Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2014 Apr 16;9(4):e93844.
doi: 10.1371/journal.pone.0093844. eCollection 2014.

Mapping the Genetic Architecture of Gene Regulation in Whole Blood

Affiliations
Free PMC article

Mapping the Genetic Architecture of Gene Regulation in Whole Blood

Katharina Schramm et al. PLoS One. .
Free PMC article

Abstract

Background: We aimed to assess whether whole blood expression quantitative trait loci (eQTLs) with effects in cis and trans are robust and can be used to identify regulatory pathways affecting disease susceptibility.

Materials and methods: We performed whole-genome eQTL analyses in 890 participants of the KORA F4 study and in two independent replication samples (SHIP-TREND, N = 976 and EGCUT, N = 842) using linear regression models and Bonferroni correction.

Results: In the KORA F4 study, 4,116 cis-eQTLs (defined as SNP-probe pairs where the SNP is located within a 500 kb window around the transcription unit) and 94 trans-eQTLs reached genome-wide significance and overall 91% (92% of cis-, 84% of trans-eQTLs) were confirmed in at least one of the two replication studies. Different study designs including distinct laboratory reagents (PAXgene™ vs. Tempus™ tubes) did not affect reproducibility (separate overall replication overlap: 78% and 82%). Immune response pathways were enriched in cis- and trans-eQTLs and significant cis-eQTLs were partly coexistent in other tissues (cross-tissue similarity 40-70%). Furthermore, four chromosomal regions displayed simultaneous impact on multiple gene expression levels in trans, and 746 eQTL-SNPs have been previously reported to have clinical relevance. We demonstrated cross-associations between eQTL-SNPs, gene expression levels in trans, and clinical phenotypes as well as a link between eQTLs and human metabolic traits via modification of gene regulation in cis.

Conclusions: Our data suggest that whole blood is a robust tissue for eQTL analysis and may be used both for biomarker studies and to enhance our understanding of molecular mechanisms underlying gene-disease associations.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Manhattan plot of all analyzed cis-eQTLs with their calculated p-values in the KORA F4 study.
Figure 2
Figure 2. All trans-eQTLs with p-values below 2.81E-12 in the KORA F4 study.
Figure 3
Figure 3. a–d): eQTLs with simultaneous impact on expression levels of at least five genes in trans.
a) Chromosome 12. The eQTL was located upstream of lysozyme (LYZ), a gene residing on chromosome 12q15. It is associated with expression levels of the seven transcripts cAMP responsive element binding protein 1 (CREB1), SHC SH2-domain binding protein 1 (SHCBP1), arylformamidase (AFMID), KIAA0101, ITPK1 antisense RNA 1 (ITPK1-AS1), EP300 interacting inhibitor of differentiation 2B (EID2B), and CDKN2A interacting protein N-terminal like (CDKN2AIPNL). b) Chromosome 11. The eQTL was found intronic of the hemoglobin beta (HBB) gene on chromosome 11p15.4 and was associated with the regulation of 13 genes distributed across the genome in trans: PWP1 homolog (PWP1), phosphatidylserine synthase 1 (PTDSS1), CCHC-type zinc finger, nucleic acid binding protein (CNBP), trafficking protein particle complex 11 (TRAPPC11), histone deacetylase 1 (HDAC1), WD repeat domain 59 (WDR59), G protein pathway suppressor 1 (GPS1), ArfGAP with SH3 domain, ankyrin repeat and PH domain 1 (ASAP1), aarF domain containing kinase 2 (ADCK2), deoxythymidylate kinase (thymidylate kinase) (DTYMK), WD repeat domain 37 (WDR37), spectrin repeat containing, nuclear envelope 2 (SYNE2), and RAD51 paralog C (RAD51C). c) Chromosome 3. The eQTL on chromosome 3 was located intronic of the rho guanin nucleotid exchange factor 3 (ARHGEF3) gene at 3p14.3. We observed a significant impact on the regulation of twelve genes, integrin beta 5 (ITGB5), platelet glycoprotein IX (GP9), carboxy-terminal domain, RNA polymerase II, polypeptide A small phosphatase-like (CTDSPL), protein S alpha (PROS1), guanylate cyclase soluble subunit alpha-3 (GUCY1A3), caldesmon 1 (CALD1), tetraspanin 9 (TSPAN9), arachidonate 12-lipoxygenase (ALOX12), parvin beta (PARVB), N-acetyltransferase 8B (NAT8B), multimerin 1 (MMRN1), and C-type lectin domain family 1, member B (CLEC1B). d) Chromosome 2. The eQTL upstream of atonal homolog 8 (ATOH8) residing on chromosome 2p11.2 exerts simultaneous impact on expression levels of six genes: paroxysmal nonkinesigenic dyskinesia (PNKD) and calcium homeostasis modulator 1 (CALHM1), zink finger protein 93 (ZNF93), dynein, light chain, roadblock-type 2 (DYNLRB2), growth hormone-releasing hormone receptor (GHRHR), and MutL-homolog 3(MLH3).
Figure 4
Figure 4. Triangular relationships between eQTL-SNPs, gene expression levels in trans and phenotypic traits.
Figure 4a: Adiponectin. 1 Measured in the fasting state. 2 Measured 2-hours after an oral glucose load in oral glucose tolerance test. Figure 4b: Mean Platelet Volume (MPV). The association between SNP and mean platelet volume was assessed in 4,159 KORA S4 participants, those between gene expression levels and mean platelet volume in 889 participants of the KORA F4 study. Figure 4c–e: Correlation analyses combining genetic, metabolomics and transcriptomics data in 712 participants of the KORA F4 study.

Similar articles

See all similar articles

Cited by 18 articles

  • Comprehensive Multiple eQTL Detection and Its Application to GWAS Interpretation.
    Zeng B, Lloyd-Jones LR, Montgomery GW, Metspalu A, Esko T, Franke L, Vosa U, Claringbould A, Brigham KL, Quyyumi AA, Idaghdour Y, Yang J, Visscher PM, Powell JE, Gibson G. Zeng B, et al. Genetics. 2019 Jul;212(3):905-918. doi: 10.1534/genetics.119.302091. Epub 2019 May 22. Genetics. 2019. PMID: 31123039
  • Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits.
    Wheeler HE, Ploch S, Barbeira AN, Bonazzola R, Andaleon A, Fotuhi Siahpirani A, Saha A, Battle A, Roy S, Im HK. Wheeler HE, et al. Version 2. Genet Epidemiol. 2019 Sep;43(6):596-608. doi: 10.1002/gepi.22205. Epub 2019 Apr 4. Genet Epidemiol. 2019. PMID: 30950127 Free PMC article.
  • Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing.
    Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, Boland A, Vronskaya M, van der Lee SJ, Amlie-Wolf A, Bellenguez C, Frizatti A, Chouraki V, Martin ER, Sleegers K, Badarinarayan N, Jakobsdottir J, Hamilton-Nelson KL, Moreno-Grau S, Olaso R, Raybould R, Chen Y, Kuzma AB, Hiltunen M, Morgan T, Ahmad S, Vardarajan BN, Epelbaum J, Hoffmann P, Boada M, Beecham GW, Garnier JG, Harold D, Fitzpatrick AL, Valladares O, Moutet ML, Gerrish A, Smith AV, Qu L, Bacq D, Denning N, Jian X, Zhao Y, Del Zompo M, Fox NC, Choi SH, Mateo I, Hughes JT, Adams HH, Malamon J, Sanchez-Garcia F, Patel Y, Brody JA, Dombroski BA, Naranjo MCD, Daniilidou M, Eiriksdottir G, Mukherjee S, Wallon D, Uphill J, Aspelund T, Cantwell LB, Garzia F, Galimberti D, Hofer E, Butkiewicz M, Fin B, Scarpini E, Sarnowski C, Bush WS, Meslage S, Kornhuber J, White CC, Song Y, Barber RC, Engelborghs S, Sordon S, Voijnovic D, Adams PM, Vandenberghe R, Mayhaus M, Cupples LA, Albert MS, De Deyn PP, Gu W, Himali JJ, Beekly D, Squassina A, Hartmann AM, Orellana A, Blacker D, Rodriguez-Rodriguez E, Lovestone S, Garcia ME, Doody RS, Munoz-Fernadez C, Sussams R, Lin H, Fairchild TJ, Benito YA, Holmes C, Karamujić-Čomić H, Frosch MP, Thonberg H, Maier W, Roshchupkin G, Ghetti B, Giedraitis V, Kawalia A, Li S, Huebinger RM, Kilander L, Moebus S, Hernández I, Kamboh MI, Brundin R, Turton J, Yang Q, Katz MJ, Concari L, Lord J, Beiser AS, Keene CD, Helisalmi S, Kloszewska I, Kukull WA, Koivisto AM, Lynch A, Tarraga L, Larson EB, Haapasalo A, Lawlor B, Mosley TH, Lipton RB, Solfrizzi V, Gill M, Longstreth WT Jr, Montine TJ, Frisardi V, Diez-Fairen M, Rivadeneira F, Petersen RC, Deramecourt V, Alvarez I, Salani F, Ciaramella A, Boerwinkle E, Reiman EM, Fievet N, Rotter JI, Reisch JS, Hanon O, Cupidi C, Andre Uitterlinden AG, Royall DR, Dufouil C, Maletta RG, de Rojas I, Sano M, Brice A, Cecchetti R, George-Hyslop PS, Ritchie K, Tsolaki M, Tsuang DW, Dubois B, Craig D, Wu CK, Soininen H, Avramidou D, Albin RL, Fratiglioni L, Germanou A, Apostolova LG, Keller L, Koutroumani M, Arnold SE, Panza F, Gkatzima O, Asthana S, Hannequin D, Whitehead P, Atwood CS, Caffarra P, Hampel H, Quintela I, Carracedo Á, Lannfelt L, Rubinsztein DC, Barnes LL, Pasquier F, Frölich L, Barral S, McGuinness B, Beach TG, Johnston JA, Becker JT, Passmore P, Bigio EH, Schott JM, Bird TD, Warren JD, Boeve BF, Lupton MK, Bowen JD, Proitsi P, Boxer A, Powell JF, Burke JR, Kauwe JSK, Burns JM, Mancuso M, Buxbaum JD, Bonuccelli U, Cairns NJ, McQuillin A, Cao C, Livingston G, Carlson CS, Bass NJ, Carlsson CM, Hardy J, Carney RM, Bras J, Carrasquillo MM, Guerreiro R, Allen M, Chui HC, Fisher E, Masullo C, Crocco EA, DeCarli C, Bisceglio G, Dick M, Ma L, Duara R, Graff-Radford NR, Evans DA, Hodges A, Faber KM, Scherer M, Fallon KB, Riemenschneider M, Fardo DW, Heun R, Farlow MR, Kölsch H, Ferris S, Leber M, Foroud TM, Heuser I, Galasko DR, Giegling I, Gearing M, Hüll M, Geschwind DH, Gilbert JR, Morris J, Green RC, Mayo K, Growdon JH, Feulner T, Hamilton RL, Harrell LE, Drichel D, Honig LS, Cushion TD, Huentelman MJ, Hollingworth P, Hulette CM, Hyman BT, Marshall R, Jarvik GP, Meggy A, Abner E, Menzies GE, Jin LW, Leonenko G, Real LM, Jun GR, Baldwin CT, Grozeva D, Karydas A, Russo G, Kaye JA, Kim R, Jessen F, Kowall NW, Vellas B, Kramer JH, Vardy E, LaFerla FM, Jöckel KH, Lah JJ, Dichgans M, Leverenz JB, Mann D, Levey AI, Pickering-Brown S, Lieberman AP, Klopp N, Lunetta KL, Wichmann HE, Lyketsos CG, Morgan K, Marson DC, Brown K, Martiniuk F, Medway C, Mash DC, Nöthen MM, Masliah E, Hooper NM, McCormick WC, Daniele A, McCurry SM, Bayer A, McDavid AN, Gallacher J, McKee AC, van den Bussche H, Mesulam M, Brayne C, Miller BL, Riedel-Heller S, Miller CA, Miller JW, Al-Chalabi A, Morris JC, Shaw CE, Myers AJ, Wiltfang J, O'Bryant S, Olichney JM, Alvarez V, Parisi JE, Singleton AB, Paulson HL, Collinge J, Perry WR, Mead S, Peskind E, Cribbs DH, Rossor M, Pierce A, Ryan NS, Poon WW, Nacmias B, Potter H, Sorbi S, Quinn JF, Sacchinelli E, Raj A, Spalletta G, Raskind M, Caltagirone C, Bossù P, Orfei MD, Reisberg B, Clarke R, Reitz C, Smith AD, Ringman JM, Warden D, Roberson ED, Wilcock G, Rogaeva E, Bruni AC, Rosen HJ, Gallo M, Rosenberg RN, Ben-Shlomo Y, Sager MA, Mecocci P, Saykin AJ, Pastor P, Cuccaro ML, Vance JM, Schneider JA, Schneider LS, Slifer S, Seeley WW, Smith AG, Sonnen JA, Spina S, Stern RA, Swerdlow RH, Tang M, Tanzi RE, Trojanowski JQ, Troncoso JC, Van Deerlin VM, Van Eldik LJ, Vinters HV, Vonsattel JP, Weintraub S, Welsh-Bohmer KA, Wilhelmsen KC, Williamson J, Wingo TS, Woltjer RL, Wright CB, Yu CE, Yu L, Saba Y, Pilotto A, Bullido MJ, Peters O, Crane PK, Bennett D, Bosco P, Coto E, Boccardi V, De Jager PL, Lleo A, Warner N, Lopez OL, Ingelsson M, Deloukas P, Cruchaga C, Graff C, Gwilliam R, Fornage M, Goate AM, Sanchez-Juan P, Kehoe PG, Amin N, Ertekin-Taner N, Berr C, Debette S, Love S, Launer LJ, Younkin SG, Dartigues JF, Corcoran C, Ikram MA, Dickson DW, Nicolas G, Campion D, Tschanz J, Schmidt H, Hakonarson H, Clarimon J, Munger R, Schmidt R, Farrer LA, Van Broeckhoven C, C O'Donovan M, DeStefano AL, Jones L, Haines JL, Deleuze JF, Owen MJ, Gudnason V, Mayeux R, Escott-Price V, Psaty BM, Ramirez A, Wang LS, Ruiz A, van Duijn CM, Holmans PA, Seshadri S, Williams J, Amouyel P, Schellenberg GD, Lambert JC, Pericak-Vance MA; Alzheimer Disease Genetics Consortium (ADGC),; European Alzheimer’s Disease Initiative (EADI),; Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE),; Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium (GERAD/PERADES),. Kunkle BW, et al. Nat Genet. 2019 Mar;51(3):414-430. doi: 10.1038/s41588-019-0358-2. Epub 2019 Feb 28. Nat Genet. 2019. PMID: 30820047 Free PMC article.
  • Inborn-like errors of metabolism are determinants of breast cancer risk, clinical response and survival: a study of human biochemical individuality.
    da Silva I, da Costa Vieira R, Stella C, Loturco E, Carvalho AL, Veo C, Neto C, Silva SM, D'Amora P, Salzgeber M, Matos D, Silva CR, Oliveira JR, Rabelo I, Yamakawa P, Maciel R, Biscolla R, Chiamolera M, Fraietta R, Reis F, Mori M, Marchioni D, Carioca A, Maciel G, Tomioka R, Baracat E, Silva C, Granato C, Diaz R, Scarpellini B, Egle D, Fiegl H, Himmel I, Troi C, Nagourney R. da Silva I, et al. Oncotarget. 2018 Aug 3;9(60):31664-31681. doi: 10.18632/oncotarget.25839. eCollection 2018 Aug 3. Oncotarget. 2018. PMID: 30167086 Free PMC article.
  • DNA methylation age is associated with an altered hemostatic profile in a multiethnic meta-analysis.
    Ward-Caviness CK, Huffman JE, Everett K, Germain M, van Dongen J, Hill WD, Jhun MA, Brody JA, Ghanbari M, Du L, Roetker NS, de Vries PS, Waldenberger M, Gieger C, Wolf P, Prokisch H, Koenig W, O'Donnell CJ, Levy D, Liu C, Truong V, Wells PS, Trégouët DA, Tang W, Morrison AC, Boerwinkle E, Wiggins KL, McKnight B, Guo X, Psaty BM, Sotoodenia N, Boomsma DI, Willemsen G, Ligthart L, Deary IJ, Zhao W, Ware EB, Kardia SLR, Van Meurs JBJ, Uitterlinden AG, Franco OH, Eriksson P, Franco-Cereceda A, Pankow JS, Johnson AD, Gagnon F, Morange PE, de Geus EJC, Starr JM, Smith JA, Dehghan A, Björck HM, Smith NL, Peters A. Ward-Caviness CK, et al. Blood. 2018 Oct 25;132(17):1842-1850. doi: 10.1182/blood-2018-02-831347. Epub 2018 Jul 24. Blood. 2018. PMID: 30042098 Free PMC article.
See all "Cited by" articles

