. 2024 May 24;384(6698):eadh0829.

doi: 10.1126/science.adh0829. Epub 2024 May 24.

Cross-ancestry 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}, Miao Tang^{6

7}, Chuan Jiao^{4

12}, Minsoo Kim^{2

3}, Ellen Tsai^{2

3}, Celine Hoh^{2

3}, Nil Aygün^{10

11}, Rebecca L Walker^{1

2

3}, Christos Chatzinakos^{13

14

15}, Declan Clarke¹⁶, Henry Pratt¹⁷; PsychENCODE Consortium†; Mette A Peters¹⁸, Mark Gerstein^{16

19

20

21}, Nikolaos P Daskalakis^{13

14

15}, Zhiping Weng¹⁷, Andrew E Jaffe^{22

23

24

25

26

27

28}, Joel E Kleinman^{22

23}, Thomas M Hyde^{22

23

29}, Daniel R Weinberger^{22

23

24

25

29}, Nicholas J Bray³⁰, Nenad Sestan^{31

32}, Daniel H Geschwind^{3

33

34}, Kathryn Roeder^{35

36}, Alexander Gusev^{37

38

39

40}, Bogdan Pasaniuc^{1

3

8

34

41}, Jason L Stein^{10

11}, Michael I Love^{10

42}, Katherine S Pollard^{5

43

44}, Chunyu Liu^{4

45}, Michael J Gandal^{1

2

3

6

7}; PsychENCODE Consortium

Collaborators, Affiliations

Collaborators

Schahram Akbarian, Alexej Abyzov, Nadav Ahituv, Dhivya Arasappan, Jose Juan Almagro Armenteros, Brian J Beliveau, Jaroslav Bendl, Sabina Berretta, Rahul A Bharadwaj, Lucy Bicks, Kristen Brennand, Davide Capauto, Frances A Champagne, Tanima Chatterjee, Chris Chatzinakos, Yuhang Chen, H Isaac Chen, Yuyan Cheng, Lijun Cheng, Andrew Chess, Jo-Fan Chien, Zhiyuan Chu, Ashley Clement, Leonardo Collado-Torres, Gregory M Cooper, Gregory E Crawford, Jose Davila-Velderrain, Amy Deep-Soboslay, Chengyu Deng, Christopher P DiPietro, Stella Dracheva, Shiron Drusinsky, Ziheng Duan, Duc Duong, Cagatay Dursun, Nicholas J Eagles, Jonathan Edelstein, Prashant S Emani, John F Fullard, Kiki Galani, Timur Galeev, Sophia Gaynor, Kiran Girdhar, Fernando S Goes, William Greenleaf, Jennifer Grundman, Hanmin Guo, Qiuyu Guo, Chirag Gupta, Yoav Hadas, Joachim Hallmayer, Xikun Han, Vahram Haroutunian, Natalie Hawken, Chuan He, Ella Henry, Stephanie C Hicks, Marcus Ho, Li-Lun Ho, Gabriel E Hoffman, Yiling Huang, Louise A Huuki-Myers, Ahyeon Hwang, Artemis Iatrou, Fumitaka Inoue, Aarti Jajoo, Matthew Jensen, Lihua Jiang, Peng Jin, Ting Jin, Connor Jops, Alexandre Jourdon, Riki Kawaguchi, Manolis Kellis, Steven P Kleopoulos, Alex Kozlenkov, Arnold Kriegstein, Anshul Kundaje, Soumya Kundu, Cheyu Lee, Donghoon Lee, Junhao Li, Mingfeng Li, Xiao Lin, Shuang Liu, Jason Liu, Jianyin Liu, Shuang Liu, Shaoke Lou, Jacob M Loupe, Dan Lu, Shaojie Ma, Liang Ma, Jessica Mariani, Keri Martinowich, Kristen R Maynard, Samantha Mazariegos, Ran Meng, Richard M Myers, Courtney Micallef, Tatiana Mikhailova, Guo-Li Ming, Shahin Mohammadi, Emma Monte, Kelsey S Montgomery, Jill E Moore, Jennifer R Moran, Eran A Mukamel, Angus C Nairn, Charles B Nemeroff, Pengyu Ni, Scott Norton, Tomasz Nowakowski, Larsson Omberg, Stephanie C Page, Saejeong Park, Ashok Patowary, Reenal Pattni, Geo Pertea, Nishigandha Phalke, Dalila Pinto, Milos Pjanic, Sirisha Pochareddy, Alex Pollen, Carolin Purmann, Zhaohui S Qin, Ping-Ping Qu, Diana Quintero, Towfique Raj, Ananya S Rajagopalan, Sarah Reach, Thomas Reimonn, Kerry J Ressler, Deanna Ross, Panos Roussos, Joel Rozowsky, Misir Ruth, W Brad Ruzicka, Stephan J Sanders, Juliane M Schneider, Soraya Scuderi, Robert Sebra, Nicholas Seyfried, Zhiping Shao, Nicole Shedd, Annie W Shieh, Joo Heon Shin, Mario Skarica, Clara Snijders, Hongjun Song, Matthew W State, Marilyn Steyert, Sivan Subburaju, Thomas Sudhof, Michael Snyder, Ran Tao, Karen Therrien, Li-Huei Tsai, Alexander E Urban, Flora M Vaccarino, Harm van Bakel, Georgios Voloudakis, Brie Wamsley, Tao Wang, Sidney H Wang, Daifeng Wang, Yifan Wang, Jonathan Warrell, Yu Wei, Annika K Weimer, Sean Whalen, Kevin P White, A Jeremy Willsey, Hyejung Won, Wing Wong, Hao Wu, Feinan Wu, Stefan Wuchty, Dennis Wylie, Siwei Xu, Chloe X Yap, Biao Zeng, Chunling Zhang, Bin Zhang, Jing Zhang, Yanqiong Zhang, Xiao Zhou, Ryan Ziffra, Zane R Zeier, Trisha M Zintel

