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From expression QTLs to personalized transcriptomics

Abstract

Approaches that combine expression quantitative trait loci (eQTLs) and genome-wide association (GWA) studies are offering new functional information about the aetiology of complex human traits and diseases. Improved study designs — which take into account technological advances in resolving the transcriptome, cell history and state, population of origin and diverse endophenotypes — are providing insights into the architecture of disease and the landscape of gene regulation in humans. Furthermore, these advances are helping to establish links between cellular effects and organismal traits.

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Figure 1: Gene regulatory architecture through expression quantitative trait locus studies.

References

  1. 1

    Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    CAS  Article  Google Scholar 

  2. 2

    Manolio, T. A. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363, 166–176 (2010).

    CAS  Article  Google Scholar 

  3. 3

    Nica, A. C. et al. Candidate causal regulatory effects by integration of expression QTLs with complex trait genetic associations. PLoS Genet. 6, e1000895 (2010).

    Article  Google Scholar 

  4. 4

    Nicolae, D. L. et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 6, e1000888 (2010).

    Article  Google Scholar 

  5. 5

    Montgomery, S. B. et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464, 773–777 (2010).

    CAS  Article  Google Scholar 

  6. 6

    Pickrell, A. M. & Moraes, C. T. What role does mitochondrial stress play in neurodegenerative diseases? Methods Mol. Biol. 648, 63–78 (2010).

    CAS  Article  Google Scholar 

  7. 7

    Birney, E. et al. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799–816 (2007).

    CAS  Article  Google Scholar 

  8. 8

    Heintzman, N. D. et al. Histone modifications at human enhancers reflect global cell-type-specific gene expression. Nature 459, 108–112 (2009).

    CAS  Article  Google Scholar 

  9. 9

    Schadt, E. E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6, e107 (2008).

    Article  Google Scholar 

  10. 10

    Myers, A. J. et al. A survey of genetic human cortical gene expression. Nature Genet. 39, 1494–1499 (2007).

    CAS  Article  Google Scholar 

  11. 11

    Emilsson, V. et al. Genetics of gene expression and its effect on disease. Nature 452, 423–428 (2008).

    CAS  Article  Google Scholar 

  12. 12

    Heinzen, E. L. et al. Tissue-specific genetic control of splicing: implications for the study of complex traits. PLoS Biol. 6, e1 (2008).

    Article  Google Scholar 

  13. 13

    Dimas, A. S. et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 325, 1246–1250 (2009).

    CAS  Article  Google Scholar 

  14. 14

    Gerrits, A. et al. Expression quantitative trait loci are highly sensitive to cellular differentiation state. PLoS Genet. 5, e1000692 (2009).

    Article  Google Scholar 

  15. 15

    Grundberg, E. et al. Population genomics in a disease targeted primary cell model. Genome Res. 19, 1942–1952 (2009).

    CAS  Article  Google Scholar 

  16. 16

    Kwan, T. et al. Tissue effect on genetic control of transcript isoform variation. PLoS Genet. 5, e1000608 (2009).

    Article  Google Scholar 

  17. 17

    Altshuler, D. M. et al. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

    CAS  Article  Google Scholar 

  18. 18

    Frazer, K. A. et al. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

    CAS  Article  Google Scholar 

  19. 19

    Spielman, R. S. et al. Common genetic variants account for differences in gene expression among ethnic groups. Nature Genet. 39, 226–231 (2007).

    CAS  Article  Google Scholar 

  20. 20

    Storey, J. D. et al. Gene-expression variation within and among human populations. Am. J. Hum. Genet. 80, 502–509 (2007).

    CAS  Article  Google Scholar 

  21. 21

    Stranger, B. E. et al. Population genomics of human gene expression. Nature Genet. 39, 1217–1224 (2007).

    CAS  Article  Google Scholar 

  22. 22

    Zaitlen, N., Pasaniuc, B., Gur, T., Ziv, E. & Halperin, E. Leveraging genetic variability across populations for the identification of causal variants. Am. J. Hum. Genet. 86, 23–33 (2010).

    CAS  Article  Google Scholar 

  23. 23

    Schadt, E. E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nature Genet. 37, 710–717 (2005).

    CAS  Article  Google Scholar 

  24. 24

    Moffatt, M. F. et al. Genetic variants regulating ORMDL3 expression contribute to the risk of childhood asthma. Nature 448, 470–473 (2007).

