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It has not yet been peer reviewed by a journal.

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[Preprint]. 2023 Mar 6:2023.03.03.23286706.

doi: 10.1101/2023.03.03.23286706.

Cross-ancestry, cell-type-informed atlas of gene, isoform, and splicing regulation in the developing human brain

Cindy Wen^{1

2

3}, Michael Margolis^{2

3}, Rujia Dai⁴, Pan Zhang^{2

3}, Pawel F Przytycki⁵, Daniel D Vo^{2

3

6

7}, Arjun Bhattacharya^{8

9}, Nana Matoba^{10

11}, Chuan Jiao⁴, Minsoo Kim^{2

3}, Ellen Tsai^{2

3}, Celine Hoh^{2

3}, Nil Aygün^{10

11}, Rebecca L Walker^{1

2

3}, Christos Chatzinakos^{12

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14}, Declan Clarke¹⁵, Henry Pratt¹⁶, PsychENCODE Consortium^{1

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31

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44}, Mette A Peters¹⁷, Mark Gerstein^{15

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20}, Nikolaos P Daskalakis^{12

13

14}, Zhiping Weng¹⁶, Andrew E Jaffe^{21

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25

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27}, Joel E Kleinman^{21

22}, Thomas M Hyde^{21

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28}, Daniel R Weinberger^{21

22

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28}, Nicholas J Bray²⁹, Nenad Sestan^{30

31}, Daniel H Geschwind^{3

32

33}, Kathryn Roeder^{34

35}, Alexander Gusev^{36

37

38

39}, Bogdan Pasaniuc^{1

3

8

33

40}, Jason L Stein^{10

11}, Michael I Love^{10

41}, Katherine S Pollard^{5

42

43}, Chunyu Liu^{4

44}, Michael J Gandal^{1

2

3

6

7}

Affiliations

¹ Interdepartmental Program in Bioinformatics, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
² Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
³ Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
⁴ Department of Psychiatry, SUNY Upstate Medical University; Syracuse, NY, 13210, USA.
⁵ Gladstone Institute of Data Science and Biotechnology; San Francisco, CA, 94158, USA.
⁶ Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA, 19104, USA.
⁷ Lifespan Brain Institute, The Children's Hospital of Philadelphia; Philadelphia, PA, 19104, USA.
⁸ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
⁹ Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
¹⁰ Department of Genetics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA.
¹¹ UNC Neuroscience Center, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA.
¹² Department of Psychiatry, Harvard Medical School; Boston, MA, 02215, USA.
¹³ McLean Hospital; Belmont, MA, 02478, USA.
¹⁴ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA.
¹⁵ Department of Molecular Biophysics and Biochemistry, Yale University; New Haven, CT, 06520, USA.
¹⁶ Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School; Worcester, MA, 01605, USA.
¹⁷ CNS Data Coordination Group, Sage Bionetworks; Seattle, WA, 98109, USA.
¹⁸ Program in Computational Biology and Bioinformatics, Yale University; New Haven, CT, 06520, USA.
¹⁹ Department of Computer Science, Yale University; New Haven, CT, 06520, USA.
²⁰ Department of Statistics and Data Science, Yale University; New Haven, CT, 06520, USA.
²¹ Lieber Institute for Brain Development; Baltimore, MD, 21205, USA.
²² Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA.
²³ Department of Neuroscience, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA.
²⁴ Department of Genetic Medicine, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA.
²⁵ Department of Mental Health, Johns Hopkins Bloomberg School of Public Health; Baltimore, MD, 21205, USA.
²⁶ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health; Baltimore, MD, 21205, USA.
²⁷ Neumora Therapeutics; Watertown, MA, 02472, USA.
²⁸ Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA.
²⁹ MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University School of Medicine; Cardiff, CF24 4HQ, UK.
³⁰ Department of Comparative Medicine, Yale University School of Medicine; New Haven, CT, 06520, USA.
³¹ Department of Neuroscience, Yale University School of Medicine; New Haven, CT, 06520, USA.
³² Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
³³ Institute for Precision Health, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
³⁴ Department of Statistics & Data Science, Carnegie Mellon University; Pittsburgh, PA, 15213, USA.
³⁵ Computational Biology Department, Carnegie Mellon University; Pittsburgh, PA, 15213, USA.
³⁶ Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute; Boston, MA, 02215, USA.
³⁷ Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA.
³⁸ Harvard Medical School; Boston, MA, 02215, USA.
³⁹ Division of Genetics, Brigham and Women's Hospital; Boston, MA, 02215, USA.
⁴⁰ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
⁴¹ Department of Biostatistics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA.
⁴² Department of Epidemiology & Biostatistics, University of California, San Francisco; San Francisco, CA, 94158, USA.
⁴³ Chan Zuckerberg Biohub; San Francisco, CA, 94158, USA.
⁴⁴ Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University; Changsha, Hunan, 410008, China.

