# Psychiatric disorders converge on common pathways but diverge in cellular context, spatial distribution, and directionality of genetic effects

Worrawat Engchuan, Omar Shanta, Kuldeep Kumar, Jeffrey R Macdonald, Bhooma Thiruvahindrapuram, Omar Hamdan, Marieke Klein, Adam Maihofer, James Guevara, Oanh Hong, et al.

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## To cite this version:

Worrawat Engchuan, Omar Shanta, Kuldeep Kumar, Jeffrey R Macdonald, Bhooma Thiruvahindrapuram, et al. Psychiatric disorders converge on common pathways but diverge in cellular context, spatial distribution, and directionality of genetic effects. 2025. inserm-05294360

HAL Id: inserm-05294360

https://inserm.hal.science/inserm-05294360v1

Preprint submitted on 2 Oct 2025

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1 Psychiatric disorders converge on common pathways but diverge in cellular context,

2 spatial distribution, and directionality of genetic effects

3

4 Worrawat Engchuan<sup>1,2</sup>, Omar Shanta<sup>3,4</sup>, Kuldeep Kumar<sup>5</sup>, Jeffrey R. MacDonald<sup>1,2</sup>, Bhooma

5 Thiruvahindrapuram<sup>1,2</sup>, Omar Hamdan<sup>1,2</sup>, Marieke Klein<sup>4,6,7</sup>, Adam Maihofer<sup>4,8,9</sup>, James

6 Guevara<sup>4</sup>, Oanh Hong<sup>4</sup>, Guillaume Huguet<sup>10</sup>, Molly Sacks<sup>3,4</sup>, Mohammad Ahangari<sup>4</sup>, Rayssa

7 M.M.W. Feitosa<sup>1,2,11</sup>, Kara Han<sup>1,2</sup>, Marla Mendes<sup>1,2</sup>, Xiaopu Zhou<sup>1,2</sup>, Nelson X. Bautista<sup>1,2</sup>,

8 Giovanna Pellecchia<sup>1,2</sup>, Zhouzhi Wang<sup>1,2</sup>, Daniele Merico<sup>1,12</sup>, Ryan K.C. Yuen<sup>2,13</sup>, Brett

9 Trost<sup>2,13,14</sup>, Ida Sønderby<sup>15,16,17</sup>, Mark J. Adams<sup>18</sup>, Rolf Adolfsson<sup>19</sup>, Ingrid Agartz<sup>20,21,22</sup>, Allison E.

10 Aiello<sup>23</sup>, Martin Alda<sup>24,25</sup>, Judith Allardyce<sup>26</sup>, Ananda B. Amstadter<sup>27</sup>, Till F.M. Andlauer<sup>28</sup>, Ole

11 A. Andreassen<sup>15,16,29</sup>, María S. Artigas<sup>30,31,32,33</sup>, S. Bryn Austin<sup>34,35,36</sup>, Muhammad Ayub<sup>37</sup>,

12 Dewleen G. Baker<sup>4</sup>, Nick Bass<sup>38</sup>, Bernhard T. Baune<sup>39,40,41</sup>, Maximilian Bayas<sup>42</sup>, Klaus Berger<sup>43</sup>,

13 Joanna M. Biernacka<sup>44,45</sup>, Tim Bigdeli<sup>46</sup>, Jonathan I. Bisson<sup>47</sup>, Douglas Blackwood<sup>26</sup>, Marco

14 Boks<sup>48</sup>, David Braff<sup>4</sup>, Elvira Bramon<sup>38</sup>, Gerome Breen<sup>49</sup>, Tanja Brueckl<sup>50</sup>, Richard A. Bryant<sup>51</sup>,

15 Cynthia M. Bulik<sup>52,53,54</sup>, Joseph Buxbaum<sup>55</sup>, Murray J. Cairns<sup>56,57</sup>, Jose M. Caldas-de-Almeida<sup>58</sup>,

16 Megan Campbell<sup>59</sup>, Dominique Campion<sup>60</sup>, Vaughan J. Carr<sup>61,62</sup>, Enrique Castelao<sup>63</sup>, Boris

17 Chaumette<sup>64</sup>, Sven Cichon<sup>65</sup>, David Cohen<sup>66,67</sup>, Aiden Corvin<sup>68</sup>, Nicholas Craddock<sup>69</sup>, Jennifer

18 Crosbie<sup>70,71</sup>, Darrina Czamara<sup>72</sup>, Udo Dannlowski<sup>73,74</sup>, Franziska Degenhardt<sup>65</sup>, Douglas L.

19 Delahanty<sup>75</sup>, Astrid Dempfle<sup>76</sup>, Guillaume Desachy<sup>77,78</sup>, Arianna Di Florio<sup>79</sup>, Faith B.

20 Dickerson<sup>80</sup>, Srdjan Djurovic<sup>15,81,82</sup>, Katharina Domschke<sup>83</sup>, Lisa Douglas<sup>84</sup>, Ole K. Oranje<sup>82,85</sup>,

21 Laramie E. Duncan<sup>86,87</sup>, Howard J. Edenberg<sup>88,89</sup>, Tonu Esko<sup>90</sup>, Steve Faraone<sup>91</sup>, Norah C.

22 Feeny<sup>92</sup>, Andreas J. Forstner<sup>93,94,95,96,97</sup>, Barbara Franke<sup>98,99</sup>, Mark Frye<sup>100</sup>, Dong-jing Fu<sup>101</sup>,

23 Janice M. Fullerton<sup>102,103</sup>, Anna Gareeva<sup>104,105,106,107</sup>, Linda Garvert<sup>108</sup>, Justine M. Gatt<sup>51</sup>, Pablo

24 Gejman<sup>109</sup>, Daniel H. Geschwind<sup>110</sup>, Ina Giegling<sup>111</sup>, Stephen J. Glatt<sup>112</sup>, Joe Glessner<sup>113,114,115</sup>,

25 Fernando S. Goes<sup>116</sup>, Katherine Gordon-Smith<sup>117</sup>, Hans Grabe<sup>108</sup>, Melissa J. Green<sup>118</sup>, Michael

26 F. Green<sup>119,120</sup>, Tiffany Greenwood<sup>4</sup>, Maria Grigoroiu-Serbanescu<sup>121</sup>, Raquel E. Gur<sup>122</sup>, Ruben C.

27 Gur<sup>122</sup>, Jose Guzman-Parra<sup>123</sup>, Jan Haavik<sup>124</sup>, Tim Hahn<sup>74</sup>, Hakon Hakonarson<sup>113,114,115</sup>, Joachim

28 Hallmayer<sup>125,126</sup>, Marian L. Hamshere<sup>127</sup>, Annette M. Hartmann<sup>128</sup>, Arsalan Hassan<sup>129</sup>, Caroline

29 Hayward<sup>130</sup>, Johannes Hebebrand<sup>131,132</sup>, Sian M.J. Hemmings<sup>133,134</sup>, Stefan Herms<sup>65</sup>, Marisol

30 Herrera-Rivero<sup>39,73,135</sup>, Anke Hinney<sup>132,136,137</sup>, Georg Homuth<sup>138</sup>, Andrés Ingason<sup>139</sup>, Lucas T.

31 Ito<sup>140,141,142,143,144</sup>, Nakao Iwata<sup>145</sup>, Ian Jones<sup>146</sup>, Lisa A. Jones<sup>117</sup>, Lina Jonsson<sup>147</sup>, Erik G.

32 Jönsson<sup>20,21</sup>, René S. Kahn<sup>143</sup>, Robert Karlsson<sup>148</sup>, Milissa L. Kaufman<sup>149,150</sup>, John R. Kelsoe<sup>4</sup>,

33 James L. Kennedy<sup>70,151</sup>, Anthony King<sup>152</sup>, Tilo Kircher<sup>153,154</sup>, George Kirov<sup>155</sup>, Per

34 Knappskog<sup>156,157</sup>, James A. Knowles<sup>158,159</sup>, Nene Kobayashi<sup>160</sup>, Karestan C. Koenen<sup>161</sup>, Bettina

35 Konte<sup>128</sup>, Mayuresh Korgaonkar<sup>162</sup>, Kaarina Kowalec<sup>148</sup>, Marie-Odile Krebs<sup>64</sup>, Mikael

36 Landén<sup>147,148</sup>, Claudine Laurent-Levinson<sup>66,163</sup>, Lauren A. Lebois<sup>150,164</sup>, Doug Levinson<sup>86</sup>, Cathryn

37 Lewis<sup>49,165</sup>, Qingqin Li<sup>101</sup>, Israel Liberzon<sup>166</sup>, Greg Light<sup>4</sup>, Sandra K. Loo<sup>167</sup>, Yi Lu<sup>148,168</sup>, Susanne

38 Lucae<sup>50</sup>, Charles Marmar<sup>169</sup>, Nicholas G. Martin<sup>170</sup>, Fermin Mayoral<sup>123</sup>, Andrew M.

39 McIntosh<sup>171</sup>, Katie A. McLaughlin<sup>172</sup>, Samuel A. McLean<sup>173,174</sup>, Andrew McQuillin<sup>38</sup>, Sarah E.

40 Medland<sup>175,176,177</sup>, Andreas Meyer-Lindenberg<sup>178</sup>, Vihra Milanova<sup>179</sup>, Philip B. Mitchell<sup>118</sup>,

41 Esther Molina<sup>180,181</sup>, Bryan Mowry<sup>182,183</sup>, Bertram Muller-Myhsok<sup>184,185</sup>, Niamh Mullins<sup>140,143,144</sup>,

42 Robin Murray<sup>186</sup>, Markus M. Nöthen<sup>65</sup>, John I. Nurnberger Jr<sup>187,188</sup>, Kevin S. O'Connell<sup>29,82</sup>, Roel

43 A. Ophoff<sup>189</sup>, Holly K. Orcutt<sup>190</sup>, Michael J. Owen<sup>191</sup>, Aarno Palotie<sup>192,193,194</sup>, Carlos Pato<sup>195</sup>,

44 Michele Pato<sup>195</sup>, Joanna Pawlak<sup>196</sup>, Triinu Peters<sup>132,136,137</sup>, Tracey L. Petryshen<sup>197</sup>, Giorgio

45 Pistis<sup>63</sup>, James B. Potash<sup>116</sup>, John Powell<sup>198</sup>, Martin Preisig<sup>63</sup>, Digby Quested<sup>199</sup>, Josep A.

46 Rønning, J. Quiping, J. reports new, research that has not been certified by peer review and should not be used as a guide to clinical practice.
1 Marcella Rietschel<sup>203</sup>, Victoria B. Risbrough<sup>4,8</sup>, Margarita Rivera<sup>181,204</sup>, Alex O. Rothbaum<sup>201,205</sup>,

2 Barbara O. Rothbaum<sup>201</sup>, Dan Rujescu<sup>111</sup>, Takeo Saito<sup>145</sup>, Alan R. Sanders<sup>206</sup>, Russell J.

3 Schachar<sup>70,71</sup>, Peter R. Schofield<sup>118</sup>, Eva C. Schulte<sup>65,207,208,209,210</sup>, Thomas G. Schulze<sup>209</sup>, Laura J.

4 Scott<sup>211</sup>, Soraya Seedat<sup>212,213</sup>, Christina Sheerin<sup>27</sup>, Jianxin Shi<sup>214</sup>, Pamela Sklar<sup>215</sup>, Susan

5 Smalley<sup>167,216</sup>, Olav B. Smeland<sup>29,82</sup>, Jordan W. Smoller<sup>217,218</sup>, Edmund Sonuga-Barke<sup>219</sup>, David

6 St. Clair<sup>220</sup>, Nils Eiel Steen<sup>29,82,221</sup>, Dan Stein<sup>222</sup>, Frederike Stein<sup>153,154</sup>, Murray B. Stein<sup>4,223,224</sup>,

7 Fabian Streit<sup>178,203,225,226</sup>, Neal Swerdlow<sup>4</sup>, Florence Thibaut<sup>227,228</sup>, Johan H. Thygesen<sup>38,229</sup>, Ilgiz

8 Timerbulatov<sup>106,230,231</sup>, Claudio Toma<sup>232,233</sup>, Edward Trapido<sup>234</sup>, Micheline Tremblay<sup>235</sup>, Ming T.

9 Tsuang<sup>4</sup>, Monica Uddin<sup>236</sup>, Marquis P. Vawter<sup>237</sup>, John B. Vincent<sup>70,151</sup>, Henry Völzke<sup>238</sup>, James

10 T. Walters<sup>191</sup>, Cynthia S. Weickert<sup>103,118</sup>, Lauren A. Weiss<sup>77</sup>, Myrna M. Weissman<sup>239,240</sup>, Thomas

11 Werge<sup>241</sup>, Stephanie H. Witt<sup>203,225</sup>, Miguel Xavier<sup>242</sup>, Robert Yolken<sup>243</sup>, Ross M. Young<sup>244,245</sup>,

12 Tetyana Zayats<sup>246,247,248</sup>, Lori A. Zoellner<sup>249</sup>, AGP Consortium, PEIC Psychosis Endophenotypes

13 International Consortium, ADHD Working Group of the Psychiatric Genomics Consortium,

14 Autism Working Group of the Psychiatric Genomics Consortium, Bipolar Disorder Working

15 Group of the Psychiatric Genomics Consortium, Major Depressive Disorder Working Group

16 of the Psychiatric Genomics Consortium, PTSD Working Group of the Psychiatric Genomics

17 Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, CNV

18 Working Group of the Psychiatric Genomics Consortium, Kimberley Kendall<sup>69</sup>, Brien Riley<sup>250</sup>,

19 Naomi R. Wray<sup>251,252</sup>, Michael C. O'Donovan<sup>69</sup>, Patrick F. Sullivan<sup>148,253</sup>, Sandra

20 Sanchez-Roige<sup>4,254,255</sup>, Caroline M. Nievergelt<sup>4,256</sup>, Sébastien Jacquemont<sup>5,257</sup>, Stephen W.

