Ph.D. # Michael P Epstein

Ph.D. # Nigel M Williams
email: williamsnm@cardiff.ac.uk

M.D. # Anne S Bassett
email: anne.bassett@utoronto.ca.

Ph.D. # Isabelle Cleynen

Ph.D Worrawat Engchuan

Ph.D. # Matthew S Hestand

Tracy Heung

Ph.D. # Aaron M Holleman

Ph.D. # H Richard Johnston

Ph.D. # Thomas Monfeuga

M.Sc Donna M Mcdonald-Mcginn

M.D Raquel E Gur

Ph.D Bernice E 12

Ph.D Morrow

Ann 13

Ph.D Swillen

M.D Jacob A S Vorstman

Ph.D Carrie E Bearden

Eva W C 18

M.D Chow

Ph.D Marianne Van Den Bree

Ph.D Beverly S Emanuel

Joris R 11

Ph.D Vermeesch

Ph.D Stephen T Warren

M.D Michael J Owen

Ph.D Pankaj Chopra

Ph.D David J Cutler

Ph.D Richard Duncan

Ph.D Alex V Kotlar

Ph.D Jennifer G Mulle

B.Sc Anna J Voss

Ph.D Michael E Zwick

B.Sc Alexander Diacou

Aaron 13

Ph.D Golden

Tingwei 13

Ph.D Guo

Jhih-Rong 13

Ph.D Lin

Tao 13

Ph.D Wang

Zhengdong 19

Ph.D Zhang

Yingjie 13

Ph.D Zhao

Christian 13

Ph.D Marshall

Ph.D Daniele Merico

B.Sc Andrea Jin

Brenna 11

B.Sc Lilley

Harold I 11

B.Sc Salmons

Oanh 11

B.Sc Tran

Peter 11

Ph.D Holmans

Ph.D Antonio Pardinas

M.D James T R Walters

Ph.D Wolfram Demaerel

M.D Erik Boot

Ph.D Nancy J Butcher

M.D Gregory A Costain

Ph.D Chelsea Lowther

M.D Rens Evers

Ph.D Therese A M J 24

M.D Van Amelsvoort

Ph.D Esther 24

Ph.D Van Duin

Claudia 24

Ph.D Vingerhoets

Jeroen 24

M.D Breckpot

M.D Koen Devriendt

M.D Elfi Vergaelen

Ph.D Annick 14

M.D Vogels

T Blaine Crowley

B.Sc Daniel E Mcginn

Edward M 11

Ph.D Moss

Robert J 11

B.Sc Sharkus

Marta 11

M.D Unolt

Elaine H 11

M.D Zackai

Ph.D Monica E Calkins

Robert S 12

M.S Gallagher

Ph.D Ruben C Gur

Sunny X 12

M.D Tang

Rosemarie 12

M.D Fritsch

Claudia 25

M A Ornstein

Gabriela M 25

M.D Repetto

Elemi 26

M.D Breetvelt

Ph.D Sasja N Duijff

Ania 28

M.Sc Fiksinski

Ph.D Hayley Moss

Ph.D Maria Niarchou

M.D Kieran C Murphy

Ph.D Sarah E 30

M.D Prasad

Eileen M 30

Ph.D Daly

Maria 31

M.Sc Gudbrandsen

Cleynen 31

MD Clodagh M Murphy

Ph.D Declan G 31

M.D, FRCPsych Murphy

Ph.D Antonio Buzzanca

Fabio Di 32

M.D Fabio

Ph.D Maria C 32

M.D Digilio

Maria 33

Ph.D Pontillo

Bruno 34

M.D Marino

Stefano 35

M.D Vicari

Karlene 34

MSN, CGC Coleman

M.D Joseph F Cubells

Ph.D Opal Y 36

Ph.D Ousley

Miri 37

M.D Carmel

M.D Doron Gothelf

M.D Ehud Mekori-Domachevsky

Ph.D Elena Michaelovsky

Ph.D Ronnie Weinberger

Abraham 40

M.D Weizman

M.Sc Leila Kushan

Maria 18

Ph.D Jalbrzikowski

Marco 42

M.D Armando

Ph.D Stéphan 43

M.D Eliez

Corrado 43

M.Sc Sandini

Maude 43

Ph.D Schneider

Frédérique Sloan 43

Ph.D Béna

Kevin M 44

Ph.D Antshel

Wanda 45

M.D Fremont

Wendy R 46

Ph.D Kates

Raoul 46

M.D Belzeaux

Ph.D Tiffany 47

M.D Busa

Nicole 48

M.D Philip

Linda E 49

Ph.D Campbell

Kathryn L 50

Ph.D Mccabe

Ph.D Stephen R Hooper

Kelly 52

M.Sc Schoch

Vandana 53

MBBS Shashi

M.D Tony J 53

Ph.D Simon

Flora 54

Ph.D Tassone

Celso 55

