Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders

Journal name:
Nature Genetics
Volume:
49,
Pages:
978–985
Year published:
DOI:
doi:10.1038/ng.3863
Received
Accepted
Published online

Abstract

Autism spectrum disorder (ASD) risk is influenced by common polygenic and de novo variation. We aimed to clarify the influence of polygenic risk for ASD and to identify subgroups of ASD cases, including those with strongly acting de novo variants, in which polygenic risk is relevant. Using a novel approach called the polygenic transmission disequilibrium test and data from 6,454 families with a child with ASD, we show that polygenic risk for ASD, schizophrenia, and greater educational attainment is over-transmitted to children with ASD. These findings hold independent of proband IQ. We find that polygenic variation contributes additively to risk in ASD cases who carry a strongly acting de novo variant. Lastly, we show that elements of polygenic risk are independent and differ in their relationship with phenotype. These results confirm that the genetic influences on ASD are additive and suggest that they create risk through at least partially distinct etiologic pathways.

At a glance

Figures

  1. ASD probands over-inherit polygenic risk for ASD, schizophrenia, and greater educational attainment.
    Figure 1: ASD probands over-inherit polygenic risk for ASD, schizophrenia, and greater educational attainment.

    Transmission disequilibrium is shown in terms of standard deviation on the mid-parent distribution ± 1.96 standard error (95% confidence intervals). P values denote the probability that the mean of the pTDT deviation distribution is 0 (two-sided, one-sample t test). (a) ASD probands over-inherit ASD-associated polygenic risk in the SSC (n = 2,584), PGC ASD (n = 3,870), and combined (n = 6,454) cohorts. Unaffected siblings in SSC (n = 2,091) do not over-inherit ASD-associated polygenic risk. (b) Both male (n = 5,490) and female (n = 962) probands over-inherit ASD-associated polygenic risk in the SSC + PGC ASD combined cohort. (c) ASD probands with (n = 1,341) and without (n = 2,743) intellectual disability (full-scale IQ < 70) over-inherit ASD-associated polygenic risk in the SSC + PGC ASD combined cohort. ASD, autism spectrum disorders; SCZ, schizophrenia; EA, educational attainment.

  2. Contributing de novo mutations are associated with adverse neurological and developmental outcomes and act additively with polygenic burden to influence ASD risk.
    Figure 2: Contributing de novo mutations are associated with adverse neurological and developmental outcomes and act additively with polygenic burden to influence ASD risk.

    (a) SSC probands are grouped by their count of the following: delayed walking (≥19 months); presence of seizures; intellectual disability (full-scale IQ < 70) (n = 1,476 with no outcomes; n = 719 with one outcome; n = 134 with two outcomes; n = 16 with three outcomes). The CDNV rate is calculated by dividing the count of CDNVs by the count of individuals. The OR was calculated via Poisson regression predicting CDNV count from case/control status for all controls (n = 1,736) and cases in the outcome category, controlling for maternal and paternal age at birth of the child. P values above each diamond are from the Poisson regression and indicate the probability that the CDNV rate in cases is not different from the CDNV rate in controls. P values between the diamonds were calculated by Poisson exact test and indicate the probability that there is no difference in CDNV rate between the two noted groups. Error bars, ±1 standard error. (b) pTDT analysis for SSC CDNV proband carriers (n = 221). Transmission disequilibrium is shown in terms of standard deviation on the mid-parent distribution ± 1.96 standard error (95% confidence intervals). P values denote the probability that the mean of the pTDT deviation distribution is 0 (two-sided, one-sample t test).

  3. Polygenic risk factors for ASD are partially independent and differ in their relationship with cognition.
    Figure 3: Polygenic risk factors for ASD are partially independent and differ in their relationship with cognition.

    (a) Additivity among orthogonal risk factors can yield high cumulative risk. (b) PRSs for ASD, SCZ, and educational attainment are not strongly associated at either the mid-parent level (above the diagonal) or the pTDT deviation level (below the diagonal). The table contains Pearson correlation coefficients and associated P values indicating the probability with which the true correlation is 0. Mid-parent correlations are controlled for the first ten principal components of parental ancestry. PRSs are from European-ancestry SSC families (n = 1,851). (c) Polygenic risk factors for ASD exhibit independent, distinct effects on IQ in European-ancestry SSC probands (n = 1,674). P values, which estimate the probability of no association between each PRS and IQ, were calculated from linear regression. We predicted full-scale IQ from each PRS, z normalized following residualization for the other two PRSs, CDNV presence/absence, proband sex, and the first ten principal components of proband ancestry. Each panel displays the linear association between full-scale proband IQ and the normalized PRS.

  4. Illustrative example of pTDT using height
    Supplementary Fig. 1: Illustrative example of pTDT using height

    The expected value of a child’s polygenic risk score (PRS) for a trait is the average of maternal and paternal PRS values. For example, if a mother’s PRS is A, the expected PRS of an egg, which contains half of the maternal genetic material, is A/2. If the father’s PRS is B, the expected PRS of a sperm is B/2. The expected value of the child’s PRS is then (A + B)/2. (a) In a randomly selected cohort of parent–child trios, the average of the children’s PRSs for height, in light blue, is expected to equal the average of the mid-parent PRS for height, in dark blue; for each pair of parents, the mid-parent PRS is calculated by averaging the maternal and paternal PRSs; the variance of the mid-parent PRS is reduced because it is the average of the maternal and paternal values. (b) In a cohort of trios phenotypically selected for very high height in the offspring (offspring who are taller than expected based on the height of the parents), we expect offspring PRS to exceed mid-parent PRS. The difference between the mean of the offspring PRS distribution and mid-parent PRS distribution, n, we refer to as polygenic transmission disequilibrium.

  5. ASD probands of European ancestry over-inherit ASD-associated polygenic risk.
    Supplementary Fig. 2: ASD probands of European ancestry over-inherit ASD-associated polygenic risk.