21 Scherer<sup>1,2,13,258*</sup>, Jonathan Sebat<sup>4,254,259,260*</sup>

22 <sup>1</sup>The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada,

23 <sup>2</sup>Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON,

24 Canada, <sup>3</sup>Bioinformatics and Systems Biology Graduate Program, University of California San

25 Diego, La Jolla, CA, USA, <sup>4</sup>Department of Psychiatry, University of California San Diego, La

26 Jolla, CA, USA, <sup>5</sup>Centre Hospitalier Universitaire Sainte-Justine Research Center, Montreal,

27 QC, Canada, <sup>6</sup>Donders Institute for Brain, Cognition and Behaviour, Radboud University

28 Medical Center, Nijmegen, The Netherlands, <sup>7</sup>Department of Human Genetics, Radboud

29 University Medical Center, Nijmegen, The Netherlands, <sup>8</sup>Veterans Affairs San Diego

30 Healthcare System, Center of Excellence for Stress and Mental Health, San Diego, CA, USA,

31 <sup>9</sup>Veterans Affairs San Diego Healthcare System, Research Service, San Diego, CA, USA, <sup>10</sup>CHU

32 Sainte-Justine Azrieli Research Center, Université de Montréal, Montreal, QC, Canada,

33 <sup>11</sup>Institute of Medical Science, University of Toronto, Toronto, ON, Canada, <sup>12</sup>Tahoe

34 Therapeutics (formerly Vevo), South San Francisco, CA, USA, <sup>13</sup>Department of Molecular

35 Genetics, University of Toronto, Toronto, ON, Canada, <sup>14</sup>Molecular Medicine Program, The

36 Hospital for Sick Children, Toronto, ON, Canada, <sup>15</sup>KG Jebsen Centre for Neurodevelopmental

37 disorders, University of Oslo, Oslo, Norway, <sup>16</sup>Centre for Precision Psychiatry, University of

38 Oslo, Oslo, Norway, <sup>17</sup>Department of Medical Genetics, Oslo University Hospital, Oslo,

39 Norway, <sup>18</sup>Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK,

40 <sup>19</sup>Department of Clinical Science, Umeå University, Umeå, Sweden, <sup>20</sup>Institute of Clinical

41 Medicine, University of Oslo, Oslo, Norway, <sup>21</sup>Centre for Psychiatry Research, Department of

42 Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm

43 Region, Stockholm, Sweden, <sup>22</sup>Department of Psychiatric Research, Diakonhemmet Hospital,

44 Oslo, Norway, <sup>23</sup>Department of Epidemiology, Columbia University, Robert N Butler Columbia

45 Aging Center, New York, NY, US, <sup>24</sup>National Institute of Mental Health, Klecany, Czech

46 Republic, <sup>25</sup>Department of Psychiatry, Dalhousie University, Halifax, NS, Canada, <sup>26</sup>Division of
1 Psychiatry, University of Edinburgh, Edinburgh, UK, ²⁷Department of Psychiatry, Virginia

2 Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University,

3 Richmond, VA, USA, ²⁸Department of Neurology, Klinikum rechts der Isar, School of

4 Medicine, Technical University of Munich, Munich, Germany, ²⁹Division of Mental Health and

5 Addiction, Oslo University Hospital, Oslo, Norway, ³⁰Biomedical Network Research Centre on

6 Mental Health (CIBERSAM), Madrid, Spain, ³¹Department of Mental Health, Hospital

7 Universitari Vall d'Hebron, Barcelona, Spain, ³²Department of Genetics, Microbiology, and

8 Statistics, Faculty of Biology, Universitat de Barcelona (UB), Barcelona, Spain, ³³Psychiatric

9 Genetics Unit, Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research

10 Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain, ³⁴Boston Children's

11 Hospital, Division of Adolescent and Young Adult Medicine, Boston, MA, USA, ³⁵Department

12 of Pediatrics, Harvard Medical School, Boston, MA, USA, ³⁶Department of Social and

13 Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA,

14 ³⁷University College London, London, UK, ³⁸Division of Psychiatry, University College London,

15 London, UK, ³⁹Department of Psychiatry, University of Münster, Münster, Germany, ⁴⁰The

16 Flore Institute of Neuroscience and Mental Health, Melbourne, Australia, ⁴¹Department of

17 Psychiatry, University of Melbourne, Melbourne, Australia, ⁴²Department of Psychiatry,

18 Psychosomatic Medicine and Psychotherapy; University Hospital Frankfurt - Goethe

19 University, Frankfurt, Germany, ⁴³Institute of Epidemiology and Social Medicine, University of

20 Münster, Münster, Germany, ⁴⁴Department of Quantitative Health Sciences, Mayo Clinic,

21 Rochester, MN, USA, ⁴⁵Department of Psychiatry and Psychology, Mayo Clinic, Rochester,

22 MN, USA, ⁴⁶Department of Psychiatry and Behavioral Sciences, Institute for Genomics in

23 Health, State University of New York Downstate Health Sciences University, Brooklyn, NY,

24 USA, ⁴⁷Cardiff University, National Centre for Mental Health, MRC Centre for Psychiatric

25 Genetics and Genomics, Cardiff, UK, ⁴⁸University Medical Center, Division of Neurosciences,

26 Department of Psychiatry, Heidelberglaan, Utrecht, the Netherlands, ⁴⁹Social, Genetic and

27 Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience,

28 King's College London, London, UK, ⁵⁰Department of Translational Research in Psychiatry,

29 Max Planck Institute of Psychiatry, Munich, Germany, ⁵¹School of Psychology, Faculty of

30 Science, University of New South Wales, Sydney, New South Wales, Australia, ⁵²Department

31 of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, ⁵³Department

32 of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden, ⁵⁴Department of

33 Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, ⁵⁵Seaver Autism

34 Center, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY,

35 USA, ⁵⁶Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle,

36 New South Wales, Australia, ⁵⁷School of Biomedical Sciences and Pharmacy, University of

37 Newcastle, Callaghan, NSW, Australia, ⁵⁸Chronic Diseases Research Centre (CEDOC), Lisbon

38 Institute of Global Mental Health, Lisbon, Portugal, ⁵⁹Department of Psychiatry and

39 Neuroscience Institute, University of Cape Town, Cape Town, South Africa, ⁶⁰INSERM EPI

40 9906, Faculté de Médecine et de Pharmacie, Institut Fédératif de Recherches

41 Multidisciplinaires sur les peptides, Rouen, France, ⁶¹Department of Psychiatry, Monash

42 University, Melbourne, Australia, ⁶²School of Psychiatry, University of New South Wales,

43 Sydney, New South Wales, Australia, ⁶³Psychiatric Epidemiology and Psychopathology

44 Research Center, Department of Psychiatry, Lausanne University Hospital and University of

45 Lausanne, Prilly, Switzerland, ⁶⁴Université Paris Cité, Institute of Psychiatry and Neuroscience

46 of Paris (INSERM U1266), GHU Paris Psychiatrie et Neurosciences, Paris, France, ⁶⁵Institute of

47 Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn,
1. Germany, ⁶⁶Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital,

2. Assistance Publique-Hôpitaux de Paris-Sorbonne University, Paris, France, ⁶⁷CNRS UMR 7222,

3. Institute for Intelligent Systems and Robotics, Sorbonne University, Paris, France,

4. ⁶⁸Department of Psychiatry, Trinity College Dublin, Dublin, Ireland, ⁶⁹Centre for

5. Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical

6. Neurosciences, Cardiff University, Cardiff, UK, ⁷⁰Department of Psychiatry, University of

7. Toronto, Toronto, ON, Canada, ⁷¹Neurosciences & Mental Health, The Hospital for Sick

8. Children, Toronto, ON, Canada, ⁷²Department Genes and Environment, Max-Planck-Institute

9. of Psychiatry, Munich, Germany, ⁷³Joint Institute for Individualisation in a Changing

10. Environment (JICE), University of Münster and Bielefeld University, Münster, Germany,

11. ⁷⁴Institute for Translational Psychiatry, University of Münster, Münster, Germany,

12. ⁷⁵Department of Psychological Sciences, Kent State University, Kent, OH, USA, ⁷⁶Institute of

13. Medical Informatics and Statistics, UKSH University Hospital of Schleswig-Holstein Kiel

14. Campus, Arnold-Heller-Strasse, Kiel, Germany, ⁷⁷Institute for Human Genetics, Department

15. of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of

16. California San Francisco, San Francisco, CA, USA, ⁷⁸Data Science & Biometrics, Research &

17. Development, Pierre Fabre Group, Toulouse, France, ⁷⁹School of Medicine, Division of

18. Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK, ⁸⁰Sheppard

19. Pratt Health System, Baltimore, MD, USA, ⁸¹Department of Medical Genetics, Oslo University

20. Hospital and University of Oslo, Oslo, Norway, ⁸²Centre for Precision Psychiatry, Oslo

21. University Hospital & University of Oslo, Oslo, Norway, ⁸³Department of Psychiatry and

22. Psychotherapy, University of Freiburg, Freiburg, Germany, ⁸⁴Cheshire and Wirral Partnership

23. NHS Trust, Ellesmere Port, Cheshire, UK, ⁸⁵Department of Psychiatry, Sørlandet hospital,

24. Arendal/Kristiansand, Norway, ⁸⁶Department of Psychiatry and Behavioral Sciences, Stanford

25. University, Stanford, CA, USA, ⁸⁷Wu Tsai Neurosciences Institute, Stanford University,

26. Stanford, CA, USA, ⁸⁸Department of Biochemistry & Molecular Biology, Indiana University,

27. Indianapolis, IN, USA, ⁸⁹Department of Medical and Molecular Genetics, Indiana University,

28. Indianapolis, IN, USA, ⁹⁰Estonian Genome Centre, Institute of Genomics, University of Tartu,

29. Tartu, Estonia, ⁹¹Departments of Psychiatry and of Neuroscience and Physiology, SUNY

30. Upstate Medical University, Syracuse, New York, NY, USA, ⁹²Department of Psychological

31. Sciences, Case Western Reserve University, Cleveland, OH, USA, ⁹³Institute of Medical

32. Genetics and Pathology, University Hospital Basel, Basel, Switzerland, ⁹⁴Centre for Human

33. Genetics, University of Marburg, Marburg, Germany, ⁹⁵Institute of Human Genetics,

34. University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany,

35. ⁹⁶Department of Biomedicine, University of Basel, Basel, Switzerland, ⁹⁷Department of

36. Psychiatry (UPK), University of Basel, Basel, Switzerland, ⁹⁸Department of Medical

37. Neuroscience, Donders Institute of Brain, Cognition and Behaviour, Radboud University

38. Medical Center, Nijmegen, The Netherlands, ⁹⁹Department of Human Genetics, Donders

39. Institute of Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen,

40. The Netherlands, ¹⁰⁰Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN,

41. US, ¹⁰¹Janssen Research and Development, LLC, ¹⁰²School of Biomedical Sciences, University

42. of New South Wales, Sydney, New South Wales, Australia, ¹⁰³Neuroscience Research

43. Australia, Randwick, New South Wales, Australia, ¹⁰⁴FSBSI Institute of Biochemistry and

44. Genetics of the Ufa Federal Research Center of the Russian Academy of Sciences, Russia,

45. ¹⁰⁵FSBEI HE Bashkir State Medical University of Health Ministry of Russia, Russia, ¹⁰⁶FSBEI

46. APGE Russian Medical Academy of Continuing Professional Education of the Ministry of

47. Health of Russia, Russia, ¹⁰⁷FSBEI HE Kemerovo State University, Russia, ¹⁰⁸Department of
1 Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany,

2 <sup>109</sup>Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL,

3 USA, <sup>110</sup>School of Medicine, University of California Los Angeles, Los Angeles, CA, USA,

4 <sup>111</sup>Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical

5 Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Austria,

6 <sup>112</sup>Director, Psychiatric Genetic Epidemiology & Neurobiology Laboratory (PsychGENe Lab),

7 SUNY Upstate Medical University, <sup>113</sup>Division of Human Genetics, Children's Hospital of

8 Philadelphia, Philadelphia, PA, USA, <sup>114</sup>Center for Applied Genomics, Children's Hospital of

9 Philadelphia, Philadelphia, PA, USA, <sup>115</sup>Department of Pediatrics, Perelman School of

10 Medicine, University of Pennsylvania, Philadelphia, PA, USA, <sup>116</sup>Department of Psychiatry and

11 Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA,

12 <sup>117</sup>Psychological Medicine, University of Worcester, Worcester, UK, <sup>118</sup>Discipline of Psychiatry

13 and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of

14 New South Wales, Sydney, New South Wales, Australia, <sup>119</sup>Center on Enhancement of

15 Community Integration for Homeless Veterans, VA Greater Los Angeles Healthcare System,

16 Los Angeles, CA, USA, <sup>120</sup>Semel Institute for Neuroscience and Human Behavior, University of

17 California Los Angeles, Los Angeles, CA, USA, <sup>121</sup>Psychiatric Genetics Research Unit,

18 Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania, <sup>122</sup>Department of

19 Psychiatry, University of Pennsylvania, Philadelphia, PA, USA, <sup>123</sup>Unidad de Gestión Clínica de

20 Salud Mental del Hospital Regional Universitario de Málaga, Instituto de Investigación

21 Biomédica de Málaga y Plataforma en Nanomedicina - IBIMA Plataforma Bionand, Málaga,

22 Spain, <sup>124</sup>JanK.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine,

23 University of Bergen, Bergen, Norway, <sup>125</sup>Department of Psychiatry and Behavioral Sciences

24 (JKF, PL, BJ, MWM, JH, JY), Stanford University School of Medicine, Stanford, CA, USA, <sup>126</sup>VISN

25 21 Mental Illness Research, Education, and Clinical Center (JKF, PL, MWM, JH, JY), Veterans

26 Affairs Palo Alto Health Care System, Palo Alto, CA, USA, <sup>127</sup>Medical Research Council Centre

27 for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical

28 Neurosciences, Cardiff University, Cardiff, UK, <sup>128</sup>Department of Psychiatry and

29 Psychotherapy, Medical University of Vienna, Austria, <sup>129</sup>University of Peshawar, Peshawar,

30 Pakistan, <sup>130</sup>MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine,

31 Edinburgh, UK, <sup>131</sup>Department of Child and Adolescent Psychiatry, Psychotherapy and

32 Psychosomatics, University Hospital Essen (AÖR), University of Duisburg-Essen, Essen,

33 Germany, <sup>132</sup>Center for Translational Neuro- and Behavioral Sciences, University Hospital

34 Essen, University of Duisburg-Essen, Essen, Germany, <sup>133</sup>Faculty of Medicine and Health

35 Sciences, Department of Psychiatry, Stellenbosch University, Cape Town, Western Cape, ZA,

36 <sup>134</sup>SAMRC Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town,

37 Western Cape, ZA, <sup>135</sup>Department of Genetic Epidemiology, Institute of Human Genetics,

38 University of Münster, Münster, Germany, <sup>136</sup>Section of Molecular Genetics of Mental

39 Disorders, LVR-University Clinic Essen, Essen, Germany, <sup>137</sup>Institute of Sex and

40 Gender-Sensitive Medicine, University Hospital Essen, University of Duisburg-Essen,

41 Virchowstr, Essen, Germany, <sup>138</sup>Interfaculty Institute for Genetics and Functional Genomics,

42 University Medicine Greifswald, Greifswald, Germany, <sup>139</sup>Institute of Biological Psychiatry,

43 Mental Health Services, Copenhagen University Hospital, Roskilde, Denmark, <sup>140</sup>Charles

44 Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New

45 York, NY, USA, <sup>141</sup>Laboratory of Integrative Neuroscience, Universidade Federal de São Paulo,

46 São Paulo, Brazil, <sup>142</sup>Department of Biochemistry, Universidade Federal de São Paulo, São

47 Paulo, Brazil, <sup>143</sup>Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New
1 York, NY, USA, ¹⁴⁴Department of Genetics and Genomic Sciences, Icahn School of Medicine at
2 Mount Sinai, New York, NY, USA, ¹⁴⁵Department of Psychiatry, Fujita Health University School
3 of Medicine, Toyoake, Aichi, Japan, ¹⁴⁶Cardiff University, National Centre for Mental Health,
4 Cardiff University Centre for Psychiatric Genetics and Genomics, Cardiff, UK, ¹⁴⁷Institute of
5 Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden,
6 ¹⁴⁸Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm,
7 Sweden, ¹⁴⁹McLean Hospital, Belmont, MA, USA, ¹⁵⁰Department of Psychiatry, Harvard
8 Medical School, Boston, MA, USA, ¹⁵¹Campbell Family Mental Health Research Institute,
9 Centre for Addiction and Mental Health, Toronto, ON, Canada, ¹⁵²The Ohio State University,
10 College of Medicine, Institute for Behavioral Medicine Research, Columbus, OH, USA,
11 ¹⁵³Department of Psychiatry and Psychotherapy, University of Marburg, Marburg, Germany,
12 ¹⁵⁴Center for Mind, Brain and Behavior, University of Marburg, Marburg, Germany,
13 ¹⁵⁵Department of Psychological Medicine and Neurology, MRC Centre for Neuropsychiatric
14 Genetics and Genomics, School of Medicine, Neuroscience and Mental Health Research
15 Institute, Cardiff University, Cardiff, UK, ¹⁵⁶Department of Clinical Science, University of
16 Bergen, Bergen, Norway, ¹⁵⁷Department of Medical Genetics, Haukeland University Hospital,
17 Bergen, Norway, ¹⁵⁸Department of Genetics, Rutgers University, Piscataway, NJ, USA,
18 ¹⁵⁹Human Genetics Institute of New Jersey (HGINJ), Rutgers University, Piscataway, NJ, USA,
19 ¹⁶⁰Goethe University Frankfurt, University Hospital, Department of Psychiatry, Psychosomatic
20 Medicine and Psychotherapy, Frankfurt, Germany, ¹⁶¹Department of Epidemiology, Harvard T.
21 H. Chan School of Public Health, Boston, MA, USA., ¹⁶²Brain Dynamics Centre, Westmead
22 Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia,
23 ¹⁶³Childhood Genetic Disease Laboratory, INSERM UMR S933, Trousseau University Hospital,
24 Paris, France, ¹⁶⁴McLean Hospital, Center of Excellence in Depression and Anxiety Disorders,
25 Belmont, MA, USA, ¹⁶⁵Department of Medical and Molecular Genetics, Faculty of Life
26 Sciences and Medicine, King's College London, London, UK, ¹⁶⁶Department of Psychiatry and
27 Behavioral Sciences, Texas A&M University College of Medicine, Bryan, TX, USA,
28 ¹⁶⁷Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles,
29 Los Angeles, CA, USA, ¹⁶⁸College of Pharmacy, University of Manitoba, Winnipeg, MB,
30 Canada, ¹⁶⁹New York University, Grossman School of Medicine, New York City, NY, USA,
31 ¹⁷⁰Brain and Mental Health Program, QIMR Berghofer Institute of Medical Research,
32 Brisbane, Australia, ¹⁷¹Division of Psychiatry, Centre for Clinical Brain Sciences, The University
33 of Edinburgh, Edinburgh, UK, ¹⁷²Department of Psychology, Harvard University, Boston, MA,
34 USA, ¹⁷³Department of Emergency Medicine, UNC Institute for Trauma Recovery, Chapel Hill,
35 NC, USA, ¹⁷⁴Department of Anesthesiology, UNC Institute for Trauma Recovery, Chapel Hill,
36 NC, USA, ¹⁷⁵School of Psychology, The University of Queensland, Brisbane, Queensland,
37 Australia, ¹⁷⁶School of Psychology and Counselling, Queensland University of Technology,
38 Brisbane, Queensland, Australia, ¹⁷⁷Mental Health and Neuroscience, QIMR Berghofer
39 Medical Research Institute, Brisbane, Queensland, Australia, ¹⁷⁸Department of Psychiatry
40 and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim,
41 Heidelberg University, Mannheim, Germany, ¹⁷⁹Department of Psychiatry, Faculty of
42 Medicine, Medical University of Sofia, University Hospital Alexandrovska, Bulgaria,
43 ¹⁸⁰Department of Nursing, Faculty of Health Sciences, University of Granada, Granada,
44 Spain., ¹⁸¹Institute of Neurosciences 'Federico Olóriz', Biomedical Research Centre (CIBM),
45 University of Granada, and Instituto de Investigación Biosanitaria, Ibs Granada, Granada,
46 Spain., ¹⁸²Queensland Centre for Schizophrenia Research, Wolston Park Hospital, Wacol,
47 Queensland, Australia, ¹⁸³Department of Psychiatry, University of Queensland, Brisbane,
1 Australia, ¹⁸⁴HMNC Holding GmbH, Munich, Germany, ¹⁸⁵Max Planck Institute of Psychiatry,
2 Munich, Germany, ¹⁸⁶Department of Psychosis Studies, Institute of Psychiatry, Psychology
3 and Neuroscience, King's College London, UK, ¹⁸⁷Departments of Psychiatry & Medical and
4 Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, ¹⁸⁸Stark
5 Neurosciences Research Institute, ¹⁸⁹Department of Psychiatry and Biobehavioral Science
6 and Department of Human Genetics, David Geffen School of Medicine, University of
7 California Los Angeles, Los Angeles, CA, USA, ¹⁹⁰Department of Psychology, Northern Illinois
8 University, DeKalb, IL, USA, ¹⁹¹Centre for Neuropsychiatric Genetics and Genomics, Division
9 of Psychiatry and Clinical Neurosciences, Cardiff University, Hadyn Ellis Building, Maindy
10 Road, Cardiff, CF24 4HQ, ¹⁹²Analytic and Translational Genetics Unit, Department of
11 Medicine, Department of Neurology, and Department of Psychiatry, Massachusetts General
12 Hospital, Boston, MA, USA, ¹⁹³The Stanley Center for Psychiatric Research and Program in
13 Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA,
14 USA, ¹⁹⁴Institute for Molecular Medicine Finland and the Helsinki Institute of Life Science,
15 University of Helsinki, Helsinki, Finland, ¹⁹⁵Department of Psychiatry, Rutgers University,
16 Piscataway, NJ, USA, ¹⁹⁶Department of Psychiatric Genetics, Poznan University of Medical
17 Sciences, Poznan, Poland, ¹⁹⁷Psychiatric and Neurodevelopmental Genetics Unit, Department
18 of Psychiatry and Center for Genomic Medicine, Massachusetts General Hospital, Harvard
19 Medical School, Boston, MA, USA, ¹⁹⁸King's College London, London, UK, ¹⁹⁹Oxford NHS
20 Foundation Trust, Oxford, UK, ²⁰⁰Department of Psychiatry, Psychosomatic Medicine and
21 Psychotherapy, University Hospital Frankfurt - Goethe University, Frankfurt am Main,
22 Germany, ²⁰¹Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta,
23 GA, USA, ²⁰²Division of Depression and Anxiety, McLean Hospital, Belmont, MA, US,
24 ²⁰³Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health,
25 Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ²⁰⁴Department of
26 Biochemistry and Molecular Biology II, Faculty of Pharmacy, University of Granada, Granada,
27 Spain, ²⁰⁵Department of Research and Outcomes, Skyland Trail, Atlanta, GA, USA,
28 ²⁰⁶Department of Psychiatry and Behavioral Neuroscience, University of Chicago, ²⁰⁷German
29 Center for Mental Health (DZPG), Munich, Germany, ²⁰⁸Department of Psychiatry, University
30 Hospital, Faculty of Medicine, University of Bonn, Bonn, Germany, ²⁰⁹Institute of Psychiatric
31 Phenomics and Genomics (IPPG), LMU University Hospital, LMU Munich, Munich, Germany,
32 ²¹⁰Department of Psychiatry and Psychotherapy, LMU University Hospital, LMU Munich,
33 Munich, Germany, ²¹¹Department of Biostatistics and Center for Statistical Genetics, School
34 of Public Health, University of Michigan, Ann Arbor, MI, USA, ²¹²SAMRC Extramural Genomics
35 of Brain Disorders Research Unit, Stellenbosch University, Cape Town, Western Cape, ZA,
36 ²¹³SAMRC Unit on the Genomics of Brain Disorders, Faculty of Medicine and Health Sciences,
37 Department of Psychiatry, Stellenbosch University, Cape Town, Western Cape, ZA, ²¹⁴Division
38 of Cancer Epidemiology and Genetics National Cancer Institute, Rockville, MD, USA, ²¹⁵Icahn
39 School of Medicine at Mount Sinai, New York, NY, USA, ²¹⁶Net.bio Inc, Los Angeles, CA, USA,
40 ²¹⁷Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine,
41 Massachusetts General Hospital, Boston, MA, USA, ²¹⁸Center for Precision Psychiatry,
42 Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA, ²¹⁹Department
43 of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience,
44 King's College London, London, UK, ²²⁰Department of Mental Health, University of Aberdeen,
45 Royal Cornhill Hospital, Aberdeen, UK., ²²¹Division of Mental Health and Substance abuse,
46 Diakonhjemmet Hospital, Oslo, Norway, ²²²SAMRC Unit on Risk and Resilience in Mental
47 Disorders, Dept of Psychiatry and Neuroscience Institute, University of Cape Town, Cape
1 Town, South Africa, ²²³School of Public Health, University of California San Diego, La Jolla, CA,
2 USA, ²²⁴Veterans Affairs San Diego Healthcare System, Psychiatry Service, San Diego, CA,
3 USA, ²²⁵German Center for Mental Health (DZPG), Partner Site Mannheim - Heidelberg - Ulm,
4 Germany, ²²⁶Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of
5 Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,
6 ²²⁷Cochin University Hospital, Paris Cité University, Paris, France, ²²⁸INSERM Unit 894,
7 Institute of Psychiatry And Neuroscience of Paris, Paris, France, ²²⁹Institute for Health
8 Informatics, University College London, London, UK, ²³⁰SBI MD Central Clinical Psychiatric
9 Hospital F. Usoltseva, ²³¹FSBEI HE "Russian University of Medicine" of the Ministry of Health
10 of Russia, Russia, ²³²Neuroscience Research Australia, Sydney, New South Wales, Australia,
11 ²³³School of Biomedical Sciences, Faculty of Medicine and Health, University of New South
12 Wales, Sydney, New South Wales, Australia, ²³⁴School of Public Health and Department of
13 Epidemiology, Louisiana State University Health Sciences Center, New Orleans, LA, US,
14 ²³⁵Cheshire and Wirral Partnership NHS Foundation Trust, Winsford, Cheshire, UK,
15 ²³⁶Genomics Program, University of South Florida College of Public Health, Tampa, FL, USA,
16 ²³⁷Department of Psychiatry & Human Behavior, University of California, Irvine, CA, USA,
17 ²³⁸Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany,
18 ²³⁹New York State Psychiatric Institute, New York, NY, USA, ²⁴⁰Department of Psychiatry,
19 Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA,
20 ²⁴¹Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital,
21 Copenhagen, Denmark, ²⁴²Universidade Nova de Lisboa, Nova Medical School, Lisboa,
22 Portugal, ²⁴³Stanley Division of Developmental Neurovirology, Johns Hopkins School of
23 Medicine, Baltimore, MD, USA, ²⁴⁴University of the Sunshine Coast, The Chancellory, Sippy
24 Downs, Queensland, Australia, ²⁴⁵Queensland University of Technology, School of Clinical
25 Sciences, Kelvin Grove, Queensland, Australia, ²⁴⁶Department of Biomedicine, University of
26 Bergen, Bergen, Norway, ²⁴⁷Stanley Center for Psychiatric Research, Broad Institute of MIT,
27 and Harvard, Cambridge, MA, USA, ²⁴⁸Analytic and Translational Genetics Unit, Department
28 of Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, MA,
29 USA, ²⁴⁹Department of Psychology, University of Washington, Seattle, WA, USA,
30 ²⁵⁰Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics,
31 Virginia Commonwealth University, Richmond, VA, USA, ²⁵¹Department of Psychiatry,
32 University of Oxford, Warneford Hospital, Oxford, UK, ²⁵²Institute for Molecular Bioscience,
33 The University of Queensland, St Lucia, Queensland, Australia, ²⁵³Departments of Genetics
34 and Psychiatry, University of North Carolina, Chapel Hill, NC, USA, ²⁵⁴Institute for Genomic
35 Medicine, University of California San Diego, La Jolla, CA, USA, ²⁵⁵Department of Medicine,
36 Division of Genetic Medicine, Vanderbilt University, Nashville, TN, USA,
37 ²⁵⁶Research/Psychiatry, Veterans Affairs San Diego Healthcare System, San Diego, CA, USA,
38 ²⁵⁷Department of Pediatrics, Université de Montréal, Montreal, QC, Canada, ²⁵⁸McLaughlin
39 Centre, University of Toronto, Toronto, ON, Canada, ²⁵⁹Beyster Center for Psychiatric
40 Genomics, University of California San Diego, La Jolla, CA, USA, ²⁶⁰Department of Cellular and
41 Molecular Medicine, University of California San Diego, La Jolla, CA, USA

42 *Correspondence at jsebat@ucsd.edu, stephen.scherer@sickkids.ca

43

44

45
## 1 Abstract

2 Psychiatric conditions share common genes, but mechanisms that differentiate diagnoses

3 remain unclear. We present a multidimensional framework for functional analysis of rare

4 copy number variants (CNVs) across 6 diagnostic categories, including schizophrenia (SCZ),

5 autism (ASD), bipolar disorder (BD), depression (MDD), PTSD, and ADHD (N = 574,965).