M.D Arango

Ph.D David 56

M.D Fraguas

Ph.D Sixto 56

M.D García-Miñaúr

Jaume 57

B.Ch M B Morey-Canyelles

Jordi 58

Rosell

Ph.D Damià H Suñer

Jasna 59

B.Ch. 58 , International 22q11.2DS Brain M B Raventos-Simic

Schizophrenia occurs in about one in four individuals with 22q11.2 deletion syndrome (22q11.2DS). The aim of this International Brain and Behavior 22q11.2DS Consortium (IBBC) study was to identify genetic factors that contribute to schizophrenia, in addition to the ~20-fold increased risk conveyed by the 22q11.2 deletion. Using whole-genome sequencing data from 519 unrelated individuals with 22q11.2DS, we conducted genome-wide comparisons of common and rare variants between those with schizophrenia and those with no psychotic disorder at age ≥25 years. Available microarray data enabled direct comparison of polygenic risk for schizophrenia between 22q11.2DS and independent population samples with no 22q11.2 deletion, with and without schizophrenia (total n=35,182). Polygenic risk for schizophrenia within 22q11.2DS was significantly greater for those with schizophrenia (p adj =6.73×10 -6 ). Novel reciprocal case-control comparisons between the 22q11.2DS and population-based cohorts showed that polygenic risk score was significantly greater in individuals with psychotic illness, regardless of the presence of the 22q11.2 deletion. Within the 22q11.2DS cohort, results of gene-set analyses showed some support for rare variants affecting synaptic genes. No common or rare variants within the 22q11.2 deletion region were significantly associated with schizophrenia. These findings suggest that in addition to conferring a greatly increased risk to schizophrenia, the risk is higher when the 22q11.2 deletion and common polygenic risk factors that contribute to schizophrenia in the general population are both present.

INTRODUCTION

There is accumulated evidence that the recurrent 22q11.2 deletion associated with 22q11.2 deletion syndrome (22q11.2DS) represents a clinically relevant cause of schizophrenia, and is one of the strongest known risk factors for schizophrenia [START_REF] Mcdonald-Mcginn | 22q11.2 deletion syndrome[END_REF]. Schizophrenia occurs in about one in four individuals with 22q11.2DS, representing a more than 20-fold increase over the general population risk of 1% [START_REF] Mcdonald-Mcginn | 22q11.2 deletion syndrome[END_REF][START_REF] Fung | Practical guidelines for managing adults with 22q11.2 deletion syndrome[END_REF]. Identifying additional genetic modifiers of expressing schizophrenia in addition to the deletion is of scientific and clinical interest [START_REF] Mcdonald-Mcginn | 22q11.2 deletion syndrome[END_REF][START_REF] Gur | A neurogenetic model for the study of schizophrenia spectrum disorders: the International 22q11.2 Deletion Syndrome Brain Behavior Consortium[END_REF][START_REF] Insel | Rethinking schizophrenia[END_REF].

