Skip to main content
SpringerLink
Log in

Methods for Association Studies

  • Chapter
  • First Online:
Human Population Genomics

Abstract

Association studies are a key approach to evaluating the relationship between genetic factors and phenotypes or traits. This chapter presents general methods for genetic association studies in unrelated humans. Topics covered include types of association studies, study design considerations, measurement of genetic information, and analytical techniques. This material provides readers with background for interpreting results from association studies and for undertaking their own studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 84.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Altshuler D, Daly MJ, Lander ES (2008) Genetic mapping in human disease. Science 322:881–888

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Aragaki CC, Greenland S, Probst-Hensch N, Haile RW (1997) Hierarchical modeling of gene-environment interactions: estimating NAT2 genotype-specific dietary effects on adenomatous polyps. Cancer Epidemiol Biomark Prev 6:307–314

    CAS  Google Scholar 

  • Asimit J, Zeggini E (2010) Rare variant association analysis methods for complex traits. Annu Rev Genet 44:293–308

    Article  CAS  PubMed  Google Scholar 

  • Asimit JL, Day-Williams AG, Morris AP, Zeggini E (2012) ARIEL and AMELIA: testing for an accumulation of rare variants using next-generation sequencing data. Hum Hered 73:84–94

    Article  CAS  PubMed  Google Scholar 

  • Aulchenko YS, Ripke S, Isaacs A, van Duijn CM (2007) GenABEL: an R library for genome-wide association analysis. Bioinformatics 23:1294–1296

    Article  CAS  PubMed  Google Scholar 

  • Barbeira AN, Dickinson SP, Bonazzola R et al (2018) Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun 9:1825

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B 57:289–300

    Google Scholar 

  • Bhattacharjee S, Rajaraman P, Jacobs KB et al (2012) A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits. Am J Hum Genet 90:821–835

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Botstein D, White RL, Skolnick M, Davis RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet 32:314–331

    CAS  PubMed  PubMed Central  Google Scholar 

  • Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J (2017) A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med 36:1783–1802

    Article  PubMed  PubMed Central  Google Scholar 

  • Brzyski D, Peterson CB, Sobczyk P, Candes EJ, Bogdan M, Sabatti C (2017) Controlling the rate of GWAS false discoveries. Genetics 205:61–75

    Article  PubMed  Google Scholar 

  • Bulik-Sullivan B, Loh P-R, Finucane H et al (2015) LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet 47:291–295

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Buniello A, MacArthur JAL, Cerezo M et al (2019) The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47:D1005–D1012

    Article  CAS  PubMed  Google Scholar 

  • Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37:658–665

    Article  PubMed  PubMed Central  Google Scholar 

  • Burton PR, Clayton DG, Cardon LR et al (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678

    Article  CAS  Google Scholar 

  • Cantor RM, Lange K, Sinsheimer JS (2010) Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet 86:6–22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cardin NJ, Mefford JA, Witte JS (2012) Joint association testing of common and rare genetic variants using hierarchical modeling. Genet Epidemiol 36:642–651

    Article  PubMed  PubMed Central  Google Scholar 

  • Carlson CS, Matise TC, North KE et al (2013) Generalization and dilution of association results from European GWAS in populations of non-European ancestry: the PAGE study. PLoS Biol 11:e1001661

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chanock SJ, Manolio T, Boehnke M et al (2007) Replicating genotype-phenotype associations. Nature 447:655–660

    Article  CAS  PubMed  Google Scholar 

  • Chen GK, Witte JS (2007) Enriching the analysis of genomewide association studies with hierarchical modeling. Am J Hum Genet 81:397–404

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Chen H, Lumley T, Brody J et al (2014) Sequence kernel association test for survival traits. Genet Epidemiol 38:191–197

    Article  PubMed  PubMed Central  Google Scholar 

  • Chen H, Huffman JE, Brody JA et al (2019) Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole-genome sequencing studies. Am J Hum Genet 104:260–274

