis families using clustered bi-allelic markers. Chest 138:151–157
Mccarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JPA, Hirschhorn JN (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9:356–369
Purcell S, Cherny SS, Sham PC (2003) Genetic power calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19:149–150
WTCCC (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678
Bishop DT, Demenais F, Iles MM, Harland M, Taylor JC, Corda E, Randerson-Moor J, Aitken JF, Avril MF, Azizi E, Bakker B, Bianchi-Scarra G, Bressac-De Paillerets B, Calista D, Cannon-Albright LA, Chin-A-Woeng T, Debniak T, Galore-Haskel G, Ghiorzo P, Gut I, Hansson J, Hocevar M, Hoiom V, Hopper JL, Ingvar C, Kanetsky PA, Kefford RF, Landi MT, Lang J, Lubinski J, Mackie R, Malvehy J, Mann GJ, Martin NG, Montgomery GW, Van Nieuwpoort FA, Novakovic S, Olsson H, Puig S, Weiss M, Van Workum W, Zelenika D, Brown KM, Goldstein AM, Gillanders EM, Boland A, Galan P, Elder DE, Gruis NA, Hayward NK, Lathrop GM, Barrett JH, Bishop JAN (2009) Genome-wide association study identifies three loci associated with melanoma risk. Nat Genet 41:920–925
Amos CI (2007) Successful design and conduct of genome-wide association studies. Hum Mol Genet 2:R220–R225
Zondervan KT, Cardon LR, Kennedy SH (2002) What makes a good case-control study? Design issues for complex traits such as endometriosis. Hum Reprod 17:1415–1423
Newton-Cheh C, Hirschhorn JN (2005) Genetic association studies of complex traits: design and analysis issues. Mutat Res 573:54–69
Spencer CCA, 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
Morton NE, Collins A (1998) Tests and estimates of allelic association in complex inheritance. Proc Natl Acad Sci U S a 95:11389–11393
Clayton DG, Walker NM, Smyth DJ, Pask R, Cooper JD, Maier LM, Smink LJ, Lam AC, Ovington NR, Stevens HE, Nutland S, Howson JMM, Faham M, Moorhead M, Jones HB, Falkowski M, Hardenbol P, Willis TD, Todd JA (2005) Population structure, differential bias and genomic control in a large-scale, case-control association study. Nat Genet 37:1243–1246
Plagnol V, Cooper JD, Todd JA, Clayton DG (2007) A method to address differential bias in genotyping in large-scale association studies. PLoS Genet 3:E74
Pluzhnikov A, Below J, Konkashbaev A, Tikhomirov A, Kistner-Griffin E, Roe C, Nicolae D, Cox Nj (2010) Spoiling the whole bunch: quality control aimed at preserving the integrity of high-throughput genotyping. Am J Hum Genet 87:123–128
Tabor HK, Risch NJ, Myers RM (2002) Candidate-gene approaches for studying complex genetic traits: practical considerations. Nat Rev Genet 3:391–397
Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6:95–108
Pettersson FH, Anderson CA, Clarke GM, Barrett JC, Cardon LR, Morris AP, Zondervan KT (2009) Marker selection for genetic case-control association studies. Nat Protoc 4:743–752
Panoutsopoulou K, Zeggini E (2009) Finding common susceptibility variants for complex disease: past, present and future. Brief Funct Genomic Proteomic 8:345–352
Das S, Forer L, Schonherr S, Sidore C, Locke AE, Kwong A, Vrieze SI, Chew EY, Levy S, Mcgue M, Schlessinger D, Stambolian D, Loh PR, Iacono WG, Swaroop A, Scott LJ, Cucca F, Kronenberg F, Boehnke M, Abecasis GR, Fuchsberger C (2016) Next-generation genotype imputation service and methods. Nat Genet 48:1284–1287
Pahl R, Schafer H, Muller HH (2009) Optimal multistage designs general framework for efficient genome-wide association studies. Biostatistics 10:297–309
Skol AD, Scott LJ, Abecasis GR, Boehnke M (2006) Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 38:209–213
Bowden J, Dudbridge F (2009) Unbiased estimation and inference for replicated associations following a genome scan. Genet Epidemiol 33(5):406–418
Garner C (2007) Upward bias in odds ratio estimates from genome-wide association studies. Genet Epidemiol 31:288–295
Goldgar D, Venne V, Conner T, Buys S (2007) BRCA phenocopies or ascertainment bias? J Med Genet 44:10–15
Terwilliger JD, Weiss KM (2003) Confounding, ascertainment bias, and the blind quest for a genetic ‘fountain of youth’. Ann Med 35:532–544
Astle W, Balding DJ (2009) Population structure and cryptic relatedness in genetic association studies. Stat Sci 24:451–471
Voight BF, Pritchard JK (2005) Confounding from cryptic relatedness in case-control association studies. PLoS Genet 1:302–311
Marchini J, Cardon LR, Phillips MS, Donnelly P (2004) The effects of human population structure on large genetic association studies. Nat Genet 36:512–517
Choi Y, Wijsman EM, Weir BS (2009) Case-control association testing in the presence of unknown relationships. Genet Epidemiol 33:668–678
Slager SL, Schaid DJ (2001) Evaluation of candidate genes in case-control studies: a statistical method to account for related subjects. Am J Hum Genet 68:1457–1462
Bourgain C, Hoffjan S, Nicolae R, Newman D, Steiner L, Walker K, Reynolds R, Ober C, Mcpeek MS (2003) Novel case-control test in a founder population identifies p-selectin as an atopy-susceptibility locus. Am J Hum Genet 73:612–626
Sillanpaa M (2010) Overview of techniques to account for confounding due to population stratification and cryptic relatedness in genomic data association analyses. Heredity 106(4):511–519. doi:10.1038/Hdy.2010.91
Price AL, Zaitlen NA, Reich D, Patterson N (2010) New approaches to population stratification in genome-wide association studies. Nat Rev Genet 11:459–463
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
Laird NM, Lange C (2009) The role of family-based designs in genome-wide association studies. Statist Sci 24:388–397
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Nsengimana, J., Bishop, D.T. (2017). Design Considerations for Genetic Linkage and Association Studies. In: Elston, R. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 1666. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7274-6_13
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