PMID: 36945630
PMCID: PMC10029021
DOI: 10.1101/2023.03.03.23286706

Cross-ancestry, cell-type-informed atlas of gene, isoform, and splicing regulation in the developing human brain

Cindy Wen et al. medRxiv. 2023.

[Preprint]. 2023 Mar 6:2023.03.03.23286706.

doi: 10.1101/2023.03.03.23286706.

Authors

Affiliations

¹ Interdepartmental Program in Bioinformatics, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
² Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
³ Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
⁴ Department of Psychiatry, SUNY Upstate Medical University; Syracuse, NY, 13210, USA.
⁵ Gladstone Institute of Data Science and Biotechnology; San Francisco, CA, 94158, USA.
⁶ Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA, 19104, USA.
⁷ Lifespan Brain Institute, The Children's Hospital of Philadelphia; Philadelphia, PA, 19104, USA.
⁸ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
⁹ Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
¹⁰ Department of Genetics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA.
¹¹ UNC Neuroscience Center, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA.
¹² Department of Psychiatry, Harvard Medical School; Boston, MA, 02215, USA.
¹³ McLean Hospital; Belmont, MA, 02478, USA.
¹⁴ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA.
¹⁵ Department of Molecular Biophysics and Biochemistry, Yale University; New Haven, CT, 06520, USA.
¹⁶ Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School; Worcester, MA, 01605, USA.
¹⁷ CNS Data Coordination Group, Sage Bionetworks; Seattle, WA, 98109, USA.
¹⁸ Program in Computational Biology and Bioinformatics, Yale University; New Haven, CT, 06520, USA.
¹⁹ Department of Computer Science, Yale University; New Haven, CT, 06520, USA.
²⁰ Department of Statistics and Data Science, Yale University; New Haven, CT, 06520, USA.
²¹ Lieber Institute for Brain Development; Baltimore, MD, 21205, USA.
²² Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA.
²³ Department of Neuroscience, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA.
²⁴ Department of Genetic Medicine, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA.
²⁵ Department of Mental Health, Johns Hopkins Bloomberg School of Public Health; Baltimore, MD, 21205, USA.
²⁶ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health; Baltimore, MD, 21205, USA.
²⁷ Neumora Therapeutics; Watertown, MA, 02472, USA.
²⁸ Department of Neurology, Johns Hopkins University School of Medicine; Baltimore, MD, 21205, USA.
²⁹ MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University School of Medicine; Cardiff, CF24 4HQ, UK.
³⁰ Department of Comparative Medicine, Yale University School of Medicine; New Haven, CT, 06520, USA.
³¹ Department of Neuroscience, Yale University School of Medicine; New Haven, CT, 06520, USA.
³² Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
³³ Institute for Precision Health, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
³⁴ Department of Statistics & Data Science, Carnegie Mellon University; Pittsburgh, PA, 15213, USA.
³⁵ Computational Biology Department, Carnegie Mellon University; Pittsburgh, PA, 15213, USA.
³⁶ Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute; Boston, MA, 02215, USA.
³⁷ Broad Institute of MIT and Harvard; Cambridge, MA, 02142, USA.
³⁸ Harvard Medical School; Boston, MA, 02215, USA.
³⁹ Division of Genetics, Brigham and Women's Hospital; Boston, MA, 02215, USA.
⁴⁰ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles; Los Angeles, CA, 90095, USA.
⁴¹ Department of Biostatistics, University of North Carolina at Chapel Hill; Chapel Hill, NC, 27599, USA.
⁴² Department of Epidemiology & Biostatistics, University of California, San Francisco; San Francisco, CA, 94158, USA.
⁴³ Chan Zuckerberg Biohub; San Francisco, CA, 94158, USA.
⁴⁴ Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University; Changsha, Hunan, 410008, China.