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Choi M, Scholl UI, Ji W et al (2009) Genetic diagnosis by whole exome capture and massively parallel DNA sequencing. Proc Natl Acad Sci USA 106:19096–19101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Clarke GM, Anderson CA, Pettersson FH, Cardon LR, Morris AP, Zondervan KT (2011) Basic statistical analysis in genetic case-control studies. Nat Protoc 6:121–133

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Claussnitzer M, Cho JH, Collins R et al (2020) A brief history of human disease genetics. Nature 577:179–189

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Clayton DG (2009) Sex chromosomes and genetic association studies. Genome Med 1:110

    Article  PubMed  PubMed Central  Google Scholar 

  • Cordell HJ, Clayton DG (2005) Genetic association studies. Lancet 366:1121–1131

    Article  PubMed  Google Scholar 

  • Daly MJ, Rioux JD, Schaffner SF, Hudson TJ, Lander ES (2001) High-resolution haplotype structure in the human genome. Nat Genet 29:229–232

    Article  CAS  PubMed  Google Scholar 

  • Devlin B, Roeder K (1999) Genomic control for association studies. Biometrics 55:997–1004

    Article  CAS  PubMed  Google Scholar 

  • Dick DM, Agrawal A, Keller MC et al (2015) Candidate gene-environment interaction research: reflections and recommendations. Perspect Psychol Sci 10:37–59

    Article  PubMed  PubMed Central  Google Scholar 

  • Dinu I, Potter JD, Mueller T et al (2009) Gene-set analysis and reduction. Brief Bioinform 10:24–34

    Article  CAS  PubMed  Google Scholar 

  • Dutta D, Scott L, Boehnke M, Lee S (2019) Multi-SKAT: general framework to test for rare-variant association with multiple phenotypes. Genet Epidemiol 43:4–23

    Article  PubMed  Google Scholar 

  • Elbers CC, van der Schouw YT, Wijmenga C, Onland-Moret NC (2009a) Comment on: Perry et al. (2009) interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach. Diabetes 58:1463–1467. e9; author reply e10

    Google Scholar 

  • Elbers CC, van Eijk KR, Franke L et al (2009b) Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol 33:419–431

    Article  PubMed  Google Scholar 

  • Evangelou E, Ioannidis JPA (2013) Meta-analysis methods for genome-wide association studies and beyond. Nat Rev Genet 14:379–389

    Article  CAS  PubMed  Google Scholar 

  • Evangelou M, Dudbridge F, Wernisch L (2014) Two novel pathway analysis methods based on a hierarchical model. Bioinformatics 30:690–697

    Article  CAS  PubMed  Google Scholar 

  • Evangelou E, Warren HR, Mosen-Ansorena D et al (2018) Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet 50:1412–1425

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Fehringer G, Kraft P, Pharoah PD et al (2016) Cross-cancer genome-wide analysis of lung, ovary, breast, prostate, and colorectal cancer reveals novel pleiotropic associations. Cancer Res 76:5103–5114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Frazer KA, Ballinger DG, Cox DR et al (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449:851–861

    Article  CAS  PubMed  Google Scholar 

  • Frazer KA, Murray SS, Schork NJ, Topol EJ (2009) Human genetic variation and its contribution to complex traits. Nat Rev Genet 10:241–251

    Article  CAS  PubMed  Google Scholar 

  • Gabriel SB, Schaffner SF, Nguyen H et al (2002) The structure of haplotype blocks in the human genome. Science 296:2225–2229

    Article  CAS  PubMed  Google Scholar 

  • Gamazon ER, Wheeler HE, Shah KP et al (2015) A gene-based association method for mapping traits using reference transcriptome data. Nat Genet 47:1091–1098

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Genovese CR, Roeder K, Wasserman L (2006) False discovery control with p-value weighting. Biometrika 93:509–524