Discovering such additional genetic factors is a primary goal of the International 22q11.2DS Brain and Behavior Consortium (IBBC) [START_REF] Gur | A neurogenetic model for the study of schizophrenia spectrum disorders: the International 22q11.2 Deletion Syndrome Brain Behavior Consortium[END_REF]. We recently reported evidence from genome wide microarray data that other rare copy number variations (CNVs) outside of the 22q11.2 deletion region increase risk to schizophrenia in 22q11.2DS [START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF]. A key question is whether other genetic contributions to schizophrenia, known to exert risk in the general population, are modifying risk of schizophrenia within the 22q11.2DS subgroup.

Schizophrenia is polygenic, with risk variants spanning the full spectrum of population frequencies. Common alleles identified from genome-wide association studies (GWAS) each contribute only a small effect to disease risk, but their aggregate effect can confer a far greater risk, quantifiable as a polygenic risk score (PRS). Analyzing by aggregating genes can also be a means to observe relevant associations [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF]. Studies of rare protein-coding variants implicate functional gene-sets relevant to neuronal functions and synaptic components including the activity-regulated cytoskeleton-associated protein (ARC) complex, amongst others [START_REF] Singh | The contribution of rare variants to risk of schizophrenia in individuals with and without intellectual disability[END_REF][START_REF] Genovese | Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia[END_REF][START_REF] Purcell | A polygenic burden of rare disruptive mutations in schizophrenia[END_REF][START_REF] Marshall | Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects[END_REF][START_REF] Kirov | De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia[END_REF][START_REF] Fromer | De novo mutations in schizophrenia implicate synaptic networks[END_REF].

The current study used whole genome sequencing and microarray data from the IBBC (3) to investigate the contribution of common (single nucleotide polymorphisms, SNPs) and rare genetic variants (comprising mostly single nucleotide variants (SNVs) in protein coding regions of genes), i.e., sequence variant classes with prior evidence from idiopathic schizophrenia [START_REF] Genovese | Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia[END_REF][START_REF] Purcell | A polygenic burden of rare disruptive mutations in schizophrenia[END_REF][START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF]. To improve power, we examined collective signals imparted by the schizophrenia PRS for SNPs [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF][START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF], and functional gene-sets [START_REF] Marshall | Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects[END_REF] for rare variants. We examined these variant effects both within the 22q11.2 deletion region and genome-wide, comparing individuals with schizophrenia and related major psychotic disorders to those with no psychotic disorder and aged ≥25 years, therefore largely past the age at greatest risk for onset of psychotic illness.

METHODS

Sample collection and diagnostic classification

The IBBC ascertainment and assessment methods have been previously detailed [START_REF] Gur | A neurogenetic model for the study of schizophrenia spectrum disorders: the International 22q11.2 Deletion Syndrome Brain Behavior Consortium[END_REF][START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF]. Briefly, individuals with a presumed 22q11.2 deletion, who were recruited from 22 international sites in studies approved by local institutional research ethics boards, and for whom existing DNA samples were available, were ascertained for the study. The focus of this study was on unrelated individuals with sufficient data to discern psychotic illness status, who were molecularly confirmed to have a typical hemizygous deletion at 22q11.2, i.e., that spanned the LCR22A-LCR22B region, most commonly extending from LCR22A LCR22D [START_REF] Mcdonald-Mcginn | 22q11.2 deletion syndrome[END_REF][START_REF] Gur | A neurogenetic model for the study of schizophrenia spectrum disorders: the International 22q11.2 Deletion Syndrome Brain Behavior Consortium[END_REF]. As before, individuals were eligible for inclusion in this study if they met DSM diagnostic criteria for a major psychotic disorder (mostly schizophrenia) at any age, or had no history of any psychotic illness when assessed at age ≥25 years [START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF]. Subjects with affective psychosis were excluded. A case consensus procedure assured stringent diagnostic categorization [START_REF] Gur | A neurogenetic model for the study of schizophrenia spectrum disorders: the International 22q11.2 Deletion Syndrome Brain Behavior Consortium[END_REF]. Additional details are provided in the Supplement.