    CAS  Article  Google Scholar 

  25. 25

    Dixon, A. L. et al. A genome-wide association study of global gene expression. Nature Genet. 39, 1202–1207 (2007).

    CAS  Article  Google Scholar 

  26. 26

    Arnosti, D. N. & Kulkarni, M. M. Transcriptional enhancers: Intelligent enhanceosomes or flexible billboards? J. Cell Biochem. 94, 890–898 (2005).

    CAS  Article  Google Scholar 

  27. 27

    van Nas, A. et al. Expression quantitative trait loci: replication, tissue- and sex-specificity in mice. Genetics 185, 1059–1068 (2010).

    CAS  Article  Google Scholar 

  28. 28

    Nica, A. E. A. The architecture of gene regulatory variation across multiple human tissues: the MuTHER Study. PLoS Genet. 7, e1002003 (2011).

    CAS  Article  Google Scholar 

  29. 29

    Gilad, Y., Rifkin, S. A. & Pritchard, J. K. Revealing the architecture of gene regulation: the promise of eQTL studies. Trends Genet. 24, 408–415 (2008).

    CAS  Article  Google Scholar 

  30. 30

    Price, A. L. et al. Effects of cis and trans genetic ancestry on gene expression in African Americans. PLoS Genet. 4, e1000294 (2008).

    Article  Google Scholar 

  31. 31

    Cheung, V. G. et al. Polymorphic cis- and trans-regulation of human gene expression. PLoS Biol. 8, e1000480 (2010).

    Article  Google Scholar 

  32. 32

    Goring, H. H. et al. Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nature Genet. 39, 1208–1216 (2007).

    Article  Google Scholar 

  33. 33

    Petretto, E. et al. New insights into the genetic control of gene expression using a Bayesian multi-tissue approach. PLoS Comput. Biol. 6, e1000737 (2010).

    Article  Google Scholar 

  34. 34

    Yvert, G. et al. Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nature Genet. 35, 57–64 (2003).

    CAS  Article  Google Scholar 

  35. 35

    Breitling, R. et al. Genetical genomics: spotlight on QTL hotspots. PLoS Genet. 4, e1000232 (2008).

    Article  Google Scholar 

  36. 36

    Sun, W., Yu, T. & Li, K. C. Detection of eQTL modules mediated by activity levels of transcription factors. Bioinformatics 23, 2290–2297 (2007).

    CAS  Article  Google Scholar 

  37. 37

    Wu, C. et al. Gene set enrichment in eQTL data identifies novel annotations and pathway regulators. PLoS Genet. 4, e1000070 (2008).

    Article  Google Scholar 

  38. 38

    Ghazalpour, A. et al. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet. 2, e130 (2006).

    Article  Google Scholar 

  39. 39

    Ren, X., Zhou, X., Wu, L. Y. & Zhang, X. S. An information-flow-based model with dissipation, saturation and direction for active pathway inference. BMC Syst. Biol. 4, 72 (2010).

    Article  Google Scholar 

  40. 40

    Li, S., Lu, Q. & Cui, Y. A systems biology approach for identifying novel pathway regulators in eQTL mapping. J. Biopharm. Stat. 20, 373–400 (2010).

    Article  Google Scholar 

  41. 41

    Rashid, I., McDermott, J. & Samudrala, R. Inferring molecular interactions pathways from eQTL data. Methods Mol. Biol. 541, 211–223 (2009).

    CAS  Article  Google Scholar 

  42. 42

    Wessel, J., Zapala, M. A. & Schork, N. J. Accommodating pathway information in expression quantitative trait locus analysis. Genomics 90, 132–142 (2007).

    CAS  Article  Google Scholar 

  43. 43

    Suthram, S., Beyer, A., Karp, R. M., Eldar, Y. & Ideker, T. eQED: an efficient method for interpreting eQTL associations using protein networks. Mol. Syst. Biol. 4, 162 (2008).

    Article  Google Scholar 

  44. 44

    Lee, E. & Bussemaker, H. J. Identifying the genetic determinants of transcription factor activity. Mol. Syst. Biol. 6, 412 (2010).

    Article  Google Scholar 

  45. 45

    Pickrell, J. K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).

    CAS  Article  Google Scholar 

  46. 46

    McDaniell, R. et al. Heritable individual-specific and allele-specific chromatin signatures in humans. Science 328, 235–239 (2010).