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 Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team Krebs, 75014 Paris, France.
¹³ Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA.
¹⁴ McLean Hospital, Belmont, MA 02478, USA.
¹⁵ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
¹⁶ Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
¹⁷ Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
¹⁸ CNS Data Coordination Group, Sage Bionetworks, Seattle, WA 98109, USA.
¹⁹ Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
²⁰ Department of Computer Science, Yale University, New Haven, CT 06520, USA.
²¹ Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA.
²² Lieber Institute for Brain Development, Baltimore, MD 21205, USA.
²³ Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
²⁴ Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
²⁵ Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
²⁶ Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
²⁷ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
²⁸ Neumora Therapeutics, Watertown, MA 02472, USA.
²⁹ Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
³⁰ MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University School of Medicine, Cardiff CF24 4HQ, UK.
³¹ Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT 06520, USA.
³² Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA.
³³ Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
³⁴ Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA.
³⁵ Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
³⁶ Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
³⁷ Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
³⁸ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
³⁹ Harvard Medical School, Boston, MA 02215, USA.
⁴⁰ Division of Genetics, Brigham and Women's Hospital, Boston, MA 02215, USA.
⁴¹ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁴² Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
⁴³ Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA.
⁴⁴ Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
⁴⁵ Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China.

PMID: 38781368
PMCID: PMC12147038
DOI: 10.1126/science.adh0829

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

Cindy Wen et al. Science. 2024.

. 2024 May 24;384(6698):eadh0829.

doi: 10.1126/science.adh0829. Epub 2024 May 24.