Whole-genome sequencing (WGS) and processing of DNA samples

Raw Illumina WGS data were mapped with PEMapper [START_REF] Johnston | PEMapper and PECaller provide a simplified approach to whole-genome sequencing[END_REF] to genome build GRCh38/hg38 with a median sequencing depth of 39X. The 22q11.2 deletion region was called based on sequencing depth, and variants were called independently with PECaller [START_REF] Johnston | PEMapper and PECaller provide a simplified approach to whole-genome sequencing[END_REF] for the genome-wide diploid and 22q11.2 haploid regions of the genomes. Routine QC metrics were performed (Table S1) and resulted in a multi-ethnic sample of 519 subjects (259 schizophrenia, 260 no psychotic illness). Using principal component analysis anchored with HapMap reference samples to infer ancestry, we further defined a European subsample of 435 subjects (214 schizophrenia, 221 no psychotic illness) based on subjects whose principal components were within 1 standard deviation of the average PC value for HapMap3 CEU subjects. Additional details on principal components and related QC are provided in the Supplement. These samples were then used for subsequent variant annotation and analyses.

Additional samples and array-based genotyping for polygenic risk assessment

An important aspect of our study was to assess the contribution of polygenic risk to schizophrenia in 22q11.2DS given previous results for schizophrenia in the general population [START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF].

To achieve this, we used an idiopathic schizophrenia case-control study (CLOZUK/ WTCCC) [START_REF] Rees | Analysis of Intellectual Disability Copy Number Variants for Association With Schizophrenia[END_REF] where samples were independent of those from the schizophrenia case control training dataset used for PRS construction [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF]. To allow comparisons between the PRS derived from this dataset and our 22q11.2DS cohort, we used available Affymetrix Genome-Wide Human SNP 6.0 array genotype data [START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF][START_REF] Guo | Deletion size analysis of 1680 22q11.2DS subjects identifies a new recombination hotspot on chromosome 22q11.2[END_REF]. Analyses were restricted to samples of European ancestry and autosomal SNPs. Identity by descent analysis identified three subjects from the 22q11.2DS cohort who were also present in the CLOZUK cohort. After excluding these individuals there were 432 (212 with schizophrenia; 220 non-psychotic controls) individuals from the 22q11.2DS cohort included for the primary PRS and rare variant analyses.

After alignment (HRCv1.1) [START_REF] Mccarthy | A reference panel of 64,976 haplotypes for genotype imputation[END_REF] and phasing with Eagle2 [START_REF] Loh | Reference based phasing using the Haplotype Reference Consortium panel[END_REF], genotype imputation of the 22q11.2DS cohort was performed using Minimac3 [START_REF] Das | Next-generation genotype imputation service and methods[END_REF]. SNPs with common variants (MAF >10%), low missingness (<1%), HWE mid p >10 -5 and high imputation quality (INFO >0.9) were retained for PRS analysis. PRS results obtained from imputed SNP data were compared to those obtained from WGS data using a sub-sample of 365 individuals (171 with schizophrenia; 194 non-psychotic) that had passed QC for both methods. Identity by descent analysis ensured correspondence between samples. Only overlapping SNPs that had passed QC in both datasets were kept for this analysis.

To establish a homogenous dataset that would enable a joint analysis of the array-based data from 22q11.2DS and CLOZUK/WTCCC, we initially conducted a QC step that used SNPs that were shared between both datasets (see Supplement for additional details). Following principal component analysis, samples >6 standard deviations from the mean of first 20 principal components were excluded to ensure homogeneity of the merged 22q11.2DS and CLOZUK/WTCCC datasets of European ancestry. This QC resulted in a dataset of 322 individuals with 22q11.2DS (147 with schizophrenia; 175 non-psychotic controls), 10,791 schizophrenia CLOZUK cases and 24,391 WTCCC controls for joint PRS analyses.