PMID: 36945630
PMCID: PMC10029021
DOI: 10.1101/2023.03.03.23286706

Update in

Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain.
Wen C, Margolis M, Dai R, Zhang P, Przytycki PF, Vo DD, Bhattacharya A, Matoba N, Tang M, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker RL, Chatzinakos C, Clarke D, Pratt H; PsychENCODE Consortium†; Peters MA, Gerstein M, Daskalakis NP, Weng Z, Jaffe AE, Kleinman JE, Hyde TM, Weinberger DR, Bray NJ, Sestan N, Geschwind DH, Roeder K, Gusev A, Pasaniuc B, Stein JL, Love MI, Pollard KS, Liu C, Gandal MJ; PsychENCODE Consortium. Wen C, et al. Science. 2024 May 24;384(6698):eadh0829. doi: 10.1126/science.adh0829. Epub 2024 May 24. Science. 2024. PMID: 38781368 Free PMC article.

Abstract

Genomic regulatory elements active in the developing human brain are notably enriched in genetic risk for neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia, and bipolar disorder. However, prioritizing the specific risk genes and candidate molecular mechanisms underlying these genetic enrichments has been hindered by the lack of a single unified large-scale gene regulatory atlas of human brain development. Here, we uniformly process and systematically characterize gene, isoform, and splicing quantitative trait loci (xQTLs) in 672 fetal brain samples from unique subjects across multiple ancestral populations. We identify 15,752 genes harboring a significant xQTL and map 3,739 eQTLs to a specific cellular context. We observe a striking drop in gene expression and splicing heritability as the human brain develops. Isoform-level regulation, particularly in the second trimester, mediates the greatest proportion of heritability across multiple psychiatric GWAS, compared with eQTLs. Via colocalization and TWAS, we prioritize biological mechanisms for ~60% of GWAS loci across five neuropsychiatric disorders, nearly two-fold that observed in the adult brain. Finally, we build a comprehensive set of developmentally regulated gene and isoform co-expression networks capturing unique genetic enrichments across disorders. Together, this work provides a comprehensive view of genetic regulation across human brain development as well as the stage-and cell type-informed mechanistic underpinnings of neuropsychiatric disorders.

PubMed Disclaimer

Conflict of interest statement

M.J.G. and D.H.G. receive grant funding from Mitsubishi Tanabe Pharma America.

Figures

**Fig. 1.. The landscape of gene, splicing, and isoform regulation in the developing human brain.**
**(A)** The number of eGenes discovered here versus sample size, compared with other human brain studies. **(B)** Overlap of eGenes between fetal brain (n=629), GTEx v8 Brain Cortex (n=205), and PsychENCODE (n=1,387). **(C)** Correlation of eQTL effect size, measured by allelic fold change (aFC), between fetal and adult brain. Each dot is a shared pair of eGene-primary eQTL between fetal brain and GTEx (247 pairs) or PsychENCODE (253 pairs). **(D)** Overlap among eGenes, isoGenes, and sGenes. **(E)** Distance between the transcription start site (TSS) of each target gene of *cis*-eQTL, *cis*-isoQTL, and *cis*-sQTL SNPs. **(F)** Enrichment of *cis*-eQTL, *cis*-isoQTL, and *cis*-sQTLs within functional regions of the genome. **(G)** Loss-of-function mutation intolerance, as measured by pLI score, of eGenes, isoGenes, and sGenes. **(H)** Storey’s pi1 statistic of the proportion of true associations in the discovery group of QTL (y-axis) that are also true associations in the replication group of QTL (x-axis). **(I)** Number of fine-mapping credible sets versus number of conditionally independent eQTLs discovered. The size of the dots is scaled to the number of genes. **(J)** Common recurrent Inversion-QTLs in the developing brain. Inversions are displayed according to their length and the number of overlapping SNPs. Inversions with significantly associated eGenes have filled circles. The size of the circle indicates the population frequency of the inversions.