Authors

Collaborators

Schahram Akbarian, Alexej Abyzov, Nadav Ahituv, Dhivya Arasappan, Jose Juan Almagro Armenteros, Brian J Beliveau, Jaroslav Bendl, Sabina Berretta, Rahul A Bharadwaj, Lucy Bicks, Kristen Brennand, Davide Capauto, Frances A Champagne, Tanima Chatterjee, Chris Chatzinakos, Yuhang Chen, H Isaac Chen, Yuyan Cheng, Lijun Cheng, Andrew Chess, Jo-Fan Chien, Zhiyuan Chu, Ashley Clement, Leonardo Collado-Torres, Gregory M Cooper, Gregory E Crawford, Jose Davila-Velderrain, Amy Deep-Soboslay, Chengyu Deng, Christopher P DiPietro, Stella Dracheva, Shiron Drusinsky, Ziheng Duan, Duc Duong, Cagatay Dursun, Nicholas J Eagles, Jonathan Edelstein, Prashant S Emani, John F Fullard, Kiki Galani, Timur Galeev, Sophia Gaynor, Kiran Girdhar, Fernando S Goes, William Greenleaf, Jennifer Grundman, Hanmin Guo, Qiuyu Guo, Chirag Gupta, Yoav Hadas, Joachim Hallmayer, Xikun Han, Vahram Haroutunian, Natalie Hawken, Chuan He, Ella Henry, Stephanie C Hicks, Marcus Ho, Li-Lun Ho, Gabriel E Hoffman, Yiling Huang, Louise A Huuki-Myers, Ahyeon Hwang, Artemis Iatrou, Fumitaka Inoue, Aarti Jajoo, Matthew Jensen, Lihua Jiang, Peng Jin, Ting Jin, Connor Jops, Alexandre Jourdon, Riki Kawaguchi, Manolis Kellis, Steven P Kleopoulos, Alex Kozlenkov, Arnold Kriegstein, Anshul Kundaje, Soumya Kundu, Cheyu Lee, Donghoon Lee, Junhao Li, Mingfeng Li, Xiao Lin, Shuang Liu, Jason Liu, Jianyin Liu, Shuang Liu, Shaoke Lou, Jacob M Loupe, Dan Lu, Shaojie Ma, Liang Ma, Jessica Mariani, Keri Martinowich, Kristen R Maynard, Samantha Mazariegos, Ran Meng, Richard M Myers, Courtney Micallef, Tatiana Mikhailova, Guo-Li Ming, Shahin Mohammadi, Emma Monte, Kelsey S Montgomery, Jill E Moore, Jennifer R Moran, Eran A Mukamel, Angus C Nairn, Charles B Nemeroff, Pengyu Ni, Scott Norton, Tomasz Nowakowski, Larsson Omberg, Stephanie C Page, Saejeong Park, Ashok Patowary, Reenal Pattni, Geo Pertea, Nishigandha Phalke, Dalila Pinto, Milos Pjanic, Sirisha Pochareddy, Alex Pollen, Carolin Purmann, Zhaohui S Qin, Ping-Ping Qu, Diana Quintero, Towfique Raj, Ananya S Rajagopalan, Sarah Reach, Thomas Reimonn, Kerry J Ressler, Deanna Ross, Panos Roussos, Joel Rozowsky, Misir Ruth, W Brad Ruzicka, Stephan J Sanders, Juliane M Schneider, Soraya Scuderi, Robert Sebra, Nicholas Seyfried, Zhiping Shao, Nicole Shedd, Annie W Shieh, Joo Heon Shin, Mario Skarica, Clara Snijders, Hongjun Song, Matthew W State, Marilyn Steyert, Sivan Subburaju, Thomas Sudhof, Michael Snyder, Ran Tao, Karen Therrien, Li-Huei Tsai, Alexander E Urban, Flora M Vaccarino, Harm van Bakel, Georgios Voloudakis, Brie Wamsley, Tao Wang, Sidney H Wang, Daifeng Wang, Yifan Wang, Jonathan Warrell, Yu Wei, Annika K Weimer, Sean Whalen, Kevin P White, A Jeremy Willsey, Hyejung Won, Wing Wong, Hao Wu, Feinan Wu, Stefan Wuchty, Dennis Wylie, Siwei Xu, Chloe X Yap, Biao Zeng, Chunling Zhang, Bin Zhang, Jing Zhang, Yanqiong Zhang, Xiao Zhou, Ryan Ziffra, Zane R Zeier, Trisha M Zintel

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 Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team Krebs, 75014 Paris, France.
¹³ Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA.
¹⁴ McLean Hospital, Belmont, MA 02478, USA.
¹⁵ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
¹⁶ Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA.
¹⁷ Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
¹⁸ CNS Data Coordination Group, Sage Bionetworks, Seattle, WA 98109, USA.
¹⁹ Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
²⁰ Department of Computer Science, Yale University, New Haven, CT 06520, USA.
²¹ Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA.
²² Lieber Institute for Brain Development, Baltimore, MD 21205, USA.
²³ Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
²⁴ Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
²⁵ Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
²⁶ Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
²⁷ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
²⁸ Neumora Therapeutics, Watertown, MA 02472, USA.
²⁹ Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
³⁰ MRC Centre for Neuropsychiatric Genetics & Genomics, Division of Psychological Medicine & Clinical Neurosciences, Cardiff University School of Medicine, Cardiff CF24 4HQ, UK.
³¹ Department of Comparative Medicine, Yale University School of Medicine, New Haven, CT 06520, USA.
³² Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06520, USA.
³³ Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
³⁴ Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA.
³⁵ Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
³⁶ Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
³⁷ Department of Medical Oncology, Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA 02215, USA.
³⁸ Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
³⁹ Harvard Medical School, Boston, MA 02215, USA.
⁴⁰ Division of Genetics, Brigham and Women's Hospital, Boston, MA 02215, USA.
⁴¹ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁴² Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
⁴³ Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA.
⁴⁴ Chan Zuckerberg Biohub, San Francisco, CA 94158, USA.
⁴⁵ Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan 410008, China.

PMID: 38781368
PMCID: PMC12147038
DOI: 10.1126/science.adh0829

Abstract

Neuropsychiatric genome-wide association studies (GWASs), including those for autism spectrum disorder and schizophrenia, show strong enrichment for regulatory elements in the developing brain. However, prioritizing risk genes and mechanisms is challenging without a unified regulatory atlas. Across 672 diverse developing human brains, we identified 15,752 genes harboring gene, isoform, and/or splicing quantitative trait loci, mapping 3739 to cellular contexts. Gene expression heritability drops during development, likely reflecting both increasing cellular heterogeneity and the intrinsic properties of neuronal maturation. Isoform-level regulation, particularly in the second trimester, mediated the largest proportion of GWAS heritability. Through colocalization, we prioritized mechanisms for about 60% of GWAS loci across five disorders, exceeding adult brain findings. Finally, we contextualized results within gene and isoform coexpression networks, revealing the comprehensive landscape of transcriptome regulation in development and disease.

PubMed Disclaimer

Conflict of interest statement

This article was prepared while M.A.P. was employed at Sage Bionetworks. M.J.G. and D.H.G. receive grant funding from Mitsubishi Tanabe Pharma America. Z.W. cofounded Rgenta Therapeutics and serves as a scientific adviser for the company and a member of its board. The remaining authors declare tno competing interests. The opinions expressed in this article are the authors’ own and do not reflect the view of the National Institute on Aging, the National Institutes of Health, the Department of Health and Human Services, or the US Government.

Figures

**Fig. 1.. The landscape of gene, splicing, and isoform regulation in the developing human brain.**
(A) Number of eGenes versus sample size discovered here compared with other human brain studies (–, , –27). (B) Overlap of eGenes between developing brain (N = 629), GTEx v8 Brain Cortex (N = 205), and PsychENCODE adult brain studies (N = 1387). (C) Correlation of eQTL effect size, measured by allelic fold change (aFC), between developing and adult brain datasets. Each dot is a shared eGene-primary eQTL pair between the developing brain and GTEx (247 pairs) or PsychENCODE (253 pairs) datasets. (D) Overlap among eGenes, isoGenes, and sGenes. (E) Distance between the 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. isoGenes and sGenes exhibit significantly less tolerance to loss-of-function mutations than eGenes (Wilcoxon). (H) Storey’s pi1 statistic of the proportion of true associations in the discovery group of QTL (y axis; permutation q value < 0.05) that are also true associations in the replication group of QTL (x axis; all nominal P values). (I) Number of fine-mapped credible sets versus number of conditionally independent eQTLs discovered. The size of the dots is scaled to the number of genes. (J) Common recurrent inv-eQTLs 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 (FDR-adjusted P < 0.05). The size of the circle indicates the population frequency of the inversions.

**Fig. 2.. Cross-ancestry gene regulation and fine-mapping.**
(A) Genotype principal components analysis of the developing human brain samples. Sample ancestry was inferred by merging imputed genotypes with 1000 Genomes (47). (B) Comparison of eGenes discovered in the full cross-ancestry dataset (“ALL,” N = 629) and in the separate subancestries, 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 is colored by its nominal significance in EUR. Gray lines denote the lower and upper bounds of aFC. (D) Comparison of fine-mapping credible set sizes between the ancestries. (E) *Cis* associations for the gene *MTFR1* in the cross-ancestry, EUR, AMR, and AFR datasets.

**Fig. 3.. Trimester-specific patterns of gene expression and splicing regulation.**
(A) Comparison of eGenes and sGenes identified in Tri1, Tri2, and the full dataset. We identified many more eGenes and sGenes in Tri1 than in Tri2 despite similar sample sizes. (B) Example of a trimester-specific eGene for *WARS2*, where rs146862216 (G>A) is an eQTL in Tri1 (beta = −0.89, FDR = 3.88 × 10⁻¹³) but not in Tri2 (beta = −;0.03, P = 0.71). (C) *cis*-Heritability of gene expression drops from Tri1 to Tri2 time points, as well as between developing and adult (PsychENCODE) samples (left). A similar drop is observed in splicing heritability between Tri1 and Tri2 (right). (D) Sliding-window analysis of gene expression (left) and splicing (right) *cis*-heritability for samples from 10 to 18 weeks. 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 gene biotype enrichment of Tri1-only and Tri2-only eGenes and sGenes. Values associated with each gene type represent the proportion of genes classified within that category. Red boxes highlight significant post hoc P value. (F) Estimated proportion of seven major cell classes over development assessed by bulk tissue cell-type deconvolution using CIBERSORTx (53).

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

**Fig. 5.. Neuropsychiatric risk gene prioritization through colocalization and isoTWASs.**
(A) Total number of GWAS loci exhibiting significant colocalization (CLPP > 0.01 or PP4 > 0.7) with specific developing brain xQTL annotations. (B) Colocalization between SCZ GWAS and developing brain xQTLs ranked by CLPP and grouped by GWAS locus, as indicated by the index SNP to the right. Only top colocalization results for protein-coding genes at genome-wide significant loci are shown. (C) Top: locus plots of SCZ GWAS with *SP4* eQTLs and sQTLs. A significant colocalization (CLPP = 0.01) is observed for SCZ GWAS with a cryptic splicing event in *SP4*. *SP4* does not have a detectable eQTL in the developing brain. Middle: Gene structure of *SP4* with and without cryptic exon inclusion, likely resulting in nonsense-mediated decay. Bottom left: sashimi plot showing 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) Developing brain isoTWAS associations with SCZ GWASs. Each dot represents an isoform, and adjusted P < 0.05 isoforms are shown in red. Genes of fine-mapped isoforms near a GWAS locus are labeled.

**Fig. 6.. Systems-level integration of risk variation with developmental gene and isoform coexpression.**
(A) Workflow for construction of gene- and isoform-level coexpression networks, followed by cell-type, pathway, and disease gene enrichment analyses. Separate gene coexpression 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 biweight midcorrelation of eigengenes. Middle, heatmaps depicting module-level enrichment for neuropsychiatric GWAS signal (−log₁₀P_enrich from S-LDSC and MAGMA) and ORs 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, and 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 (orange) variation, GO terms (dark green), and module overlap (light green). (D) 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 developing brain cell types as mapped by CellWalker. Outermost numbers denote results from single-cell-type label analysis. Inner numbers denote results from a broader, multilevel label analysis. (B) 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 NPCs, molecular feature (gene or isoform), number of ieQTLs, and cell-type enrichment. A total of 62 modules with pi1 > 0.2 in either neurons or 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.

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Update of

Cross-ancestry, cell-type-informed 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, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker RL, Chatzinakos C, Clarke D, Pratt H, Consortium P, 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. Wen C, et al. medRxiv [Preprint]. 2023 Mar 6:2023.03.03.23286706. doi: 10.1101/2023.03.03.23286706. medRxiv. 2023. Update in: Science. 2024 May 24;384(6698):eadh0829. doi: 10.1126/science.adh0829. PMID: 36945630 Free PMC article. Updated. Preprint.

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

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

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