Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genetic variation and gene expression across multiple tissues and developmental stages in a nonhuman primate


By analyzing multitissue gene expression and genome-wide genetic variation data in samples from a vervet monkey pedigree, we generated a transcriptome resource and produced the first catalog of expression quantitative trait loci (eQTLs) in a nonhuman primate model. This catalog contains more genome-wide significant eQTLs per sample than comparable human resources and identifies sex- and age-related expression patterns. Findings include a master regulatory locus that likely has a role in immune function and a locus regulating hippocampal long noncoding RNAs (lncRNAs), whose expression correlates with hippocampal volume. This resource will facilitate genetic investigation of quantitative traits, including brain and behavioral phenotypes relevant to neuropsychiatric disorders.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Principal-components analysis of the 1,000 genes with the most variable expression levels.
Figure 2: Box plots of log-transformed expression in samples of BA46 from 58 animals by time point for three genes with a strong relationship between expression pattern and age.
Figure 3: Master regulatory locus on vervet chromosome CAE9.
Figure 4: Hippocampal volume QTL and local hippocampal eQTLs in RNA-seq analysis.
Figure 5: Correlation in 16 animals of hippocampal volume (MRI) with hippocampal expression of three genes.

Accession codes


European Nucleotide Archive

Gene Expression Omnibus


  1. Hindorff, L.A. et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. USA 106, 9362–9367 (2009).

    CAS  Article  Google Scholar 

  2. 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 

  3. Albert, F.W. & Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212 (2015).

    CAS  Article  Google Scholar 

  4. 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 

  5. Gibson, G., Powell, J.E. & Marigorta, U.M. Expression quantitative trait locus analysis for translational medicine. Genome Med. 7, 60 (2015).

    Article  Google Scholar 

  6. Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).

    CAS  Article  Google Scholar 

  7. Melé, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).

    Article  Google Scholar 

  8. Jennings, C.G. et al. Opportunities and challenges in modeling human brain disorders in transgenic primates. Nat. Neurosci. 19, 1123–1130 (2016).

    Article  Google Scholar 

  9. Rogers, J. & Gibbs, R.A. Comparative primate genomics: emerging patterns of genome content and dynamics. Nat. Rev. Genet. 15, 347–359 (2014).

    CAS  Article  Google Scholar 

  10. Jasinska, A.J. et al. Systems biology of the vervet monkey. ILAR J. 54, 122–143 (2013).

    CAS  Article  Google Scholar 

  11. Huang, Y.S. et al. Sequencing strategies and characterization of 721 vervet monkey genomes for future genetic analyses of medically relevant traits. BMC Biol. 13, 41 (2015).

    Article  Google Scholar 

  12. Jasinska, A.J. et al. Identification of brain transcriptional variation reproduced in peripheral blood: an approach for mapping brain expression traits. Hum. Mol. Genet. 18, 4415–4427 (2009).

    CAS  Article  Google Scholar 

  13. Stein, J.L. et al. Identification of common variants associated with human hippocampal and intracranial volumes. Nat. Genet. 44, 552–561 (2012).

    CAS  Article  Google Scholar 

  14. Warren, W.C. et al. The genome of the vervet (Chlorocebus aethiopssabaeus). Genome Res. 25, 1921–1933 (2015).

    CAS  Article  Google Scholar 

  15. Arnett, M.G., Muglia, L.M., Laryea, G. & Muglia, L.J. Genetic approaches to hypothalamic–pituitary–adrenal axis regulation. Neuropsychopharmacology 41, 245–260 (2016).

    CAS  Article  Google Scholar 

  16. McEwen, B.S., Gray, J.D. & Nasca, C. Redefining neuroendocrinology: stress, sex and cognitive and emotional regulation. J. Endocrinol. 226, T67–T83 (2015).

    CAS  Article  Google Scholar 

  17. Nestler, E., Hyman, S., Holtzman, D. & Malenka, R. Molecular Neuropharmacology: A Foundation for Clinical Neuroscience (McGraw-Hill Education/Medical, 2015).

  18. Cáceres, M., Suwyn, C., Maddox, M., Thomas, J.W. & Preuss, T.M. Increased cortical expression of two synaptogenic thrombospondins in human brain evolution. Cereb. Cortex 17, 2312–2321 (2007).