**Fig. 2.. Cross-population gene regulation and fine-mapping.**
**(A)** Genotype PCA of the fetal brain samples. Population structure was inferred by merging imputed genotypes with 1000 Genomes. **(B)** Comparison of eGenes discovered in the full multi-population dataset (“ALL”, n=629) and in the separate sub-populations, EUR (n=280), AMR (n=162), and AFR (n=135). **(C)** Correlation of eQTL effect sizes between AMR/AFR (top/bottom) and EUR, as measured by allelic fold change. Each dot is an AMR/AFR eGene-primary eQTL pair and colored by its nominal significance in EUR. Grey lines denote the lower and upper bounds of aFC. **(D)** Comparison of fine-mapping credible set sizes between the populations. **(E)** *Cis* associations for the gene *MTFR1* in the cross-population, EUR, AMR, and AFR datasets.

**Fig. 3.. Trimester-specific patterns of gene expression and splicing regulation.**
**(A)** Comparison of eGenes discovered in Tri1, Tri2, and the full dataset. Notably, we identify many more eGenes in Tri1 than Tri2 despite similar sample sizes. **(B)** Trimester-specific eQTLs for *WARS2,* where rs146862216 (G>A) is an eQTL in Tri1 (beta=−0.89, FDR=3.88e-13) but not in Tri2 (beta=−0.03, P-value=0.71). **(C)** *cis*-heritability of gene expression drops from Tri1 to Tri2 timepoints, and between fetal and adult (PsychENCODE) samples. **(D)** *cis*-heritability of gene expression across 10-18 post-conception weeks (PCW) in fetal brain EUR samples. For D and G, each dot represents a sliding set of temporally ordered samples (n=150), with mean age (+/−SD) on the x-axis, and median *cis*-h²_SNP (+/−SD) on the y-axis. **(E)** Comparison of sGenes discovered in Tri1, Tri2, and the full dataset. **(F)** *cis* heritability of intron excision ratios in Tri1 vs Tri2. **(G)** *cis* heritability of local splining in fetal brain EUR samples over development, as in (D). **(H)** Comparison of gene and cell type enrichment of Tri1-only and Tri2-only e/sGenes.

**Fig. 4.. Integrative analysis of fetal xQTLs with neuropsychiatric GWAS.**
**(A)** Quantile-quantile plot of SCZ GWAS p-values, subsetted by top *cis*-eQTLs, *cis*-isoQTLs, and *cis*-sQTLs in comparison to all background GWAS SNPs. (B) s-LDSC enrichment of SCZ GWAS heritability within fetal brain xQTLx, adult brain cortex eQTLs (GTEx v8), compared with background functional annotations. The proportional genomic coverage of SNPs within each annotation are shown in parentheses. For B, C, D: *** FDR<0.001, ** FDR<0.01, * FDR<0.05. (C) Estimated proportion (+/−SE) of GWAS h²_SNP mediated by the *cis* genetic component of gene, isoform, and intron (splicing) regulation. Isoform-level QTLs mediate the greatest degree of heritability for multiple neuropsychiatric traits in the developing brain, compared with e/sQTLs. (D) Estimated proportion of GWAS h²_SNP mediated by the *cis* genetic component of trimester-stratified gene-, isoform-, and intron (splicing)-regulation.

