. 2022 Nov;54(11):1630-1639.

doi: 10.1038/s41588-022-01203-y. Epub 2022 Oct 24.

Statistical and functional convergence of common and rare genetic influences on autism at chromosome 16p

Daniel J Weiner^{1

2}, Emi Ling^{3

4}, Serkan Erdin^{5

6

7}, Derek J C Tai^{5

6

7}, Rachita Yadav^{5

6

7}, Jakob Grove^{8

9

10

11}, Jack M Fu^{3

5

6

7}, Ajay Nadig^{3

12}, Caitlin E Carey^{3

7

13}, Nikolas Baya³, Jonas Bybjerg-Grauholm^{11

14}; iPSYCH Consortium; ASD Working Group of the Psychiatric Genomics Consortium; ADHD Working Group of the Psychiatric Genomics Consortium; Sabina Berretta^{15

16}, Evan Z Macosko^{3

17}, Jonathan Sebat¹⁸, Luke J O'Connor⁵, David M Hougaard^{11

14}, Anders D Børglum^{8

9

11}, Michael E Talkowski^{3

5

6

7}, Steven A McCarroll^{3

4}, Elise B Robinson^{19

20

21}

Collaborators, Affiliations

Collaborators

Preben B Mortensen, Thomas Werge, Ditte Demontis, Ole Mors, Merete Nordentoft, Thomas D Als, Marie Baekvad-Hansen, Anders Rosengren, Alexandra Havdahl, Anne Hedemand, Aarno Palotie, Aravinda Chakravarti, Dan Arking, Arvis Sulovari, Anna Starnawska, Bhooma Thiruvahindrapuram, Christiaan de Leeuw, Caitlin Carey, Christine Ladd-Acosta, Celia van der Merwe, Bernie Devlin, Edwin H Cook, Evan Eichler, Elisabeth Corfield, Gwen Dieleman, Gerard Schellenberg, Hakon Hakonarson, Hilary Coon, Isabel Dziobek, Jacob Vorstman, Jessica Girault, James S Sutcliffe, Jinjie Duan, John Nurnberger, Joachim Hallmayer, Joseph Buxbaum, Joseph Piven, Lauren Weiss, Lea Davis, Magdalena Janecka, Manuel Mattheisen, Matthew W State, Michael Gill, Mark Daly, Mohammed Uddin, Ole Andreassen, Peter Szatmari, Phil Hyoun Lee, Richard Anney, Stephan Ripke, Kyle Satterstrom, Susan Santangelo, Susan Kuo, Ludger Tebartz van Elst, Thomas Rolland, Thomas Bougeron, Tinca Polderman, Tychele Turner, Jack Underwood, Veera Manikandan, Vamsee Pillalamarri, Varun Warrier, Alexandra Philipsen, Andreas Reif, Anke Hinney, Bru Cormand, Claiton H D Bau, Diego Luiz Rovaris, Edmund Sonuga-Barke, Elizabeth Corfield, Eugenio Horacio Grevet, Giovanni Salum, Henrik Larsson, Jan Buitelaar, Jan Haavik, James McGough, Jonna Kuntsi, Josephine Elia, Klaus-Peter Lesch, Marieke Klein, Mark Bellgrove, Martin Tesli, Patrick W L Leung, Pedro M Pan, Soren Dalsgaard, Sandra Loo, Sarah Medland, Stephen V Faraone, Ted Reichborn-Kjennerud, Tobias Banaschewski, Ziarih Hawi

Affiliations

¹ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. dweiner@broadinstitute.org.
² Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. dweiner@broadinstitute.org.
³ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁴ Department of Genetics, Harvard Medical School, Boston, MA, USA.
⁵ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁶ Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁷ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁸ Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark.
⁹ Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, Denmark.
¹⁰ Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.
¹¹ The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
¹² Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
¹³ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
¹⁴ Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark.
¹⁵ Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
¹⁶ McLean Hospital, Belmont, MA, USA.
¹⁷ Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
¹⁸ Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
¹⁹ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. erob@broadinstitute.org.
²⁰ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. erob@broadinstitute.org.
²¹ Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. erob@broadinstitute.org.

PMID: 36280734
PMCID: PMC9649437
DOI: 10.1038/s41588-022-01203-y

Statistical and functional convergence of common and rare genetic influences on autism at chromosome 16p

Daniel J Weiner et al. Nat Genet. 2022 Nov.

. 2022 Nov;54(11):1630-1639.

doi: 10.1038/s41588-022-01203-y. Epub 2022 Oct 24.

Authors

Collaborators

Preben B Mortensen, Thomas Werge, Ditte Demontis, Ole Mors, Merete Nordentoft, Thomas D Als, Marie Baekvad-Hansen, Anders Rosengren, Alexandra Havdahl, Anne Hedemand, Aarno Palotie, Aravinda Chakravarti, Dan Arking, Arvis Sulovari, Anna Starnawska, Bhooma Thiruvahindrapuram, Christiaan de Leeuw, Caitlin Carey, Christine Ladd-Acosta, Celia van der Merwe, Bernie Devlin, Edwin H Cook, Evan Eichler, Elisabeth Corfield, Gwen Dieleman, Gerard Schellenberg, Hakon Hakonarson, Hilary Coon, Isabel Dziobek, Jacob Vorstman, Jessica Girault, James S Sutcliffe, Jinjie Duan, John Nurnberger, Joachim Hallmayer, Joseph Buxbaum, Joseph Piven, Lauren Weiss, Lea Davis, Magdalena Janecka, Manuel Mattheisen, Matthew W State, Michael Gill, Mark Daly, Mohammed Uddin, Ole Andreassen, Peter Szatmari, Phil Hyoun Lee, Richard Anney, Stephan Ripke, Kyle Satterstrom, Susan Santangelo, Susan Kuo, Ludger Tebartz van Elst, Thomas Rolland, Thomas Bougeron, Tinca Polderman, Tychele Turner, Jack Underwood, Veera Manikandan, Vamsee Pillalamarri, Varun Warrier, Alexandra Philipsen, Andreas Reif, Anke Hinney, Bru Cormand, Claiton H D Bau, Diego Luiz Rovaris, Edmund Sonuga-Barke, Elizabeth Corfield, Eugenio Horacio Grevet, Giovanni Salum, Henrik Larsson, Jan Buitelaar, Jan Haavik, James McGough, Jonna Kuntsi, Josephine Elia, Klaus-Peter Lesch, Marieke Klein, Mark Bellgrove, Martin Tesli, Patrick W L Leung, Pedro M Pan, Soren Dalsgaard, Sandra Loo, Sarah Medland, Stephen V Faraone, Ted Reichborn-Kjennerud, Tobias Banaschewski, Ziarih Hawi

Affiliations

¹ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. dweiner@broadinstitute.org.
² Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. dweiner@broadinstitute.org.
³ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁴ Department of Genetics, Harvard Medical School, Boston, MA, USA.
⁵ Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
⁶ Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁷ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁸ Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark.
⁹ Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, Denmark.
¹⁰ Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.
¹¹ The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark.
¹² Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
¹³ Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
¹⁴ Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark.
¹⁵ Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
¹⁶ McLean Hospital, Belmont, MA, USA.
¹⁷ Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
¹⁸ Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
¹⁹ Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA. erob@broadinstitute.org.
²⁰ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. erob@broadinstitute.org.
²¹ Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. erob@broadinstitute.org.

PMID: 36280734
PMCID: PMC9649437
DOI: 10.1038/s41588-022-01203-y

Abstract

The canonical paradigm for converting genetic association to mechanism involves iteratively mapping individual associations to the proximal genes through which they act. In contrast, in the present study we demonstrate the feasibility of extracting biological insights from a very large region of the genome and leverage this strategy to study the genetic influences on autism. Using a new statistical approach, we identified the 33-Mb p-arm of chromosome 16 (16p) as harboring the greatest excess of autism's common polygenic influences. The region also includes the mechanistically cryptic and autism-associated 16p11.2 copy number variant. Analysis of RNA-sequencing data revealed that both the common polygenic influences within 16p and the 16p11.2 deletion were associated with decreased average gene expression across 16p. The transcriptional effects of the rare deletion and diffuse common variation were correlated at the level of individual genes and analysis of Hi-C data revealed patterns of chromatin contact that may explain this transcriptional convergence. These results reflect a new approach for extracting biological insight from genetic association data and suggest convergence of common and rare genetic influences on autism at 16p.

PubMed Disclaimer

Conflict of interest statement

M.E.T. consults for BrigeBio Pharma and receives research funding and/or reagents from Illumina Inc., Levo Therapeutics and Microsoft Inc. The remaining authors disclose no competing interests.

Figures

**Fig. 1. S-pTDT identifies exceptional polygenic signal at chromosome 16p.**
a, S-pTDT estimates transmission of stratified PGSs from parents to their children. PGS_S is a stratified PGS constructed from a continuous block of SNPs, denoted in green. The S-pTDT value for a parent–child trio is the normalized difference between the stratified PGS in the child and the average stratified PGS of their parents. b, Genome-wide S-pTDT analysis using European ancestry autism probands in the combined SSC + SPARK cohorts (x axis, n = 5,048 trios) and the PGC (y axis, n = 4,335 trios). Blue points are partitions on 16p and red points are partitions including an autism GWAS locus. Score weights are derived from an autism GWAS of the iPSYCH consortium. The axes are in units of S-pTDT residual z-scores (Methods) and dashed lines denote z-scores ± 1.96. c, The three partitions that are nominally significant in both combined SSC + SPARK and PGC (blue bars) collectively span 16p. d, Autism S-pTDT analysis of the combined SSC + SPARK cohorts with stratified PGSs constructed from the 33-Mb partitions. Inset: the 16p partition (blue bar) compared with 73 other 33-Mb partitions (gray bars) spanning the genome. e, SSC + SPARK autism S-pTDT (n = 5,048 trios) signal decaying gradually with successive removal of the most associated remaining linkage disequilibrium-independent block on 16p. The y axis is the S-pTDT estimate (±95% confidence interval (CI)) for transmission of a stratified PGS constructed from the union of the remaining blocks. f, Each point is shown as a 33-Mb partition as defined in d. Brain-specific genes are defined as genes in the top 10% of specific expression in cortex relative to nonbrain tissues in GTEx. The P value is calculated from the residual z-score in a linear model regressing the number of cortex-specific genes on the number of genes in the partition (two sided). Inset: the P value is from Pearson’s correlation of number of genes in partition and S-pTDT residual z-score (two sided). For both the inset and the main panel, the trend line is a linear best fit and the shaded regions denote a 95% CI.

**Fig. 2. In vitro deletion of 16p11.2 causes decreased average expression of neuronally expressed 16p genes.**
a, Experimental design of 16p11.2 in vitro deletion resource. The iPSCs undergo CRISPR–Cas9-mediated deletion of the 16p11.2 CNV locus, differentiation into induced neurons and transcriptome profiling with RNA-seq (n = 7 biological replicates). Differential expression analysis compares these samples with controls (n = 6 biological replicates) without deletion of the locus. b, Differential expression of neuronally expressed genes on 16p (n = 200 genes) after deletion of the 16p11.2 locus (neuronally expressed is defined as above-median normalized expression level of genes over all samples in analysis of induced neurons). FC, fold-change. Genes in the deletion region ±0.1 Mb are green, whereas all other genes on 16p are in blue. The y axis is the log₂(fold-change) per gene. c, The 16p11.2 deletion causing decreased expression of neuronally expressed genes on 16p (n = 200 genes), but not of all other neuronally expressed genes in the genome (n = 8,533 genes). Point estimates are of mean differential expression t-statistic for the group of genes ± s.e.m. The P value is from two-sided, two-sample Student’s t-test comparing groups.

**Fig. 3. The 16p PGS is associated with decreased average expression of neuronally expressed genes on 16p.**
a, Per-gene association to 16p autism PGS in combined analysis of HBTRC and CommonMind resources (n = 544 samples). Each point is a gene expressed in glutamatergic neurons (n = 183; Methods), the x position its midpoint and the y position its t-statistic from a linear model of normalized expression on 16p autism PGS, controlling for donor schizophrenia diagnosis, European ancestry and single-cell/bulk expression measurement. b, Association between average regional gene expression and regional PGS across 16p (blue point) and 73 other 33-Mb control regions (gray points). Point estimates and s.e.m. are from regression of average regional gene expression on regional autism PGS, controlling for donor schizophrenia diagnosis, European ancestry and single-cell/bulk expression measurement (n = 544 samples). c, Consistency of association between regional autism PGS and average gene expression across the genome. Inset: association between mean 16p gene expression and autism 16p PGS in each of the three contributing cohorts: HBTRC snRNA-seq (glutamatergic neurons (Glut.)), n = 122 European ancestry samples; CommonMind bulk RNA-seq (cortical tissue (PFC)), n = 229 European ancestry samples; and CommonMind bulk RNA-seq (cortical tissue), n = 193 African ancestry samples. Point estimates and s.e.m. are from regression of mean 16p gene expression on 16p autism PGS, controlling for donor schizophrenia diagnosis. The 16p region exhibits the most consistent negative association between PGS and gene expression across the three cohorts compared with other 33-Mb regions. The y axis is the most positive regression coefficient in the model described in c (inset). Analysis is repeated for the other 33-Mb control regions (gray points).

**Fig. 4. Integrative model of genetic influences on autism at 16p.**
a, On the x axis, the association t-statistics from the all sample meta-analyses of 16p PGS and 16p gene expression; on the y axis, the association t-statistics from the 16p11.2 in vitro deletion analysis. The shaded region is the 95% CI. Genes are colored by their location on 16p; telomeric is defined as a gene midpoint <5.2 Mb. A single outlying point has been truncated from the plot for visualization; the untruncated plot is shown in Supplementary Fig. 22. b, Hi-C analysis revealing elevated within-region chromatin interaction at 16p. Each point represents a 33-Mb partition, with 16p colored blue. Both axes are in units of residual z-score, where the residual is from a linear model regressing out segmental duplication content and gene count from the mean within-region Hi-C contact value (Methods). The x axis is a dataset of LCLs, whereas the y axis is a dataset of midgestational cortical plate. c, Hi-C analysis revealing elevated contact between the 16p11.2 locus and the 5.2-Mb gene-dense telomeric region of 16p in midgestational cortical plate. The triangle depicts the 16p contact matrix: the blue shaded region denotes contacts between the 16p11.2 locus (29.5–30.2 Mb) and the 0- to 5.2-Mb telomeric region (n = 291 100-kb × 100-kb contacts), whereas the red shaded region is the distance between matched controls (n = 1,808 100-kb × 100-kb contacts). The inset shows the distribution of contact values for 16p11.2–telomeric versus control contacts. The P value is from a two-sided, two-sample Student’s t-test. The lower whisker, lower hinge, center, upper hinge and upper whisker correspond to (lower hinge − 1.5× interquartile range (IQR)) and the 25th percentile, median, 75th percentile, and (upper hinge + 1.5× IQR), respectively. d, A model of genetic influences on autism at 16p. Two independent genetic influences on autism—the 16p11.2 deletion and polygenic variation at 16p—are located in a region of elevated 16p chromatin interaction and enriched in brain-specific expression and are associated with coordinated decreased gene expression at 16p.

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Comment in

Rare and common autism risk variants converge across 16p.
Won H, Huguet G, Jacquemont S. Won H, et al. Nat Genet. 2022 Nov;54(11):1587-1588. doi: 10.1038/s41588-022-01219-4. Nat Genet. 2022. PMID: 36303073 No abstract available.

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Statistical and functional convergence of common and rare genetic influences on autism at chromosome 16p

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Statistical and functional convergence of common and rare genetic influences on autism at chromosome 16p

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