    We performed pTDT after restricting the cohort to European ancestry (Supplementary Note; n = 1,851 SSC probands, n = 3,209 PGC ASD probands, n = 5,060 SSC and PGC ASD probands combined, n = 1,509 SSC unaffected siblings). Transmission disequilibrium is shown in terms of standard deviations on mid-parent distribution ± 1.96 standard error (95% confidence interval). P values denote the probability that the mean of the pTDT deviation distribution is 0 (two-sided, one-sample t test).

  6. Polygenic risk for schizophrenia stratifies by ancestry.
    Supplementary Fig. 3: Polygenic risk for schizophrenia stratifies by ancestry.

    See the Supplementary Note for discussion of ancestral stratification of schizophrenia polygenic risk score.

  7. Large de novo deletions and de novo deletions in constrained genes were associated with ASD case status.
    Supplementary Fig. 4: Large de novo deletions and de novo deletions in constrained genes were associated with ASD case status.

    Constrained genes are intolerant of heterozygous loss-of-function variations (probability of being loss-of-function intolerant (pLI) ≥ 0.9); P values are from Fisher’s exact test and estimate the probability with which the variant type is equally likely to be seen in cases (n = 2,587 subjects) and controls (n = 2,100 subjects); error bars are ±1 standard error.

  8. Unconstrained de novo deletions were not associated with ASD case status.
    Supplementary Fig. 5: Unconstrained de novo deletions were not associated with ASD case status.

    Contributing deletions are deletions in either category in Supplementary Figure 4 (constrained or ≥500 kb and unconstrained); error bars are ±1 standard error; P values are from Fisher’s exact tests and estimate the probability that the variant type is equally likely to be seen in cases (n = 2,587 subjects) and controls (n = 2,100 subjects).

  9. Ancestry of Simons Simplex Collection probands
    Supplementary Fig. 6: Ancestry of Simons Simplex Collection probands

    Included/excluded denotes whether the first two proband principal components of ancestry were within the study-defined bounds of European ancestry; HapMap population CEU, individuals of Northern and Western European ancestry residing in Utah, USA; HapMap population TSI, Tuscans in Italy; non-European, all HapMap cohorts excluding CEU and TSI; see the Online Methods for more information.

  10. Ancestry of parents of ASD probands in the Psychiatric Genomics Consortium Autism Group.
    Supplementary Fig. 7: Ancestry of parents of ASD probands in the Psychiatric Genomics Consortium Autism Group.

    Included/excluded denotes whether the first two parent principal components of ancestry were within the study-defined bounds of European ancestry; families were marked as European ancestry if both parents were marked as included; HapMap population CEU, individuals of Northern and Western European ancestry residing in Utah, USA; HapMap population TSI, Tuscans in Italy; non-European, all HapMap cohorts excluding CEU and TSI; see the Online Methods for more information.

  11. Association between constrained PTV rate and proband IQ in SSC.
    Supplementary Fig. 8: Association between constrained PTV rate and proband IQ in SSC.

    The red line denotes the linear relationship between contributing PTVs (Online Methods) and full-scale IQ in SSC probands (n = 2,492 subjects); the blue line denotes the linear relationship between all other PTVs and full-scale IQ in SSC probands (n = 2,492 subjects). Shaded regions denote 95% confidence intervals. The red line P value is associated with a Poisson regression predicting count of contributing de novo PTVs from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of contributing de novo PTVs; the blue line P value is associated with a Poisson regression predicting count of non-contributing de novo PTVs from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of non-contributing de novo PTVs. Control rate dots were calculated from n = 1,902 unaffected sibling controls.

  12. Association between contributing deletions and proband IQ in SSC.
    Supplementary Fig. 9: Association between contributing deletions and proband IQ in SSC.

    The red line denotes the linear relationship between rate of contributing deletions (Online Methods) and full-scale IQ in SSC probands (n = 2,581 subjects); the blue line denotes the linear relationship between all other de novo deletions and full-scale IQ in SSC probands (n = 2,581 subjects). Shaded regions denote 95% confidence interval. The red line P value is associated with a Poisson regression predicting count of contributing de novo deletions from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of contributing de novo deletions; the blue line P value is associated with a Poisson regression predicting count of non-contributing de novo deletions from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of non-contributing de novo deletions. Control rate dots were calculated from n = 2,100 unaffected sibling controls.

  13. De novo deletions, but not duplications, in constrained genes were associated with ASD.
    Supplementary Fig. 10: De novo deletions, but not duplications, in constrained genes were associated with ASD.

    Rates are the fraction of CNVs that include a constrained gene. P values are from Fisher’s exact tests and estimate the probability with which case (n = 82 with duplication, n = 116 with deletion) and control (n = 27 with duplication, n = 45 with deletion) carriers are equally likely to have a deletion that includes a constrained gene.

  14. Association between CDNV rate and proband IQ in SSC.
    Supplementary Fig. 11: Association between CDNV rate and proband IQ in SSC.

    The red line denotes the linear relationship between the rate of CDNVs (contributing de novo variants; Online Methods) and full-scale IQ in SSC probands (n = 2,340 subjects); the blue line denotes the linear relationship between all other de novo deletions and PTVs and full-scale IQ in SSC probands (n = 2,340 subjects). Shaded regions denote 95% confidence intervals. The red line P value is associated with a Poisson regression predicting count of CDNVs from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of CDNVs; the blue line P value is associated with a Poisson regression predicting count of non-CDNV de novo deletions and PTVs from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of non-CDNV de novo deletions and PTVs. Control rate dots were calculated from n = 1,736 unaffected sibling controls.

  15. Association between the male:female carrier ratio and de novo variant category.
    Supplementary Fig. 12: Association between the male:female carrier ratio and de novo variant category.

    P values were generated using Fisher’s exact tests and estimate the probability that there is no difference between male proband (n = 2,029) and female proband (n = 317) variant rates; see the Online Methods for variant description.