    Article  Google Scholar 

  • Gilbert W, Maxam A (1973) The nucleotide sequence of the lac operator. Proc Natl Acad Sci USA 70:3581–3584

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gnirke A, Melnikov A, Maguire J et al (2009) Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nat Biotechnol 27:182–189

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Goring HH, Terwilliger JD, Blangero J (2001) Large upward bias in estimation of locus-specific effects from genomewide scans. Am J Hum Genet 69:1357–1369

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gorlov IP, Gorlova OY, Sunyaev SR, Spitz MR, Amos CI (2008) Shifting paradigm of association studies: value of rare single-nucleotide polymorphisms. Am J Hum Genet 82:100–112

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gray R, Wheatley K (1991) How to avoid bias when comparing bone marrow transplantation with chemotherapy. Bone Marrow Transplant 7(Suppl 3):9–12

    PubMed  Google Scholar 

  • Greenwood CM, Rangrej J, Sun L (2007) Optimal selection of markers for validation or replication from genome-wide association studies. Genet Epidemiol 31:396–407

    Article  PubMed  Google Scholar 

  • Guey LT, Kravic J, Melander O et al (2011) Power in the phenotypic extremes: a simulation study of power in discovery and replication of rare variants. Genet Epidemiol 35:236–246

    PubMed  Google Scholar 

  • Gusev A, Ko A, Shi H et al (2016) Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet 48:245–252

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hahn LW, Ritchie MD, Moore JH (2003) Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19:376–382

    Article  CAS  PubMed  Google Scholar 

  • Han B, Eskin E (2011) Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am J Hum Genet 88:586–598

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Haycock PC, Burgess S, Wade KH, Bowden J, Relton C, Davey SG (2016) Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies. Am J Clin Nutr 103:965–978

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hindorff LA, Sethupathy P, Junkins HA et al (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA 106:9362–9367

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hirschhorn JN, Altshuler D (2002) Once and again-issues surrounding replication in genetic association studies. J Clin Endocrinol Metab 87:4438–4441

    Article  CAS  PubMed  Google Scholar 

  • Hirschhorn JN, Lohmueller K, Byrne E, Hirschhorn K (2002) A comprehensive review of genetic association studies. Genet Med 4:45–61

    Article  CAS  PubMed  Google Scholar 

  • Ho LA, Lange EM (2010) Using public control genotype data to increase power and decrease cost of case–control genetic association studies. Hum Genet 128:597–608

    Article  PubMed  PubMed Central  Google Scholar 

  • Hodges E, Xuan Z, Balija V et al (2007) Genome-wide in situ exon capture for selective resequencing. Nat Genet 39:1522–1527

    Article  CAS  PubMed  Google Scholar 

  • Hoffmann TJ, Kvale MN, Hesselson SE et al (2011a) Next generation genome-wide association tool: design and coverage of a high-throughput European-optimized SNP array. Genomics 98:79–89

    Article  CAS  PubMed  Google Scholar 

  • Hoffmann TJ, Zhan Y, Kvale MN et al (2011b) Design and coverage of high throughput genotyping arrays optimized for individuals of East Asian, African American, and Latino race/ethnicity using imputation and a novel hybrid SNP selection algorithm. Genomics 98:422–430

    Article  CAS  PubMed  Google Scholar 

  • Hoffmann TJ, Van Den Eeden SK, Sakoda LC et al (2015) A large multiethnic genome-wide association study of prostate cancer identifies novel risk variants and substantial ethnic differences. Cancer Discov 5:878–891

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hoffmann TJ, Passarelli MN, Graff RE et al (2017) Genome-wide association study of prostate-specific antigen levels identifies novel loci independent of prostate cancer. Nat Commun 8:14248