Statistical analyses

Single-variant association analysis of common variants-We performed association analyses of WGS data using PLINK 1.9. Variants were defined as common if sample MAF was either ≥1% outside the 22q11.2 deletion, or ≥2% within the 22q11.2 deletion (given the inherent reduction in the number of haploid variants observed). For analysis within the 22q11.2 deletion region, we only considered hemizygous SNPs, ie SNPs located in the deleted region. We therefore performed two sets of analyses: one where all individuals were included and where we considered SNPs located in the region spanning LCR22A-LCR22B, as this region is deleted in all study participants; and one where only individuals with the larger LCR22A-LCR22D deletion were included, using SNPs spanning that entire interval. We used logistic regression to regress disease status on a variant (additive coding), adjusting for sex and top principal components of ancestry. For analyses of the entire sample (n=519: 259 schizophrenia, 260 no psychotic illness), we adjusted for top 5 principal components derived from this sample plus HapMap reference samples (Fig. SF1). For analyses of the European subset (n=435: 214 schizophrenia, 221 no psychotic illness), we adjusted for the top 3 principal components derived from this subset. Given the possibility of unreliable asymptotic results for variants with sample MAF of 1-5%, we further studied these variants using Fisher's Exact Test.

Polygenic risk score (PRS) analyses-We used PRSice software (PRSice-2) [START_REF] Euesden | PRSice: Polygenic Risk Score software[END_REF] to construct PRS in the European subset. We compared WGS and array data to assess robustness of results. Two different datasets were used as the discovery (training) sample. For the 22q11.2DS-only analyses (n=432) that used WGS data, we used summary results from the second Psychiatric Genomics Consortium mega-analysis of schizophrenia (PGC2 SCZ) [START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF]. For the analysis of 22q11.2DS (n=322) and CLOZUK/WTCCC cohorts (n=10,791/24,391) that used array data, we used a version of the PGC2 SCZ data that excluded the CLOZUK/WTCCC samples [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF]. Only autosomal SNPs with high imputation quality (INFO > 0.9) were kept in these discovery datasets. In all target datasets, we used only variants with MAF >10% and excluded variants from the X chromosome, the 22q11.2 deletion region, and the Major Histocompatibility Complex (due to complex linkage disequilibrium patterns in the region) to calculate PRS. We also excluded ambiguous SNPs (A/T and C/G) that posed challenges in matching between discovery and validation sets. Specific coordinates of excluded regions are described in the Supplement.

Using PRSice, we clumped SNPs from the discovery datasets (R2 >0.1, 250 kb window), and then selected clumped SNPs at 13 P-value thresholds (ranging from <10 -6 to 1). For each P-value threshold, PRSice constructed a PRS for each individual from the target datasets as an average of the per-allele score for each eligible SNP. The per-allele score for a SNP was calculated as half the product of the number of reference alleles (0, 1 or 2) and the estimated log odds ratio for the SNP in the discovery dataset.

For the 22q11.2DS-only analysis using WGS data, we used logistic regression to test for association between disease status and standardized PRS adjusting for the same covariates (sex, top 3 principal components for ancestry) as for the analyses of individual common variants above. For the 22q11.2DS-CLOZUK/WTCCC joint analysis, we adjusted for sex and the top 3 principal components from the array data. For the PRS comparison between WGS and array data the same number of covariates were used (top 3 principal components for ancestry and sex) but using the principal components from each respective dataset. For all models, we used PRSice-2 to calculate both odds ratios per standard deviation of PRS as well as Nagelkerke's pseudo-R2 values to estimate the proportion of variance in schizophrenia explained by the PRS. We adjusted for testing of multiple correlated PRSs under different p-value thresholds using the P_ACT method [START_REF] Conneely | So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests[END_REF].

