Discovery of novel hepatocyte eQTLs in African Americans

Autoři: Yizhen Zhong aff001;  Tanima De aff001;  Cristina Alarcon aff001;  C. Sehwan Park aff001;  Bianca Lec aff001;  Minoli A. Perera aff001
Působiště autorů: Department of Pharmacology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America aff001
Vyšlo v časopise: Discovery of novel hepatocyte eQTLs in African Americans. PLoS Genet 16(4): e32767. doi:10.1371/journal.pgen.1008662
Kategorie: Research Article


African Americans (AAs) are disproportionately affected by metabolic diseases and adverse drug events, with limited publicly available genomic and transcriptomic data to advance the knowledge of the molecular underpinnings or genetic associations to these diseases or drug response phenotypes. To fill this gap, we obtained 60 primary hepatocyte cultures from AA liver donors for genome-wide mapping of expression quantitative trait loci (eQTL) using LAMatrix. We identified 277 eGenes and 19,770 eQTLs, of which 67 eGenes and 7,415 eQTLs are not observed in the Genotype-Tissue Expression Project (GTEx) liver eQTL analysis. Of the eGenes found in GTEx only 25 share the same lead eQTL. These AA-specific eQTLs are less correlated to GTEx eQTLs. in effect sizes and have larger Fst values compared to eQTLs found in both cohorts (overlapping eQTLs). We assessed the overlap between GWAS variants and their tagging variants with AA hepatocyte eQTLs and demonstrated that AA hepatocyte eQTLs can decrease the number of potential causal variants at GWAS loci. Additionally, we identified 75,002 exon QTLs of which 48.8% are not eQTLs in AA hepatocytes. Our analysis provides the first comprehensive characterization of AA hepatocyte eQTLs and highlights the unique discoveries that are made possible due to the increased genetic diversity within the African ancestry genome.

Klíčová slova:

Europe – Exon mapping – Gene expression – Gene mapping – Gene regulation – Genome-wide association studies – Hepatocytes – Quantitative trait loci


1. Cappellini MD, Fiorelli G. Glucose-6-phosphate dehydrogenase deficiency. The lancet. 2008;371(9606):64–74.

2. Dang M-TN, Hambleton J, Kayser SR. The influence of ethnicity on warfarin dosage requirement. Annals of Pharmacotherapy. 2005;39(6):1008–12. doi: 10.1345/aph.1E566 15855242

3. Hernandez W, Gamazon ER, Smithberger E, O'Brien TJ, Harralson AF, Tuck M, et al. Novel genetic predictors of venous thromboembolism risk in African Americans. Blood. 2016:blood-2015-09-668525.

4. Perera MA, Cavallari LH, Limdi NA, Gamazon ER, Konkashbaev A, Daneshjou R, et al. Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study. The Lancet. 2013;382(9894):790–6.

5. Peprah E, Xu H, Tekola-Ayele F, Royal CD. Genome-wide association studies in Africans and African Americans: expanding the framework of the genomics of human traits and disease. Public Health Genomics. 2015;18(1):40–51. doi: 10.1159/000367962 25427668

6. Johnson JA, Caudle KE, Gong L, Whirl‐Carrillo M, Stein CM, Scott SA, et al. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for pharmacogenetics‐guided warfarin dosing: 2017 update. Clinical Pharmacology & Therapeutics. 2017;102(3):397–404.

7. Gamazon ER, Segrè AV, van de Bunt M, Wen X, Xi HS, Hormozdiari F, et al. Using an atlas of gene regulation across 44 human tissues to inform complex disease-and trait-associated variation. Nature genetics. 2018;50(7):956. doi: 10.1038/s41588-018-0154-4 29955180

8. Naranbhai V, Fairfax BP, Makino S, Humburg P, Wong D, Ng E, et al. Genomic modulators of gene expression in human neutrophils. Nature communications. 2015;6:7545. doi: 10.1038/ncomms8545 26151758

9. Schadt EE, Molony C, Chudin E, Hao K, Yang X, Lum PY, et al. Mapping the genetic architecture of gene expression in human liver. PLoS biology. 2008;6(5):e107. doi: 10.1371/journal.pbio.0060107 18462017

10. Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, Lau E, et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science. 2014;343(6175):1246949. doi: 10.1126/science.1246949 24604202

11. Kim-Hellmuth S, Bechheim M, Pütz B, Mohammadi P, Nédélec Y, Giangreco N, et al. Genetic regulatory effects modified by immune activation contribute to autoimmune disease associations. Nature communications. 2017;8(1):266. doi: 10.1038/s41467-017-00366-1 28814792

12. Ongen H, Brown AA, Delaneau O, Panousis NI, Nica AC, Dermitzakis ET, et al. Estimating the causal tissues for complex traits and diseases. Nature genetics. 2017;49(12):1676. doi: 10.1038/ng.3981 29058715

13. MacParland SA, Liu JC, Ma X-Z, Innes BT, Bartczak AM, Gage BK, et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nature communications. 2018;9(1):4383. doi: 10.1038/s41467-018-06318-7 30348985

14. Ponsoda X, Pareja E, Gómez-Lechón Ma-J, Fabra R, Carrasco E, Trullenque R, et al. Drug biotransformation by human hepatocytes. In vitro/in vivo metabolism by cells from the same donor. Journal of hepatology. 2001;34(1):19–25. doi: 10.1016/s0168-8278(00)00085-4 11211902

15. Strunz T, Grassmann F, Gayán J, Nahkuri S, Souza-Costa D, Maugeais C, et al. A mega-analysis of expression quantitative trait loci (eQTL) provides insight into the regulatory architecture of gene expression variation in liver. Scientific reports. 2018;8(1):5865. doi: 10.1038/s41598-018-24219-z 29650998

16. Wang X, Tang H, Teng M, Li Z, Li J, Fan J, et al. Mapping of hepatic expression quantitative trait loci (eQTLs) in a Han Chinese population. Journal of medical genetics. 2014;51(5):319–26. doi: 10.1136/jmedgenet-2013-102045 24665059

17. Mogil LS, Andaleon A, Badalamenti A, Dickinson SP, Guo X, Rotter JI, et al. Genetic architecture of gene expression traits across diverse populations. PLoS genetics. 2018;14(8):e1007586. doi: 10.1371/journal.pgen.1007586 30096133

18. Pala M, Zappala Z, Marongiu M, Li X, Davis JR, Cusano R, et al. Population-and individual-specific regulatory variation in Sardinia. Nature genetics. 2017;49(5):700. doi: 10.1038/ng.3840 28394350

19. Nédélec Y, Sanz J, Baharian G, Szpiech ZA, Pacis A, Dumaine A, et al. Genetic ancestry and natural selection drive population differences in immune responses to pathogens. Cell. 2016;167(3):657–69. e21. doi: 10.1016/j.cell.2016.09.025 27768889

20. Hindorff LA, Bonham VL, Brody LC, Ginoza ME, Hutter CM, Manolio TA, et al. Prioritizing diversity in human genomics research. Nature Reviews Genetics. 2018;19(3):175. doi: 10.1038/nrg.2017.89 29151588

21. De T, Park CS, Perera MA. Cardiovascular Pharmacogenomics: Does It Matter If You're Black or White? Annual review of pharmacology and toxicology. 2019;59:577–603. doi: 10.1146/annurev-pharmtox-010818-021154 30296897

22. Sajuthi SP, Sharma NK, Chou JW, Palmer ND, McWilliams DR, Beal J, et al. Mapping adipose and muscle tissue expression quantitative trait loci in African Americans to identify genes for type 2 diabetes and obesity. Human genetics. 2016;135(8):869–80. doi: 10.1007/s00439-016-1680-8 27193597

23. Storey JD, Madeoy J, Strout JL, Wurfel M, Ronald J, Akey JM. Gene-expression variation within and among human populations. The American Journal of Human Genetics. 2007;80(3):502–9. doi: 10.1086/512017 17273971

24. Stranger BE, Montgomery SB, Dimas AS, Parts L, Stegle O, Ingle CE, et al. Patterns of cis regulatory variation in diverse human populations. PLoS genetics. 2012;8(4):e1002639. doi: 10.1371/journal.pgen.1002639 22532805

25. Zhang W, Duan S, Kistner EO, Bleibel WK, Huang RS, Clark TA, et al. Evaluation of genetic variation contributing to differences in gene expression between populations. The American Journal of Human Genetics. 2008;82(3):631–40. doi: 10.1016/j.ajhg.2007.12.015 18313023

26. Albert FW, Kruglyak L. The role of regulatory variation in complex traits and disease. Nature Reviews Genetics. 2015;16(4):197–212. doi: 10.1038/nrg3891 25707927

27. Baharian S, Barakatt M, Gignoux CR, Shringarpure S, Errington J, Blot WJ, et al. The great migration and African-American genomic diversity. PLoS genetics. 2016;12(5):e1006059. doi: 10.1371/journal.pgen.1006059 27232753

28. Zhong Y, Perera MA, Gamazon ER. On Using Local Ancestry to Characterize the Genetic Architecture of Human Traits: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations. The American Journal of Human Genetics. 2019.

29. Mitchell O, Feldman DM, Diakow M, Sigal SH. The pathophysiology of thrombocytopenia in chronic liver disease. Hepatic medicine: evidence and research. 2016;8:39.

30. Hasan S, Dinh K, Lombardo F, Kark J. Doxorubicin cardiotoxicity in African Americans. Journal of the National Medical Association. 2004;96(2):196. 14977278

31. Hernandez W, Gamazon ER, Smithberger E, O’Brien TJ, Harralson AF, Tuck M, et al. Novel genetic predictors of venous thromboembolism risk in African Americans. Blood. 2016;127(15):1923–9. doi: 10.1182/blood-2015-09-668525 26888256

32. White RH, Keenan CR. Effects of race and ethnicity on the incidence of venous thromboembolism. Thrombosis research. 2009;123:S11–S7. doi: 10.1016/S0049-3848(09)70136-7 19303496

33. Hormozdiari F, Kostem E, Kang EY, Pasaniuc B, Eskin E. Identifying causal variants at loci with multiple signals of association. Genetics. 2014;198(2):497–508. doi: 10.1534/genetics.114.167908 25104515

34. Kimura H. Histone modifications for human epigenome analysis. Journal of human genetics. 2013;58(7):439. doi: 10.1038/jhg.2013.66 23739122

35. Babeu J-P, Boudreau F. Hepatocyte nuclear factor 4-alpha involvement in liver and intestinal inflammatory networks. World journal of gastroenterology: WJG. 2014;20(1):22. doi: 10.3748/wjg.v20.i1.22 24415854

36. Hoffmann TJ, Theusch E, Haldar T, Ranatunga DK, Jorgenson E, Medina MW, et al. A large electronic-health-record-based genome-wide study of serum lipids. Nature genetics. 2018;50(3):401. doi: 10.1038/s41588-018-0064-5 29507422

37. Musunuru K, Strong A, Frank-Kamenetsky M, Lee NE, Ahfeldt T, Sachs KV, et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature. 2010;466(7307):714. doi: 10.1038/nature09266 20686566

38. Waterworth DM, Ricketts SL, Song K, Chen L, Zhao JH, Ripatti S, et al. Genetic variants influencing circulating lipid levels and risk of coronary artery disease. Arteriosclerosis, thrombosis, and vascular biology. 2010;30(11):2264–76. doi: 10.1161/ATVBAHA.109.201020 20864672

39. Segal JB, Moliterno AR. Platelet counts differ by sex, ethnicity, and age in the United States. Annals of epidemiology. 2006;16(2):123–30. doi: 10.1016/j.annepidem.2005.06.052 16246584

40. Sylman JL, Mitrugno A, Tormoen GW, Wagner TH, Mallick P, McCarty OJ. Platelet count as a predictor of metastasis and venous thromboembolism in patients with cancer. Convergent science physical oncology. 2017;3(2):023001. doi: 10.1088/2057-1739/aa6c05 29081989

41. Simanek R, Vormittag R, Ay C, Alguel G, Dunkler D, Schwarzinger I, et al. High platelet count associated with venous thromboembolism in cancer patients: results from the Vienna Cancer and Thrombosis Study (CATS). Journal of Thrombosis and Haemostasis. 2010;8(1):114–20. doi: 10.1111/j.1538-7836.2009.03680.x 19889150

42. Kanai M, Akiyama M, Takahashi A, Matoba N, Momozawa Y, Ikeda M, et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nature genetics. 2018;50(3):390. doi: 10.1038/s41588-018-0047-6 29403010

43. Rosemond E, Rossi M, McMillin SM, Scarselli M, Donaldson JG, Wess J. Regulation of M3 muscarinic receptor expression and function by transmembrane protein 147. Molecular pharmacology. 2011;79(2):251–61. doi: 10.1124/mol.110.067363 21056967

44. Greliche N, Germain M, Lambert J-C, Cohen W, Bertrand M, Dupuis A-M, et al. A genome-wide search for common SNP x SNP interactions on the risk of venous thrombosis. BMC medical genetics. 2013;14(1):36.

45. Lamba J, Hebert JM, Schuetz EG, Klein TE, Altman RB. PharmGKB summary: very important pharmacogene information for CYP3A5. Pharmacogenetics and genomics. 2012;22(7):555. doi: 10.1097/FPC.0b013e328351d47f 22407409

46. Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature News. 2016;538(7624):161.

47. Innocenti F, Cooper GM, Stanaway IB, Gamazon ER, Smith JD, Mirkov S, et al. Identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue. PLoS genetics. 2011;7(5):e1002078. doi: 10.1371/journal.pgen.1002078 21637794

48. Vockley CM, Guo C, Majoros WH, Nodzenski M, Scholtens DM, Hayes MG, et al. Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort. Genome research. 2015;25(8):1206–14. doi: 10.1101/gr.190090.115 26084464

49. Roden DM, Wilke RA, Kroemer HK, Stein CM. Pharmacogenomics: the genetics of variable drug responses. Circulation. 2011;123(15):1661–70. doi: 10.1161/CIRCULATIONAHA.109.914820 21502584

50. Owen RP, Gong L, Sagreiya H, Klein TE, Altman RB. VKORC1 pharmacogenomics summary. Pharmacogenetics and genomics. 2010;20(10):642. doi: 10.1097/FPC.0b013e32833433b6 19940803

51. Ferreira PG, Muñoz-Aguirre M, Reverter F, Godinho CPS, Sousa A, Amadoz A, et al. The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nature communications. 2018;9(1):490. doi: 10.1038/s41467-017-02772-x 29440659

52. Tolbert M, Finley SJ, Visonà SD, Soni S, Osculati A, Javan GT. The thanatotranscriptome: gene expression of male reproductive organs after death. Gene. 2018;675:191–6. doi: 10.1016/j.gene.2018.06.090 30180965

53. Zhu Y, Wang L, Yin Y, Yang E. Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Scientific reports. 2017;7(1):5435. doi: 10.1038/s41598-017-05882-0 28710439

54. Park SC, De T, Xu Y, Zhoug Y, Gamazon E, Alarcon C, et al. Uncovering the role of admixture in disease and drug response: Association of hepatocyte gene expression and DNA methylation with African Ancestry in African Americans. bioRxiv. 2019:491225.

55. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics. 2007;81(3):559–75. doi: 10.1086/519795 17701901

56. Delaneau O, Marchini J, Zagury J-F. A linear complexity phasing method for thousands of genomes. Nature methods. 2012;9(2):179.

57. Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nature genetics. 2007;39(7):906. doi: 10.1038/ng2088 17572673

58. Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010. Available from:

59. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. doi: 10.1093/bioinformatics/bts635 23104886

60. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. doi: 10.1093/bioinformatics/btp352 19505943

61. Battle A, Brown CD, Engelhardt BE, Montgomery SB, Consortium G. Genetic effects on gene expression across human tissues. Nature. 2017;550(7675):204–13. doi: 10.1038/nature24277 29022597

62. DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 2012;28(11):1530–2. doi: 10.1093/bioinformatics/bts196 22539670; PubMed Central PMCID: PMC3356847.

63. Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9. doi: 10.1093/bioinformatics/btu638 25260700

64. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8 25516281; PubMed Central PMCID: PMC4302049.

65. Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome biology. 2010;11(3):R25. doi: 10.1186/gb-2010-11-3-r25 20196867

66. Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CN. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics. 2009;26(4):493–500. doi: 10.1093/bioinformatics/btp692 20022975

67. Stegle O, Parts L, Durbin R, Winn J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput Biol. 2010;6(5):e1000770. doi: 10.1371/journal.pcbi.1000770 20463871; PubMed Central PMCID: PMC2865505.

68. Shabalin AA. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics. 2012;28(10):1353–8. doi: 10.1093/bioinformatics/bts163 22492648

69. Huang QQ, Ritchie SC, Brozynska M, Inouye M. Power, false discovery rate and Winner's Curse in eQTL studies. bioRxiv. 2017:209171.

70. Peterson CB, Bogomolov M, Benjamini Y, Sabatti C. TreeQTL: hierarchical error control for eQTL findings. Bioinformatics. 2016;32(16):2556–8. doi: 10.1093/bioinformatics/btw198 27153635

71. Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Annals of statistics. 2001:1165–88.

72. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society Series B (Methodological). 1995:289–300.

73. Reimand J, Arak T, Adler P, Kolberg L, Reisberg S, Peterson H, et al. g: Profiler—a web server for functional interpretation of gene lists (2016 update). Nucleic acids research. 2016;44(W1):W83–W9. doi: 10.1093/nar/gkw199 27098042

74. Maples BK, Gravel S, Kenny EE, Bustamante CD. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference. The American Journal of Human Genetics. 2013;93(2):278–88. doi: 10.1016/j.ajhg.2013.06.020 23910464

75. Jansen R, Hottenga J-J, Nivard MG, Abdellaoui A, Laport B, de Geus EJ, et al. Conditional eQTL analysis reveals allelic heterogeneity of gene expression. Human molecular genetics. 2017;26(8):1444–51. doi: 10.1093/hmg/ddx043 28165122

76. Delaneau O, Ongen H, Brown AA, Fort A, Panousis NI, Dermitzakis ET. A complete tool set for molecular QTL discovery and analysis. Nature communications. 2017;8:15452. doi: 10.1038/ncomms15452 28516912

77. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics. 2011;88(1):76–82. doi: 10.1016/j.ajhg.2010.11.011 21167468

78. Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nature genetics. 2015;47(3):291. doi: 10.1038/ng.3211 25642630

79. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42(Database issue):D1001–6. doi: 10.1093/nar/gkt1229 24316577; PubMed Central PMCID: PMC3965119.

80. Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS genetics. 2014;10(5):e1004383. doi: 10.1371/journal.pgen.1004383 24830394

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