Epistasis pp 19-33 | Cite as

Finding the Epistasis Needles in the Genome-Wide Haystack

  • Marylyn D. RitchieEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1253)


Genome-wide association studies (GWAS) have dominated the field of human genetics for the past 10 years. This study design allows for an unbiased, dense exploration of the genome and provides researchers with a vast array of SNPs to look for association with their trait or disease of interest. GWAS has been referred to as finding needles in a haystack and while many of these “needles,” or SNPs associating with disease, have been identified, there is still a great deal of heritability yet to be explained. The missing or phantom heritability is due, at least in part, to epistasis or gene–gene interactions, which have not been extensively explored in GWAS. Part of the challenge for epistasis analysis in GWAS is the sheer magnitude of the search and the computational complexity associated with it. An exhaustive search for epistasis models is not computationally feasible; thus, alternate approaches must be considered. In this chapter, these approaches will be reviewed briefly, and the incorporation of biological knowledge to guide this process will be further expanded upon. Real biological data examples where this approach has yielded successful identification of epistasis will also be provided. Epistasis has been known to be important since the early 1900s; however, its prevalence in mainstream research has been somewhat overshadowed by molecular technology advances. Due to the increasing evidence of epistasis in complex traits, it continues to emerge as a likely explanation for missing heritability.

Key words

Epistasis Prior knowledge Missing heritability Filtering Enrichment Pathways 


  1. 1.
    Maher B (2008) Personal genomes: the case of the missing heritability. Nature 456:18–21. doi: 10.1038/456018a PubMedCrossRefGoogle Scholar
  2. 2.
    Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, McCarthy MI, Ramos EM, Cardon LR, Chakravarti A, Cho JH, Guttmacher AE, Kong A, Kruglyak L, Mardis E, Rotimi CN, Slatkin M, Valle D, Whittemore AS, Boehnke M, Clark AG, Eichler EE, Gibson G, Haines JL, Mackay TFC, McCarroll SA, Visscher PM (2009) Finding the missing heritability of complex diseases. Nature 461:747–753. doi: 10.1038/nature08494 PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA (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. doi: 10.1073/pnas.0903103106 PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Zuk O, Hechter E, Sunyaev SR, Lander ES (2012) The mystery of missing heritability: genetic interactions create phantom heritability. Proc Natl Acad Sci U S A 109(4):1193–1198, 201119675. doi: 10.1073/pnas.1119675109PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Moore JH (2003) The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered 56:73–82PubMedCrossRefGoogle Scholar
  6. 6.
    Moore JH, Williams SM (2005) Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. Bioessays 27:637–646PubMedCrossRefGoogle Scholar
  7. 7.
    Cordell HJ (2009) Detecting gene-gene interactions that underlie human diseases. Nat Rev Genet 10:392–404. doi: 10.1038/nrg2579 PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Templeton AR (2000) Epistasis and complex traits. Epistasis and the evolutionary process. Oxford University Press, New York, pp 41–57Google Scholar
  9. 9.
    Gibson G (1996) Epistasis and pleiotropy as natural properties of transcriptional regulation. Theor Popul Biol 49:58–89PubMedCrossRefGoogle Scholar
  10. 10.
    Moore JH (2005) A global view of epistasis. Nat Genet 37:13–14. doi: 10.1038/ng0105-13 PubMedCrossRefGoogle Scholar
  11. 11.
    McKinney BA, Pajewski NM (2011) Six degrees of epistasis: statistical network models for GWAS. Front Genet 2:109. doi: 10.3389/fgene.2011.00109 PubMedCentralPubMedGoogle Scholar
  12. 12.
    Bush WS, Dudek SM, Ritchie MD (2006) Parallel multifactor dimensionality reduction: a tool for the large-scale analysis of gene-gene interactions. Bioinformatics 22:2173–2174PubMedCrossRefGoogle Scholar
  13. 13.