Rare variant analyses-Rare variant analyses were conducted on the 432 subjects in the European subset used for PRS analysis. We defined rare variants as those with gnomAD global frequency ≤1% and 22q11.2DS sample frequency ≤1% for all variants, but analyzed those from the 22q11.2 deletion region separately (Table S2). We also performed analyses using 5 different sets of filtered variants, based on: 1) any variant with Combined Annotation Dependent Depletion, CADD,(17) >30, 2) any variant with CADD >15, 3) any missense variant, 4) any missense variant with CADD >15, and 5) any predicted loss-of-function (LOF) mutation (defined as stop-gains, frameshift, core splice site alterations, stop-loss, or start-loss). For each filtered set, we first performed single-variant analysis using Fisher's Exact Test within PLINK 1.9. We then used the R package SKAT [START_REF] Wu | Rare-variant association testing for sequencing data with the sequence kernel association test[END_REF] to perform burden and variance-component (i.e., SKAT) tests within genes. For each filtered variant set, we considered only those genes with ≥5 rare variants observed in the 22q11.2DS European sample. We performed both unweighted and weighted analyses in a gene with the latter upweighting effects of extremely rare variants using a beta (0.5,0.5) distribution. For all analyses, we adjusted for sex, top 3 principal components of ancestry, total number of genome-wide variants, and PRS based on WGS data.

In addition to gene-based analyses, we performed weighted and unweighted gene-set burden analyses with 30 functional pathways (Table S3) previously used to study CNV enrichment in schizophrenia, involving genes relevant to neuronal functions and synaptic components, or genes with homologues in mouse genes grouped by organ system [START_REF] Marshall | Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects[END_REF]. These analyses used the same filtering scheme described above and adjusted for multiple correlated tests using P_ACT [START_REF] Conneely | So many correlated tests, so little time! Rapid adjustment of P values for multiple correlated tests[END_REF]. As a complementary analysis, we constructed logistic-regression models that, for a given pathway, assumed distinct effects for burden of LOF mutations and burden of missense variants passing or not passing CADD >15. We then used a likelihood-ratio statistic to test the null hypothesis that coefficients for all burden variables were equal to 0. These logistic-regression models used the same rare-variant weighting schemes and covariate adjustment as above. To adjust for multiple testing, we estimated the false discovery rate (FDR) based on outcome-label permutations balanced on covariates using the R package BiasedUrn [START_REF] Epstein | A permutation procedure to correct for confounders in case-control studies, including tests of rare variation[END_REF]. Finally, we also performed genome-wide and deletion region wide burden tests of global rare variation (i.e., regardless of gene function) between cases and controls using the same filtering schemes, variant-weighting schemes, and covariate adjustments as above.

RESULTS

22q11.2DS sample

The main sample comprised 435 unrelated individuals of European ancestry with a 22q11.2 deletion: 214 with schizophrenia (mean age at onset 22.1 (SD 8.3) years; mean age at assessment 30.9 (SD 12.8) years) and 221 with no lifetime diagnosis of psychotic disorder and age ≥25 years (mean age at assessment 36.4 (SD 10.0) years) [START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF]. Sample characteristics are representative of those for all ethnicities (Table S4). Results for 22q11.2 deletion sizes showed the expected ~90% of subjects with the typical 2.5 Mb LCR22A-LCR22D 22q11.2 deletion (Fig. 1) [START_REF] Mcdonald-Mcginn | 22q11.2 deletion syndrome[END_REF][START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF] and no significant difference in distribution between the two phenotypic subgroups (Χ 2 =0.0024, df=1, p=0.96).

Genome-wide schizophrenia polygenic risk score (PRS)

Using the PRS to analyze common variants collectively in 22q11.2DS showed that the PRS was significantly higher in those with schizophrenia than in those with no psychotic illness; significance was greatest using a p-value threshold (pT) of 0.05 to select individual SNPs from the training set (13) (Nagelkerke's pseudo-R 2 0.077, p adjusted =6.73 × 10 -6 ). The risk of schizophrenia in individuals with 22q11.2DS increased by 1.77-fold (95% CI 1.41-2.22) for every incremental standard deviation in PRS (Fig. 2a and2b; Table S5 shows results for other pT values). Using pT=0.05, individuals within the highest quartile of PRS had a 3.42-fold (95% CI: 1.88-6.22) increased risk of schizophrenia compared to those in the lowest quartile (Fig. SF2).