**Fig. 5.. Neuropsychiatric risk gene prioritization through colocalization and isoTWAS.**
(A) Total number of GWAS loci exhibiting significant colocalization (CLPP>0.01) with specific fetal brain xQTL annotations. From left to right, bars represent the cumulative number of GWAS loci colocalized with each additional annotation. Shown in grey are GWAS loci that are not colocalized with any annotation. (B) Colocalization between SCZ GWAS and fetal brain xQTLs, ranked by CLPP and grouped by GWAS locus, as indicated by the number to the right. (C) Top: locus plots of SCZ GWAS with *SP4* e- and sQTLs. A significant colocalization is observed for SCZ GWAS with a cryptic splicing event in *SP4*. Notably, *SP4* does not have a detectable eQTL in the fetal brain. Middle: Gene structure of *SP4* with and without cryptic exon inclusion, likely resulting in nonsense mediated decay. Bottom left: sashimi plot shows the density of exon and junction read mapping for intron cluster chr7:21516925-21521542, stratified by the colocalized sQTL. Bottom right: sQTL rs10276352 (G>A) increases the contribution to cluster of annotated intron chr:21516925-21521542. The SCZ risk allele increases cryptic exon inclusion. (D) Fetal brain isoTWAS associations with SCZ GWAS. Each dot represents an isoform and genes with significant, prioritized isoforms are colored in red.

**Fig. 6.. Systems-level integration of risk variation with developmental gene and isoform co-expression.**
(A) Workflow for construction of gene and isoform-level co-expression networks, followed by cell-type, pathway, and disease gene enrichment analyses. Separate gene co-expression networks were built to capture trimester and sex-specific effects. (B) Top: hierarchical clustering of modules from gene, isoform, trimester and sex-stratified networks through bi-weight mid-correlation of eigengenes. Middle: heatmaps depict module-level enrichment for neuropsychiatric GWAS signal (−log₁₀P_enrich from s-LDSC and MAGMA) and odds ratios (OR) for rare variation and cell type enrichment (truncated at 10). Triangles indicate FDR-corrected P<0.1 significance. (C) Annotations for M2, a development-wide disease-associated chromatin regulation module. Center: top module (‘hub’) genes with circle size reflecting module membership (kME) and orange shading indicating genes with associated high-confidence neuropsychiatric disorder associated rare variants. Thin edges represent topologic overlap, solid edges indicate protein-protein interactions from the STRING database. Surrounding: circular bar plot highlighting module enrichment for cell types (purple), common (red) and rare (red-orange) variation, gene ontology terms (dark green), and module overlap (light green). (D) Annotations for M82, a SCZ/BIP-associated deep layer neuron-projection isoform module. (E) Annotations for M59, an ADHD-associated mitochondrial/proteasome isoform module.

**Fig. 7.. Module interacting eQTLs and context-specific GWAS colocalization.**
(A) Hierarchical clustering of *cis*-eQTL enrichments among specific fetal brain cell-types as mapped by CellWalker. Outermost numbers denote results from single cell type label analysis. Inner numbers denote results from a broader, multi-level label analysis. See Methods for details. (B) A schematic of module interaction ieQTL mapping and validation in cultured neurons and progenitors. (C) Results from module ieQTL mapping. From top to bottom, pi1 statistics depicting ieQTL overlap with eQTLs from cultured neurons or neural progenitor cells (NPCs), molecular feature (gene/isoform), number of ieQTLs, and cell type enrichment. 62 modules with pi1>0.2 in either neurons/NPCs are shown. (D) Colocalization between SCZ GWAS and *BRINP2* ieQTL rs17659437 in M93 (CLPP=0.02). (E) Annotation for M93, a SCZ/BIP enriched deep-layer neuronal/synaptic module. (F) Trajectory of M93 eigengene expression across brain development, colored by biological sex.

See this image and copyright information in PMC

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Cross-ancestry, cell-type-informed atlas of gene, isoform, and splicing regulation in the developing human brain

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Cross-ancestry, cell-type-informed atlas of gene, isoform, and splicing regulation in the developing human brain

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