On the cross-population generalizability of gene expression prediction models


Autoři: Kevin L. Keys aff001;  Angel C. Y. Mak aff001;  Marquitta J. White aff001;  Walter L. Eckalbar aff001;  Andrew W. Dahl aff001;  Joel Mefford aff001;  Anna V. Mikhaylova aff003;  María G. Contreras aff001;  Jennifer R. Elhawary aff001;  Celeste Eng aff001;  Donglei Hu aff001;  Scott Huntsman aff001;  Sam S. Oh aff001;  Sandra Salazar aff001;  Michael A. Lenoir aff005;  Jimmie C. Ye aff006;  Timothy A. Thornton aff003;  Noah Zaitlen aff008;  Esteban G. Burchard aff001;  Christopher R. Gignoux aff009
Působiště autorů: Department of Medicine, University of California, San Francisco, California, United States of America aff001;  Berkeley Institute for Data Science, University of California, Berkeley, California, United States of America aff002;  Department of Biostatistics, University of Washington, Seattle, Washington, United States of America aff003;  San Francisco State University, San Francisco, California, United States of America aff004;  Bay Area Pediatrics, Oakland, California, United States of America aff005;  Department of Epidemiology and Biostatistics, University of California, San Francisco, California, United States of America aff006;  Department of Bioengineering and Therapeutic Biosciences, University of California, San Francisco, California, United States of America aff007;  Department of Neurology, University of California, Los Angeles, California, United States of America aff008;  Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America aff009;  Department of Biostatistics and Informatics, School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America aff010
Vyšlo v časopise: On the cross-population generalizability of gene expression prediction models. PLoS Genet 16(8): e32767. doi:10.1371/journal.pgen.1008927
Kategorie: Research Article
doi: 10.1371/journal.pgen.1008927

Souhrn

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.

Klíčová slova:

African American people – Europe – Forecasting – Gene expression – Gene prediction – Phenotypes – Population genetics – Serial analysis of gene expression


Zdroje

1. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med. 2015;12. doi: 10.1371/journal.pmed.1001779 25826379

2. NHLBI Trans-Omics for Precision Medicine. [cited 13 Nov 2018]. Available: https://www.nhlbiwgs.org/

3. NHGRI Genome Sequencing Program (GSP). In: National Human Genome Research Institute (NHGRI) [Internet]. [cited 13 Nov 2018]. Available: https://www.genome.gov/10001691/nhgri-genome-sequencing-program-gsp/

4. The 1000 Genomes Consortium. An integrated map of genetic variation from 1,092 human genomes | Nature. [cited 13 Nov 2018]. Available: https://www.nature.com/articles/nature11632

5. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48: 245–252. doi: 10.1038/ng.3506 26854917

6. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. 2015;47: 1091–1098. doi: 10.1038/ng.3367 26258848

7. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45: 580–585. doi: 10.1038/ng.2653 23715323

8. Battle A, Mostafavi S, Zhu X, Potash JB, Weissman MM, McCormick C, et al. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. 2014;24: 14–24. doi: 10.1101/gr.155192.113 24092820

9. Barbeira AN, Dickinson SP, Torres JM, Bonazzola R, Zheng J, Torstenson ES, et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun. 2018;9. doi: 10.1038/s41467-018-03621-1 29739930

10. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48: 481–487. doi: 10.1038/ng.3538 27019110

11. Mostafavi S, Gaiteri C, Sullivan SE, White CC, Tasaki S, Xu J, et al. A molecular network of the aging human brain provides insights into the pathology and cognitive decline of Alzheimer’s disease. Nat Neurosci. 2018;21: 811. doi: 10.1038/s41593-018-0154-9 29802388

12. Ferreira MAR, Jansen R, Willemsen G, Penninx B, Bain LM, Vicente CT, et al. Gene-based analysis of regulatory variants identifies four putative novel asthma risk genes related to nucleotide synthesis and signaling. J Allergy Clin Immunol. 2017;139: 1148–1157. doi: 10.1016/j.jaci.2016.07.017 27554816

13. Lamontagne M, Bérubé J-C, Obeidat M, Cho MH, Hobbs BD, Sakornsakolpat P, et al. Leveraging lung tissue transcriptome to uncover candidate causal genes in COPD genetic associations. Hum Mol Genet. 2018;27: 1819–1829. doi: 10.1093/hmg/ddy091 29547942

14. Thériault S, Gaudreault N, Lamontagne M, Rosa M, Boulanger M-C, Messika-Zeitoun D, et al. A transcriptome-wide association study identifies PALMD as a susceptibility gene for calcific aortic valve stenosis. Nat Commun. 2018;9: 988. doi: 10.1038/s41467-018-03260-6 29511167