Using a subset of samples for which imputed data were available from microarray-based genotypes allowed us to directly compare schizophrenia PRS between 22q11.2DS (n=322; 147 with schizophrenia, 175 without psychotic illness) and an idiopathic schizophrenia case-control cohort with no 22q11.2 deletion (10,791 schizophrenia CLOZUK cases, 24,391 WTCCC controls) [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF]. We first ensured the concordance of genotypes obtained by array and WGS (mean [median] variant concordance of 99.4% [99.6%]) and observed similar PRS distributions across methods (Fig. SF3); see Supplementary results for more details. Using this smaller sample and imputed data we again found significant differences in PRS for the 22q11.2DS schizophrenia group compared to those with no psychotic illness (pT=0.05: R 2 =0.063, p adjusted =5.5×10 -4 , OR=1.63, 95% CI:1.27-2.08; Fig. 3a(i) and 3b(i)), confirming that the PRS results were comparable to those using WGS data. We also confirmed the significant difference in PRS between the CLOZUK-schizophrenia and the WTCCC-control samples, which was comparable to the results obtained when using larger case-control datasets (6, 13): pT=0.05: R 2 =0.11, p adjusted <2×10 -16 , OR=2.05, 95% CI: 2.00-2.11; Fig. 3a(vi) and 3b(vi).

We then proceeded to examine novel reciprocal case-control comparisons between the 22q11.2DS and population-based cohorts (Fig. 1). Individuals with 22q11.2DS schizophrenia had a significantly higher PRS than WTCCC-controls (pT=0.05: R 2 =0.014, p adjusted 3.8×10 -6 , OR=1.53, 95% CI: 1.29-1.81; Fig. 3a(ii) and 3b(ii)). Similarly, the CLOZUK-schizophrenia group had a significantly higher PRS than the 22q11.2DS-non psychotic group (pT=0.05: R 2 =0.047, p adjusted <2×10 -16 , OR=2.05, 95% CI: 1.74-2.41; Fig. 3a(v) and 3b(v)). These results indicate that PRS was significantly greater in individuals with psychotic illness, regardless of the presence of the 22q11.2 deletion.

Within diagnostic group analyses revealed that the PRS observed in schizophrenia cases from the CLOZUK cohort was significantly greater that that seen in those with schizophrenia and 22q11.2DS (pT=0.05: R 2 =0.0065, p adjusted 0.01, OR=1.31, 95% CI:1.10-1.55; Fig. 3a(iv) and 3b(iv)). The PRS results for the non-psychotic 22q11.2DS group were not significantly different than those for WTCCC controls (Fig. 3a(iii) and 3b(iii)); however, the 22q11.2DS sample size was underpowered for this comparison given the low effect size (Table S6).

We assessed the consistency of these results with a liability threshold model that assumed additive effects of the 22q11.2 deletion and PRS on risk for schizophrenia; details of our methodology and results are provided in the Supplemental methods and Table S7. The model that best fit the observed data (where the sum of the absolute difference in observed and expected PRS was the lowest (0.005) across all 4 cohorts) estimated that the PRS contributes to 5.2% (range 4.9% to 5.5%) of the variance of liability for schizophrenia. This best-fit model also provided an estimate of the shift in liability attributable to the 22q11.2 deletion (1.0, range 0.2-1.9) from population-based expectations of schizophrenia prevalence (2.33, based on top 1% CI of the normal distribution). In comparison, an increase in the PRS by one standard deviation corresponds to a shift in liability of 0.23. Working back from the estimated shift in liability attributable to the 22q11.2 deletion (i.e., 2.33-1.00=1.33) provides a predicted prevalence of schizophrenia in 22q11.2DS of 9.2% (range 1.7% to 33.7%) (Table S7). Using this best-fit liability threshold model to calculate the sample sizes required to provide 80% power to detect a nominally significant difference in PRS at pT=0.05, indicated that apart from the comparison of PRS between 22q11.2DS individuals without psychosis and WTCCC controls, all other primary analyses of polygenic risk were sufficiently powered (Table S6).