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hong MG, Pawitan Y, Magnusson PK, Prince JA (2009) Strategies and issues in the detection of pathway enrichment in genome-wide association studies. Hum Genet 126:289–301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hong J, Lunetta KL, Cupples LA, Dupuis J, Liu CT (2016) Evaluation of a two-stage approach in trans-ethnic meta-analysis in genome-wide association studies. Genet Epidemiol 40:284–292

    Article  PubMed  PubMed Central  Google Scholar 

  • Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44:955–959

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Huang BE, Lin DY (2007) Efficient association mapping of quantitative trait loci with selective genotyping. Am J Hum Genet 80:567–576

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Huang L, Li Y, Singleton AB et al (2009) Genotype-imputation accuracy across worldwide human populations. Am J Hum Genet 84:235–250

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Huang QQ, Ritchie SC, Brozynska M, Inouye M (2018) Power, false discovery rate and Winner’s curse in eQTL studies. Nucleic Acids Res 46:e133

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Huedo-Medina TB, Sanchez-Meca J, Marin-Martinez F, Botella J (2006) Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods 11:193–206

    Article  PubMed  Google Scholar 

  • International HapMap Consortium (2003) The International HapMap Project. Nature 426:789–796

    Google Scholar 

  • International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437:1299–1320

    Google Scholar 

  • Ioannidis JP (2005) Why most published research findings are false. PLoS Med 2:e124

    Article  PubMed  PubMed Central  Google Scholar 

  • Ioannidis JP (2006) Common genetic variants for breast cancer: 32 largely refuted candidates and larger prospects. J Natl Cancer Inst 98:1350–1353

    Article  CAS  PubMed  Google Scholar 

  • Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG (2001) Replication validity of genetic association studies. Nat Genet 29:306–309

    Article  CAS  PubMed  Google Scholar 

  • Ionita-Laza I, Lee S, Makarov V, Buxbaum JD, Lin X (2013) Sequence kernel association tests for the combined effect of rare and common variants. Am J Hum Genet 92:841–853

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jorgenson E, Witte JS (2006) Coverage and power in genomewide association studies. Am J Hum Genet 78:884–888

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Karlsson Linner R, Biroli P, Kong E et al (2019) Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences. Nat Genet 51:245–257

    Article  CAS  PubMed  Google Scholar 

  • Katan MB (1986) Apolipoprotein E isoforms, serum cholesterol, and cancer. Lancet 1:507–508

    Article  CAS  PubMed  Google Scholar 

  • Kraft P, Wacholder S, Cornelis MC et al (2009) Beyond odds ratios—communicating disease risk based on genetic profiles. Nat Rev Genet 10:264–269

    Article  CAS  PubMed  Google Scholar 

  • Lander ES, Linton LM, Birren B et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921

    Article  CAS  PubMed  Google Scholar 

  • Larson NB, McDonnell S, Cannon Albright L et al (2017) gsSKAT: rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels. Genet Epidemiol 41:297–308

    Article  PubMed  PubMed Central  Google Scholar 

  • Lee S, Emond MJ, Bamshad MJ et al (2012) Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet 91:224–237

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lettre G, Lange C, Hirschhorn JN (2007) Genetic model testing and statistical power in population-based association studies of quantitative traits. Genet Epidemiol 31:358–362

    Article  PubMed  Google Scholar 

  • Li B, Leal SM (2008) Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet 83:311–321

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li Y, Willer C, Sanna S, Abecasis G (2009) Genotype imputation. Annu Rev Genomics Hum Genet 10:387–406

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li R, Conti DV, Diaz-Sanchez D, Gilliland F, Thomas DC (2012) Joint analysis for integrating two related studies of different data types and different study designs using hierarchical modeling approaches. Hum Hered 74:83–96

    Article  CAS  PubMed  Google Scholar 

  • Li Z, Li X, Liu Y et al (2019) Dynamic scan procedure for detecting rare-variant association regions in whole-genome sequencing studies. Am J Hum Genet 104:802–814