    Steffens M, Becker T, Sander T, Fimmers R, Herold C, Holler DA, Leu C, Herms S, Cichon S, Bohn B, Gerstner T, Griebel M, Nöthen MM, Wienker TF, Baur MP (2010) Feasible and successful: genome-wide interaction analysis involving all 1.9 × 1011 pair-wise interaction tests. Hum Hered 69:268–284. doi: 10.1159/000295896 PubMedCrossRefGoogle Scholar
  14. 14.
    Evans DM, Marchini J, Morris AP, Cardon LR (2006) Two-stage two-locus models in genome-wide association. PLoS Genet 2:e157. doi: 10.1371/journal.pgen.0020157 PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Ueki M, Cordell HJ (2012) Improved statistics for genome-wide interaction analysis. PLoS Genet 8:e1002625. doi: 10.1371/journal.pgen.1002625 PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Herold C, Steffens M, Brockschmidt FF, Baur MP, Becker T (2009) INTERSNP: genome-wide interaction analysis guided by a priori information. Bioinform Oxf Engl 25:3275–3281. doi: 10.1093/bioinformatics/btp596 CrossRefGoogle Scholar
  17. 17.
    Kooperberg C, Leblanc M (2008) Increasing the power of identifying gene x gene interactions in genome-wide association studies. Genet Epidemiol 32:255–263. doi: 10.1002/gepi.20300 PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Sha Q1, Zhang Z, Schymick JC, Traynor BJ, Zhang S. Genome-wide association reveals three SNPs associated with sporadic amyotrophic lateral sclerosis through a two-locus analysis. BMC Med Genet. 2009 Sep 9;10:86Google Scholar
  19. 19.
    Baranzini SE, Galwey NW, Wang J, Khankhanian P, Lindberg R, Pelletier D, Wu W, Uitdehaag BMJ, Kappos L, GeneMSA Consortium, Polman CH, Matthews PM, Hauser SL, Gibson RA, Oksenberg JR, Barnes MR (2009) Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum Mol Genet 18:2078–2090. doi: 10.1093/hmg/ddp120 CrossRefGoogle Scholar
  20. 20.
    Greene CS, Penrod NM, Kiralis J, Moore JH (2009) Spatially uniform relieff (SURF) for computationally-efficient filtering of gene-gene interactions. BioData Min 2:5. doi: 10.1186/1756-0381-2-5 PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Moore JH, White BC (2007) Tuning relieff for genome-wide genetic analysis. In: Moore JH, Rajapakse JC, Marchiori E (eds) Evolutionary computation, machine learning and data mining, bioinformatics. Springer, Berlin, pp 166–175CrossRefGoogle Scholar
  22. 22.
    Ritchie MD (2011) Using biological knowledge to uncover the mystery in the search for epistasis in genome-wide association studies. Ann Hum Genet 75:172–182. doi: 10.1111/j.1469-1809.2010.00630.x PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Carlson CS, Eberle MA, Kruglyak L, Nickerson DA (2004) Mapping complex disease loci in whole-genome association studies. Nature 429:446–452PubMedCrossRefGoogle Scholar
  24. 24.
    Sun X, Lu Q, Mukheerjee S, Crane PK, Elston R, Ritchie MD (2014) Analysis pipeline for the epistasis search – statistical versus biological filtering. Front Genet 5:106. doi: 10.3389/fgene.2014.00106 PubMedCentralPubMedGoogle Scholar
  25. 25.
    Pattin KA, Moore JH (2008) Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases. Hum Genet 124:19–29. doi: 10.1007/s00439-008-0522-8 PubMedCentralPubMedCrossRefGoogle Scholar
  26. 26.
    Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D (2004) The database of interacting proteins: 2004 update. Nucleic Acids Res 32:D449–D451. doi: 10.1093/nar/gkh086 PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Breitkreutz B-J, Stark C, Reguly T, Boucher L, Breitkreutz A, Livstone M, Oughtred R, Lackner DH, Bahler J, Wood V, Dolinski K, Tyers M (2008) The BioGRID interaction database: 2008 update. Nucleic Acids Res 36:D637–D640. doi: 10.1093/nar/gkm1001 PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Mishra GR, Suresh M, Kumaran K, Kannabiran N, Suresh S, Bala P, Shivakumar K, Anuradha N, Reddy R, Raghavan TM, Menon S, Hanumanthu G, Gupta M, Upendran S, Gupta S, Mahesh M, Jacob B, Mathew P, Chatterjee P, Arun KS, Sharma S, Chandrika KN, Deshpande N, Palvankar K, Raghavnath R, Krishnakanth R, Karathia H, Rekha B, Nayak R, Vishnupriya G, Kumar HGM, Nagini M, Kumar GSS, Jose R, Deepthi P, Mohan SS, Gandhi TKB, Harsha HC, Deshpande KS, Sarker M, Prasad TSK, Pandey A (2006) Human protein reference database – 2006 update. Nucleic Acids Res 34:D411–D414. doi: 10.1093/nar/gkj141 PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Perry JRB, McCarthy MI, Hattersley AT, Zeggini E, Wellcome Trust Case Control Consortium, Weedon MN, Frayling TM (2009) Interrogating type 2 diabetes genome-wide association data using a biological pathway-based approach. Diabetes 58:1463–1467. doi: 10.2337/db08-1378 CrossRefGoogle Scholar
  30. 30.
    Beyene J, Hu P, Hamid JS, Parkhomenko E, Paterson AD, Tritchler D (2009) Pathway-based analysis of a genome-wide case-control association study of rheumatoid arthritis. BMC Proc 3(Suppl 7):S128PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    O’Dushlaine C, Kenny E, Heron EA, Segurado R, Gill M, Morris DW, Corvin A (2009) The SNP ratio test: pathway analysis of genome-wide association datasets. Bioinform Oxf Engl 25:2762–2763. doi: 10.1093/bioinformatics/btp448 CrossRefGoogle Scholar
  32. 32.
    Askland K, Read C, Moore J (2009) Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission. Hum Genet 125:63–79. doi: 10.1007/s00439-008-0600-y PubMedCrossRefGoogle Scholar
  33. 33.
    Huebinger RM, Garner HR, Barber RC (2010) Pathway genetic load allows simultaneous evaluation of multiple genetic associations. Burns 36:787–792. doi: 10.1016/j.burns.2010.02.001 PubMedCrossRefGoogle Scholar
  34. 34.
    Elbers CC, van Eijk KR, Franke L, Mulder F, van der Schouw YT, Wijmenga C, Onland-Moret NC (2009) Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol 33:419–431. doi: 10.1002/gepi.20395 PubMedCrossRefGoogle Scholar
  35. 35.
    Guo Y-F, Li J, Chen Y, Zhang L-S, Deng H-W (2009) A new permutation strategy of pathway-based approach for genome-wide association study. BMC Bioinformatics 10:429. doi: 10.1186/1471-2105-10-429 PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    Holmans P, Green EK, Pahwa JS, Ferreira MAR, Purcell SM, Sklar P, Owen MJ, O’Donovan MC, Craddock N (2009) Gene ontology analysis of GWA study data sets provides insights into the biology of bipolar disorder. Am J Hum Genet 85:13–24. doi: 10.1016/j.ajhg.2009.05.011 PubMedCentralPubMedCrossRefGoogle Scholar
  37. 37.
    Bush WS, Dudek SM, Ritchie MD (2009) Biofilter: a knowledge-integration system for the multi-locus analysis of genome-wide association studies. Pac Symp Biocomput 368–379Google Scholar
  38. 38.
    Pendergrass SA, Frase AT, Wallace JR, Wolfe D, Katiyar N, Moore C, Ritchie MD (2013) Genomic analyses with biofilter 20: knowledge driven filtering, annotation, and model development. BioData Min 6(1):25PubMedCentralPubMedCrossRefGoogle Scholar
  39. 39.
    Bush WS, Chen G, Torstenson ES, Ritchie MD (2009) LD-spline: mapping SNPs on genotyping platforms to genomic regions using patterns of linkage disequilibrium. BioData Min 2:7. doi: 10.1186/1756-0381-2-7 PubMedCentralPubMedCrossRefGoogle Scholar
  40. 40.
    Bush WS, McCauley JL, DeJager PL, Dudek SM, Hafler DA, Gibson RA, Matthews PM, Kappos L, Naegelin Y, Polman CH, Hauser SL, Oksenberg J, Haines JL, Ritchie MD (2011) A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility. Genes Immun 12:335–340. doi: 10.1038/gene.2011.3 PubMedCentralPubMedCrossRefGoogle Scholar
  41. 41.
    Turner SD, Berg RL, Linneman JG, Peissig PL, Crawford DC, Denny JC, Roden DM, McCarty CA, Ritchie MD, Wilke RA (2011) Knowledge-driven multi-locus analysis reveals gene-gene interactions influencing HDL cholesterol level in two independent EMR-linked biobanks. PLoS One 6:e19586. doi: 10.1371/journal.pone.0019586 PubMedCentralPubMedCrossRefGoogle Scholar
  42. 42.
    Grady BJ, Torstenson ES, McLaren PJ, De Bakker PIW, Haas DW, Robbins GK, Gulick RM, Haubrich R, Ribaudo H, Ritchie MD (2011) Use of biological knowledge to inform the analysis of gene-gene interactions involved in modulating virologic failure with efavirenz-containing treatment regimens in art-naïve actg clinical trials participants. Pac Symp Biocomput 2011:253–264Google Scholar
  43. 43.
    Pendergrass SA, Verma SS, Holzinger ER, Moore CB, Wallace J, Dudek SM, Huggins W, Kitchner T, Waudby C, Berg R, McCarty CA, Ritchie MD (2013) Next-generation analysis of cataracts: determining knowledge driven gene-gene interactions using Biofilter, and gene-environment interactions using the PhenX Toolkit. Pac Symp Biocomput 147–158Google Scholar
  44. 44.
    Teslovich TM, Musunuru K, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Ripatti S, Chasman DI, Willer CJ, Johansen CT, Fouchier SW, Isaacs A, Peloso GM, Barbalic M, Ricketts SL, Bis JC, Aulchenko YS, Thorleifsson G, Feitosa MF, Chambers J, Orho-Melander M, Melander O, Johnson T, Li X, Guo X, Li M, Shin Cho Y, Jin Go M, Jin Kim Y, Lee J-Y, Park T, Kim K, Sim X, Twee-Hee Ong R, Croteau-Chonka DC, Lange LA, Smith JD, Song K, Hua Zhao J, Yuan X, Luan J, Lamina C, Ziegler A, Zhang W, Zee RYL, Wright AF, Witteman JCM, Wilson JF, Willemsen G, Wichmann H-E, Whitfield JB, Waterworth DM, Wareham NJ, Waeber G, Vollenweider P, Voight BF, Vitart V, Uitterlinden AG, Uda M, Tuomilehto J, Thompson JR, Tanaka T, Surakka I, Stringham HM, Spector TD, Soranzo N, Smit JH, Sinisalo J, Silander K, Sijbrands EJG, Scuteri A, Scott J, Schlessinger D, Sanna S, Salomaa V, Saharinen J, Sabatti C, Ruokonen A, Rudan I, Rose LM, Roberts R, Rieder M, Psaty BM, Pramstaller PP, Pichler I, Perola M, Penninx BWJH, Pedersen NL, Pattaro C, Parker AN, Pare G, Oostra BA, O’Donnell CJ, Nieminen MS, Nickerson DA, Montgomery GW, Meitinger T, McPherson R, McCarthy MI, McArdle W, Masson D, Martin NG, Marroni F, Mangino M, Magnusson PKE, Lucas G, Luben R, Loos RJF, Lokki M-L, Lettre G, Langenberg C, Launer LJ, Lakatta EG, Laaksonen R, Kyvik KO, Kronenberg F, König IR, Khaw K-T, Kaprio J, Kaplan LM, Johansson A, Jarvelin M-R, Janssens ACJW, Ingelsson E, Igl W, Kees Hovingh G, Hottenga J-J, Hofman A, Hicks AA, Hengstenberg C, Heid IM, Hayward C, Havulinna AS, Hastie ND, Harris TB, Haritunians T, Hall AS, Gyllensten U, Guiducci C, Groop LC, Gonzalez E, Gieger C, Freimer NB, Ferrucci L, Erdmann J, Elliott P, Ejebe KG, Döring A, Dominiczak AF, Demissie S, Deloukas P, de Geus EJC, de Faire U, Crawford G, Collins FS, Chen YI, Caulfield MJ, Campbell H, Burtt NP, Bonnycastle LL, Boomsma DI, Boekholdt SM, Bergman RN, Barroso I, Bandinelli S, Ballantyne CM, Assimes TL, Quertermous T, Altshuler D, Seielstad M, Wong TY, Tai E-S, Feranil AB, Kuzawa CW, Adair LS, Taylor HA Jr, Borecki IB, Gabriel SB, Wilson JG, Holm H, Thorsteinsdottir U, Gudnason V, Krauss RM, Mohlke KL, Ordovas JM, Munroe PB, Kooner JS, Tall AR, Hegele RA, Kastelein JJP, Schadt EE, Rotter JI, Boerwinkle E, Strachan DP, Mooser V, Stefansson K, Reilly MP, Samani NJ, Schunkert H, Cupples LA, Sandhu MS, Ridker PM, Rader DJ, van Duijn CM, Peltonen L, Abecasis GR, Boehnke M, Kathiresan S (2010) Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466:707–713. doi:10.1038/nature09270Google Scholar
  45. 45.
    Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang H-Y, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkilä K, Hyppönen E, Isaacs A, Jackson AU, Johansson A, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikäinen L-P, Magnusson PKE, Mangino M, Mihailov E, Montasser ME, Müller-Nurasyid M, Nolte IM, O’Connell JR, Palmer CD, Perola M, Petersen A-K, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney ASF, Döring A, Elliott P, Epstein SE, Eyjolfsson GI, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen A-L, Hayward C, Hernandez D, Hicks AA, Holm H, Hung Y-J, Illig T, Jones MR, Kaleebu P, Kastelein JJP, Khaw K-T, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimäki T, Lin S-Y, Lindström J, Loos RJF, Mach F, McArdle WL, Meisinger C, Mitchell BD, Müller G, Nagaraja R, Narisu N, Nieminen TVM, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stancáková A, Stirrups K, Swift AJ, Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D, Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen Y-DI, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrières J, Ferrucci L, Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Iribarren C, Järvelin M-R, Jula A, Kähönen M, Kaprio J, Kesäniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, März W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njølstad I, Pedersen NL, Power C, Pramstaller PP, Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK, Saramies J, Schwarz PEH, Sheu WH-H, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BHR, Ordovas JM, Boerwinkle E, Palmer CNA, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Kathiresan S, Mohlke KL, Ingelsson E, Abecasis GR (2013) Discovery and refinement of loci associated with lipid levels. Nat Genet 45:1274–1283. doi:10.1038/ng.2797Google Scholar
  46. 46.
    Ma L, Brautbar A, Boerwinkle E, Sing CF, Clark AG, Keinan A (2012) Knowledge-driven analysis identifies a gene-gene interaction affecting high-density lipoprotein cholesterol levels in multi-ethnic populations. PLoS Genet 8:e1002714. doi: 10.1371/journal.pgen.1002714 PubMedCentralPubMedCrossRefGoogle Scholar
  47. 47.
    Ma L, Clark AG, Keinan A (2013) Gene-based testing of interactions in association studies of quantitative traits. PLoS Genet 9:e1003321. doi: 10.1371/journal.pgen.1003321 PubMedCentralPubMedCrossRefGoogle Scholar
  48. 48.
    Ma L, Ballantyne C, Brautbar A, Keinan A (2014) Analysis of multiple association studies provides evidence of an expression QTL hub in gene-gene interaction network affecting HDL cholesterol levels. PLoS One 9:e92469. doi: 10.1371/journal.pone.0092469 PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Biochemistry and Molecular BiologyCenter for Systems GenomicsUniversity ParkUSA

Personalised recommendations