References

  1. Sanders, S.J. et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87, 12151233 (2015).
  2. Gaugler, T. et al. Most genetic risk for autism resides with common variation. Nat. Genet. 46, 881885 (2014).
  3. De Rubeis, S. et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515, 209215 (2014).
  4. Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216221 (2014).
  5. Bulik-Sullivan, B.K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291295 (2015).
  6. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 12361241 (2015).
  7. Krumm, N. et al. Excess of rare, inherited truncating mutations in autism. Nat. Genet. 47, 582588 (2015).
  8. Anney, R. et al. Individual common variants exert weak effects on the risk for autism spectrum disorders. Hum. Mol. Genet. 21, 47814792 (2012).
  9. Klei, L. et al. Common genetic variants, acting additively, are a major source of risk for autism. Mol. Autism 3, 9 (2012).
  10. World Health Organization. WHO Motor Development Study: windows of achievement for six gross motor development milestones. Acta Paediatr. 450, 8695 (2006).
  11. Deciphering Developmental Disorders Study. Large-scale discovery of novel genetic causes of developmental disorders. Nature 519, 223228 (2015).
  12. Okbay, A. et al. Genome-wide association study identifies 74 loci associated with educational attainment. Nature 533, 539542 (2016).
  13. Clarke, T.K. et al. Common polygenic risk for autism spectrum disorder (ASD) is associated with cognitive ability in the general population. Mol. Psychiatry 21, 419425 (2016).
  14. Hagenaars, S.P. et al. Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia. Mol. Psychiatry 21, 16241632 (2016).
  15. Robinson, E.B. et al. Autism spectrum disorder severity reflects the average contribution of de novo and familial influences. Proc. Natl. Acad. Sci. USA 111, 1516115165 (2014).
  16. Munafo, M.R., Tilling, K., Taylor, A.E., Evans, D.M. & Davey Smith, G. Collider Scope: how selection bias can induce spurious associations. Preprint at bioRxiv http://dx.doi.org/10.1101/079707 (2016).
  17. Spielman, R.S., McGinnis, R.E. & Ewens, W.J. Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am. J. Hum. Genet. 52, 506516 (1993).
  18. Fischbach, G.D. & Lord, C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron 68, 192195 (2010).
  19. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421427 (2014).
  20. Cross-Disorder Group of the Psychiatric Genomic Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat. Genet. 45, 984994 (2013).
  21. Kosmicki, J.A. et al. Refining the role of de novo protein-truncating variants in neurodevelopmental disorders by using population reference samples. Nat. Genet. 49, 504510 (2017).
  22. Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285291 (2016).
  23. Samocha, K.E. et al. A framework for the interpretation of de novo mutation in human disease. Nat. Genet. 46, 944950 (2014).
  24. Robinson, E.B. et al. Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nat. Genet. 48, 552555 (2016).
  25. 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 5665 (2012).
  26. International HapMap 3 Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 5258 (2010).
  27. Yang, J., Lee, S.H., Goddard, M.E. & Visscher, P.M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 7682 (2011).
  28. Wray, N.R., Goddard, M.E. & Visscher, P.M. Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res. 17, 15201528 (2007).
  29. Locke, A.E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197206 (2015).
  30. Chang, C.C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).
  31. Elliott, C. Differential Ability Scales (The Psychological Corporation, 2007).
  32. Mullen, E. Mullen Scales of Early Learning (American Guidance Service, 1995).
  33. Wechsler, D. Wechsler Abbreviated Scale of Intelligence (Psychological Corporation, 1999).
  34. Chaste, P. et al. A genome-wide association study of autism using the Simons Simplex Collection: does reducing phenotypic heterogeneity in autism increase genetic homogeneity? Biol. Psychiatry 77, 775784 (2015).
  35. Provost, B., Lopez, B.R. & Heimerl, S. A comparison of motor delays in young children: autism spectrum disorder, developmental delay, and developmental concerns. J. Autism Dev. Disord. 37, 321328 (2007).

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Author information

Affiliations

  1. Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

    • Daniel J Weiner,
    • Emilie M Wigdor,
    • Stephan Ripke,
    • Raymond K Walters,
    • Jack A Kosmicki,
    • Kaitlin E Samocha,
    • Jacqueline I Goldstein,
    • Jacob Taylor,
    • Daniel Howrigan,
    • Timothy Poterba,
    • Verneri Anttila,
    • Hailiang Huang,
    • Phil H Lee,
    • Benjamin Neale,
    • Mark J Daly &
    • Elise B Robinson
  2. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Daniel J Weiner,
    • Emilie M Wigdor,
    • Stephan Ripke,
    • Raymond K Walters,
    • Jack A Kosmicki,
    • Kaitlin E Samocha,
    • Jacqueline I Goldstein,
    • Jacob Taylor,
    • Ashley Dumont,
    • Daniel Howrigan,
    • Jennifer Moran,
    • Timothy Poterba,
    • Christine Stevens,
    • Verneri Anttila,
    • Hailiang Huang,
    • Phil H Lee,
    • Benjamin Neale,
    • Mark J Daly &
    • Elise B Robinson
  3. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

    • Daniel J Weiner,
    • Emilie M Wigdor,
    • Stephan Ripke,
    • Jack A Kosmicki,
    • Kaitlin E Samocha,
    • Jacqueline I Goldstein,
    • Jacob Taylor,
    • Daniel Howrigan,
    • Timothy Poterba,
    • Mark J Daly &
    • Elise B Robinson
  4. Department of Psychiatry and Psychotherapy, Charité, Campus Mitte, Berlin, Germany.

    • Stephan Ripke
  5. Program in Genetics and Genomics, Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA.

    • Jack A Kosmicki &
    • Christopher A Walsh
  6. Department of Biomedicine (Human Genetics), Aarhus University, Aarhus, Denmark.

    • Jakob Grove,
    • Manuel Mattheisen,
    • Preben Bo Mortensen &
    • Anders D Børglum
  7. Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Copenhagen, Denmark.

    • Jakob Grove,
    • Jonas Bybjerg-Grauholm,
    • Thomas Werge,
    • Marie Bækvad-Hansen,
    • Christine Hansen,
    • Thomas F Hansen,
    • Manuel Mattheisen,
    • Ole Mors,
    • Merete Nordentoft,
    • Bent Nørgaard-Pedersen,
    • Jesper Poulsen,
    • Marianne G Pedersen,
    • Carsten B Pedersen,
    • Christine S Hansen,
    • Preben Bo Mortensen &
    • Anders D Børglum
  8. Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark.

    • Jakob Grove,
    • Manuel Mattheisen &
    • Anders D Børglum
  9. Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.

    • Jakob Grove
  10. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.

    • Aysu Okbay
  11. Danish Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark.

    • Jonas Bybjerg-Grauholm,
    • David M Hougaard,
    • Marie Bækvad-Hansen,
    • Christine Hansen,
    • Bent Nørgaard-Pedersen,
    • Jesper Poulsen &
    • Christine S Hansen
  12. Institute of Biological Psychiatry, Mental Health Center Sct. Hans, Mental Health Services Copenhagen, Copenhagen, Denmark.

    • Thomas Werge &
    • Thomas F Hansen
  13. Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.

    • Thomas Werge
  14. Department of Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA.

    • Jacob Taylor
  15. A list of members and affiliations appears at the end of the paper.

    • iPSYCH-Broad Autism Group &
    • Psychiatric Genomics Consortium Autism Group
  16. Behavioural Sciences Unit, Institute of Child Health, University College London, London, UK.

    • David Skuse
  17. Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.

    • Lambertus Klei,
    • Nancy Minshew &
    • Bernie Devlin
  18. Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, Wales, UK.

    • Richard Anney
  19. Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA.

    • Stephan J Sanders &
    • Somer Bishop
  20. National Centre for Register-based Research, University of Aarhus, Aarhus, Denmark.

    • Marianne G Pedersen,
    • Carsten B Pedersen &
    • Preben Bo Mortensen
  21. Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

    • George Davey Smith
  22. Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark.

    • Ole Mors
  23. Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark.

    • Merete Nordentoft
  24. MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.

    • Peter Holmans
  25. Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.

    • Phil H Lee
  26. Queensland Institute of Medical Research, Brisbane, Queensland, Australia.

    • Sarah E Medland
  27. Department of Psychiatry, University of California, San Francisco, San Francisco, California, USA.

    • Lauren A Weiss,
    • A Jeremy Willsey,
    • Kathryn Tsang,
    • Matthew W State,
    • Robert Hendren &
    • Vanessa H Bal
  28. Institute of Human Genetics, University of California, San Francisco, San Francisco, California, USA.

    • Lauren A Weiss &
    • Kathryn Tsang
  29. Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada.

    • Lonnie Zwaigenbaum
  30. Division of Genetics, Children's Hospital Boston, Harvard Medical School, Boston, Massachusetts, USA.

    • Timothy W Yu &
    • Christopher A Walsh
  31. School of Education, University of Birmingham, Birmingham, UK.

    • Kerstin Wittemeyer
  32. Department of Medicine, University of Washington, Seattle, Washington, USA.

    • Ellen M Wijsman
  33. Department of Biostatistics, University of Washington, Seattle, Washington, USA.

    • Ellen M Wijsman
  34. Department of Psychiatry, Carver College of Medicine, Iowa City, Iowa, USA.

    • Thomas H Wassink
  35. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, JW Goethe University Frankfurt, Frankfurt am Main, Germany.

    • Regina Waltes,
    • Fritz Poustka,
    • Christine M Freitag,
    • Eftichia Duketis,
    • Andreas G Chiocchetti &
    • Sven Bölte
  36. Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts, USA.

    • Christopher A Walsh
  37. Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.

    • Christopher A Walsh
  38. Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA.

    • Christopher A Walsh
  39. Department of Psychiatry, University of Oxford and Warneford Hospital, Oxford, UK.

    • Simon Wallace &
    • Anthony J Bailey
  40. Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands.

    • Jacob A S Vorstman,
    • Herman van Engeland &
    • Maretha V De Jonge
  41. Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA.

    • Veronica J Vieland
  42. Instituto Nacional de Saœde Dr. Ricardo Jorge, Lisboa, Portugal.

    • Astrid M Vicente,
    • Catarina T Correia &
    • Inês C Conceição
  43. Center for Biodiversity, Functional and Integrative Genomics, Campus da FCUL, Lisboa, Portugal.

    • Astrid M Vicente,
    • Catarina T Correia &
    • Inês C Conceição
  44. Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, Ontario, Canada.

    • Ann P Thompson
  45. Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.

    • Peter Szatmari
  46. Karolinska Institutet, Solna, Sweden.

    • Oscar Svantesson,
    • Bozenna Iliadou &
    • Christina M Hultman
  47. deCODE Genetics, Reykjavik, Iceland.

    • Stacy Steinberg,
    • Kari Stefansson &
    • Hreinn Stefansson
  48. Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Latha Soorya,
    • Jennifer Reichert,
    • Abraham Reichenberg,
    • Christopher S Poultney,
    • Dalila Pinto,
    • Alexander Kolevzon,
    • Silvia De Rubeis &
    • Joseph D Buxbaum
  49. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Latha Soorya,
    • Jennifer Reichert,
    • Abraham Reichenberg,
    • Christopher S Poultney,
    • Dalila Pinto,
    • Alexander Kolevzon,
    • Dorothy E Grice,
    • Silvia De Rubeis &
    • Joseph D Buxbaum
  50. Department of Psychiatry, Rush University Medical Center, Chicago, Illinois, USA.

    • Latha Soorya
  51. Department of Psychiatry and Drug Addiction, Tbilisi State Medical University, Tbilisi, Georgia.

    • Teimuraz Silagadze
  52. The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, Ontario, Canada.

    • Stephen W Scherer &
    • Andrew D Paterson
  53. McLaughlin Centre, University of Toronto, Toronto, Ontario, Canada.

    • Stephen W Scherer
  54. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.

    • Stephen W Scherer &
    • Andrew D Paterson
  55. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Gerard D Schellenberg
  56. State Diagnostic and Counseling Centre, Kopavogur, Iceland.

    • Sven Sandin &
    • Evald Saemundsen
  57. Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.

