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.
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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).
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).
Albert, F.W. & Kruglyak, L. The role of regulatory variation in complex traits and disease. Nat. Rev. Genet. 16, 197–212 (2015).
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).
Gibson, G., Powell, J.E. & Marigorta, U.M. Expression quantitative trait locus analysis for translational medicine. Genome Med. 7, 60 (2015).
Kang, H.J. et al. Spatio-temporal transcriptome of the human brain. Nature 478, 483–489 (2011).
Melé, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).
Jennings, C.G. et al. Opportunities and challenges in modeling human brain disorders in transgenic primates. Nat. Neurosci. 19, 1123–1130 (2016).
Rogers, J. & Gibbs, R.A. Comparative primate genomics: emerging patterns of genome content and dynamics. Nat. Rev. Genet. 15, 347–359 (2014).
Jasinska, A.J. et al. Systems biology of the vervet monkey. ILAR J. 54, 122–143 (2013).
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).
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).
Stein, J.L. et al. Identification of common variants associated with human hippocampal and intracranial volumes. Nat. Genet. 44, 552–561 (2012).
Warren, W.C. et al. The genome of the vervet (Chlorocebus aethiopssabaeus). Genome Res. 25, 1921–1933 (2015).
Arnett, M.G., Muglia, L.M., Laryea, G. & Muglia, L.J. Genetic approaches to hypothalamic–pituitary–adrenal axis regulation. Neuropsychopharmacology 41, 245–260 (2016).
McEwen, B.S., Gray, J.D. & Nasca, C. Redefining neuroendocrinology: stress, sex and cognitive and emotional regulation. J. Endocrinol. 226, T67–T83 (2015).
Nestler, E., Hyman, S., Holtzman, D. & Malenka, R. Molecular Neuropharmacology: A Foundation for Clinical Neuroscience (McGraw-Hill Education/Medical, 2015).
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).
Gaujoux, R. & Seoighe, C. CellMix: a comprehensive toolbox for gene expression deconvolution. Bioinformatics 29, 2211–2212 (2013).
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).
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).
Almasy, L. & Blangero, J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am. J. Hum. Genet. 62, 1198–1211 (1998).
Mähler, N. et al. Gene co-expression network connectivity is an important determinant of selective constraint. PLoS Genet. 13, e1006402 (2017).
Bogomolov, M., Peterson, C.B., Benjamini, Y. & Sabatti, C. Testing hypotheses on a tree: new error rates and controlling strategies. Preprint at https://arxiv.org/abs/1705.07529 (2017).
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
Maurano, M.T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
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).
Vermunt, M.W. et al. Epigenomic annotation of gene regulatory alterations during evolution of the primate brain. Nat. Neurosci. 19, 494–503 (2016).
Villar, D. et al. Enhancer evolution across 20 mammalian species. Cell 160, 554–566 (2015).
Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).
Young, R.S. et al. The frequent evolutionary birth and death of functional promoters in mouse and human. Genome Res. 25, 1546–1557 (2015).
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).
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).
Fears, S.C. et al. Identifying heritable brain phenotypes in an extended pedigree of vervet monkeys. J. Neurosci. 29, 2867–2875 (2009).
Mattick, J.S. & Rinn, J.L. Discovery and annotation of long noncoding RNAs. Nat. Struct. Mol. Biol. 22, 5–7 (2015).
Ulitsky, I. & Bartel, D.P. lincRNAs: genomics, evolution, and mechanisms. Cell 154, 26–46 (2013).
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
GTEx Consortium. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).
Wang, J. et al. Imputing gene expression in uncollected tissues within and beyond GTEx. Am. J. Hum. Genet. 98, 697–708 (2016).
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).
Bergoffen, J. et al. Connexin mutations in X-linked Charcot–Marie–Tooth disease. Science 262, 2039–2042 (1993).
Bond, J. et al. ASPM is a major determinant of cerebral cortical size. Nat. Genet. 32, 316–320 (2002).
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).
Eriksson, P.S. et al. Neurogenesis in the adult human hippocampus. Nat. Med. 4, 1313–1317 (1998).
van Praag, H. et al. Functional neurogenesis in the adult hippocampus. Nature 415, 1030–1034 (2002).
Pichlmair, A. et al. IFIT1 is an antiviral protein that recognizes 5′-triphosphate RNA. Nat. Immunol. 12, 624–630 (2011).
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).
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).
Azevedo, C. et al. The RAR1 interactor SGT1, an essential component of R gene–triggered disease resistance. Science 295, 2073–2076 (2002).
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).
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).
Bakken, T.E. et al. A comprehensive transcriptional map of primate brain development. Nature 535, 367–375 (2016).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Anders, S., Pyl, P.T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
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).
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).
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).
Jasinska, A.J. et al. A genetic linkage map of the vervet monkey (Chlorocebus aethiops sabaeus). Mamm. Genome 18, 347–360 (2007).
Kang, H.M. et al. Variance component model to account for sample structure in genome-wide association studies. Nat. Genet. 42, 348–354 (2010).
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).
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).
Simes, R.J. An improved Bonferroni procedure for multiple tests of significance. Biometrika 73, 751–754 (1986).
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).
Benjamini, Y. & Bogomolov, M. Selective inference on multiple families of hypotheses. J. R. Stat. Soc. B 76, 297–318 (2014).
Peterson, C.B., Bogomolov, M., Benjamini, Y. & Sabatti, C. TreeQTL: hierarchical error control for eQTL findings. Bioinformatics 32, 2556–2558 (2016).
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).
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).
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).
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.
Deconvolution analysis was applied to vervet BA46. The distribution of cell type proportions is plotted for six vervet age groups.
Deconvolution analysis was applied to vervet caudate. The distribution of cell type proportions is plotted for six vervet age groups.
Deconvolution analysis was applied to vervet hippocampus. The distribution of cell type proportions is plotted for six vervet age groups.
Deconvolution analysis was applied to vervet blood. The distribution of cell type proportions is plotted for six vervet age groups.
The intersection of FDR-significant eGenes among seven vervet tissues used in data set 2.
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 Figures 1–12, Supplementary Tables 1, 2, 4–6 and 10–24, and Supplementary Note
Genes with high expression in one of the seven tissues evaluated with RNA-seq.
Genes in the top or bottom 10% of loadings on PC1–PC3.
Genes in the top or bottom 10% of loadings on PC1 for caudate and BA46.
Genes with a significant age effect from either ANOVA or linear regression.
Listing of all 22,184 probes on the HumanRef-8 v2 array and indicators for different probe filtering criteria.
Estimates of heritability for 6,018 probes that passed filtering criteria described in Supplementary Table 1.
All significant probe–SNP combinations (at Bonferroni thresholds) from the analysis of microarray gene expression.
All significant gene–SNP combinations (at Bonferroni thresholds) from the analysis of RNA-seq expression data.
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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). https://doi.org/10.1038/ng.3959
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