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lindquist KJ, Jorgenson E, Hoffmann TJ, Witte JS (2013) The impact of improved microarray coverage and larger sample sizes on future genome-wide association studies. Genet Epidemiol 37:383–392

    Article  PubMed  PubMed Central  Google Scholar 

  • Liu M, Jiang Y, Wedow R et al (2019) Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 51:237–244

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lloyd-Jones LR, Robinson MR, Yang J, Visscher PM (2018) Transformation of summary statistics from linear mixed model association on all-or-none traits to odds ratio. Genetics 208:1397–1408

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Loh PR, Tucker G, Bulik-Sullivan BK et al (2015) Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat Genet 47:284–290

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN (2003) Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet 33:177–182

    Article  CAS  PubMed  Google Scholar 

  • Luca D, Ringquist S, Klei L et al (2008) On the use of general control samples for genome-wide association studies: genetic matching highlights causal variants. Am J Hum Genet 82:453–463

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Madsen BE, Browning SR (2009) A groupwise association test for rare mutations using a weighted sum statistic. PLoS Genet 5:e1000384

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Magi R, Morris AP (2010) GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11:288

    Article  PubMed  PubMed Central  Google Scholar 

  • Maher B (2008) Personal genomes: the case of the missing heritability. Nature 456:18–21

    Article  CAS  PubMed  Google Scholar 

  • Manichaikul A, Mychaleckyj JC, Rich SS, Daly K, Sale M, Chen WM (2010) Robust relationship inference in genome-wide association studies. Bioinformatics 26:2867–2873

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • 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  CAS  PubMed  PubMed Central  Google Scholar 

  • Marchini J, Howie B (2008) Comparing algorithms for genotype imputation. Am J Hum Genet 83:535–539. author reply 539-540

    Article  PubMed  PubMed Central  Google Scholar 

  • Marchini J, Howie B, Myers S, McVean G, Donnelly P (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39:906–913

    Article  CAS  PubMed  Google Scholar 

  • Marquez A, Kerick M, Zhernakova A et al (2018) Meta-analysis of immunochip data of four autoimmune diseases reveals novel single-disease and cross-phenotype associations. Genome Med 10:97

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mavaddat N, Michailidou K, Dennis J et al (2019) Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am J Hum Genet 104:21–34

    Article  CAS  PubMed  Google Scholar 

  • McAllister K, Mechanic LE, Amos C et al (2017) Current challenges and new opportunities for gene-environment interaction studies of complex diseases. Am J Epidemiol 186:753–761

    Article  PubMed  PubMed Central  Google Scholar 

  • Mefford J, Witte JS (2012) The covariate’s dilemma. PLoS Genet 8:e1003096

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mitchell BD, Fornage M, McArdle PF et al (2014) Using previously genotyped controls in genome-wide association studies (GWAS): application to the stroke genetics network (SiGN). Front Genet 5

    Google Scholar 

  • Morgenthaler S, Thilly WG (2007) A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat Res 615:28–56

    Article  CAS  PubMed  Google Scholar 

  • Morris AP (2011) Transethnic meta-analysis of genomewide association studies. Genet Epidemiol 35:809–822

    Article  PubMed  PubMed Central  Google Scholar 

  • Morris AP, Zeggini E (2010) An evaluation of statistical approaches to rare variant analysis in genetic association studies. Genet Epidemiol 34:188–193

    Article  PubMed  Google Scholar 

  • Motsinger AA, Ritchie MD (2006) Multifactor dimensionality reduction: an analysis strategy for modelling and detecting gene-gene interactions in human genetics and pharmacogenomics studies. Hum Genomics 2:318–328

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mutsuddi M, Morris DW, Waggoner SG, Daly MJ, Scolnick EM, Sklar P (2006) Analysis of high-resolution HapMap of DTNBP1 (Dysbindin) suggests no consistency between reported common variant associations and schizophrenia. Am J Hum Genet 79:903–909