    CAS  Article  Google Scholar 

  47. 47

    Dubois, P. C. et al. Multiple common variants for celiac disease influencing immune gene expression. Nature Genet. 42, 295–302 (2010).

    CAS  Article  Google Scholar 

  48. 48

    Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature Genet. 42, 937–948 (2010).

    CAS  Article  Google Scholar 

  49. 49

    Anttila, V. et al. Genome-wide association study of migraine implicates a common susceptibility variant on 8q22.1. Nature Genet. 42, 869–873 (2010).

    CAS  Article  Google Scholar 

  50. 50

    Yamanaka, S. & Blau, H. M. Nuclear reprogramming to a pluripotent state by three approaches. Nature 465, 704–712 (2010).

    CAS  Article  Google Scholar 

  51. 51

    Durbin, R. M. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

    CAS  Article  Google Scholar 

  52. 52

    Willer, C. J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nature Genet. 41, 25–34 (2009).

    CAS  Article  Google Scholar 

  53. 53

    Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).

    CAS  Article  Google Scholar 

  54. 54

    Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    CAS  Article  Google Scholar 

  55. 55

    Crawford, N. P. et al. Rrp1b, a new candidate susceptibility gene for breast cancer progression and metastasis. PLoS Genet. 3, e214 (2007).

    Article  Google Scholar 

  56. 56

    Libioulle, C. et al. Novel Crohn disease locus identified by genome-wide association maps to a gene desert on 5p13.1 and modulates expression of PTGER4. PLoS Genet. 3, e58 (2007).

    Article  Google Scholar 

  57. 57

    Heid, I. M. et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nature Genet. 42, 949–960 (2010).

    CAS  Article  Google Scholar 

  58. 58

    Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

    CAS  Article  Google Scholar 

  59. 59

    Soranzo, N. et al. Meta-analysis of genome-wide scans for human adult stature identifies novel loci and associations with measures of skeletal frame size. PLoS Genet. 5, e1000445 (2009).

    Article  Google Scholar 

  60. 60

    Wheeler, H. E. et al. Sequential use of transcriptional profiling, expression quantitative trait mapping, and gene association implicates MMP20 in human kidney aging. PLoS Genet. 5, e1000685 (2009).

    Article  Google Scholar 

  61. 61

    Cunnington, M. S., Santibanez Koref, M., Mayosi, B. M., Burn, J. & Keavney, B. Chromosome 9p21 SNPs associated with multiple disease phenotypes correlate with ANRIL expression. PLoS Genet. 6, e1000899 (2010).

    Article  Google Scholar 

  62. 62

    Hsu, Y. H. et al. An integration of genome-wide association study and gene expression profiling to prioritize the discovery of novel susceptibility loci for osteoporosis-related traits. PLoS Genet. 6, e1000977 (2010).

    Article  Google Scholar 

  63. 63

    Rivadeneira, F. et al. Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nature Genet. 41, 1199–1206 (2009).

    CAS  Article  Google Scholar 

  64. 64

    Simon-Sanchez, J. et al. Genome-wide association study reveals genetic risk underlying Parkinson's disease. Nature Genet. 41, 1308–1312 (2009).

    CAS  Article  Google Scholar 

  65. 65

    Hamza, T. H. et al. Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson's disease. Nature Genet. 42, 781–785 (2010).

    CAS  Article  Google Scholar 

  66. 66

    Stuart, P. E. et al. Genome-wide association analysis identifies three psoriasis susceptibility loci. Nature Genet. 42, 1000–1004 (2010).

    CAS  Article  Google Scholar 

  67. 67

    Sotoodehnia, N. et al. Common variants in 22 loci are associated with QRS duration and cardiac ventricular conduction. Nature Genet. 42, 1068–1076 (2010).

    CAS  Article  Google Scholar 

  68. 68

    Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nature Genet. 42, 105–116 (2010).

    CAS  Article  Google Scholar 

  69. 69

    Voight, B. F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nature Genet. 42, 579–589 (2010).

    CAS  Article  Google Scholar 

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Acknowledgements

We acknowledge funds from the Louis-Jeantet Foundation, the Swiss National Science Foundation and the European Commission and the help and comments of our Functional Population Genomics group in Geneva.

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Genotype-Tissue Expression (GTEx) Project

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Montgomery, S., Dermitzakis, E. From expression QTLs to personalized transcriptomics. Nat Rev Genet 12, 277–282 (2011). https://doi.org/10.1038/nrg2969

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