15. Porcu E, Rüeger S, Consortium eQTLGen, Santoni FA, Reymond A, Kutalik Z. Mendelian Randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. bioRxiv. 2018; 377267. doi: 10.1101/377267

16. Gusev A, Lawrenson K, Segato F, Fonseca M, Kar S, Lee J, et al. Multi-Tissue Transcriptome-Wide Association Studies Identify 21 Novel Candidate Susceptibility Genes for High Grade Serous Epithelial Ovarian Cancer. bioRxiv. 2018; 330613. doi: 10.1101/330613

17. Huckins LM, Dobbyn A, Ruderfer D, Hoffman G, Wang W, Pardinas AF, et al. Gene expression imputation across multiple brain regions reveals schizophrenia risk throughout development. bioRxiv. 2017; 222596. doi: 10.1101/222596

18. Consortium GTEx. Genetic effects on gene expression across human tissues. Nature. 2017;550: 204–213. doi: 10.1038/nature24277 29022597

19. Bustamante CD, Burchard EG, De la Vega FM. Genomics for the world. Nature. 2011;475: 163–165. doi: 10.1038/475163a 21753830

20. Popejoy AB, Fullerton SM. Genomics is failing on diversity. Nature. 2016;538: 161–164. doi: 10.1038/538161a 27734877

21. Bentley AR, Callier S, Rotimi CN. Diversity and inclusion in genomic research: why the uneven progress? J Community Genet. 2017;8: 255–266. doi: 10.1007/s12687-017-0316-6 28770442

22. Hindorff LA, Bonham VL, Brody LC, Ginoza MEC, Hutter CM, Manolio TA, et al. Prioritizing diversity in human genomics research. Nat Rev Genet. 2018;19: 175–185. doi: 10.1038/nrg.2017.89 29151588

23. Asimit JL, Hatzikotoulas K, McCarthy M, Morris AP, Zeggini E. Trans-ethnic study design approaches for fine-mapping. Eur J Hum Genet. 2016;24: 1330–1336. doi: 10.1038/ejhg.2016.1 26839038

24. Wang X, Cheng C-Y, Liao J, Sim X, Liu J, Chia K-S, et al. Evaluation of transethnic fine mapping with population-specific and cosmopolitan imputation reference panels in diverse Asian populations. Eur J Hum Genet. 2016;24: 592–599. doi: 10.1038/ejhg.2015.150 26130488

25. Li YR, Keating BJ. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med. 2014;6: 91. doi: 10.1186/s13073-014-0091-5 25473427

26. Kumar R, Seibold MA, Aldrich MC, Williams LK, Reiner AP, Colangelo L, et al. Genetic ancestry in lung-function predictions. N Engl J Med. 2010;363: 321–330. doi: 10.1056/NEJMoa0907897 20647190

27. Yang JJ, Cheng C, Devidas M, Cao X, Fan Y, Campana D, et al. Ancestry and pharmacogenomics of relapse in acute lymphoblastic leukemia. Nat Genet. 2011;43: 237–241. doi: 10.1038/ng.763 21297632

28. Acuña-Alonzo V, Flores-Dorantes T, Kruit JK, Villarreal-Molina T, Arellano-Campos O, Hünemeier T, et al. A functional ABCA1 gene variant is associated with low HDL-cholesterol levels and shows evidence of positive selection in Native Americans. Hum Mol Genet. 2010;19: 2877–2885. doi: 10.1093/hmg/ddq173 20418488

29. Adeyemo A, Rotimi C. Genetic variants associated with complex human diseases show wide variation across multiple populations. Public Health Genomics. 2010;13: 72–79. doi: 10.1159/000218711 19439916

30. Manrai AK, Funke BH, Rehm HL, Olesen MS, Maron BA, Szolovits P, et al. Genetic Misdiagnoses and the Potential for Health Disparities. N Engl J Med. 2016;375: 655–665. doi: 10.1056/NEJMsa1507092 27532831

31. Petrovski S, Goldstein DB. Unequal representation of genetic variation across ancestry groups creates healthcare inequality in the application of precision medicine. Genome Biol. 2016;17: 157. doi: 10.1186/s13059-016-1016-y 27418169

32. Oh SS, White MJ, Gignoux CR, Burchard EG. Making Precision Medicine Socially Precise. Take a Deep Breath. Am J Respir Crit Care Med. 2016;193: 348–350. doi: 10.1164/rccm.201510-2045ED 26871667