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nam D, Kim SY (2008) Gene-set approach for expression pattern analysis. Brief Bioinform 9:189–197

    Article  PubMed  Google Scholar 

  • Neale BM, Rivas MA, Voight BF et al (2011) Testing for an unusual distribution of rare variants. PLoS Genet 7:e1001322

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nelson SC, Doheny KF, Pugh EW et al (2013) Imputation-based genomic coverage assessments of current human genotyping arrays. G3 (Bethesda) 3:1795–1807

    Article  CAS  Google Scholar 

  • Ng SB, Turner EH, Robertson PD et al (2009) Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461:272–276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nolte IM, van der Most PJ, Alizadeh BZ et al (2017) Missing heritability: is the gap closing? An analysis of 32 complex traits in the lifelines cohort study. Eur J Hum Genet 25:877–885

    Article  PubMed  PubMed Central  Google Scholar 

  • Nyholt DR (2004) A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet 74:765–769

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Paltoo DN, Rodriguez LL, Feolo M et al (2014) Data use under the NIH GWAS data sharing policy and future directions. Nat Genet 46:934–938

    Article  PubMed  CAS  Google Scholar 

  • Pan W (2009) Asymptotic tests of association with multiple SNPs in linkage disequilibrium. Genet Epidemiol 33:497–507

    Article  PubMed  PubMed Central  Google Scholar 

  • Panagiotou OA, Willer CJ, Hirschhorn JN, Ioannidis JPA (2013) The power of meta-analysis in genome-wide association studies. Annu Rev Genomics Hum Genet 14:441–465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pierce BL, Burgess S (2013) Efficient design for Mendelian randomization studies: subsample and 2-sample instrumental variable estimators. Am J Epidemiol 178:1177–1184

    Article  PubMed  PubMed Central  Google Scholar 

  • Pirinen M, Donnelly P, Spencer CC (2012) Including known covariates can reduce power to detect genetic effects in case-control studies. Nat Genet 44:848–851

    Article  CAS  PubMed  Google Scholar 

  • Piro RM, Di Cunto F (2012) Computational approaches to disease-gene prediction: rationale, classification and successes: computational disease-gene prediction. FEBS J 279:678–696

    Article  CAS  PubMed  Google Scholar 

  • 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  CAS  PubMed  Google Scholar 

  • 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pritchard JK, Rosenberg NA (1999) Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 65:220–228

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rava M, Ahmed I, Demenais F, Sanchez M, Tubert-Bitter P, Nadif R (2013) Selection of genes for gene-environment interaction studies: a candidate pathway-based strategy using asthma as an example. Environ Health 12:56

    Article  PubMed  PubMed Central  Google Scholar 

  • Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273:1516–1517

    Article  CAS  PubMed  Google Scholar 

  • Ritchie MD, Hahn LW, Roodi N et al (2001) Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet 69:138–147

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Ritchie MD, Hahn LW, Moore JH (2003) Power of multifactor dimensionality reduction for detecting gene-gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity. Genet Epidemiol 24:150–157

    Article  PubMed  Google Scholar 

  • Roeder K, Wasserman L (2009) Genome-wide significance levels and weighted hypothesis testing. Stat Sci 24:398–413

    Article  PubMed  PubMed Central  Google Scholar 

  • Roeder K, Bacanu SA, Wasserman L, Devlin B (2006) Using linkage genome scans to improve power of association in genome scans. Am J Hum Genet 78:243–252

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Roshan U, Chikkagoudar S, Wei Z, Wang K, Hakonarson H (2011) Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest. Nucleic Acids Res 39:e62–e62

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Santorico SA, Hendricks AE (2016) Progress in methods for rare variant association. BMC Genet 17(Suppl 2):6

    Article  PubMed  PubMed Central  Google Scholar 

  • Servin B, Stephens M (2007) Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet 3:e114