    Article  Google Scholar 

  19. Gaujoux, R. & Seoighe, C. CellMix: a comprehensive toolbox for gene expression deconvolution. Bioinformatics 29, 2211–2212 (2013).

    CAS  Article  Google Scholar 

  20. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    CAS  Article  Google Scholar 

  21. Yu, Q. & He, Z. Comprehensive investigation of temporal and autism-associated cell type composition–dependent and independent gene expression changes in human brains. Sci. Rep. 7, 4121 (2017).

    Article  Google Scholar 

  22. Almasy, L. & Blangero, J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am. J. Hum. Genet. 62, 1198–1211 (1998).

    CAS  Article  Google Scholar 

  23. Mähler, N. et al. Gene co-expression network connectivity is an important determinant of selective constraint. PLoS Genet. 13, e1006402 (2017).

    Article  Google Scholar 

  24. Bogomolov, M., Peterson, C.B., Benjamini, Y. & Sabatti, C. Testing hypotheses on a tree: new error rates and controlling strategies. Preprint at (2017).

  25. Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).

    CAS  Article  Google Scholar 

  26. Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  Article  Google Scholar 

  27. Tung, J., Zhou, X., Alberts, S.C., Stephens, M. & Gilad, Y. The genetic architecture of gene expression levels in wild baboons. eLife 4, e04729 (2015).

    Article  Google Scholar 

  28. Vermunt, M.W. et al. Epigenomic annotation of gene regulatory alterations during evolution of the primate brain. Nat. Neurosci. 19, 494–503 (2016).

    CAS  Article  Google Scholar 

  29. Villar, D. et al. Enhancer evolution across 20 mammalian species. Cell 160, 554–566 (2015).

    CAS  Article  Google Scholar 

  30. Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    CAS  Article  Google Scholar 

  31. Young, R.S. et al. The frequent evolutionary birth and death of functional promoters in mouse and human. Genome Res. 25, 1546–1557 (2015).

    CAS  Article  Google Scholar 

  32. Daugherty, M.D., Schaller, A.M., Geballe, A.P. & Malik, H.S. Evolution-guided functional analyses reveal diverse antiviral specificities encoded by IFIT1 genes in mammals. eLife 5, e14228 (2016).

    Article  Google Scholar 

  33. Pierce, B.L. et al. Mediation analysis demonstrates that trans-eQTLs are often explained by cis-mediation: a genome-wide analysis among 1,800 South Asians. PLoS Genet. 10, e1004818 (2014).

    Article  Google Scholar 

  34. Fears, S.C. et al. Identifying heritable brain phenotypes in an extended pedigree of vervet monkeys. J. Neurosci. 29, 2867–2875 (2009).

    CAS  Article  Google Scholar 

  35. Mattick, J.S. & Rinn, J.L. Discovery and annotation of long noncoding RNAs. Nat. Struct. Mol. Biol. 22, 5–7 (2015).

    CAS  Article  Google Scholar 

  36. Ulitsky, I. & Bartel, D.P. lincRNAs: genomics, evolution, and mechanisms. Cell 154, 26–46 (2013).

    CAS  Article  Google Scholar 

  37. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Article  Google Scholar 

  38. GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

  39. Wang, J. et al. Imputing gene expression in uncollected tissues within and beyond GTEx. Am. J. Hum. Genet. 98, 697–708 (2016).

    CAS  Article  Google Scholar 

  40. Sargiannidou, I. et al. Connexin32 mutations cause loss of function in Schwann cells and oligodendrocytes leading to PNS and CNS myelination defects. J. Neurosci. 29, 4736–4749 (2009).

    CAS  Article  Google Scholar 

  41. Bergoffen, J. et al. Connexin mutations in X-linked Charcot–Marie–Tooth disease. Science 262, 2039–2042 (1993).

    CAS  Article  Google Scholar 

  42. Bond, J. et al. ASPM is a major determinant of cerebral cortical size. Nat. Genet. 32, 316–320 (2002).

    CAS  Article  Google Scholar 

  43. Tang, B.S. et al. Small heat-shock protein 22 mutated in autosomal dominant Charcot–Marie–Tooth disease type 2L. Hum. Genet. 116, 222–224 (2005).

