Skip to main content
SpringerLink
Log in

Biological Knowledge-Driven Analysis of Epistasis in Human GWAS with Application to Lipid Traits

  • Protocol
  • First Online:
Epistasis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1253))

Abstract

While the importance of epistasis is well established, specific gene–gene interactions have rarely been identified in human genome-wide association studies (GWAS), mainly due to low power associated with such interaction tests. In this chapter, we integrate biological knowledge and human GWAS data to reveal epistatic interactions underlying quantitative lipid traits, which are major risk factors for coronary artery disease. To increase power to detect interactions, we only tested pairs of SNPs filtered by prior biological knowledge, including GWAS results, protein–protein interactions (PPIs), and pathway information. Using published GWAS and 9,713 European Americans (EA) from the Atherosclerosis Risk in Communities (ARIC) study, we identified an interaction between HMGCR and LIPC affecting high-density lipoprotein cholesterol (HDL-C) levels. We then validated this interaction in additional multiethnic cohorts from ARIC, the Framingham Heart Study, and the Multi-Ethnic Study of Atherosclerosis. Both HMGCR and LIPC are involved in the metabolism of lipids and lipoproteins, and LIPC itself has been marginally associated with HDL-C. Furthermore, no significant interaction was detected using PPI and pathway information, mainly due to the stringent significance level required after correcting for the large number of tests conducted. These results suggest the potential of biological knowledge-driven approaches to detect epistatic interactions in human GWAS, which may hold the key to exploring the role gene–gene interactions play in connecting genotypes and complex phenotypes in future GWAS.

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

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

  1. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP et al (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106:9362–9367

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA et al (2009) Finding the missing heritability of complex diseases. Nature 461:747–753

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. 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 

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

    Article  CAS  PubMed  Google Scholar 

  5. Eichler EE, Flint J, Gibson G, Kong A, Leal SM et al (2010) Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet 11:446–450

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Ma L, Ballantyne CM, Belmont JW, Keinan A, Brautbar A (2012) Interaction between SNPs in the RXRA and near ANGPTL3 gene region inhibit apolipoprotein B reduction following statin-fenofibric acid therapy in individuals with mixed dyslipidemia. J Lipid Res 53(11):2425–2428

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM et al (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466:707–713

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  8. Asselbergs FW, Guo YR, van Iperen EPA, Sivapalaratnam S, Tragante V et al (2012) Large-scale gene-centric meta-analysis across 32 studies identifies multiple lipid loci. Am J Hum Genet 91:823–838

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Cheverud JM, Routman EJ (1995) Epistasis and its contribution to genetic variance components. Genetics 139:1455–1461

    CAS  PubMed Central  PubMed  Google Scholar 

  10. Cockerham CC (1954) An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 39:859–882

    CAS  PubMed Central  PubMed  Google Scholar 

  11. Zuk O, Hechter E, Sunyaev SR, Lander ES (2012) The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci 109:1193–1198

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Bateson W, Saunders ER, Punnett RC, Hurst CC (eds) (1905) Reports to the Evolution Committee of the Royal Society, report II. Harrison and Sons, London

    Google Scholar 

  13. Carlborg O, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nat Rev Genet 5:618–625

    Article  CAS  PubMed  Google Scholar 

  14. Cordell HJ (2009) Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 10:392–404

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Moore JH, Williams SM (2009) Epistasis and its implications for personal genetics. Am J Hum Genet 85:309–320

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Gao H, Granka JM, Feldman MW (2010) On the classification of epistatic interactions. Genetics 184:827–837

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Shimomura K, Low-Zeddies SS, King DP, Steeves TDL, Whiteley A et al (2001) Genome-wide epistatic interaction analysis reveals complex genetic determinants of circadian behavior in mice. Genome Res 11:959–980

    Article  CAS  PubMed  Google Scholar 

  18. Carlborg Ö, Kerje S, Schütz K, Jacobsson L, Jensen P et al (2003) A global search reveals epistatic interaction between QTL for early growth in the chicken. Genome Res 13:413–421

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Caicedo AL, Stinchcombe JR, Olsen KM, Schmitt J, Purugganan MD (2004) Epistatic interaction between Arabidopsis FRI and FLC flowering time genes generates a latitudinal cline in a life history trait. Proc Natl Acad Sci U S A 101:15670

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Clark AG, Doane WW (1984) Interactions between the amylase and adipose chromosomal regions of Drosophila melanogaster. Evolution 957–982

    Google Scholar 

  21. Ma L, Dvorkin D, Garbe J, Da Y (2007) Genome-wide analysis of single-locus and epistasis single-nucleotide polymorphism effects on anti-cyclic citrullinated peptide as a measure of rheumatoid arthritis. BMC Proc 1:S127

    Article  PubMed Central  PubMed  Google Scholar 

  22. Ma L, Yang J, Runesha HB, Tanaka T, Ferrucci L et al (2010) Genome-wide association analysis of total cholesterol and high-density lipoprotein cholesterol levels using the Framingham Heart Study data. BMC Med Genet 11:55

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Ma L, Runesha HB, Dvorkin D, Garbe JR, Da Y (2008) Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies. BMC Bioinformatics 9:315

    Article  PubMed Central  PubMed  Google Scholar 

  24. Marchini J, Donnelly P, Cardon LR (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Locus 2:0.0

    Google Scholar 

  25. Jia P, Zheng S, Long J, Zheng W, Zhao Z (2011) DmGWAS: dense module searching for genome-wide association studies in protein–protein interaction networks. Bioinformatics 27:95

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Sun YV, Kardia SLR (2010) Identification of epistatic effects using a protein–protein interaction database. Hum Mol Genet 19:4345

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Wu X, Dong H, Luo L, Zhu Y, Peng G et al (2010) A novel statistic for genome-wide interaction analysis. PLoS Genet 6:e1001131

    Article  PubMed Central  PubMed  Google Scholar 

  28. Williams OD (1989) The atherosclerosis risk in communities (ARIC) study – design and objectives. Am J Epidemiol 129:687–702

    Google Scholar 

  29. Dawber TR, Meadors GF, Moore FE (1951) Epidemiological approaches to heart disease: the Framingham study. Am J Public Health Nations Health 41:279–286

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  30. Bild DE, Bluemke DA, Burke GL, Detrano R, Roux AVD et al (2002) Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol 156:871–881

    Article  PubMed  Google Scholar 

  31. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K et al (2007) The NCBI dbGaP database of genotypes and phenotypes. Nat Genet 39:1181–1186

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. Howie BN, Donnelly P, Marchini J (2009) A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet 5:e1000529

    Article  PubMed Central  PubMed  Google Scholar 

  33. Altshuler DM, Gibbs RA, Peltonen L, Dermitzakis E, Schaffner SF et al (2010) Integrating common and rare genetic variation in diverse human populations. Nature 467:52–58

    Article  CAS  PubMed  Google Scholar 

  34. Altshuler DL, Durbin RM, Abecasis GR, Bentley DR, Chakravarti A et al (2010) A map of human genome variation from population-scale sequencing. Nature 467:1061–1073

    Article  PubMed  Google Scholar 

  35. Cordell HJ (2002) Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans. Hum Mol Genet 11:2463–2468

    Article  CAS  PubMed  Google Scholar 

  36. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR 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 Central  PubMed  Google Scholar 

  37. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA et al (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909

    Article  CAS  PubMed  Google Scholar 

  38. Haas BE, Horvath S, Pietilainen KH, Cantor RM, Nikkola E et al (2012) Adipose co-expression networks across Finns and Mexicans identify novel triglyceride-associated genes. BMC Med Genomics 5:61. doi:10.1186/1755-8794-1185-1161