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Sinnott JA, Kraft P (2012) Artifact due to differential error when cases and controls are imputed from different platforms. Hum Genet 131:111–119

    Article  PubMed  Google Scholar 

  • 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

    Article  CAS  PubMed  Google Scholar 

  • Skol AD, Scott LJ, Abecasis GR, Boehnke M (2007) Optimal designs for two-stage genome-wide association studies. Genet Epidemiol 31:776–788

    Article  PubMed  Google Scholar 

  • Smith GD, Ebrahim S (2003) ‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 32:1–22

    Article  PubMed  Google Scholar 

  • Stephens M, Balding DJ (2009) Bayesian statistical methods for genetic association studies. Nat Rev Genet 10:681–690

    Article  CAS  PubMed  Google Scholar 

  • Sun R, Hui S, Bader GD, Lin X, Kraft P (2019) Powerful gene set analysis in GWAS with the generalized Berk-Jones statistic. PLoS Genet 15:e1007530

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Teer JK, Mullikin JC (2010) Exome sequencing: the sweet spot before whole genomes. Hum Mol Genet 19:R145–R151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Thomas D (2010) Methods for investigating gene-environment interactions in candidate pathway and genome-wide association studies. Annu Rev Public Health 31:21–36

    Article  PubMed  PubMed Central  Google Scholar 

  • Thomas DC, Haile RW, Duggan D (2005) Recent developments in genomewide association scans: a workshop summary and review. Am J Hum Genet 77:337–345

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Thomas DC, Casey G, Conti DV, Haile RW, Lewinger JP, Stram DO (2009) Methodological issues in multistage genome-wide association studies. Stat Sci 24:414

    Article  PubMed  PubMed Central  Google Scholar 

  • Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ (2005) Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci USA 102:13544–13549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Toland AE (2019) Polygenic risk scores for prostate cancer: testing considerations. Can J Urol 26:17–18

    PubMed  Google Scholar 

  • Torkamani A, Wineinger NE, Topol EJ (2018) The personal and clinical utility of polygenic risk scores. Nat Rev Genet 19:581–590

    Article  CAS  PubMed  Google Scholar 

  • Venter JC, Adams MD, Myers EW et al (2001) The sequence of the human genome. Science 291:1304–1351

    Article  CAS  PubMed  Google Scholar 

  • Wacholder S, McLaughlin JK, Silverman DT, Mandel JS (1992) Selection of controls in case-control studies. I Principles. Am J Epidemiol 135:1019–1028

    Article  CAS  PubMed  Google Scholar 

  • Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N (2004) Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 96:434–442

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang DG, Fan JB, Siao CJ et al (1998) Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science 280:1077–1082

    Article  CAS  PubMed  Google Scholar 

  • Wang K, Li M, Hakonarson H (2010) Analysing biological pathways in genome-wide association studies. Nat Rev Genet 11:843–854

    Article  CAS  PubMed  Google Scholar 

  • Wang L, Jia P, Wolfinger RD, Chen X, Zhao Z (2011) Gene set analysis of genome-wide association studies: methodological issues and perspectives. Genomics 98:1–8

    Article  CAS  PubMed  Google Scholar 

  • Ware JH (2006) The limitations of risk factors as prognostic tools. N Engl J Med 355:2615–2617

    Article  CAS  PubMed  Google Scholar 

  • Wild CP (2005) Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomark Prev 14:1847–1850

    Article  CAS  Google Scholar 

  • Willer CJ, Li Y, Abecasis GR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26:2190–2191

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Witte JS (1997) Genetic analysis with hierarchical models. Genet Epidemiol 14:1137–1142

    Article  CAS  PubMed  Google Scholar 

  • Witte JS, Greenland S (1996) Simulation study of hierarchical regression. Stat Med 15:1161–1170

    Article  CAS  PubMed  Google Scholar 

  • Witte JS, Elston RC, Schork NJ (1996) Genetic dissection of complex traits. Nat Genet 12:355–356. author reply 357–358

    Article  CAS  PubMed  Google Scholar 

  • Witte JS, Elston RC, Cardon LR (2000) On the relative sample size required for multiple comparisons. Stat Med 19:369–372

    Article  CAS  PubMed  Google Scholar 

  • Wojcik GL, Fuchsberger C, Taliun D et al (2018) Imputation-aware tag SNP selection to improve power for large-scale, multi-ethnic association studies. G3 (Bethesda) 8:3255–3267

    Article  Google Scholar 

  • Wu R, Kaiser AD (1968) Structure and base sequence in the cohesive ends of bacteriophage lambda DNA. J Mol Biol 35:523–537

    Article  CAS  PubMed  Google Scholar 

  • Wu Michael C, Lee S, Cai T, Li Y, Boehnke M, Lin X (2011) Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet 89:82–93

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wu R, Taylor E (1971) Nucleotide sequence analysis of DNA. II. Complete nucleotide sequence of the cohesive ends of bacteriophage lambda DNA. J Mol Biol 57:491–511

    Article  CAS  PubMed  Google Scholar 

  • Wu MC, Kraft P, Epstein MP et al (2010) Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet 86:929–942

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wu MC, Maity A, Lee S et al (2013) Kernel machine SNP-set testing under multiple candidate kernels. Genet Epidemiol 37:267–275

    Article  PubMed  PubMed Central  Google Scholar 

  • Xing C, Huang J, Hsu YH et al (2016) Evaluation of power of the Illumina HumanOmni5M-4v1 BeadChip to detect risk variants for human complex diseases. Eur J Hum Genet 24:1029–1034

    Article  CAS  PubMed  Google Scholar 

  • Xiong M, Guo SW (1997) Fine-scale genetic mapping based on linkage disequilibrium: theory and applications. Am J Hum Genet 60:1513–1531

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yang J, Zaitlen NA, Goddard ME, Visscher PM, Price AL (2014) Advantages and pitfalls in the application of mixed-model association methods. Nat Genet 46:100–106

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zaitlen N, Lindstrom S, Pasaniuc B et al (2012) Informed conditioning on clinical covariates increases power in case-control association studies. PLoS Genet 8:e1003032

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zawistowski M, Gopalakrishnan S, Ding J, Li Y, Grimm S, Zollner S (2010) Extending rare-variant testing strategies: analysis of noncoding sequence and imputed genotypes. Am J Hum Genet 87:604–617

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zeggini E, Ioannidis JP (2009) Meta-analysis in genome-wide association studies. Pharmacogenomics 10:191–201

    Article  PubMed  Google Scholar 

  • Zhang Z, Ersoz E, Lai CQ et al (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhou X, Stephens M (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821–824

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu Z, Lee PH, Chaffin MD et al (2018) A genome-wide cross-trait analysis from UK biobank highlights the shared genetic architecture of asthma and allergic diseases. Nat Genet 50:857–864

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by National Institutes of Health grants R25CA112355, R01CA088164, and R01CA201358.

Author information

Authors and Affiliations

  1. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Rebecca E. Graff, Caroline G. Tai, Linda Kachuri & John S. Witte

Authors
  1. Rebecca E. Graff
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Caroline G. Tai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linda Kachuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. John S. Witte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rebecca E. Graff .

Editor information

Editors and Affiliations

  1. Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA

    Kirk E. Lohmueller

  2. Integrative Biology, University of California, Berkeley, CA, USA

    Rasmus Nielsen

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Graff, R.E., Tai, C.G., Kachuri, L., Witte, J.S. (2021). Methods for Association Studies. In: Lohmueller, K.E., Nielsen, R. (eds) Human Population Genomics. Springer, Cham. https://doi.org/10.1007/978-3-030-61646-5_5

Download citation

Publish with us

Policies and ethics

Search

Navigation