    CAS  Article  Google Scholar 

  44. Eriksson, P.S. et al. Neurogenesis in the adult human hippocampus. Nat. Med. 4, 1313–1317 (1998).

    CAS  Article  Google Scholar 

  45. van Praag, H. et al. Functional neurogenesis in the adult hippocampus. Nature 415, 1030–1034 (2002).

    CAS  Article  Google Scholar 

  46. Pichlmair, A. et al. IFIT1 is an antiviral protein that recognizes 5′-triphosphate RNA. Nat. Immunol. 12, 624–630 (2011).

    CAS  Article  Google Scholar 

  47. Brodziak, F., Meharg, C., Blaut, M. & Loh, G. Differences in mucosal gene expression in the colon of two inbred mouse strains after colonization with commensal gut bacteria. PLoS One 8, e72317 (2013).

    CAS  Article  Google Scholar 

  48. Sato, Y. et al. Cellular transcriptional coactivator RanBP10 and herpes simplex virus 1 ICP0 interact and synergistically promote viral gene expression and replication. J. Virol. 90, 3173–3186 (2016).

    CAS  Article  Google Scholar 

  49. Azevedo, C. et al. The RAR1 interactor SGT1, an essential component of R gene–triggered disease resistance. Science 295, 2073–2076 (2002).

    CAS  Article  Google Scholar 

  50. Mayor, A., Martinon, F., De Smedt, T., Pétrilli, V. & Tschopp, J. A crucial function of SGT1 and HSP90 in inflammasome activity links mammalian and plant innate immune responses. Nat. Immunol. 8, 497–503 (2007).

    CAS  Article  Google Scholar 

  51. Naitza, S. et al. A genome-wide association scan on the levels of markers of inflammation in Sardinians reveals associations that underpin its complex regulation. PLoS Genet. 8, e1002480 (2012).

    CAS  Article  Google Scholar 

  52. Bakken, T.E. et al. A comprehensive transcriptional map of primate brain development. Nature 535, 367–375 (2016).

    CAS  Article  Google Scholar 

  53. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  54. Anders, S., Pyl, P.T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Article  Google Scholar 

  55. Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Article  Google Scholar 

  56. Andersen, C.L., Jensen, J.L. & Ørntoft, T.F. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 64, 5245–5250 (2004).

    CAS  Article  Google Scholar 

  57. Heath, S.C., Snow, G.L., Thompson, E.A., Tseng, C. & Wijsman, E.M. MCMC segregation and linkage analysis. Genet. Epidemiol. 14, 1011–1016 (1997).

    CAS  Article  Google Scholar 

  58. Jasinska, A.J. et al. A genetic linkage map of the vervet monkey (Chlorocebus aethiops sabaeus). Mamm. Genome 18, 347–360 (2007).

    CAS  Article  Google Scholar 

  59. Kang, H.M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).

    CAS  Article  Google Scholar 

  60. Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).

    CAS  Article  Google Scholar 

  61. Peterson, C.B., Bogomolov, M., Benjamini, Y. & Sabatti, C. Many phenotypes without many false discoveries: error controlling strategies for multitrait association studies. Genet. Epidemiol. 40, 45–56 (2016).

    Article  Google Scholar 

  62. Simes, R.J. An improved Bonferroni procedure for multiple tests of significance. Biometrika 73, 751–754 (1986).

    Article  Google Scholar 

  63. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  64. Benjamini, Y. & Bogomolov, M. Selective inference on multiple families of hypotheses. J. R. Stat. Soc. B 76, 297–318 (2014).

    Article  Google Scholar 

  65. Peterson, C.B., Bogomolov, M., Benjamini, Y. & Sabatti, C. TreeQTL: hierarchical error control for eQTL findings. Bioinformatics 32, 2556–2558 (2016).

    CAS  Article  Google Scholar 

  66. Chen, H. et al. Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models. Am. J. Hum. Genet. 98, 653–666 (2016).

    CAS  Article  Google Scholar 

Download references


We thank S. Groman for assistance with tissue resources; T. Chavanne, K. Finnie, M. Long, J. Gardin and D. Swaim for technical assistance with necropsies and tissue collections; and M. Vermunt for sharing rhesus macaque epigenetic data. This work was supported by the following grants, all from the US National Institutes of Health: U54HG00307907 (to R.K.W.); P40RR019963/OD010965 (to J.R.K.); R01RR016300/OD010980 (to N.B.F.); R37MH060233 (to D. Geschwind); UL1DE019580 (to R. Bilder); PL1NS062410 (to C. Evans); RL1MH083270 (to J.D.J.); P30NS062691 (to N.B.F. and G.C.); and R01MH101782 (to C.S. and E.E.). R.N., B.L.A. and P.F. acknowledge support from the Wellcome Trust. P.F., B.L.A. and R.N. acknowledge support from the Wellcome Trust (grant WT108749/Z/15/Z) and the European Molecular Biology Laboratory. E.S.W. was supported by an EMBO Advanced Fellowship (aALTF1672-2014). A.E.F. was supported by US National Institutes of Health award T32MH073526. Data were generated as part of the CommonMind Consortium supported by funding from Takeda Pharmaceuticals Company, F. Hoffman-La Roche and US National Institutes of Health grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1MH-075916, P50M096891, P50MH084053S1, R37MH057881 and R37MH057881S1, HHSN271201300031C, AG02219, AG05138 and MH06692. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer's Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories, and the National Institute of Mental Health (NIMH) Human Brain Collection Core. CMC Leadership: P. Sklar, J. Buxbaum (Icahn School of Medicine at Mount Sinai), B. Devlin, D. Lewis (University of Pittsburgh), R. Gur, C.-G. Hahn (University of Pennsylvania), K. Hirai, H. Toyoshiba (Takeda Pharmaceuticals Company), E. Domenici, L. Essioux (F. Hoffman-La Roche), L. Mangravite, M. Peters (Sage Bionetworks), T. Lehner, B. Lipska (NIMH).

Author information

Authors and Affiliations



A.J.J., J.R.K., G.M.W., K.D., R.K.W., J.D.J., W.W., R.P.W. and N.B.F. designed the study. A.J.J., I.Z., O.-W.C., J.D., L.A.F., S.F., A.E.F., Y.S.H., V.R., C.A.S., J.D.J., G.C. and R.P.W. produced the data. A.J.J., I.Z., S.K.S., C.B.P., R.M.C., E.E., L.A.F., S.F., Y.S.H., V.R., C.A.S., H.S., D.V., B.L.A., P.F., R.N., E.S.W., J.B., T.D.D., M.B., Y.B., C.S. and G.C. analyzed the data. O.-W.C., J.D. and M.J.J. managed data and samples. A.J.J., S.K.S. and N.B.F. wrote the manuscript. All authors reviewed the final draft.

Corresponding author

Correspondence to Nelson B Freimer.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Principal components 1, 2, 3 and 6 from analysis of gene expression levels (RNA-seq) in seven tissues.

PC1 (47.5% of total variance) separates fibroblast from brain tissues and PC2 (18.2% of variance) separates blood from all other tissues, while the three brain regions do not separate until PC6 (2% of variance).

Supplementary Figure 2 Vervet age-related genes in tissues BA46 and caudate.

In BA46, MOG, MAG, OPALIN and MBP are involved in myelination. ASPM and NDRG1 are age-related genes in caudate.

Supplementary Figure 3 Expression profiles of orthologs of three vervet genes with clear developmental trajectories in BA46 obtained from similar tissues for human and rhesus macaque in Allen Brain Atlas data.

Top row: THBS1, THBS2 and THBS4 in human DLPFC (n = 18). Bottom row: THBS1, THBS2 and THBS4 in rhesus medial frontal cortex (n = 12). Genes shown are the orthologs of three vervet genes that had clear developmental trajectories in BA46 (Figure 2).

Supplementary Figure 4 Expression profiles of orthologs of six vervet genes with clear developmental trajectories in BA46 and caudate in vervet in similar tissues from human and rhesus macaque in Allen Brain Atlas data.