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  39. Aulchenko YS, Ripatti S, Lindqvist I, Boomsma D, Heid IM et al (2009) Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat Genet 41:47–55

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Burkhardt R, Kenny EE, Lowe JK, Birkeland A, Josowitz R et al (2008) Common SNPs in HMGCR in Micronesians and Whites associated with LDL-cholesterol levels affect alternative splicing of exon13. Arterioscler Thromb Vasc Biol 28:U2078–U2332

    Article  Google Scholar 

  41. Burkhardt R, Kenny EE, Lowe JK, Birkeland A, Josowitz R et al (2008) Common SNPs in HMGCR in micronesians and whites associated with LDL-cholesterol levels affect alternative splicing of exon13. Arterioscler Thromb Vasc Biol 28:2078–2084

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Das J, Yu H (2012) HINT: high-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol 6:92

    Article  PubMed Central  PubMed  Google Scholar 

  43. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH et al (2002) The human genome browser at UCSC. Genome Res 12:996–1006

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  44. Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D et al (2009) Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 37:D619–D622

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  45. Lemaitre RN, Tanaka T, Tang WH, Manichaikul A, Foy M et al (2011) Genetic loci associated with plasma phospholipid n-3 fatty acids: a meta-analysis of genome-wide association studies from the CHARGE consortium. PLoS Genet 7

    Google Scholar 

  46. Lambert CG, Black LJ (2012) Learning from our GWAS mistakes: from experimental design to scientific method. Biostatistics 13:195–203

    Article  PubMed Central  PubMed  Google Scholar 

  47. Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P 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 

  48. He J, Wang K, Edmondson AC, Rader DJ, Li C et al (2011) Gene-based interaction analysis by incorporating external linkage disequilibrium information. Eur J Hum Genet 19:164–172

    Article  PubMed Central  PubMed  Google Scholar 

  49. Oh S, Lee J, Kwon M-S, Weir B, Ha K et al (2012) A novel method to identify high order gene-gene interactions in genome-wide association studies: gene-based MDR. BMC Bioinformatics 13:S5

    Article  PubMed Central  PubMed  Google Scholar 

  50. Ma L, Clark AG, Keinan A (2013) Gene-based testing of interactions in association studies of quantitative traits. PLoS Genet 9:e1003321

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  51. Li SY, Cui YH (2012) Gene-centric gene-gene interaction: a model-based Kernel machine method. Ann Appl Stat 6:1134–1161

    Article  Google Scholar 

  52. Rajapakse I, Perlman MD, Martin PJ, Hansen JA, Kooperberg C (2012) Multivariate detection of gene-gene interactions. Genet Epidemiol 36:622–630

    Article  PubMed Central  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Animal and Avian Sciences, University of Maryland, Bldg 142, College Park, MD, 20742, USA

    Li Ma

  2. Department of Biological Statistics and Computational Biology, Cornell Center for Comparative and Population Genomics, Cornell University, Ithaca, NY, USA

    Li Ma, Alon Keinan & Andrew G. Clark

  3. Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY, USA

    Andrew G. Clark

Authors
  1. Li Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alon Keinan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrew G. Clark
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Ma .

Editor information

Editors and Affiliations

  1. Department of Genetics, Institute of Quantitative Biomedical Sciences, Geisel School of Medicine, Hanover, New Hampshire, USA

    Jason H. Moore

  2. Department of Genetics, Institute of Quantitative Biomedical Sciences, Geisel School of Medicine, Hanover, New Hampshire, USA

    Scott M. Williams

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer Science+Business Media New York

About this protocol

Cite this protocol

Ma, L., Keinan, A., Clark, A.G. (2015). Biological Knowledge-Driven Analysis of Epistasis in Human GWAS with Application to Lipid Traits. In: Moore, J., Williams, S. (eds) Epistasis. Methods in Molecular Biology, vol 1253. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-2155-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-2155-3_3

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-2154-6

  • Online ISBN: 978-1-4939-2155-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation