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

Nature Genetics volume 49, pages 1714–1721 (2017)Cite this article

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Abstract

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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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.

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European Nucleotide Archive

Gene Expression Omnibus

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Acknowledgements

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

Author notes

  1. Yu S Huang

    Present address: State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

  2. Vasily Ramensky

    Present address: Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation

  3. Christopher A Schmitt

    Present address: Department of Anthropology, Boston University, Boston, Massachusetts, USA

  4. Richard K Wilson

    Present address: Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, Ohio, USA

  5. J David Jentsch

    Present address: Department of Psychology, Binghamton University, Binghamton, New York, USA

Authors and Affiliations

  1. Department of Psychiatry and Biobehavioral Sciences, Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA

    Anna J Jasinska, Susan K Service, Rita M Cantor, Oi-Wa Choi, Joseph DeYoung, Lynn A Fairbanks, Scott Fears, Yu S Huang, Vasily Ramensky, Christopher A Schmitt, Giovanni Coppola & Nelson B Freimer

  2. Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland

    Anna J Jasinska

  3. Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, California, USA

    Ivette Zelaya

  4. Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA

    Christine B Peterson

  5. Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, USA

    Rita M Cantor, Eleazar Eskin & Nelson B Freimer

  6. Department of Computer Science, University of California, Los Angeles, Los Angeles, California, USA

    Eleazar Eskin

  7. Interdepartmental Graduate Program in Neuroscience, University of California, Los Angeles, Los Angeles, California, USA

    Allison E Furterer

  8. Wellcome Trust Sanger Institute, Hinxton, UK

    Hannes Svardal

  9. Department of Pathology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA

    Matthew J Jorgensen & Jay R Kaplan

  10. University of Cambridge, Cancer Research UK Cambridge Institute, Cambridge, UK

    Diego Villar

  11. European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, UK

    Bronwen L Aken, Paul Flicek, Rishi Nag & Emily S Wong

  12. South Texas Diabetes and Obesity Institute, UTHSCSA/UTRGV, Brownsville, Texas, USA

    John Blangero & Thomas D Dyer

  13. Faculty of Industrial Engineering and Management, Technion, Haifa, Israel

    Marina Bogomolov

  14. Department of Statistics and Operation Research, Tel Aviv University, Tel Aviv, Israel

    Yoav Benjamini

  15. Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA

    George M Weinstock

  16. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Ken Dewar

  17. Department of Biomedical Data Science, Stanford University, Stanford, California, USA

    Chiara Sabatti

  18. Department of Statistics, Stanford University, Stanford, California, USA

    Chiara Sabatti

  19. McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, USA

    Richard K Wilson & Wesley Warren

  20. Department of Psychology, University of California, Los Angeles, Los Angeles, California, USA

    J David Jentsch

  21. Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA

    J David Jentsch & Roger P Woods

  22. Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA

    Giovanni Coppola & Roger P Woods

Authors
  1. Anna J Jasinska
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  2. Ivette Zelaya
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  3. Susan K Service
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  4. Christine B Peterson
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  7. Joseph DeYoung
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  8. Eleazar Eskin
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  12. Yu S Huang
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  14. Christopher A Schmitt
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  18. Diego Villar
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  31. J David Jentsch
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  32. Wesley Warren
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  35. Nelson B Freimer
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Contributions

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.

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Supplementary Text and Figures

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

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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.

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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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