References

    1. Jones BL, Swallow DM (2011) The impact of cis-acting polymorphisms on the human phenotype. Hugo J 5: 13–23. - PMC - PubMed
    1. Fehrmann RS, Jansen RC, Veldink JH, Westra HJ, Arends D, et al. (2011) Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet 7: e1002197. - PMC - PubMed
    1. Sasayama D, Hori H, Nakamura S, Miyata R, Teraishi T, et al. (2013) Identification of single nucleotide polymorphisms regulating peripheral blood mRNA expression with genome-wide significance: an eQTL study in the Japanese population. PLoS One 8: e54967. - PMC - PubMed
    1. Mehta D, Heim K, Herder C, Carstensen M, Eckstein G, et al. (2013) Impact of common regulatory single-nucleotide variants on gene expression profiles in whole blood. Eur J Hum Genet 21: 48–54. - PMC - PubMed
    1. Menke A, Rex-Haffner M, Klengel T, Binder EB, Mehta D (2012) Peripheral blood gene expression: it all boils down to the RNA collection tubes. BMC Res Notes 5: 1. - PMC - PubMed

Publication types

Substances

Grant support

The KORA research platform and the KORA Augsburg studies are financed by the Helmholtz Zentrum München, German Research Center for Environmental Health, which is funded by the BMBF and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC Health), Ludwig Maximilians-Universität, as part of the LMUinnovative and in part by a grant from the BMBF to the German Center for Diabetes Research (DZD). The German Diabetes Center is funded by the German Federal Ministry of Health and the Ministry of School, Science and Research of the State of North-Rhine-Westphalia. This study was supported by the BMBF funded Systems Biology of Metabotypes grant (SysMBo#0315494A). Additional support was obtained from the BMBF (National Genome Research Network NGFNplus Atherogenomics, 01GS0834) and from the European Commission's Seventh Framework Programme (FP7/2007-2013, HEALTH-F2-2011, grant agreement No. 277984, TIRCON). K. Suhre is supported by ‘Biomedical Research Program’ funds at Weill Cornell Medical College in Qatar, a program funded by the Qatar Foundation. SHIP-TREND is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the BMBF (German Ministry of Education and Research), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Analyses were supported by the ‘Greifswald Approach to Individualized Medicine (GANI_MED)’ consortium funded by the BMBF (grant 03IS2061A). Genome-wide genotyping and expression data have been supported by the BMBF (grant no. 03ZIK012) and the Federal State of Mecklenburg, West Pomerania. The University of Greifswald is a member of the ‘Center of Knowledge Interchange’ program of the Siemens AG and the Caché Campus program of the InterSystems GmbH. EGCUT studies were financed by University of Tartu (grant “Center of Translational Genomics”), by Estonian Goverment (grant #SF0180142s08) and by European Commission through the European Regional Development Fund in the frame of grant “Centre of Excellence in Genomics” and Estonian Research Infrastructure's Roadmap and through FP7 grant #313010. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Feedback