33. Oh SS, Galanter J, Thakur N, Pino-Yanes M, Barcelo NE, White MJ, et al. Diversity in Clinical and Biomedical Research: A Promise Yet to Be Fulfilled. PLoS Med. 2015;12. doi: 10.1371/journal.pmed.1001918 26671224

34. Belbin GM, Nieves-Colón MA, Kenny EE, Moreno-Estrada A, Gignoux CR. Genetic diversity in populations across Latin America: implications for population and medical genetic studies. Curr Opin Genet Dev. 2018;53: 98–104. doi: 10.1016/j.gde.2018.07.006 30125792

35. Martin AR, Gignoux CR, Walters RK, Wojcik GL, Neale BM, Gravel S, et al. Human Demographic History Impacts Genetic Risk Prediction across Diverse Populations. Am J Hum Genet. 2017;100: 635–649. doi: 10.1016/j.ajhg.2017.03.004 28366442

36. Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol. 2002;156: 871–881. doi: 10.1093/aje/kwf113 12397006

37. Liu Y, Ding J, Reynolds LM, Lohman K, Register TC, De La Fuente A, et al. Methylomics of gene expression in human monocytes. Hum Mol Genet. 2013;22: 5065–5074. doi: 10.1093/hmg/ddt356 23900078

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

39. Mak ACY, White MJ, Eckalbar WL, Szpiech ZA, Oh SS, Pino-Yanes M, et al. Whole-Genome Sequencing of Pharmacogenetic Drug Response in Racially Diverse Children with Asthma. Am J Respir Crit Care Med. 2018;197: 1552–1564. doi: 10.1164/rccm.201712-2529OC 29509491

40. Thakur N, Oh SS, Nguyen EA, Martin M, Roth LA, Galanter J, et al. Socioeconomic status and childhood asthma in urban minority youths. The GALA II and SAGE II studies. Am J Respir Crit Care Med. 2013;188: 1202–1209. doi: 10.1164/rccm.201306-1016OC 24050698

41. Borrell LN, Nguyen EA, Roth LA, Oh SS, Tcheurekdjian H, Sen S, et al. Childhood Obesity and Asthma Control in the GALA II and SAGE II Studies. Am J Respir Crit Care Med. 2013;187: 697–702. doi: 10.1164/rccm.201211-2116OC 23392439

42. Nishimura KK, Galanter JM, Roth LA, Oh SS, Thakur N, Nguyen EA, et al. Early-life air pollution and asthma risk in minority children. The GALA II and SAGE II studies. Am J Respir Crit Care Med. 2013;188: 309–318. doi: 10.1164/rccm.201302-0264OC 23750510

43. Lappalainen T, Sammeth M, Friedländer MR, ‘t Hoen PA, Monlong J, Rivas MA, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501: 506–511. doi: 10.1038/nature12531 24037378

44. Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, et al. An integrated map of structural variation in 2,504 human genomes. Nature. 2015;526: 75–81. doi: 10.1038/nature15394 26432246

45. Mikhaylova AV, Thornton TA. Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations. Front Genet. 2019;10. doi: 10.3389/fgene.2019.00261 31001318

46. Fryett JJ, Morris AP, Cordell HJ. Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies. Genet Epidemiol. 2020;n/a. doi: 10.1002/gepi.22290 32190932

47. Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, et al. Population genomics of human gene expression. Nat Genet. 2007;39: 1217–1224. doi: 10.1038/ng2142 17873874

48. Viñuela A, Brown AA, Buil A, Tsai P-C, Davies MN, Bell JT, et al. Age-dependent changes in mean and variance of gene expression across tissues in a twin cohort. Hum Mol Genet. 2018;27: 732–741. doi: 10.1093/hmg/ddx424 29228364

49. McCall MN, Illei PB, Halushka MK. Complex Sources of Variation in Tissue Expression Data: Analysis of the GTEx Lung Transcriptome. Am J Hum Genet. 2016;99: 624–635. doi: 10.1016/j.ajhg.2016.07.007 27588449

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

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. Nat Commun. 2018;9: 490. doi: 10.1038/s41467-017-02772-x 29440659

52. Martin AR, Karczewski KJ, Kerminen S, Kurki MI, Sarin A-P, Artomov M, et al. Haplotype Sharing Provides Insights into Fine-Scale Population History and Disease in Finland. Am J Hum Genet. 2018;102: 760–775. doi: 10.1016/j.ajhg.2018.03.003 29706349

53. Yuan Y, Tian L, Lu D, Xu S. Analysis of Genome-Wide RNA-Sequencing Data Suggests Age of the CEPH/Utah (CEU) Lymphoblastoid Cell Lines Systematically Biases Gene Expression Profiles. Sci Rep. 2015;5: 7960. doi: 10.1038/srep07960 25609584

54. Çalışkan M, Pritchard JK, Ober C, Gilad Y. The Effect of Freeze-Thaw Cycles on Gene Expression Levels in Lymphoblastoid Cell Lines. PLOS ONE. 2014;9: e107166. doi: 10.1371/journal.pone.0107166 25192014

55. The International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature. 2010;467: 52–58. doi: 10.1038/nature09298 20811451

56. Su Z, Marchini J, Donnelly P. HAPGEN2: simulation of multiple disease SNPs. Bioinformatics. 2011;27: 2304–2305. doi: 10.1093/bioinformatics/btr341 21653516

57. Baharian S, Barakatt M, Gignoux CR, Shringarpure S, Errington J, Blot WJ, et al. The Great Migration and African-American Genomic Diversity. PLOS Genet. 2016;12: e1006059. doi: 10.1371/journal.pgen.1006059 27232753

58. Hoffmann TJ, Zhan Y, Kvale MN, Hesselson SE, Gollub J, Iribarren C, et al. Design and coverage of high throughput genotyping arrays optimized for individuals of East Asian, African American, and Latino race/ethnicity using imputation and a novel hybrid SNP selection algorithm. Genomics. 2011;98: 422–430. doi: 10.1016/j.ygeno.2011.08.007 21903159

59. Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48: 1284–1287. doi: 10.1038/ng.3656 27571263

60. Loh P-R, Danecek P, Palamara PF, Fuchsberger C, Reshef YA, Finucane HK, et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet. 2016;48: 1443–1448. doi: 10.1038/ng.3679 27694958

61. Wheeler HE, Shah KP, Brenner J, Garcia T, Aquino-Michaels K, Consortium Gte, et al. Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues. PLOS Genet. 2016;12: e1006423. doi: 10.1371/journal.pgen.1006423 27835642

62. Gravel S. Population genetics models of local ancestry. Genetics. 2012;191: 607–619. doi: 10.1534/genetics.112.139808 22491189

63. Price AL, Tandon A, Patterson N, Barnes KC, Rafaels N, Ruczinski I, et al. Sensitive detection of chromosomal segments of distinct ancestry in admixed populations. PLoS Genet. 2009;5: e1000519. doi: 10.1371/journal.pgen.1000519 19543370

64. Tange O. GNU Parallel 2018. Ole Tange; 2018. doi: 10.5281/zenodo.1146014

65. Shih DJH. argparser: Command-Line Argument Parser. 2016. Available: https://CRAN.R-project.org/package=argparser

66. Wickham H. assertthat: Easy Pre and Post Assertions. 2019. Available: https://CRAN.R-project.org/package=assertthat

67. Dowle M, Srinivasan A, Gorecki J, Chirico M, Stetsenko P, Short T, et al. data.table: Extension of “data.frame.” 2019. Available: https://CRAN.R-project.org/package=data.table

68. Calaway R, Corporation M, Weston S, Tenenbaum D. doParallel: Foreach Parallel Adaptor for the “parallel” Package. 2018. Available: https://CRAN.R-project.org/package=doParallel

69. Dinno A. dunn.test: Dunn’s Test of Multiple Comparisons Using Rank Sums. 2017. Available: https://CRAN.R-project.org/package=dunn.test

70. Xie Y, Vogt A, Andrew A, Zvoleff A, http://www.andre-simon.de) AS (the C files under inst/themes/ were derived from the H package, Atkins A, et al. knitr: A General-Purpose Package for Dynamic Report Generation in R. 2019. Available: https://CRAN.R-project.org/package=knitr

71. Davis TL, package.) AD (Some documentation and examples ported from the getopt, module.) PSF (Some documentation from the optparse P, Lianoglou S, Nikelski J, Müller K, et al. optparse: Command Line Option Parser. 2019. Available: https://CRAN.R-project.org/packa=optparsege

72. Gentleman R. annotate: Annotation for microarrays. 2018. Available: http://bioconductor.org/packages/annotate/

73. Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinforma Oxf Engl. 2005;21: 3439–3440. doi: 10.1093/bioinformatics/bti525 16082012

74. Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 2009;4: 1184–1191. doi: 10.1038/nprot.2009.97 19617889

75. Bolstad B. preprocessCore. 2017. Available: https://github.com/bmbolstad/preprocessCore

76. Wickham, Hadley, Grolemund, Garrett. R for Data Science. O’Reilly Media, Inc.; 2017. Available: https://r4ds.had.co.nz/

77. Wickham Hadley. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York; 2016. Available: http://ggplot2.org.


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