Top row: MOG, MAG, OPALIN and MBP in human DLPFC (n = 18). Bottom row: ASPM and NDRG1 in human caudate (n = 14; left and middle) and ASPM in rhesus macaque basal nuclei (n = 11; right). Genes MOG, MAG, OPALIN, MBP and NDRG1 were not represented in the rhesus macaque data set in the Allen Brain Atlas. Genes shown are the orthologs of six vervet genes with clear developmental trajectories in BA46 and caudate (Supplementary Fig. 2).

Supplementary Figure 5 Cell type composition in each animal and distribution of scaled entropy of cell type.

Deconvolution analysis was applied to vervet BA46, caudate, hippocampus and blood, and the proportion of cell types is presented for each animal, as well as the distribution, over 58 vervets, of scaled entropy for each tissue.

Supplementary Figure 6 Distribution of cell type composition by age for vervet BA46.

Deconvolution analysis was applied to vervet BA46. The distribution of cell type proportions is plotted for six vervet age groups.

Supplementary Figure 7 Distribution of cell type composition by age for vervet caudate.

Deconvolution analysis was applied to vervet caudate. The distribution of cell type proportions is plotted for six vervet age groups.

Supplementary Figure 8 Distribution of cell type composition by age for vervet hippocampus.

Deconvolution analysis was applied to vervet hippocampus. The distribution of cell type proportions is plotted for six vervet age groups.

Supplementary Figure 9 Distribution of cell type composition by age for vervet blood.

Deconvolution analysis was applied to vervet blood. The distribution of cell type proportions is plotted for six vervet age groups.

Supplementary Figure 10 eGene sharing among vervet tissues.

The intersection of FDR-significant eGenes among seven vervet tissues used in data set 2.

Supplementary Figure 11 Genic and regulatory regions show enrichment for vervet eQTLs.

Forest plot representing analysis of enrichment of eQTLs in genic and regulatory regions. The log odds ratio is on the x axis, and horizontal lines around each estimate represent the 95% confidence interval. Liver Me and Liver Ac stand for, respectively, H3K4me3 and H3K27ac marks in vervet liver. Rhesus caudate Ac and Rhesus prefrontal Ac stand for the vervet-orthologous locations of HDK27ac epigenetic marks in rhesus macaque caudate and prefrontal cortex, respectively.

Supplementary Figure 12 Increase in the proportion of eQTL SNPs, in comparison to all SNPs, in the region of the TSS and TES.

For each eQTL SNP, we noted the distance from the SNP to the TSS or TES of the gene to which it was associated. For non-eQTL SNPs, we noted the distances of the SNP to the TSS or TES of all genes within 200 kb of the SNP. Distances upstream of the TSS and downstream of the TES were binned into 10-kb intervals, and the number of SNPs in each distance bin was recorded. As genes are of different sizes, for each gene, the interval between the TSS and TES was divided into ten equally sized intervals. The ratio of the number of eQTLs to non-eQTL SNPs was noted for each distance bin. The figure represents a summation over the 27,196 genes; a formal statistical analysis of enrichment was not attempted because SNPs were often within 200 kb of the TSS or TES of multiple genes.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Tables 1, 2, 4–6 and 10–24, and Supplementary Note

Life Sciences Reporting Summary

Supplementary Table 3

Genes with high expression in one of the seven tissues evaluated with RNA-seq.

Supplementary Table 7

Genes in the top or bottom 10% of loadings on PC1–PC3.

Supplementary Table 8

Genes in the top or bottom 10% of loadings on PC1 for caudate and BA46.

Supplementary Table 9

Genes with a significant age effect from either ANOVA or linear regression.

Supplementary Data 1

Listing of all 22,184 probes on the HumanRef-8 v2 array and indicators for different probe filtering criteria.

Supplementary Data 2

Estimates of heritability for 6,018 probes that passed filtering criteria described in Supplementary Table 1.

Supplementary Data 3

All significant probe–SNP combinations (at Bonferroni thresholds) from the analysis of microarray gene expression.

Supplementary Data 4

All significant gene–SNP combinations (at Bonferroni thresholds) from the analysis of RNA-seq expression data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jasinska, A., Zelaya, I., Service, S. et al. Genetic variation and gene expression across multiple tissues and developmental stages in a nonhuman primate. Nat Genet 49, 1714–1721 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing