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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA et al (2009) Finding the missing heritability of complex diseases. Nature 461:747–753
Frazer KA, Murray SS, Schork NJ, Topol EJ (2009) Human genetic variation and its contribution to complex traits. Nat Rev Genet 10:241–251
Maher B (2008) Personal genomes: the case of the missing heritability. Nature 456:18–21
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
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
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
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
Cheverud JM, Routman EJ (1995) Epistasis and its contribution to genetic variance components. Genetics 139:1455–1461
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
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
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
Carlborg O, Haley CS (2004) Epistasis: too often neglected in complex trait studies? Nat Rev Genet 5:618–625
Cordell HJ (2009) Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 10:392–404
Moore JH, Williams SM (2009) Epistasis and its implications for personal genetics. Am J Hum Genet 85:309–320
Gao H, Granka JM, Feldman MW (2010) On the classification of epistatic interactions. Genetics 184:827–837
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
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
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
Clark AG, Doane WW (1984) Interactions between the amylase and adipose chromosomal regions of Drosophila melanogaster. Evolution 957–982
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
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
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
Marchini J, Donnelly P, Cardon LR (2005) Genome-wide strategies for detecting multiple loci that influence complex diseases. Locus 2:0.0
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
Sun YV, Kardia SLR (2010) Identification of epistatic effects using a protein–protein interaction database. Hum Mol Genet 19:4345
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
Williams OD (1989) The atherosclerosis risk in communities (ARIC) study – design and objectives. Am J Epidemiol 129:687–702
Dawber TR, Meadors GF, Moore FE (1951) Epidemiological approaches to heart disease: the Framingham study. Am J Public Health Nations Health 41:279–286
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
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
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
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
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
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
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
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
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
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
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
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
Das J, Yu H (2012) HINT: high-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol 6:92
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH et al (2002) The human genome browser at UCSC. Genome Res 12:996–1006
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
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
Lambert CG, Black LJ (2012) Learning from our GWAS mistakes: from experimental design to scientific method. Biostatistics 13:195–203
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
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
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
Ma L, Clark AG, Keinan A (2013) Gene-based testing of interactions in association studies of quantitative traits. PLoS Genet 9:e1003321
Li SY, Cui YH (2012) Gene-centric gene-gene interaction: a model-based Kernel machine method. Ann Appl Stat 6:1134–1161
Rajapakse I, Perlman MD, Martin PJ, Hansen JA, Kooperberg C (2012) Multivariate detection of gene-gene interactions. Genet Epidemiol 36:622–630
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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