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Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome


The 22q11.2 deletion syndrome (22q11DS) is associated with a 20–25% risk of schizophrenia. In a cohort of 962 individuals with 22q11DS, we examined the shared genetic basis between schizophrenia and schizophrenia-related early trajectory phenotypes: sub-threshold symptoms of psychosis, low baseline intellectual functioning and cognitive decline. We studied the association of these phenotypes with two polygenic scores, derived for schizophrenia and intelligence, and evaluated their use for individual risk prediction in 22q11DS. Polygenic scores were not only associated with schizophrenia and baseline intelligence quotient (IQ), respectively, but schizophrenia polygenic score was also significantly associated with cognitive (verbal IQ) decline and nominally associated with sub-threshold psychosis. Furthermore, in comparing the tail-end deciles of the schizophrenia and IQ polygenic score distributions, 33% versus 9% of individuals with 22q11DS had schizophrenia, and 63% versus 24% of individuals had intellectual disability. Collectively, these data show a shared genetic basis for schizophrenia and schizophrenia-related phenotypes and also highlight the future potential of polygenic scores for risk stratification among individuals with highly, but incompletely, penetrant genetic variants.

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Fig. 1: PS_SZs among phenotypic subgroups.
Fig. 2: Relationship between polygenic scores and novel phenotypes.
Fig. 3: Individual risk prediction.

Data availability

The data sets (raw data) generated and/or analyzed during the current study are available through the National Institute of Mental Health Data Archive repository at, accession number 10.15154/1519190.

To construct the polygenic score for schizophrenia, we used results from the Schizophrenia Working Group of the Psychiatric Genomics Consortium24, specifically the ‘Full SNP Results’ file ckqny.scz2snpres.gz with md5 af7b9b521a196ce711d99060426fe01e, which is available after filling out an application as described at

To construct the polygenic score for fluid intelligence, we used results from the Neale lab, which are available at, specifically the file fluid_intelligence.20016.assoc.tsv.gz with md5 685d4b5e2f35c82fe29d9d9ac6e35db4, which is available through their website,, and is additionally mirrored at

Code availability

Bespoke analysis code for analyses downstream of genotype generation is available.


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This study was supported by the National Institute of Mental Health (NIMH) International Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome (U01MH101719, U01MH0101720, U01MH0101723, U01MH101722 and EU01MH101724); the Lap-Chee Tsui Fellowship for Research Excellence and the CIHR STAGE Fellowship (to R.W.D.); the Brain and Behavior Research Foundation (to J.A.S.V.; Young Investigator Award); NIMH R01MH085953 and 1U01MH119736-01 (to C.E.B.); NIH RO1 MH064824 (to W.K.); Wellcome Trust grant 102428/Z/13/Z (to N.W. and T.M.); Canadian Institutes of Health Research grants MOP-79518, MOP-89066, MOP-97800 and MOP-111238, a McLaughlin Centre Accelerator grant, the Canada Research Chairs program and Dalglish Chair (to A.S.B); the Academic Scholars Award from the Department of Psychiatry, University of Toronto and the O’Brien Scholars Fund (to E.B.); Fondecyt 1171014 and ACT 192064 (ANID-Chile) (to G.R.); Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/024), CIBERSAM, Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds, European Union Seventh Framework Program (FP7-HEALTH-2013-2.2.1-2-603196 Project PSYSCAN) and European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement 115916, Project PRISM, and grant agreement 777394, Project AIMS-2-TRIALS), Fundación Familia Alonso and Fundación Alicia Koplowitz (to C.A); Innovative Medicines Initiative 2 Joint Undertaking (#777394 for the project AIMS-2-TRIALS), the NIHR Maudsley BRC (to D.M.); and Binational Science Foundation grant 2017369 (to R.E.G. and D.G.).

Author information





J.A.S.V., R.W.D., A.M.F. and C.E.B. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. R.W.D. and A.M.F. contributed equally to the study. Study concept and design: AIM II writing group: R.W.D., A.M.F., E.J.B., T.M., N.M.W., S.R.H., C.E.B. and J.A.S.V. Acquisition of data: M.J.O., M.v.d.B., D.G.M., C.A., J.A.S.V., A.M.F., S.N.D., S.E., M.S., M.J., M.A., S.V., V.S., S.R.H., E.W.C.C., D.M.M.-M., A.V., D.G., R.W., T.v.A., W.K., K.M.A., T.S., O.Y.O., A.S., R.E.G., C.E.B. and A.S.B. Analysis and interpretation of data: AIM II writing group: R.W.D., A.M.F., E.J.B., T.M., N.M.W., S.R.H., C.E.B. and J.A.S.V. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: R.W.D., E.J.B., T.M., N.M.W. and J.A.S.V.

Corresponding author

Correspondence to Jacob A. S. Vorstman.

Ethics declarations

Competing interests

M.J.O. and M.v.d.B. report grants from Takeda Pharmaceuticals outside of the submitted work. C.A. has been a consultant to or has received honoraria or grants from Acadia, Ambrosseti, Gedeon Richter, Janssen Cilag, Lundbeck, Otsuka, Roche, Sage, Servier, Shire, Schering Plough, Sumitomo Dainippon Pharma, Sunovion and Takeda. D.G.M. has provided consultation to Roche. S.R.H. has provided consultation to Novartis. O.Y.O. is a collaborator in a Biomarin Pharmaceutical study. None of the other authors has financial disclosures.

Additional information

Peer review information Kate Gao and Joao Monteiro were the primary editors on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Relationship between polygenic scores and previously studied phenotypes.

Results for the binary SSD phenotype show per-individual values as well as summaries per group, where minimum and maximum values are directly observable from the plot, the box-plot centre is the median, the boxplot edges represent the 25th and 75th percentiles, and the whiskers represent the lesser of the distance to the minimum or maximum value, or 1.5 times the inter-quartile range. Results are shown for logistic regression of SSD on controls (N = 802) and linear regression for FSIQ (N = 720), for both PS_SZ (left panel) and PS_IQ (right panel). P-values are reported from regression analyses and are two sided and are not corrected for multiple testing.

Extended Data Fig. 2 Inferred contribution of controls and future SSD cases given PS SZ.

Shown on the y-axis are group means of PS_SZ, on the x-axis the fraction of controls. For SDD and controls the fractions of controls were taken as 0 and 1, respectively (open circles). For subthreshold psychosis and putative controls they were inferred through the observed PS-SZ values for each group, using linear interpolation based on fitting a straight line between SSD and control values (red circles). Confidence intervals are shown for the group mean values for subthreshold psychosis and putative controls, as the mean plus or minus 1.96 times the standard error, and above and below these confidence intervals are the inferred fraction of controls this would represent. The observed PS_SZ in the subthreshold group is consistent with a scenario in which 86% (95% CI 56 - 100%) of individuals who had subthreshold psychotic symptoms at the time of the assessment for this study would subsequently transition to SSD, a proportion inconsistent with known rates of SSD in 22q11DS.

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Supplementary Tables 1–6 and Supplementary Figs. 1–8.

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Davies, R.W., Fiksinski, A.M., Breetvelt, E.J. et al. Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome. Nat Med 26, 1912–1918 (2020).

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