6 Using gene-set burden analysis (GSBA), we tested duplication (DUP) and deletion (DEL)

7 burden across 2,645 functional gene sets defined by the intersections of pathways, cell

8 types, and cortical regions. While diagnoses converge on shared pathways, mixed-effects

9 modeling revealed divergence of pathway effects by cell type, brain region, and gene

10 dosage. Factor analysis identified latent dimensions aligned with clinical axes. A primary

11 factor (F1) captured reciprocal dose-dependent effects of DUP and DEL in SCZ reflecting

12 positive and negative effects in excitatory versus inhibitory neurons and association versus

13 sensory cortex. SCZ and ASD were both strongly aligned with F1 but with opposing

14 directionalities. Orthogonal factors highlighted neuronal versus non-neuronal effects in

15 mood disorders (F2) and differential spatial distributions of DEL effects in ADHD and MDD

16 (F3). High-impact CNVs at 16p11.2 and 22q11.2 were enriched for combinations of

17 cell-type-specific genes involved in pathways consistent with our broader findings. These

18 results reveal molecular and cellular mechanisms that are broadly shared across psychiatric

19 traits but differ between diagnostic categories in context and directionality.

20

## 21 Background

22 Genes that are associated with psychiatric conditions carry rich information about the

23 timing, location, and nature of the biological processes that contribute to psychopathology

24 ¹,². The molecular functions of genes point to the cellular pathways and regulatory networks

25 that underlie vulnerability to psychiatric disorders. Furthermore, because gene expression is

26 tightly regulated in a cell-type and region-specific manner across the brain, the discovery of

27 genes can also provide insight into the neuroanatomical circuits that influence psychiatric

28 traits. The discovery of hundreds of genes and copy number variations (CNVs) that underlie

29 major psychiatric conditions such as schizophrenia (SCZ)³⁻⁶ and autism spectrum disorder

30 (ASD)⁷⁻¹¹ has implicated a variety of pathways including synaptic function, chromatin

31 regulation, cell signaling, cytoskeletal proteins, and DNA and RNA binding proteins that

32 regulate neurodevelopment ³,¹²⁻¹⁸. Similar pathways have been implicated by transcriptome

33 characterization of post-mortem brains from case samples of idiopathic ASD, SCZ and bipolar

34 disorder (BD) ¹⁹⁻²³. Genes implicated in psychiatric diagnoses are also enriched in specific

35 neural cell types. RNA sequencing in postmortem samples have identified neuronal and glial

36 signatures associated with ASD ²⁴,²⁵ and differences in the distributions of glial and neuronal

37 cells in mood disorders ²⁶. Analysis of GWAS associations has found enrichment of SCZ ²⁷,

38 major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) ²⁸ associations in

39 mature excitatory and inhibitory neurons.
1 Despite significant progress in identifying risk genes and pathways in psychiatric conditions,
2 there remains a limited understanding of how neural processes relate to specific psychiatric
3 traits or diagnoses. Many of the same biological pathways, such as those described above,
4 have been repeatedly associated with multiple diagnostic categories, including SCZ<sup>5,15,29</sup>,
5 BD<sup>14,30</sup>, ASD<sup>16</sup>, intellectual disability<sup>31</sup> and congenital heart disease<sup>32</sup>. Thus, functional
6 convergence that is evident from pathway enrichment analysis of the associated genes
7 highlights broad biological themes but lacks the resolution to differentiate neural
8 mechanisms that differ between diagnostic categories.

9 CNVs have been shown to exert dose-dependent effects on a range of complex traits,
10 including gene expression<sup>33</sup>, head size<sup>4,34</sup>, brain volume<sup>35,36</sup>, functional connectivity<sup>37</sup>, body
11 mass<sup>38</sup>, craniofacial morphology<sup>39</sup>. As described in our companion paper<sup>40</sup>, this pattern
12 extends to psychiatric traits, where reciprocal duplications (DUPs) and deletions (DELs) of
13 genes show dose-dependent effects and diverge in their genotype-phenotype associations.
14 A more detailed functional analysis of gene-dosage effects could clarify how alterations in
15 molecular pathways contribute to psychiatric traits. In this study, we developed and applied
16 an integrated framework to examine how gene-dosage effects on pathways, cell types, and
17 brain regions relate to clinical diagnoses (Fig. 1). Key elements of this approach include
18 accounting for (1) directionality of gene-dosage effects and their distribution within (2)
19 neural cell-types and (3) cortical brain regions, and we perform a comparative analysis
20 across multiple diagnostic categories.

21 Gene set association of rare CNVs in 6 psychiatric conditions

22 We leveraged large-scale rare CNV data (population frequency <2%) from the Psychiatric
23 Genomics Consortium, comprising genome-wide microarray data from 574,965 individuals
24 (133,007 cases and 441,958 controls) across six major psychiatric disorders: schizophrenia
25 (SCZ), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder
26 (MDD), post-traumatic stress disorder (PTSD), and attention-deficit/hyperactivity disorder
27 (ADHD). CNVs were uniformly processed through a centralized pipeline for calling and
28 quality control. Only rare CNVs (frequency <2%) were retained for analysis. Individuals
29 represented diverse ancestral backgrounds, with 89.3% of European ancestry. This dataset
30 enabled us to apply our multidimensional framework to identify distinct molecular and
31 cellular features of brain function associated with each psychiatric diagnosis.

32 We assembled a primary catalogue of 2,645 gene sets that capture neurobiological features
33 across multiple levels of organization. These included 2,453 molecular pathways from public
34 databases<sup>41-43</sup>. In addition, differential expression in single-cell expression data was analyzed
35 to create gene sets for 12 cell types from human fetal and adult brain (ranging from second
36 trimester to 54 years of age)<sup>44</sup>, and differential expression in bulk tissue was analyzed to
37 create 180 anatomic regions of cerebral cortex from the Allen Human Brain Atlas (AHBA)<sup>45</sup>
38 <sup>46</sup>(Table S1).
1 We investigated the association of functional gene sets with psychiatric diagnoses using
2 gene-set burden analysis (GSBA) 5. Associations detected by GSBA capture the enrichment
3 of variants in functionally-related genes in cases. However, GSBA is not equivalent to a
4 gene-set enrichment test (e.g., Subramanian et al., 2005 47). Rather, it is a statistical genetics
5 approach that quantifies the effect size of rare-variant burden across a defined set of genes
6 (e.g. a GO term) in cases and controls (Fig. 1). For each gene set, we tested the association
7 of the aggregate DEL or DUP counts across genes with case-control status by logistic
8 regression controlling for population structure, sex and overall genome-wide CNV burden
9 (collapsed across all out-of-category genes). Gene-set summary statistics were generated for
10 each genotyping platform in each diagnostic category, and results were combined by
11 meta-analysis. Combined results were corrected for multiple testing with
12 Benjamini-Hochberg False Discovery Rate (BH-FDR<5%, Fig. S1).

13
14 Fig. 1 | Investigating association of pathways, cell types and brain regions by Gene Set Burden Analysis
15 (GSBA). Gene sets were derived for Pathway (from GO, KEGG, REACTOME, and BioCarta), Cell type (from single
16 cell study, Velmeshev et al.), and Cortical regions (from Glasser parcellation of the Allen Brain Atlas).
17 Case-control association of CNV burden collapsed across gene sets, was then tested by logistic regression and
18 meta-analysis was performed across genotyping platforms. Functional gene set associations were tested for 6
19 major psychiatric conditions (ASD, ADHD, SCZ, PTSD, MDD, BD).

20 Significant functional burden associations were detected for a total of 787 gene sets in one
21 or more conditions, including SCZ (671 gene sets) and ASD (331 gene sets), ADHD (52 gene
22 sets), BD (122 gene sets) and MDD (3 gene sets) (Table S2). Comparing summary statistics
23 between trans-ancestry analysis and the European-only subset, we found a high level of
24 concordance in the z-statistics between single ancestry (European subset) and
1 trans-ancestry results across the 6 psychiatric conditions (beta-coefficients are between 0.9

2 and 1 with median beta-coefficient = 0.97; Fig. S2). All results described below are from the

3 trans-ancestry summary statistics, which has the greatest statistical power.

4 **Common neurodevelopmental pathways are implicated in multiple diagnostic categories**

5 Pathway gene sets were compiled from Gene Ontology (GO) ⁴¹, KEGG ⁴², Reactome ⁴³, and

6 BioCarta ⁴⁸, with size ranging from 50 to 500 genes. 589 gene sets were associated with one

7 or more conditions (Table S3). Using Enrichment Map ⁴⁹, overlapping gene sets implicated by

8 CNVs were grouped into 19 functionally-related clusters representing canonical pathways

9 such as MAPK signaling, nervous system development, synaptic transmission, chromatin

10 regulation, etc. (Fig. 2a, Table S4). To summarize the pathway results, effect sizes were then

11 estimated for the 19 gene sets in 6 diagnostic categories by GSBA regression (Fig. 2b, Table

12 S5)

13

<figure><img src="image_3.png" /></figure>

b

<figure><img src="image_4.png" /></figure>

14

<figure><img src="image_5.png" /></figure>

d

<figure><img src="image_6.png" /></figure>

15 **Fig. 2** Rare CNVs association analysis results in molecular pathways and neuronal cell types. (a) Enrichment map showing

16 clusters of functional modules that are significantly associated with any condition. CNV associations are color-coded as a

17 portion with a node where red indicates a DEL association in ASD, orange indicates a DEL association in SCZ, blue indicates

18 a DUP association in SCZ, and yellow indicates a DEL association in ADHD. Gene-sets not forming a cluster of 3 or more

19 members were excluded. Gene set clusters are listed in Table S4. (b) The heatmap represents the results at the

20 pathway-cluster level, with color indicating z-score from meta-analysis. (c) A UMAP plot displays cell clusters colored

21 by prenatal (teal) and postnatal (red) periods. (d) Heatmaps show association results at the cell type level with

22 color indicating z-score, where red represents a higher burden of CNVs in cases and blue represents a depletion

23 of CNVs burden in cases. An asterisk indicates statistically significant associations (q-value <0.1). Summary

24 statistics of the initial primary gene sets and for the final set of pathway clusters are in Tables S3, and S5

25 respectively.
1

2 As expected, CNV burden associations were strongest in ASD and SCZ and were attenuated

3 in other adult onset diagnoses, BD, ADHD, MDD, and PTSD. Many of the same functional

4 gene sets were implicated in ASD and SCZ, including MAPK and other cell-signaling

5 pathways, chromatin regulation, and synaptic transmission. Pathway signals in ASD were

6 driven by significant DEL associations across 10 pathways. SCZ, by contrast, showed DUP

7 associations in 9 functional gene sets such as chromatin regulation, MAPK signaling, axonal

8 transport, and DEL associations in a different set of 9 pathways including synaptic

9 transmission, axon guidance, and calcium signaling. The finding that pathway associations in

10 SCZ differ by gene dosage is notable in light of the dose-dependent CNV effects reported for

11 SCZ and other diagnoses in our companion study<sup>40</sup>.

12

13 Gene set burden associations implicate neuronal and non-neuronal cell types

14 Twelve cell-type gene sets were derived from single-cell RNA-sequencing of human cortex

15 (prefrontal, cingulate, insula, motor, and temporal regions) spanning prenatal (5-9 months)

16 and postnatal (0-54 years of age) developmental stages, based on the dataset from

17 Velmeshev et al.<sup>44</sup>. Starting from eight major cell type clusters defined in the original study,

18 we refined these to capture key developmental distinctions, resulting in the following gene

19 sets: five prenatal cell types - 1) glial progenitor cells (GpcPre), 2) oligodendrocyte precursor

20 cells (OpcPre), 3) inhibitory neurons (InNeuPre), 4) excitatory neurons (ExNeuPre), and 5)

21 astrocytes (AstPre); and seven postnatal cell types - 6) vascular cells (VascPost), 7) OpcPost,

22 8) oligodendrocytes (OligoPost), 9) microglia (MgPost), 10) inhibitory neurons (InNeuPost),

23 11) excitatory neurons (ExNeuPost), and 12) astrocytes (AstPost) (Fig. 2c). We observed

24 several cell type associations with diagnostic categories (Fig. 2d, Table S3). ASD was

25 associated with DEL burden in ExNeuPre, consistent with loss-of-function variants in ASD

26 genes being enriched in fetal excitatory neurons<sup>7,50</sup>. SCZ showed DUP association in

27 ExNeuPre, microglia, and neurovascular cells and DEL association in GpcPre. BD showed DUP

28 association in ExNeuPost and OligoPost and DEL association in ExNeuPre.

29

30 Diagnoses differ in the distribution of gene-set associations between sensorimotor and association cortex

32 Spatial variation in gene expression across the cortex reflects region-specific regulation

33 beyond differences in cell type composition<sup>51</sup>. The primary gradient of gene expression

34 (PC1) in the AHBA follows a sensorimotor-to-association (S-A) axis, spanning from primary

35 sensory (visual, auditory, sensorimotor cortex) areas to transmodal (frontal, temporal)

36 regions<sup>51-53</sup>. This axis aligns with several cortical hierarchies, including developmental timing

37 <sup>54-56</sup>, myelination<sup>54,55</sup>, anatomical projections<sup>57</sup>, and functional specialization<sup>58</sup>. Given its

38 close correspondence with the S-A axis<sup>51</sup>, we refer to AHBA PC1 as the S-A axis throughout

39 the paper.

40

41 To investigate how gene dosage effects are distributed across the cortex, we defined gene
1 sets for each of the 180 cortical regions from Glasser et al.⁴⁵. Gene expression was

2 z-transformed across regions, and highly expressed genes (z>1) were assigned to each set of

3 180 regions. DEL and DUP burden was tested across cortical gene sets within each diagnosis.

4 In total, 177 significant associations were identified. DEL and DUP associations are visualized

5 on Glasser cortical maps (Fig. 3a, c), with effect sizes (z-scores) represented by a red-blue

6 scale. We then tested whether spatial patterns of effect sizes aligned with the S-A axis using

7 the SPIN test⁵⁹ with 10,000 permutations and Kendall correlation.

8

9 CNV effect sizes varied across the cortex, and in several diagnostic categories, they showed
10 significant, but divergent, correlations with the S-A axis. DEL effect sizes were positively

11 correlated with the S-A axis in MDD, ADHD, and SCZ, indicating enrichment of DEL signal in

12 sensorimotor cortex, while BD showed a negative correlation, indicating a relative

13 enrichment of DEL signal in association cortex (Fig. 3b; Table S3). DUP associations were

14 negatively correlated with the S-A axis in SCZ, ASD, and PTSD (Fig. 3c,d), indicating an

15 enrichment in the association cortex. Our results suggest that the spatial distribution of gene

16 dosage effects differs by diagnosis. Similar correlations were observed with other functional

17 and anatomical gradients that are also aligned with the S-A axis (e.g., T1w/T2w ratio

18 reflecting myelin content; Fig. S3)⁵².

19
<figure><img src="image_7.png" /><figcaption>2 **Fig. 3** Rare (a) DEL and (c) DUP association analysis results of the cortical brain regions in the 6 conditions.</figcaption></figure>

3 Color indicates the association level (z-score) with red indicating the CNV association with the cases, while blue

4 indicates the depletion of CNVs in cases (Table S3). Correlation results between CNV associations in (b) DEL and

5 (d) DUP against the dominant transcriptomic brain gradient (PC1 of AHBA). Each circle represents a brain

6 region gene set. Kendall's Tau and corresponding q-value are shown in the title of each scatterplot. Solid

7 diagonal trend line indicates significant correlation ($q_{SPIN}<0.05$). The cortical map at the top left corner

8 illustrates the transcriptomic gradient from PC1 AHBA.

9
1 Association of pathways with diagnosis varies by cell type and gene dosage

2 Our initial findings demonstrate that there are divergent genetic influences between

3 different diagnostic categories when we stratify genetic effects by gene dosage and brain

4 region. These findings highlight a principle that is somewhat obvious in retrospect. The

5 multidimensional nature of psychopathology demands a multidimensional data analytic

6 approach.

7

8 To characterize with more granularity how CNV effects are distributed in the brain, we

9 investigated gene-dosage effects at the intersections of pathways, cell types, and brain

10 regions. Pathway gene sets were intersected with cell types to create non-overlapping

11 subsets (e.g., Chromatin_ExNeu and Chromatin_InNeu; Fig. 4a). Similarly, the transcriptome

12 was divided into sensorimotor and association gene sets based on the correlations of

13 individual genes with the S-A axis in the AHBA (76.29% of genes showed a nominally

14 significant positive or negative correlation with PC1, Tables S6-S7). Pathways were

15 intersected with these to create 2 region-specific subsets of each pathway (e.g.,

16 Chromatin_Sensori, Chromatin_Assoc). GSBA was then performed on two-way and

17 three-way intersections of pathways (N=19), cell types (N=12), and brain regions (N=2),

18 including gene sets of size ≥30. Each gene set result was labeled with four factors: pathway,

19 cell type, brain region, and dosage (Table S8).

20

21 We then evaluated which levels of biological organization best explain variation in gene-set

22 effects within each diagnosis. We performed linear modeling on effect sizes of stratified

23 gene-sets (z-scores) with different combinations of pathway, cell type, brain, and dosage as

24 independent variables. For each diagnostic category, variance explained (R²) in summary

25 statistics was calculated for main effects and interactions of these factors. Of all 2-way

26 combinations, pathway and cell type explained the greatest variance (35.3% on average

27 across diagnoses, Fig. 4b; Table S9). A full model that further stratified gene sets by dosage

28 explained a majority of the variance (80.1% on average). The pathway×celltype×dosage

29 interaction consistently explained the largest proportion of variance (Fig. 4c; Table S9),

30 explaining half of the effect of the full model. This result highlights the importance of

31 cell-type-specific and dose-dependent pathway effects across psychiatric conditions. Model

32 fits improved by 7-20% when the brain region was included in the models (Fig. 4b,c; Tables

33 S9-S10), suggesting that spatial variation in pathway expression also explains a proportion of

34 variance.

35
<figure><img src="image_8.png" /><figcaption>b Full Model R² Estimates</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>c Interaction term R² Estimates</figcaption></figure>

<figure><img src="image_10.png" /></figure>

1

2 Fig. 4 | Associations of pathways with psychiatric traits vary by cell-type and gene dosage. (a) Schematic illustrating how

3 gene sets were defined by intersecting pathway, cell type, and cortical region dimensions. Example intersections include

4 Chromatin-ExNeu, Chromatin-Assoc, ExNeu-Assoc, and Chromatin-ExNeu-Assoc. (b) Full model R² estimates showing the

5 total variance in gene-set z-scores explained by main effects and interaction terms for each diagnosis. Models included

6 pathway, cell type, brain region, dosage, and all combinations of two-way and three-way interactions. (c) R² estimates for

7 individual interaction terms, quantifying the contribution of each interaction to the explained variance. The

8 pathway×celltype×dosage interaction consistently explains the largest proportion of variance across diagnoses, highlighting

9 the importance of dosage-sensitive and cell-type-specific pathway effects (Tables S9-S10).

10

11 Diagnostic categories are differentiated based on gene-dosage effects in pathways by cell

12 type and brain region

13 To elucidate where gene-dosage effects converge at the intersection of pathways, cell types,

14 brain regions, and psychiatric traits, we performed exploratory factor analysis (EFA) 60 of

15 functional gene sets to identify latent factors that correspond to different gene-trait

16 relationships. Genetic correlations of DEL and DUP associations across 6 diagnostic

17 categories were estimated based on gene-set summary statistics (Fig. 5a; Table S11). Factor

18 analysis of gene-set summary statistics was performed to extract latent dimensions of

19 genetic effects, and a three-factor model was optimal (Fig. S4). Factor F1 captured
1 dose-dependent effects in SCZ and BD (DUP positive, DEL negative) and dose-aligned effects

2 in ASD (DUP positive, DEL positive) in shared gene sets. F2 captured DUP effects shared by

3 mood disorders and PTSD. F3 captured DEL effects shared by MDD, ADHD and SCZ.

4 Importantly, genetic correlations between diagnostic categories show greater contrast when

5 DEL and DUP results for each disorder were treated as independent components (Table S11)

6 compared to when all gene set tests for DEL and DUP were aligned between disorders (Fig.

7 S5g; Table S12). This result is consistent with diagnostic categories having involvement of

8 common functional processes with sometimes opposing directionality. Loadings of DEL and

9 DUP effects onto the 3 factors yields a unique profile for each diagnostic category (Fig. 5b;

10 Table S13).
<figure><img src="image_11.png" /><figcaption>2 Fig. 5 | Differentiation of diagnostic categories based on gene-dosage effects in pathways by cell type and</figcaption></figure>

<caption>3 brain region. (a) Genetic correlations between diagnostic categories when each diagnosis-dosage combination</caption>
1 is treated as an independent component, see also Table S11, *p<0.05) **q<0.05). Diagnosis-dosages with

2 factor loadings >0.25 were grouped and labeled to highlight psychiatric traits contributing to F1, F2 and F3. (b):

3 Factor loadings of DEL and DUP for disorders reveal a distinct profile for each diagnostic category. (c, g, k) Gene

4 set-factor scores for the three factors, cell types and pathways were ordered using a simple sign-based

5 bi-clustering algorithm (see methods) (Table S14). (d,h,i) Factor scores are representative of dose-dependent

6 effects of genes. Scatterplots of gene set effect sizes (z-score) are shown for the top 2 diagnosis-dosage

7 groupings with highest absolute factor loadings for factor F1, F2, and F3, and factor score of each gene set is

8 indicated using the same color scale as in panels c,g,k. Solid trend lines indicate significant correlation between

9 the diagnosis-dosage pair. (e,j,m) Factor analysis of gene sets with genome-wide significant loci removed

10 yielded results with highly concordant gene set factor scores (e,f,i,j,m,n; tau_F1=0.45, tau_F2=0.53,

11 tau_F3=0.32, p<2.2e-16; Table S14), demonstrating that these patterns are not attributable to a select subset

12 of major loci.

13

14 The factor scores of functional gene sets show the relationships of neural processes to these

15 latent dimensions. After a sign-based bi-clustering of the matrix, a structured pattern shows

16 dose-dependent effects on pathways within cell types. F1 in particular captures distinct

17 clusters that represent the mirror-opposite effects of DUP and DEL seen in SCZ and other

18 diagnostic categories (Fig. 5b). Positively scoring gene sets (Fig. 5c, upper left quadrant),

19 which correspond to DUP associations in SCZ (Fig. 5d), were enriched for core regulatory

20 processes (cell cycle, MAPK, chromatin) and metabolic pathways expressed in postnatal

21 neurovascular cells (VascPost), excitatory neurons (ExNeuPost), and microglia (MgPost) (Fig.

22 S6). Negatively scoring gene sets (Fig. 5c, lower right quadrant), which reflect DEL

23 associations in SCZ (Fig. 5d), were enriched for calcium signaling, axon guidance, and

24 translation pathways expressed in inhibitory neurons and glia. F1 Factor scores also reveal

25 divergent effects on synaptic transmission by cell type, with DUP associations concentrated

26 in excitatory neurons and DEL associations in inhibitory neurons, a pattern that is consistent

27 with a shift in excitatory-inhibitory balance. To assess whether these patterns might be

28 attributable to strong signals from a select subset of loci, we repeated GSBA (Table S8) and

29 factor analysis (Fig. 5e) after removing 18 loci that reached genome-wide significance (GWS)

30 in our companion study 40. The results showed highly concordant genetic correlations (Fig.

31 S5c), factor solution and factor loadings (Fig. S5f), and gene-set factor scores (Fig. 5f,i,l).

32 Thus the three factors derived in Figure 5 are not driven by a select subset of loci, and

33 appear to be generalizable to CNVs genome wide. Similar clusters of pathway-cell type

34 associations were evident in F1 (Fig. 5e), with the exception of the glial precursor cell type

35 (GpcPre)(Fig. 2d). Lastly, F1 showed modest enrichment of gene set factor scores in

36 Association cortex, a result that is consistent with the inverse dose-response of DEL (Fig.

37 3b)and DUP (Fig. 3d) effects along the S-A axis. Supplementary figures are provided that

38 illustrate all gene-set associations (Fig S6A), the subsets that are captured by each of the

39 latent factors (Fig. S6B), and functional terms that are enriched within each factor (Fig. S6C).

40

41 The orthogonal F2 factor showed divergent positive (associated with cases) and negative

42 (associated with controls) effects in developmental signaling (cell-cycle, MAPK, GTPase

43 signaling) pathways in non-neuronal and neuronal cell types, respectively (Fig. 5g,i).
1 Positively-scoring gene sets, which correspond to positive DUP associations in mood disorders (Fig. 5h, Fig. S6b), include nervous system development and metabolic pathways

2

3 concentrated in microglia (MgPost), and neurovascular cells (VascPost). Negatively-scoring

4 gene sets correspond to negative DUP associations in similar pathways in neuroectodermal lineages (ExNeuPre, ExNeuPost, InNeuPre, AstPost; Fig. 5f,g). The patterns in F2 suggest that

5

6 DUP effects in mood disorders are concentrated in core regulatory processes in

7 non-neuronal cell types, while DUP effects in core regulatory pathways may be tolerated (or

8 protective) in neurons with respect to diagnoses of MDD and BD. Thus, DUP effects on

9 regulatory pathways in postnatal excitatory neurons (e.g. GTPase_ExNeuPost,

10 CellCycle_ExNeuPost, Chromatin_ExNeupost) are a point of divergence between F1 and F2

11 that represents neural processes that are positively associated with SCZ and ASD and not

12 associated with mood disorders (Fig. S6B-C).

13

14 F3 was characterized by positive loadings of MDD-DEL and ADHD-DEL (Fig. 5b; Fig. S6b).

15 Positively-scoring gene sets consisted of DEL effects in Cell-signaling and neurotransmission

16 (SynapTrans, VesiclTraff) in inhibitory neurons (InNeuPre, InNeuPost). Negatively-scoring

17 gene sets were broadly distributed across regulatory and metabolic pathways in microglia

18 and neurovascular cells. Notably, nearly all (18/19) canonical pathways showed strong

19 positive F3 factor scores in the sensorimotor cortex (Fig. 5i,k), consistent with the positive

20 correlation of MDD-DEL and ADHD-DEL with the S-A axis in Figure 3a-b. Thus, F3 captures

21 differential DEL effects in synaptic and regulatory pathways that vary along the S-A axis and

22 in cell-type populations that align with this cortical expression gradient, such as inhibitory

23 interneurons<sup>55</sup>.

24

25 High-impact CNVs have a variety of cell-type specific gene-dosage effects

26 For CNV loci with the largest effect sizes on psychiatric traits, including reciprocal CNVs at

27 16p11.2, and 22q11.2<sup>40</sup>, clinical phenotypes are likely driven by the combined effects of

28 multiple genes within each region<sup>39,61-63</sup>. Results from this study further suggest that a CNV

29 may exert its influence through distinct pathway effects in multiple cell types.

30

31 Duplication of 16p11.2 BP4-BP5 confers significant susceptibility to SCZ and BD, and Deletion

32 is associated with ASD (Fig. 6a), consistent with some hallmarks of F1. Single-cell expression

33 datasets<sup>44</sup> confirm that expression of genes within the locus differs significantly by cell type

34 (Fig. 6b), A network was constructed representing cell-type expression of CNV genes and

35 pathways (Fig. 6c), highlighting several pathway-cell type effects that are consistent with

36 positively-scoring gene sets on factor F1 including several genes tied to regulatory pathways

37 in neurovascular cells (MAPK3, ALDOA, MVP, TMEM219, TAOK2) and microglia (CORO1A,

38 INO80E) as well as MAPK signaling and synaptic plasticity in postnatal excitatory neurons

39 (YPEL3 PRRT2).

40
1 The 22q11.2 A-D locus has mirror positive and negative effects of DEL and DUP respectively

2 on SCZ susceptibility (Fig. 6d), which is also a hallmark of F1. Pathway-cell type effects in

3 22q11.2 are consistent with negatively-scoring gene sets on F1, including chromatin,

4 translation and GTPase signaling in fetal excitatory neurons (SLC25A1, MRPL40, CLTCL1,

5 THAP7), axon guidance and endosome recycling in postnatal excitatory neurons (RTN4R,

6 POI4KA, ZDHHC8) and calcium signaling in postnatal inhibitory neurons (P2RX6)(Fig. 6e,f). As

7 mentioned previously, gene set effects listed here, persist after removing all genome-wide

8 significant loci. Thus, the functional gene sets enriched within major CNV loci generalize to

9 gene-dosage effects in the rest of the genome.

10

16p11.2 BP4-BP5

CNV effect size (z-score)

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

22q11.2 A-D

CNV effect size (z-score)

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

11

12 Fig. 6| Cell-type specific expression of genes within major CNV loci 16p11.2 BP4-BP5 and 22q11.2 A-D suggests that the

13 functional influence of a CNV in the brain may be driven by distinct pathway effects across a variety of cell types. CNV
1 associations displayed in (a) and (d) were obtained from Shanta et al.⁴⁰ Colors indicate the association direction and effect

2 size (z-score), and asterisks indicate FDR<10% results. (b) and (e) heatmaps show log2 fold-change of cell type expression of

3 the genes within each locus. The colors indicate the differential expression level. CNV-gene-gene-set networks in (c) and (f)

4 display the CNV genes and their participation in the pathway-cell-type stratified gene sets. Shapes represent different

5 entities of the network where the big circle in the middle is a GWS locus, peripheral circles are genes in the locus. A gene

6 may be linked to one or more pathways (diamond) and at the end of the pathway, a cell type (square) is connected to

7 indicate the gene membership of one or more stratified pathways of the same cell type. The color of diamond nodes

8 indicates the group of pathways.

9

## 10 Discussion

11 We present an integrative framework for characterizing the functional convergence and

12 divergence of rare genetic influences on mental health traits. Using a statistical genetic

13 approach, gene set burden analysis (GSBA) ⁵, we analyze the association of aggregate rare

14 CNV burden in functional gene sets with diagnostic categories. A key element was to apply a

15 multidimensional approach that quantified divergent effects of DEL and DUP in gene sets

16 that represent the intersections of molecular pathways, neural cell types and cortical

17 regions. This approach yields key insights into the neural basis of psychopathology. We

18 demonstrate that, while major diagnostic categories converge on common molecular

19 pathways, they diverge in the cellular context, spatial distribution, and directionality of

20 genetic effects.

21

22 Gene-set burden tests identified 19 neurodevelopmental pathways, highly overlapping

23 between ASD and SCZ, that were consistent with prior CNV ³,⁵, WES ¹⁷,¹⁸, and GWAS¹⁵,⁶⁴

24 studies. These included pathways involved in neuronal signaling, GTPase and receptor

25 mediated cell signaling, chromatin, translation, and metabolism. Cell-type associations

26 included fetal excitatory neurons in ASD; excitatory neurons and oligodendrocytes in BD; and

27 postnatal excitatory neurons, microglia, and neurovascular cells in SCZ. The involvement of

28 neurovascular gene sets is notable given prior links of SCZ⁶⁵⁻⁶⁷, BD⁶⁸ and ASD ³²,⁶⁹ to

29 cardiovascular disease. However, comparing lists of pathways and cell types does not reveal

30 clear relationships between neural functions and diagnostic categories.

31

32 A key insight, originating from our companion paper ⁴⁰, is the dose-dependent effect of

33 genes in SCZ and other diagnostic categories, evident by the inverse correlation of effect

34 sizes for reciprocal DEL and DUP of the same genes. Stratification of pathway associations by

35 gene dosage showed that pathway associations, particularly in SCZ, differ by dosage.

36 SCZ-DUP effects were concentrated in core regulatory pathways and DEL effects in neuronal

37 signaling.

38

39 In addition, incorporating spatial patterns of gene expression into GSBA revealed differential

40 genetic effects across brain regions. In several diagnostic categories, the spatial distribution

41 of gene dosage effects aligned with the S-A axis, a cortical gene expression gradient,

42 extending from transmodal association areas (frontal, temporal cortex) to sensorimotor

43 regions (visual, auditory cortex), and spatial distributions differed by diagnostic category,
1 with DEL effects in MDD, ADHD and SCZ enriched in sensorimotor cortex, while DEL effects

2 BD and DUP effects in ASD, PTSD and SCZ were enriched in association cortex.

3

4 These findings highlight how stratification of genetic effects by context and gene dosage

5 allow for the differentiation of diagnostic categories. To determine where genetic effects

6 converge and diverge at multiple levels, we investigated gene-dosage effects in the

7 interactions of pathways, cell types and cortical regions. Mixed-effects modeling

8 demonstrated that associations of gene sets captured the largest share of variance when

9 pathways were stratified by cell type, and dosage. Spatial information also contributed a

10 modest additive effect representing differential genetic effects along the S-A axis, as

11 observed for MDD-DEL and ADHD-DEL (Fig. 3).

12 Factor analysis revealed three latent dimensions of gene-dosage effects (F1, F2, F3) that

13 capture shared and distinct genetic architectures across diagnoses. A major factor F1

14 captured a set of neural processes that have a dose-dependent relationship to SCZ (DUP

15 positive, DEL negative) and dose-aligned relationship to ASD (DUP positive, DEL positive),

16 with *distinct pathway-cell type combinations at opposing ends of the dose-response curve*.

17 SCZ-DUP associations in cell-signaling (MAPK, cell-cycle) and metabolic pathways were

18 concentrated in postnatal excitatory neurons and neurovascular cells. SCZ-DEL associations

19 in neuronal signaling (synaptic, calcium) were concentrated in inhibitory interneurons,

20 consistent with an imbalance of excitation and inhibition 70. Dose-dependent effects in SCZ

21 also correlated with the S-A axis (Fig. 3) with DUP effects aligned to the association cortex

22 and DEL effects in sensorimotor regions. This pattern suggests that one major dimension of

23 psychosis consists of negative effects on inhibitory activity (disinhibition) in sensory

24 processing and positive dysregulation of excitatory processes in frontal/temporal regions.

25 Thus, our genetic findings could inform studies of neurophysiology in schizophrenia 71,72.

26 Notably, ASD contrasts with SCZ in the directionality of effects in F1. In contrast to the

27 dose-dependent effects in SCZ, In ASD, opposing effects of DUP and DEL are concentrated

28 within the same neural processes. This fact could reflect distinct linear and non-linear dose

29 responses for the cognitive traits underlying psychosis and social behavior respectively.

30 Additional factors captured orthogonal neural processes associated with mood disorders

31 and ADHD. F2 implicated cell-type specific effects in mood disorders consisting of divergent

32 positive and negative effects on cell-signaling between non-neuronal and neuronal cells

33 respectively, the latter being a point of divergence from SCZ and ASD. These findings

34 represent a possible genetic basis for differences in the densities of neurons and glia that

35 have been reported in postmortem studies of BD and MDD 26. F3 reflected differential DEL

36 effects along the S-A axis capturing broad sensorimotor enrichment in ADHD and MDD

37 consisting of synaptic and regulatory pathways in cell-type populations that align with this

38 cortical gene expression gradient, such as inhibitory interneurons 55.
1 We also show that specific high-impact CNVs are enriched for combinations of

2 cell-type-specific genes involved in pathways consistent with our broader findings. 16p11.2

3 BP4-BP5⁴ represents a genomic region that is enriched for multiple functional gene sets at

4 the positive end of factor F1 (cell signaling pathways in ExNeuPost and VascPost). Conversely

5 22q11.2 A-D⁷³ is enriched for functional gene sets at the negative end of F1, such as

6 regulatory pathways in ExNeuPre and calcium signaling InNeuPost. These results suggests

7 that the large effects of an individual CNV may result from the combined impact of genes

8 acting across multiple neural processes. Thus, 16p11.2 and 22q11.2 CNVs are monogenetic

9 conditions that could serve as models for the dose-dependent effects of the major factor F1.

10 High-risk CNVs, such as these represent patient groups that can be recruited for deep

11 phenotypic characterization ⁷⁴ and parallel functional characterization of neural processes in

12 brain organoid models ⁷⁵,⁷⁶. Thus the findings from this study can be directly applied in

13 clinical and translational studies of CNVs.

14 Our results provide a genetic basis for previous findings from other NIH-funded

15 collaborations such as the PsychEncode consortium. Consistent with findings from Gandal et

16 al., functional analysis of CNVs shows that core molecular pathways are shared by multiple

17 diagnostic categories, such as ASD, SCZ, BD and MDD including synaptic transmission and

18 neuronal signaling pathways ¹⁹ and there are divergent effects in neuronal and non-neuronal

19 cell types ²⁰. Considering just one level of biological organization at a time, such as pathways,

20 the patterns that emerge from PsychEncode, GWAS, WES and CNVs are dominated broadly

21 by "functional convergence" that seemingly spans all diagnostic boundaries. However, when

22 genomic approaches take into consideration the joint influences of cell types, spatial

23 distribution and directionality (dosage) of the pathway effects, distinct mechanisms emerge

24 that underlie different dimensions of psychopathology.

25

26 Acknowledgements

27 We thank the many research participants. The PGC is supported by grants to UCSD

28 (R01MH124847), UNC (R01MH124871), MGH (R01MH124851), Mount Sinai School of

29 Medicine (R01MH124839), Cardiff University (R01MH124873), Trinity College Dublin

30 (R01MH124875) and Washington University St. Louis (R01DA054869). Additional support

31 was provided by grants to J.S. (MH119746, MH133899), C.N. (MH106595, MH124847) and

32 S.J. (U01 MH119690, U01 MH119739, CIHR_495906). Funding for the work in Bipolar

33 Disorder was supported by the Research Council of Norway (#223273, 248778, 262656,

34 273291, 283798, 248828), South East Norway Health Authority (2017-112), and KG Jebsen

35 Stiftelsen. The iPSYCH project is supported by grants from the Lundbeck Foundation

36 (R165-2013-15320, R102-A9118, R155-2014-1724 and R248-2017-2003) and the universities

37 and university hospitals of Aarhus and Copenhagen. Genotyping of iPSYCH samples was

38 supported by grants from the Lundbeck Foundation, the Stanley Foundation, the Simons

39 Foundation (SFARI 311789 to M.J.D.), and NIMH (5U01MH094432-02 to M.J.D.). The Danish

40 National Biobank resource was supported by the Novo Nordisk Foundation. Data handling
1 and analysis on the GenomeDK HPC facility was supported by NIMH (1U01MH109514-01 to

2 A.D.B.). High-performance computer capacity for handling and statistical analysis of iPSYCH

3 data on the GenomeDK HPC facility was provided by the Center for Genomics and

4 Personalized Medicine and the Centre for Integrative Sequencing, iSEQ, Aarhus University,

5 Denmark (grant to ADB). Additional support to S.W.S from The University of Toronto

6 McLaughlin Centre, the Hospital for Sick Children (SickKids) Foundation, the Ontario Brain

7 Institute, Genome Canada/Ontario Genomics Institute and the Northbridge Chair in

8 Paediatric Research held at the Hospital for Sick Children and University of Toronto.

9

## 10 Competing interests

11 S.W.S. has served on the Scientific Advisory Committee of Population Bio and has been

12 involved in Deep Genomics. Intellectual property from aspects of his research held at the

13 Hospital for Sick Children are licensed to Athena Diagnostics and Population Bio.

14

## 15 Author Contributions

16 The multidimensional analysis framework described here was developed through a team

17 effort. Data analysis and manuscript preparation was led by WE and JS. Statistical models for

18 multidimensional analysis of gene dosage effects were developed by WE and JS. Study

19 design and statistical methods for meta-analysis of CNV across diagnostic categories was

20 developed by OS and JS. Methods for spatial mapping of gene set associations were

21 developed by KK and SJ 77. Statistical models for pathway analysis of CNV burden were

22 developed DM, WE and SWS 5. CNV calling and derivation of CNV QC metrics and SNP-based

23 ancestry PCs was performed by OS, BT, JM. Management of data access and enormous

24 amounts of data wrangling were led by OH and OS in coordination with the remaining

25 coauthors who carried out data collection.

26

27

## 28 Data and code Availability

29 A WDL workflow containing all steps of CNV calling, QC and CNV-GWAS and meta-analysis

30 code is under construction and will be released on the PGC CNV Github in conjunction with

31 this publication (https://github.com/orgs/psychiatric-genomics-consortium/teams/cnv).

32 Analysis code for GSBA and downstream analyses (https://github.com/naibank/PGC_GSBA)

33 Gene sets, see Table S1, Gene-set summary statistics, see Table S3.

34 Raw genotype and intensity files are available on subset of the cohort

35 PGC dbGAP datasets

36 https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/collection.cgi?study_id=phs001254.v1.p

37 1

38 Simons Foundation Autism Research Initiative SFARI (SSC and SPARK) https://base.sfari.org/
# 1 Methods

# 2 1. Participants and CNV data

3 The CNV subgroup of the Psychiatric Genomics Consortium (PGC) works in collaboration

4 with principal investigators from many labs to obtain large sample sizes of microarray data

5 and analyze them using a centralized pipeline. We acquired microarray intensity files from

6 GWAS for a total of 574,965 samples that included data from cases and controls for 6

7 diagnostic categories (Table S1 in our companion paper<sup>40</sup>). These samples were genotyped

8 on 25 platforms across 4 genome builds. Data from Illumina was collected as either raw

9 intensity data (IDAT) files or final report files while data from Affymetrix was collected as CEL

10 files. To harmonize data, probes for newly acquired datasets were lifted over to GRCH38 for

11 CNV calling while previously called CNVs were lifted over to GRCH38. Samples were

12 genotyped on either Illumina or Affymetrix array.

13

14 For samples that were provided as IDAT files, the Illumina command line version of Genome

15 Studio was used in conjunction with platform-specific manifest and cluster files to produce

16 genotype call (GTC) files. Relevant features were extracted from GTC files to obtain final

17 report files with probes, genotypes, Log R Ratio (LRR), and B Allele Frequency (BAF) for each

18 sample. For samples that were not mapped to GRCH38, probe genome positions were

19 converted to hg38 using the LiftOver tool. Samples within each platform were grouped into

20 batches by plate. For Illumina/PsychChip arrays, CNVs were called using two methods:

21 PennCNV and iPattern. For Affy6 arrays, CNVs were called using four methods: PennCNV,

22 iPattern, CScore, and Birdsuite. For Affy5 and Affy500K arrays, CNVs were called using two

23 methods: PennCNV and Birdsuite. For Axiom arrays, CNVs were called using two methods:

24 PennCNV and QuantiSNP. The consensus of CNV calls from multiple callers was created by

25 merging CNVs at the sample level and retaining CNVs that were called by at least 2 methods.

26

## 27 1.1 Sample QC

28 Quality control (QC) was performed first at the sample level, and conducted independently

29 for each microarray platform, according to methods from our previous CNV GWAS of

30 schizophrenia (Marshall et al. 2017<sup>5</sup>). For Illumina arrays, LRR standard deviation, BAF

31 standard deviation, and GC waviness factor were extracted from PennCNV log files. Samples

32 were retained if each of the measures were within 3 SD of the median. Affymetrix arrays

33 used MAPD and waviness-sd parameters from affy power tools. Samples were further

34 evaluated based on the number and total length of autosomal CNVs detected, and were

35 retained if these values did not exceed 3 SD of the mean. The proportion of the

36 chromosome that was tagged as a CNV was calculated and samples were excluded if >10%

37 of the chromosome was marked as a CNV region to filter possible aneuploidies.

38

## 39 1.2 CNV QC

40 Large CNVs that were fragmented were merged. CNVs <10kb in length or containing <10

41 probes were excluded. CNV calls were removed if they spanned the centromere or telomere
1 (100kb from end of chromosome) or had >50% overlap with segmental duplications,

2 immunoglobulin, or T cell receptor (recurrent CNVs were processed without segmental

3 duplications, immunoglobulin, and T cell receptor filters ). The call set was restricted to rare

4 CNVs with ≤10% frequency within-platform or across all platforms.

5

## 2. Ancestry Principal Components and Ancestry Partitioning

7 We extracted a subset of SNPs with < 1% missingness across all platforms (12,185 SNPs) and

8 performed a principal component analysis using the flashPCA software<sup>78</sup>. In order to

9 genetically infer the ancestry of each individual, we used the SNPweights software<sup>79</sup> on the

10 same subset of SNPs to calculate % ancestry based on a reference panel containing 6

11 different populations (751 EUR, 687 EAS, 630 SAS, 568 AFR, 41 AMR, 22 OCE). Samples were

12 categorized into 5 large homogeneous groupings based on the following criteria used in a

13 previous study<sup>80</sup> 39 (Table S2, Fig S1): EUR: subjects with EUR ≥ 90%, AFR/AFAM: subjects

14 with EUR < 90% & AFR ≥ 5% & EAS/SAS/AMR/OCE < 5%, ASN/ASAM: subjects with EUR <

15 90% & (EAS ≥ 5% or SAS ≥ 5%) & AFR/AMR/OCE < 5%, LAT: subjects with EUR < 90% & AMR

16 ≥ 5% & EAS/SAS/AFR/OCE < 5% or EUR < 90% & AMR ≥ 60% & EAS < 20% & SAS < 15% &

17 AFR/OCE < 5%, MIX: Uncategorized subjects.

18

## 3. Gene QC

20 To avoid having false positive findings arising due to a platform or dataset biases, we

21 performed an extra filtering step of the genes being included in the gene set analysis. For

22 each gene, separately for DELs and DUPs, CNV frequency was calculated per platform and

23 dataset. Given the reduced penetration of the most recurrent CNVs, the incident frequency

24 of such CNVs can be higher than that of disease prevalence. In particular, 15q11.2 DEL

25 (major risk locus for ASD and SCZ) has been reported to have an incident rate between

26 0.57-1.27%<sup>81</sup>, thus, using an inclusive frequency threshold, wWe then limited the CNVs to

27 those with frequency lower than 2% across platforms and datasets. In addition, we

28 calculated weight deviance score (WDS) of CNV frequency per platform/dataset and used

29 that to derive a platform/dataset specificity index (SI). Specifically, for each gene, CNV

30 frequency (C<sub>i</sub>) for a particular platform/dataset was compared to the expected CNV

31 frequency (E<sub>i</sub>) estimated from across platforms/datasets as shown in Eq.1.

32

$$E_i = N_i * C_{all} / N_{all} \qquad (\text{Eq.1})$$

34 where for a particular platform/data i, E<sub>i</sub> is the expected CNV frequency, N<sub>i</sub> is the sample

35 size, C<sub>all</sub> is the CNV frequency in the entire dataset, and N<sub>all</sub> is the entire dataset sample size.

$$WDS_i = (C_i - E_i) / \sqrt{E_i * N_i} \qquad (\text{Eq.2})$$

37

38 Then WDS<sub>i</sub> was calculated as Eq. 2. With the max WDS across platforms/datasets

39 representing the specificity index. We removed genes having dataset < 0.2 and

40 platform < 0.6 from subsequent analyses.

41
## 1 4. Gene set data

### 2 4.1 Cortical regions

3 To generate gene sets for different cortical regions of the human brain, we acquired gene

4 expression data in the brain from Allen Human Brain Atlas (AHBA);

5 https://human.brain-map.org/static/download)⁴⁶, multimodal brain parcellation from

6 Glasser's brain regions⁴⁵. Using the Abagen toolbox (version 0.1.3;

7 https://github.com/rmarkello/abagen)⁸², we mapped brain parcels and gene expression

8 data, and then performed gene expression normalization and scaling. Specifically, a robust

9 sigmoid function was used to normalize the expression data across genes to address

10 inter-sample variation, while min-max normalization was applied after to scale the gene

11 expression across tissue samples. Using the left hemisphere, we defined 180 regions from

12 Glasser's brain regions⁸³. To generate the gene sets, the region-mean expression levels of

13 each gene were z-transformed across the regions. Genes were then assigned to cortical

14 region(s) when their z-score>1. The median gene set size was 4,429 genes (see **Table S1**). To

15 visualize cortical region results, we used ggseg v1.6.5⁸⁴ and ggsegGlasser R libraries for

16 Glasser's brain regions.

17

### 18 4.2 Cell types

19 We obtained single-cell RNA-seq data from Velmeshev et al., 2023 ⁴⁴, which contains the

20 data >700,000 nuclei covering both prenatal and postnatal development periods and 8

21 defined cell type clusters. The 8 defined cell type clusters were 1. Oligodendrocyte precursor

22 cells (OPC), 2. Vascular cells (Vasc), 3. Excitatory neurons (ExNeu), 4. Oligodendrocytes

23 (Oligo), 5. Interneurons (InNeu), 6. Microglia (Mg), 7. Astrocytes (ASst), and 8. Glial

24 progenitors (Gpc). Using the cluster result from the original study, we redefined the cluster

25 by taking into account the developmental period of the cell. Doing so, we obtained 12 cell

26 type clusters; 1. postnatal Opc, 2. postnatal Vasc, 3. postnatal ExNeu, 4. postnatal Oligo, 5.

27 postnatal InNeu, 6. postnatal Mg, 7. postnatal Ast, 8. prenatal ExNeu, 9. prenatal Ast, 10.

28 prenatal Opc, 11. prenatal InNeu, and 12. prenatal Gpc. We then generated cell type marker

29 gene sets using FindAllMarkers() function from the Seurat package. Genes were assigned to

30 a particular cell type cluster with the highest average log2 fold-change only when the

31 corresponding p-value is < 0.05 (Table S1). The gene set size for cell types were smallest in

32 prenatal OPC (181 genes), and largest in postnatal Mg (2,058 genes) with a median of 1,223

33 genes.

34

### 35 4.3 Molecular pathways and pathway clusters defined using EnrichmentMap

36 We compiled gene sets from multiple databases including Gene Ontology ⁴¹, KEGG pathways

37 ⁴², and Reactome ⁴³. We filtered the gene sets to include only those with size between 50
1 and 500 genes, excluding sets with broader definition (>500 genes) and those with low

2 statistical power (<50 genes). In total, we acquired 2,453 gene sets. To reduce dependency

3 between tests for multiple testing correction, we further exclude 758 more gene sets

4 through a step-down approach. Specifically, for each gene set, we removed any smaller

5 subset with substantial gene overlap (Jaccard's index >0.75). The gene set sizes for molecular

6 pathways range from 50 genes to 495 genes with a median of 145 genes.

7

8 To summarize the pathway associations, we applied the EnrichmentMap Cytoscape plugin 49

9 on the top associated gene sets (BH-FDR<5%, with z-score>0) from all the conditions. There

10 were 361, 106, 7, and 5 gene sets associated with SCZ, ASD, BD, and ADHD, respectively. By

11 limiting to pathway clusters with at least 3 gene set members, this results in 19 pathway

12 clusters. We then constructed new gene sets by merging all gene sets within each cluster for

13 subsequent analyses.

14

15

16 5. Gene set burden analysis and sample-weighted meta analysis

17 Differences in genotyping platforms have been known to confound CNV detection given the
18 variance in probe coverage. While the most common way to tackle platform bias in CNV data

19 analysis is to model the effect as one of the covariates, however, the effect is not well

20 controlled in a single regression model. In this study, we performed gene set burden analysis

21 independently for different genotyping platforms and meta-analyzed the summary statistics

22 derived from the individual platform analysis. Using ASD and SCZ as a preliminary

23 experiments, in both conditions, we found a smaller genomic inflation factor or lambda (λ)

24 value (Eq.3) in the meta-analysis result (λ<sub>ASD</sub>=1.78, λ<sub>SCZ</sub>=3.35) compared to the mega-analysis

25 result (using platform as a covariate, λ<sub>ASD</sub>=1.82, λ<sub>SCZ</sub>=3.66).

26

27

$$
\lambda = \frac{\text{median}(\chi^2)}{0.455} \qquad (\text{Eq.3})
$$

28 where χ² is chi-square statistics, and 0.455 is the theoretical mean of chi-square

29 distribution.

30

31 Specifically, we performed the gene set analysis on platforms where there are at least 50

32 cases and 50 controls. For each platform, a univariate analysis was conducted to compare

33 the burden of genes in a gene set impacted by DELs or DUPs between cases and controls.

34 The univariate analysis was done in one of two ways, either 1) a traditional case-control

35 comparison for each individual condition, or 2) a family-based comparison. For the

36 traditional case-control comparison, logistic regression was applied by regressing the

37 number of genes in a gene set impacted by DELs or DUPs on the affection status (1 =

38 affected, 0 = unaffected). Population structure (PC1-10), sex, and the number of genes

39 outside the gene set impacted by DELs or DUPs were used as covariates to correct for any

40 biases in the population, sex and total burden load. For the family-based comparison, we

41 applied conditional logistic regression the same way logistic regression was applied, except
1 that samples were matched by family ID. A likelihood ratio test was done to estimate p-value

2 by comparing two regression models with and without the testing variable, in this case, a

3 gene set burden.

4 A sample-weighted meta-analysis was applied to account for substantial differences in

5 sample size between platforms. We derived the weight for each platform based on the

6 effective sample size as shown in Eq.4.

7

8

$$ \text{Weight}_i = \sqrt{4/(1/N\text{case}_i + 1/N\text{ctrl}_i)} \quad (\text{Eq.4}) $$

9 where Ncase_i is the number of cases in platform_i, and Nctrl_i is the number of controls in
10 platform_i.

11

## 12 6. Gene burden analysis

13 We generated gene-level summary statistics by meta-analyzing the summary statistics from
14 individual platform gene burden analysis. Similar to the gene set burden analysis, the gene
15 burden analysis was done by either performing a logistic regression for case-control dataset,
16 or conditional logistic regression for family-based dataset. We regressed the status of the
17 CNV whether or not a sample has DELs or DUPs overlapping a particular gene on the
18 affection status of the condition. Like gene set burden analysis, population structure
19 (PC1-10), and sex were corrected in the analysis, with family ID being a random effect
20 variable for conditional logistic regression. As multigenic CNVs might drive correlation
21 between tests and that would affect multiple testing correction, genes were merged when
22 the Jaccard index estimated from the proportion of CNVs commonly found between genes
23 was >0.75. Since we only used the gene burden results to visualize findings from the main
24 analysis, we did not report them in this study.

25

## 26 7. Correlation analysis of CNV association and Sensorimotor-Association axis and pathway-S-A-axis gene set stratification

28 We investigated how CNV associations distributed along the cortical gradient using the
29 dominant brain transcriptomic variance data compiled in Dear et al 51. This is the PC1 of
30 AHBA transcriptomic profile 46 projected on the Glasser parcellation 45. The data was
31 processed to exclude spatially inconsistent genes and, under sampling parcellations with a
32 low number of donors (<6 donors). As a result, the final principal component analysis was
33 performed on 134 parcellations and 7,937 genes. The CNV meta-analysis summary statistics
34 of 134 Glasser parcellations was then compared with the PC1 AHBA using Spatial
35 Permutation Inference (SPIN test 59 with 10,000 permutations) with Kendall coefficient
36 analysis.

37

38 To stratify gene set by the S-A axis, we first compute the Kendall coefficient of each gene
39 against the PC1 AHBA. The gene expression matrix was preprocessed and obtained from
40 Dear et al 51 where it contains the data for 10,028 genes, of which 8,588 genes are a member
41 of at least one gene set. This identified ~76% of the genes (n=6,552) to be correlated with
1 the S-A axis at nominal significant level (p<0.05). We then stratified each gene set into 1)

2 sensorimotor cortex set (tau>0, p<0.05), and 2) association cortex set (tau<0, p<0.05),

3 leaving out other non-correlated genes from the subsequent analysis.

4

5 **8. Genetic correlations based on gene-set summary statistics**

6 We compared each pair of summary statistics (e.g., a pair of DEL and DUP summary

7 statistics) 1) within the same condition to assess dosage sensitivity at the gene set level in

8 each condition, and 2) between two conditions to assess gene set profile similarity between

9 conditions. To do so, we performed a Kendall rank correlation analysis of the z-scores

10 estimated from the meta-analysis of gene set burden results across individual platforms.

11

12 To examine correlations between cortical maps (e.g., CNV associations, transcriptomic

13 gradient map, etc.), we applied a commonly used spatial Kendall's correlation and assessed

14 significance against a two-sided spatial autocorrelation-preserving null mode (SPIN test) ⁵⁹,

15 accounting for high inter-regional correlations as a result of spatial smoothing. To reduce the

16 influence of gene set size on the z-score and the estimated correlation, we regressed out the

17 gene set size from the z-score and performed correlation analysis on the residuals.

18

19 Using stratified gene set summary statistics, we estimated genetic correlations in 2 ways: (1)

20 First, was to treat each diagnosis-dosage combination as an independent component and to

21 examine the correlations of each (12 x 12, **Table S11**). (2) Second we examined the

22 correlation of all the gene-set effects combined by stacking DEL and DUP summary statistics

23 and aligning them directly between diagnoses (**Table S12**). Genetic correlations of

24 independent diagnosis-dosage combinations showed greater contrast between diagnostic

25 categories (**Fig. S5g**).

26

27 **9. Latent factor analysis**

28 We performed a latent factor analysis on the two-way (pathway-cell-type, pathway-brain)

29 and three-way (pathway-cell-type-brain) stratified gene set summary statistics to investigate

30 the shared and convergent dosage effects amongst the 6 psychiatric conditions using psych R

31 library. The number of factors was optimized using a scree plot (elbow plot) of PCA on the

32 summary statistic where a 3-factor solution was chosen (**Fig. S4**).

33

34 We specified a 3-factor solution using the fa() function, which estimates factor loadings that

35 describe how each diagnosis-dosage combination contributes to the latent factors. A

36 heatmap of the factor loading matrix, styled similarly to the genomic SEM plots, was

37 generated to visualize how diagnoses align with these latent dimensions. To relate individual

38 pathway gene sets to the latent factors, we computed the factor score for each gene set as a

39 product between their z-scores and the factor loadings across diagnosis-dosage

40 combinations, producing a second heatmap that highlights which biological pathways align

41 most strongly with each latent genetic factor. For this second heatmap, specifically for
1 pathway-cell-type stratified gene sets, we performed a sign-based bi-clustering on the

2 heatmap where pathways are in rows, and cell types are in columns. The pathways and cell

3 types were ordered in a descending order based on their average factor scores. This resulted

4 in two main clusters of groups of pathways and cell types for i) positive and ii) negative

5 factor scores.

6

7 All 2 way and 3 way stratification were included in the mixed-effects model analysis (Fig. 4).

8 A factor analysis of three-way stratified gene sets (Fig. S8) produced similar factor solutions,

9 genetic correlations and factor scores as the results in **Figure 5**. However, overall signal was

10 comparatively weak due to the sparsity of the counts in the 3-way stratification of the data

11 and the sparsity of gene sets that could be included in the analysis (stratifying pathway, by

12 celltype, brain and dosage resulted in >50% of gene sets meeting the minimum size of 30

13 genes, Fig. S7). Thus main results in **Figure 5** include only the 2 way (pathway-cell type and

14 pathway-brain) gene sets.

15

16 **10. Linear model analysis investigating variance explained by different genetic factors**

17 Using stratified gene set summary statistics, we evaluated which levels of biological

18 organization best explain the variation in the gene-set effects within each diagnosis. We

19 performed linear modeling on the effect sizes of the stratified gene-sets (z-scores) with

20 different combinations of pathway, cell type, brain, and dosage as independent variables.

21 For each diagnostic category, variance explained (R²) in summary statistics was calculated for

22 the full model, the main effects, and the interactions of these factors.

23 For example, suppose we would like to test for the effect of a cell type variable and its

24 interaction term. Let *m0* be the full model of all three variables (e.g., *logit(y)* ~

25 *pathway*\*\*\*celltype\*\*\*dosage), *m1* be the model without the interaction term with all three

26 variables (e.g., *logit(y)* ~ *pathway*\*\*\*celltype + celltype\*\*\*dosage), *m2* be the additive model

27 without the evaluating variable (e.g., *logit(y)* ~ *pathway* + *dosage*), and *m3* be the additive

28 model with all three variables (e.g., *logit(y)* ~ *pathway* + *celltype* + *dosage*). The R² of the full

29 model is from *m0*, the R² of the main effect is estimated as R²*m3* - R²*m2*, and the R² of the

30 interaction term is estimated as R²*m0* - R²*m1*. A likelihood ratio test was performed to

31 estimate the level of significance for each comparison through the *anova()* function in R.

32
# 1 Supplementary materials

<figure><img src="image_16.png" /></figure>

2

# 3 Fig. S1 | Gene set burden analysis (GSBA) workflow

4 A diagram showing the analytical procedure done for the gene set analysis of CNV data.

5 First, CNVs were called and filtered down to rare CNVs at 2% frequency across platform and

6 ancestry. Then, for each individual condition, to maximize the statistical power, we

7 performed a cross-ancestry analysis, and also stratified the analysis by population groups;

8 European (EUR), African (AFR), American (AMR), and Asian (ASN). For each stratified

9 analysis, the gene-set burden comparison were done independently for each genotyping

10 platform, then their summary statistics were meta-analyzed. For the burden comparison, we
1 either performed a logistic regression for case-control data, or a conditional logistic

2 regression for family-based data where family ID was used as a strata. Meta-analysis was

3 done using a sample-weighted procedure (Eq.3-4), as it has shown a better robustness

4 compared to a standard-error-based procedure. For the result of molecular pathways, we

5 further clustered them using EnrichmentMap to obtain representative pathway clusters.

<figure><img src="image_17.png" /><figcaption>7 Fig. S2 | Comparing GSBA results between the full cohort and subjects of only European ancestry - scatter plots comparing summary statistics (z statistics from the sample-weighted meta analysis) between the analysis of European subset and the analysis of all ancestry. Beta coefficients estimated from linear model regressing z statistics from European analysis on the z statistics of cross-ancestry analysis.</figcaption></figure>

12
<figure><img src="image_18.png" /><figcaption>2 Fig. S3 | Correlation of cortical gene set effects with additional sensory–association</figcaption></figure>

3 cortical gradients. Scatter plots show the correlation between gene-set burden z-scores for
4 DEL (a,b) and DUP (c,d) for two independent measures of cortical organization: the T1w/T2w
5 ratio which reflects regional variation in intracortical myelination, and the principal gradient
6 of resting-state fMRI. T1w/T2w and fMRI measures aligned to the Glasser grain maps were
7 obtained from Markello et al. 52 , both of which parallel the S-A axis derived from
8 transcriptional principal components(Fig. 3). Correlation of CNV effects with these gradients
1 supports the spatial specificity of gene-dosage associations across multiple cortical

2 modalities. Together, these analyses highlight the convergent spatial patterning of CNV

3 effects along major anatomical and functional cortical hierarchies. (a) DEL z-score and T1-T2

4 ratio, and (b) DEL z-score and fMRI. (c) DUP z-score and T1-T2 ratio, and (d) DUP z-score and

5 fMRI. Solid trend lines indicate significant correlation where $p_{SPIN}<0.05$. Brain maps of T1-T2

6 ratio and fMRI are shown in (e) and (f) where colors indicate the z-score.

7

8

pca_result

<figure><img src="image_19.png" /></figure>

9

10 **Fig. S4** | Using elbow plot (scree plot), we estimated an optimal number of factors to be 3

11 factors (variance drop threshold<5)
<figure><img src="image_20.png" /><figcaption>**d**
Factor loadings of disorders</figcaption></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /><figcaption>**c**
Correlations concordance tau=0.33, p=<2.2e-16</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>**f**
Factor loadings concordance tau=0.44, p=<2.2e-16</figcaption></figure>

<figure><img src="image_26.png" /></figure>

2 **Fig. S5** | Factor analysis on two-way pathway stratification summary statistics without (by cell type, and by brain region) genome-wide significant (GWS) loci included in the analysis.
3 Genetic correlations between diagnosis-dosage from (a) the full analysis and (b) the analysis without GWS loci. Single asterisks (*) indicate nominal significance (p<0.05), while double asterisks indicate significance after multiple testing correction (q<0.05), and a factor loading threshold of > 0.25 was applied to determine factor members. (c) Correlation of genetic correlation calculated from full analysis and no GWS loci analysis. Factor loadings of diagnoses reveal distinct signatures of diagnostic categories from (d) the full analysis and (e) no GWS loci analysis. (f) Correlation of factor loadings from full analysis and no GWS loci
1 analysis. For (c) and (f) scatterplots, solid trend lines indicate significant correlation. Kendall's

2 Tau and corresponding p-value are reported in the title of the scatterplot. (g) QQ-plot

3 comparing the distributions of correlation coefficients (Kendall's Tau) when DEL and DUP

4 effects in each diagnosis are treated as separate components (y-axis, Table S11) vs when the

5 full sum stats of DEL and DUP are aligned between diagnoses (x-axis, Table S12). The

6 negative tail of the y-axis distribution on the QQ plot was weakly skewed, suggesting that

7 the distribution was enriched for effects that diverge between diagnoses.

8

9

10

a

<figure><img src="image_27.png" /></figure>

b

<figure><img src="image_28.png" /></figure>

F1

<figure><img src="image_29.png" /></figure>

c

<figure><img src="image_30.png" /></figure>

11

12 Fig. S6| Gene sets and functional terms linked to latent factors F1, F2 and F3 highlight neural processes that underlie orthogonal dimensions of gene-trait relationships. (a) a
13
14 heatmap showing full gene set associations of all two-way pathway-stratified gene-sets (i.e.,
15 pathway-cell-type, and pathway-brain stratification). Red-white-blue color scale indicates
16 gene set effect size from sample size weighted meta-analysis (z-score). Yellow-green-blue
17 color scale indicates the F1, F2 and F3 factor scores for each gene set. Asterisks indicate gene
18 set association that meets FDR correction in the combined summary statistics on 6
19 diagnostic categories (FDR<10%). **factor scores with absolute value >1. (b) To illustrate
20 pathway-cell type and pathway-brain associations that contribute to factors, subsets of
21 diagnosis-dosage and gene-sets were selected based on factor loadings and factor scores**
22 for F1, F2 and F3 and sorted by factor score. (c) A bar plot highlighting pathway and cell-type
23 terms that were enriched among positively or negatively loaded gene sets in panel B relative
24 to the full summary statistics (fisher exact test P < 0.05).

25
<figure><img src="image_31.png" /><figcaption>**b** Cell-type stratified gene sets</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>**c** SA-axis and Cell-type stratified gene sets</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>**d**</figcaption></figure>

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

1

2 **Fig. S7** | Gene set size of stratified pathways. (a)-(c) Histograms display the distribution of

3 gene set size when stratified the pathway clusters by (a) S-A axis, (b) 12 cell types, and (c)

4 both S-A axis and cell types. Vertical dashed line indicates our 50 genes cut-off for gene sets

5 to be included in the analysis. (d) Venn diagrams show the number of genes intersected

6 between the major pathway gene sets (Chromatin regulation, MAPK signaling, Calcium

7 signaling, and Synaptic transmission), Postnatal Excitatory Neurons, and Sensorimotor or

8 Association genes.

9

10
<figure><img src="image_38.png" /><figcaption>b. Factor loadings of disorders

Diagnosis-CNV</figcaption></figure>

<figure><img src="image_39.png" /><figcaption>c. Factor scores

Sensorimotor</figcaption></figure>

<figure><img src="image_40.png" /><figcaption>d. Association</figcaption></figure>

<figure><img src="image_41.png" /><figcaption>e. F2

MAPK</figcaption></figure>

<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /><figcaption>f. F3

MAPK</figcaption></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

2 Fig. S8 | Factor analysis of three-way pathway-celltype-brain stratification. The result shows

3 that factor F2 and F3 are corresponding to the factor F1 and factor F2 of the main factor

4 analysis result (Fig 5.) (a) Genetic correlation between diagnosis-dosage components. (b)

5 Factor loadings. Factor scores for gene sets were shown as heatmaps for each of the three

6 factors; where (c) and (d) correspond to factor F1, sensorimotor, and association gene sets,

7 respectively. Similarly, (e,f,g,h) heatmaps show the factor scores for the factor F2, and F3.

8
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