    • Guy A Rouleau
  58. Centre d'Etudes et de Recherches en Psychopathologie, Toulouse University, Toulouse, France.

    • Bernadette Rogé
  59. Department of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

    • Kathryn Roeder
  60. Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

    • Kathryn Roeder
  61. Autism Research Unit, The Hospital for Sick Children, Toronto, Ontario, Canada.

    • Wendy Roberts
  62. Sanger Institute, Hinxton, UK.

    • Karola Rehnström &
    • Aarno Palotie
  63. National Childrens Research Centre, Our Lady's Hospital Crumlin, Dublin, Ireland.

    • Regina Regan &
    • Tiago Magalhaes
  64. Academic Centre on Rare Diseases, University College Dublin, Dublin, Ireland.

    • Regina Regan,
    • Tiago Magalhaes,
    • Andrew Green,
    • Sean Ennis,
    • Judith Conroy &
    • Jillian Casey
  65. Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, USA.

    • Joseph Piven
  66. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Dalila Pinto,
    • Arthur P Goldberg &
    • Joseph D Buxbaum
  67. The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Dalila Pinto,
    • Alexander Kolevzon &
    • Joseph D Buxbaum
  68. The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Dalila Pinto &
    • Arthur P Goldberg
  69. Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Dalila Pinto,
    • Alexander Kolevzon &
    • Joseph D Buxbaum
  70. The John P. Hussman Institute for Human Genomics, University of Miami, Miami, Florida, USA.

    • Margaret A Pericak-Vance,
    • John Gilbert &
    • Michael L Cuccaro
  71. Institute of Mental Health and Medical Faculty, University of Belgrade, Belgrade, Serbia.

    • Milica Pejovic-Milovancevic
  72. Centre for Integrated Register-based Research, Aarhus University, Aarhus, Denmark.

    • Marianne G Pedersen &
    • Carsten B Pedersen
  73. Dalla Lana School of Public Health, Toronto, Ontario, Canada.

    • Andrew D Paterson
  74. Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK.

    • Jeremy R Parr &
    • Ann S Le Couteur
  75. Institue of Health and Science, Newcastle University, Newcastle upon Tyne, UK.

    • Jeremy R Parr &
    • Ann S Le Couteur
  76. Wellcome Trust Centre for Human Genetics, Oxford University, Oxford, UK.

    • Alistair T Pagnamenta &
    • Anthony P Monaco
  77. Unidade de Neurodesenvolvimento e Autismo do Serviço do Centro de Desenvolvimento da Criança and Centro de Investigação e Formação Clinica, Pediatric Hospital, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.

    • Guiomar Oliveira,
    • Susana Mouga,
    • Frederico Duque,
    • Cátia Café &
    • Joana Almeida
  78. University Clinic of Pediatrics and Institute for Biomedical Imaging and Life Science, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.

    • Guiomar Oliveira,
    • Susana Mouga &
    • Frederico Duque
  79. Institute of Psychiatric Research, Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, USA.

    • John I Nurnberger
  80. Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA.

    • John I Nurnberger &
    • Patrícia B S Celestino-Soper
  81. Program in Medical Neuroscience, Indiana University School of Medicine, Indianapolis, Indiana, USA.

    • John I Nurnberger
  82. Programs on Neurogenetics, Yale University School of Medicine, New Haven, Connecticut, USA.

    • Michael T Murtha &
    • A Gulhan Ercan-Sencicek
  83. Department of Psychiatry and Human Behavior, Brown University, Providence, Rhode Island, USA.

    • Eric M Morrow &
    • Daniel Moreno De Luca
  84. Tufts University, Boston, Massachusetts, USA.

    • Anthony P Monaco
  85. Department of Psychiatry, Trinity College Dublin, Dublin, Ireland.

    • Alison Merikangas,
    • Louise Gallagher,
    • Sean Brennan,
    • Nadia Bolshakova &
    • Michael Gill
  86. Department of Psychiatry, University of Utah, Salt Lake City, Utah, USA.

    • William M McMahon &
    • Hilary Coon
  87. Department of Pediatrics, Vanderbilt University, Nashville, Tennessee, USA.

    • Susan G McGrew
  88. Department of Biomedicine–Human Genetics, Aarhus University, Aarhus, Denmark.

    • Manuel Mattheisen
  89. Department of Child, Adolescent Psychiatry and Medical-Social Rehabilitation, Ukrainian Research Institute of Social Forensic Psychiatry and Drug Abuse, Kyiv, Ukraine.

    • Igor Martsenkovsky
  90. Department of Pediatrics and Human Genetics, University of Michigan, Ann Arbor, Michigan, USA.

    • Donna M Martin
  91. Yale Center for Genomic Analysis, Yale University School of Medicine, New Haven, Connecticut, USA.

    • Shrikant M Mane
  92. Department of Child and Adolescent Psychiatry, National University Hospital, Reykjavik, Iceland.

    • Pall Magnusson
  93. Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.

    • Elena Maestrini &
    • Elena Bacchelli
  94. Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, California, USA.

    • Jennifer K Lowe &
    • Daniel H Geschwind
  95. Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.

    • Jennifer K Lowe &
    • Daniel H Geschwind
  96. Center for Neurobehavioral Genetics, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.

    • Jennifer K Lowe &
    • Daniel H Geschwind
  97. Department of Psychiatry, Weill Cornell Medical College, Cornell University, New York, New York, USA.

    • Catherine Lord
  98. Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.

    • Pat Levitt
  99. Autism & Developmental Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA.

    • Christa Lese Martin
  100. Geisinger Health System, Danville, Pennsylvania, USA.

    • David H Ledbetter
  101. FondaMental Foundation, Créteil, France.

    • Marion Leboyer,
    • Richard Delorme &
    • Thomas Bourgeron
  102. INSERM U955, Paris, France.

    • Marion Leboyer
  103. Faculté de Médecine, Université Paris Est, Créteil, France.

    • Marion Leboyer
  104. Department of Psychiatry, Henri Mondor–Albert Chenevier Hospital, Assistance Publique–Hôpitaux de Paris, Créteil, France.

    • Marion Leboyer
  105. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

    • Christine Ladd-Acosta
  106. Division of Molecular Genome Analysis and Working Group Cancer Genome Research, Deutsches Krebsforschungszentrum, Heidelberg, Germany.

    • Sabine M Klauck
  107. Institute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA.

    • Suma Jacob,
    • Stephen J Guter &
    • Edwin H Cook
  108. Institute of Translational Neuroscience and Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota, USA.

    • Suma Jacob
  109. Department of Public Health Sciences, School of Medicine, University of California, Davis, Davis, California, USA.

    • Irva Hertz-Picciotto
  110. The MIND Institute, School of Medicine, University of California, Davis, Davis, California, USA.

    • Irva Hertz-Picciotto &
    • David Amaral
  111. Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, USA.

    • Jonathan L Haines &
    • James S Sutcliffe
  112. Manchester Academic Health Sciences Centre, Manchester, UK.

    • Jonathan M Green
  113. Institute of Brain, Behaviour, and Mental Health, University of Manchester, Manchester, UK.

    • Jonathan M Green
  114. Centre for Medical Genetics, Our Lady's Hospital Crumlin, Dublin, Ireland.

    • Andrew Green &
    • Sean Ennis
  115. Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden.

    • Christopher Gillberg
  116. Department of Psychiatry and Institute for Development and Disability, Oregon Health & Science University, Portland, Oregon, USA.

    • Eric Fombonne
  117. Division of Child and Adolescent Psychiatry, Department of Psychiatry, Miller School of Medicine, University of Miami, Miami, Florida, USA.

    • Susan E Folstein
  118. Disciplines of Genetics and Medicine, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada.

    • Bridget Fernandez
  119. Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

    • M Daniele Fallin
  120. Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, France.

    • Richard Delorme &
    • Thomas Bourgeron
  121. Centre National de la Recherche Scientifique, URA 2182, Institut Pasteur, Paris, France.

    • Richard Delorme &
    • Thomas Bourgeron
  122. Department of Child and Adolescent Psychiatry, Robert Debré Hospital, Assistance Publique–Hôpitaux de Paris, Paris, France.

    • Richard Delorme
  123. Duke Center for Autism and Brain Developments, Duke University School of Medicine, Durham, North Carolina, USA.

    • Geraldine Dawson
  124. Duke Institute for Brain Sciences, Duke University School of Medicine, Durham, North Carolina, USA.

    • Geraldine Dawson
  125. Temple Street Children's University Hospital, Dublin, Ireland.

    • Judith Conroy &
    • Jillian Casey
  126. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.

    • Patrícia B S Celestino-Soper &
    • Arthur L Beaudet
  127. Department of Psychiatry, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, California, USA.

    • Rita M Cantor
  128. Department of Human Genetics, David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, California, USA.

    • Rita M Cantor &
    • Daniel H Geschwind
  129. University Paris Diderot, Sorbonne Paris Cité, Paris, France.

    • Thomas Bourgeron
  130. Institute of Psychiatry, Kings College London, London, UK.

    • Patrick F Bolton
  131. South London and Maudsley Biomedical Research Centre for Mental Health, London, UK.

    • Patrick F Bolton
  132. Department of Women's and Children's Health, Center of Neurodevelopmental Disorders, Karolinska Institutet, Stockholm, Sweden.

    • Sven Bölte
  133. Child and Adolescent Psychiatry, Center for Psychiatry Research, Stockholm County Council, Stockholm, Sweden.

    • Sven Bölte
  134. INSERM U1130, Paris, France.

    • Catalina Betancur
  135. CNRS UMR 8246, Paris, France.

    • Catalina Betancur
  136. Sorbonne Universités, UPMC Université Paris 6, Neuroscience Paris Seine, Paris, France.

    • Catalina Betancur
  137. Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington, USA.

    • Raphael Bernier
  138. Stella Maris Institute for Child and Adolescent Neuropsychiatr, Pisa, Italy.

    • Agatino Battaglia
  139. Paediatric Neurodisability, King's Health Partners, Kings College London, London, UK.

    • Gillian Baird
  140. Mental Health and Addictions Research Unit, University of British Colombia, Vancouver, British Columbia, Canada.

    • Anthony J Bailey
  141. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA.

    • Joel S Bader,
    • Aravinda Chakravarti &
    • Dan E Arking
  142. Bloorview Research Institute, University of Toronto, Toronto, Ontario, Canada.

    • Evdokia Anagnostou
  143. Department of Psychiatry, School of Medicine, University of California, Davis, Davis, California, USA.

    • David Amaral
  144. Department of Behavioural Sciences, School of Medicine, University of California, Davis, Davis, California, USA.

    • David Amaral
  145. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

    • Joseph D Buxbaum
  146. The Center for Applied Genomics and Division of Human Genetics, Children's Hospital of Philadelphia, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA.

    • Hakon Hakonarson
  147. Department of Pediatrics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

    • Hakon Hakonarson
  148. Department of Psychiatry, Stanford University, Stanford, California, USA.

    • Joachim Hallmayer
  149. Maine Medical Center Research Institute, Portland, Maine, USA.

    • Susan Santangelo
  150. Center for Human Genetics Research, Vanderbilt University, Nashville, Tennessee, USA.

    • James S Sutcliffe

Consortia

  1. iPSYCH-Broad Autism Group

    • Marie Bækvad-Hansen,
    • Ashley Dumont,
    • Christine Hansen,
    • Thomas F Hansen,
    • Daniel Howrigan,
    • Manuel Mattheisen,
    • Jennifer Moran,
    • Ole Mors,
    • Merete Nordentoft,
    • Bent Nørgaard-Pedersen,
    • Timothy Poterba,
    • Jesper Poulsen &
    • Christine Stevens
  2. Psychiatric Genomics Consortium Autism Group

    • Verneri Anttila,
    • Peter Holmans,
    • Hailiang Huang,
    • Lambertus Klei,
    • Phil H Lee,
    • Sarah E Medland,
    • Benjamin Neale,
    • Lauren A Weiss,
    • Lonnie Zwaigenbaum,
    • Timothy W Yu,
    • Kerstin Wittemeyer,
    • A Jeremy Willsey,
    • Ellen M Wijsman,
    • Thomas H Wassink,
    • Regina Waltes,
    • Christopher A Walsh,
    • Simon Wallace,
    • Jacob A S Vorstman,
    • Veronica J Vieland,
    • Astrid M Vicente,
    • Herman van Engeland,
    • Kathryn Tsang,
    • Ann P Thompson,
    • Peter Szatmari,
    • Oscar Svantesson,
    • Stacy Steinberg,
    • Kari Stefansson,
    • Hreinn Stefansson,
    • Matthew W State,
    • Latha Soorya,
    • Teimuraz Silagadze,
    • Stephen W Scherer,
    • Gerard D Schellenberg,
    • Sven Sandin,
    • Evald Saemundsen,
    • Guy A Rouleau,
    • Bernadette Rogé,
    • Kathryn Roeder,
    • Wendy Roberts,
    • Jennifer Reichert,
    • Abraham Reichenberg,
    • Karola Rehnström,
    • Regina Regan,
    • Fritz Poustka,
    • Christopher S Poultney,
    • Joseph Piven,
    • Dalila Pinto,
    • Margaret A Pericak-Vance,
    • Milica Pejovic-Milovancevic,
    • Marianne G Pedersen,
    • Carsten B Pedersen,
    • Andrew D Paterson,
    • Jeremy R Parr,
    • Alistair T Pagnamenta,
    • Guiomar Oliveira,
    • John I Nurnberger,
    • Merete Nordentoft,
    • Michael T Murtha,
    • Susana Mouga,
    • Ole Mors,
    • Eric M Morrow,
    • Daniel Moreno De Luca,
    • Anthony P Monaco,
    • Nancy Minshew,
    • Alison Merikangas,
    • William M McMahon,
    • Susan G McGrew,
    • Manuel Mattheisen,
    • Igor Martsenkovsky,
    • Donna M Martin,
    • Shrikant M Mane,
    • Pall Magnusson,
    • Tiago Magalhaes,
    • Elena Maestrini,
    • Jennifer K Lowe,
    • Catherine Lord,
    • Pat Levitt,
    • Christa Lese Martin,
    • David H Ledbetter,
    • Marion Leboyer,
    • Ann S Le Couteur,
    • Christine Ladd-Acosta,
    • Alexander Kolevzon,
    • Sabine M Klauck,
    • Suma Jacob,
    • Bozenna Iliadou,
    • Christina M Hultman,
    • Irva Hertz-Picciotto,
    • Robert Hendren,
    • Christine S Hansen,
    • Jonathan L Haines,
    • Stephen J Guter,
    • Dorothy E Grice,
    • Jonathan M Green,
    • Andrew Green,
    • Arthur P Goldberg,
    • Christopher Gillberg,
    • John Gilbert,
    • Louise Gallagher,
    • Christine M Freitag,
    • Eric Fombonne,
    • Susan E Folstein,
    • Bridget Fernandez,
    • M Daniele Fallin,
    • A Gulhan Ercan-Sencicek,
    • Sean Ennis,
    • Frederico Duque,
    • Eftichia Duketis,
    • Richard Delorme,
    • Silvia De Rubeis,
    • Maretha V De Jonge,
    • Geraldine Dawson,
    • Michael L Cuccaro,
    • Catarina T Correia,
    • Judith Conroy,
    • Inês C Conceição,
    • Andreas G Chiocchetti,
    • Patrícia B S Celestino-Soper,
    • Jillian Casey,
    • Rita M Cantor,
    • Cátia Café,
    • Sean Brennan,
    • Thomas Bourgeron,
    • Patrick F Bolton,
    • Sven Bölte,
    • Nadia Bolshakova,
    • Catalina Betancur,
    • Raphael Bernier,
    • Arthur L Beaudet,
    • Agatino Battaglia,
    • Vanessa H Bal,
    • Gillian Baird,
    • Anthony J Bailey,
    • Marie Bækvad-Hansen,
    • Joel S Bader,
    • Elena Bacchelli,
    • Evdokia Anagnostou,
    • David Amaral,
    • Joana Almeida,
    • Joseph D Buxbaum,
    • Aravinda Chakravarti,
    • Edwin H Cook,
    • Hilary Coon,
    • Daniel H Geschwind,
    • Michael Gill,
    • Hakon Hakonarson,
    • Joachim Hallmayer,
    • Aarno Palotie,
    • Susan Santangelo,
    • James S Sutcliffe &
    • Dan E Arking

Contributions

D.J.W., E.M.W., S.R., R.K.W., J.A.K., J.G., K.E.S., J.I.G., A.O., J.B.-G., T.W., D.M.H., R.A., and S.J.S. generated data and/or conducted analyses. E.B.R., M.J.D., D.J.W., S.B., and G.D.S. designed the experiment and tools. D.S., B.D., and J.T. aided in interpretation of the data. E.B.R., M.J.D., P.B.M., and A.D.B. supervised the research. E.B.R., D.J.W., and E.M.W. wrote the manuscript.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: Illustrative example of pTDT using height (89 KB)

    The expected value of a child’s polygenic risk score (PRS) for a trait is the average of maternal and paternal PRS values. For example, if a mother’s PRS is A, the expected PRS of an egg, which contains half of the maternal genetic material, is A/2. If the father’s PRS is B, the expected PRS of a sperm is B/2. The expected value of the child’s PRS is then (A + B)/2. (a) In a randomly selected cohort of parent–child trios, the average of the children’s PRSs for height, in light blue, is expected to equal the average of the mid-parent PRS for height, in dark blue; for each pair of parents, the mid-parent PRS is calculated by averaging the maternal and paternal PRSs; the variance of the mid-parent PRS is reduced because it is the average of the maternal and paternal values. (b) In a cohort of trios phenotypically selected for very high height in the offspring (offspring who are taller than expected based on the height of the parents), we expect offspring PRS to exceed mid-parent PRS. The difference between the mean of the offspring PRS distribution and mid-parent PRS distribution, n, we refer to as polygenic transmission disequilibrium.

  2. Supplementary Figure 2: ASD probands of European ancestry over-inherit ASD-associated polygenic risk. (42 KB)

    We performed pTDT after restricting the cohort to European ancestry (Supplementary Note; n = 1,851 SSC probands, n = 3,209 PGC ASD probands, n = 5,060 SSC and PGC ASD probands combined, n = 1,509 SSC unaffected siblings). Transmission disequilibrium is shown in terms of standard deviations on mid-parent distribution ± 1.96 standard error (95% confidence interval). P values denote the probability that the mean of the pTDT deviation distribution is 0 (two-sided, one-sample t test).

  3. Supplementary Figure 3: Polygenic risk for schizophrenia stratifies by ancestry. (59 KB)

    See the Supplementary Note for discussion of ancestral stratification of schizophrenia polygenic risk score.

  4. Supplementary Figure 4: Large de novo deletions and de novo deletions in constrained genes were associated with ASD case status. (62 KB)

    Constrained genes are intolerant of heterozygous loss-of-function variations (probability of being loss-of-function intolerant (pLI) ≥ 0.9); P values are from Fisher’s exact test and estimate the probability with which the variant type is equally likely to be seen in cases (n = 2,587 subjects) and controls (n = 2,100 subjects); error bars are ±1 standard error.

  5. Supplementary Figure 5: Unconstrained de novo deletions were not associated with ASD case status. (64 KB)

    Contributing deletions are deletions in either category in Supplementary Figure 4 (constrained or ≥500 kb and unconstrained); error bars are ±1 standard error; P values are from Fisher’s exact tests and estimate the probability that the variant type is equally likely to be seen in cases (n = 2,587 subjects) and controls (n = 2,100 subjects).

  6. Supplementary Figure 6: Ancestry of Simons Simplex Collection probands (74 KB)

    Included/excluded denotes whether the first two proband principal components of ancestry were within the study-defined bounds of European ancestry; HapMap population CEU, individuals of Northern and Western European ancestry residing in Utah, USA; HapMap population TSI, Tuscans in Italy; non-European, all HapMap cohorts excluding CEU and TSI; see the Online Methods for more information.

  7. Supplementary Figure 7: Ancestry of parents of ASD probands in the Psychiatric Genomics Consortium Autism Group. (97 KB)

    Included/excluded denotes whether the first two parent principal components of ancestry were within the study-defined bounds of European ancestry; families were marked as European ancestry if both parents were marked as included; HapMap population CEU, individuals of Northern and Western European ancestry residing in Utah, USA; HapMap population TSI, Tuscans in Italy; non-European, all HapMap cohorts excluding CEU and TSI; see the Online Methods for more information.

  8. Supplementary Figure 8: Association between constrained PTV rate and proband IQ in SSC. (64 KB)

    The red line denotes the linear relationship between contributing PTVs (Online Methods) and full-scale IQ in SSC probands (n = 2,492 subjects); the blue line denotes the linear relationship between all other PTVs and full-scale IQ in SSC probands (n = 2,492 subjects). Shaded regions denote 95% confidence intervals. The red line P value is associated with a Poisson regression predicting count of contributing de novo PTVs from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of contributing de novo PTVs; the blue line P value is associated with a Poisson regression predicting count of non-contributing de novo PTVs from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of non-contributing de novo PTVs. Control rate dots were calculated from n = 1,902 unaffected sibling controls.

  9. Supplementary Figure 9: Association between contributing deletions and proband IQ in SSC. (66 KB)

    The red line denotes the linear relationship between rate of contributing deletions (Online Methods) and full-scale IQ in SSC probands (n = 2,581 subjects); the blue line denotes the linear relationship between all other de novo deletions and full-scale IQ in SSC probands (n = 2,581 subjects). Shaded regions denote 95% confidence interval. The red line P value is associated with a Poisson regression predicting count of contributing de novo deletions from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of contributing de novo deletions; the blue line P value is associated with a Poisson regression predicting count of non-contributing de novo deletions from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of non-contributing de novo deletions. Control rate dots were calculated from n = 2,100 unaffected sibling controls.

  10. Supplementary Figure 10: De novo deletions, but not duplications, in constrained genes were associated with ASD. (66 KB)

    Rates are the fraction of CNVs that include a constrained gene. P values are from Fisher’s exact tests and estimate the probability with which case (n = 82 with duplication, n = 116 with deletion) and control (n = 27 with duplication, n = 45 with deletion) carriers are equally likely to have a deletion that includes a constrained gene.

  11. Supplementary Figure 11: Association between CDNV rate and proband IQ in SSC. (74 KB)

    The red line denotes the linear relationship between the rate of CDNVs (contributing de novo variants; Online Methods) and full-scale IQ in SSC probands (n = 2,340 subjects); the blue line denotes the linear relationship between all other de novo deletions and PTVs and full-scale IQ in SSC probands (n = 2,340 subjects). Shaded regions denote 95% confidence intervals. The red line P value is associated with a Poisson regression predicting count of CDNVs from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of CDNVs; the blue line P value is associated with a Poisson regression predicting count of non-CDNV de novo deletions and PTVs from proband IQ and proband sex and estimates the probability of no association between proband IQ and the rate of non-CDNV de novo deletions and PTVs. Control rate dots were calculated from n = 1,736 unaffected sibling controls.

  12. Supplementary Figure 12: Association between the male:female carrier ratio and de novo variant category. (58 KB)

    P values were generated using Fisher’s exact tests and estimate the probability that there is no difference between male proband (n = 2,029) and female proband (n = 317) variant rates; see the Online Methods for variant description.

PDF files

  1. Supplementary Text and Figures (2,215 KB)

    Supplementary Figures 1–12, Supplementary Tables 1–22 and Supplementary Note

Additional data