Common variants -intact 22q11.2 allele and genome-wide association (GWAS)

Given previous evidence for a genetic modifier locus in studies of cardiac phenotypes in 22q11.2DS [START_REF] Mlynarski | Copy Number Variation of the Glucose Transporter Gene SLC2A3 and Congenital Heart Defects in the 22q11.2 Deletion Syndrome[END_REF], we considered the possibility that individual genetic risk factors could confer an elevated effect size in association analyses of 22q11.2DS that compared those with and without schizophrenia. We examined this possibility by conducting association analyses of individual SNPs, restricted to the 22q11.2 deletion region (3,252 SNPs for full dataset, 3,236 SNPs for European ancestry) and genome-wide (4,313,064 SNPs outside this deletion region). None of these analyses identified genome-wide significant associations with schizophrenia in either the European cohort (n=435) or total cohort (n=519) (Fig. SF4 to SF9, Tables S8 to S11). Genome-wide, the top SNP was rs12824373 (p=6.91×10 -7 ), located in an intron of the CRADD gene. This SNP was not significantly associated with schizophrenia in the PGC GWAS [START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF]. Also, no SNP previously reported to reach genome wide-significant levels of association with schizophrenia (13) yielded suggestive evidence of association in this GWAS of 22q11.2DS (minimum unadjusted p-value=7.4×10 -3 ; Table S12). Results were also non-significant when restricting to 724,010 SNPs in 2925 LOF intolerant genes (6) (minimum p=4.88×10 -5 ).

Rare variants -individual, gene-based and gene-set analyses

Similar to common variant results, there was no significant association of schizophrenia with any individual rare variant, nor for individual gene-based rare variant analyses, within or outside the 22q11.2 deletion region (Fig. SF10, Table S13-15). There were few LOF variants within the 22q11.2 deletion region (i.e., in effect, knock-out mutations) (Fig. 4).

Analyzing genome-wide rare variants collectively in functional gene-sets (Table S3), comparable to methods used previously [START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF][START_REF] Marshall | Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects[END_REF], but with the addition of accounting for polygenic risk in 22q11.2DS, showed nominally significant results (Table 1, Table S16). The top four results (unadjusted p values 5.9×10 -3 -5.5×10 -4 ) implicated the Kirov_ARC gene-set [START_REF] Kirov | De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia[END_REF][START_REF] Rees | Targeted Sequencing of 10,198 Samples Confirms Abnormalities in Neuronal Activity and Implicates Voltage-Gated Sodium Channels in Schizophrenia Pathogenesis[END_REF]. Though none remained significant after adjustment for multiple testing, one result was borderline significant (p=0.053) after correcting for 300 tests (Table 1). Overall genome-wide rare-variant burden (i.e., regardless of gene function) showed no significant relationship to schizophrenia (unadjusted p values: genome-wide 0.35-0.79, 22q11.2 deletion region 0.02-0.79; Tables S17-18). Similarly, there was no significant enrichment of heterozygous truncating variants (p=0.76) in a gene-set comprising 2,925 genes considered intolerant to LOF variation (gnomAD 2.1 observed/expected LOF, upper 95% CI <0.35).

Using a complementary multivariate method to assess the functional genome-wide rare variant burden supported the Kirov_ARC gene-set [START_REF] Kirov | De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia[END_REF] as the most promising (permutation based FDR ~25%) of the 30 gene-sets tested, but revealed possible heterogeneity of findings (Fig. SF11). For rare missense variants in this gene-set, the coefficients were negative, indicating the variant burden is enriched in the non-psychotic 22q11.2DS group. (Wald p-value, coefficient: 2.6×10 -3 , -0.35 for missense CADD >15 variants; 0.04, -0.22 for other missense variants). In contrast, the contribution of LOF SNVs was positive (Wald p-value 0.21, coefficient 0.16), though non-significant, suggesting that for the Kirov_ARC gene-set, the LOF rare variant burden may be somewhat enriched in those with schizophrenia. The main gene driving the putatively protective rare missense burden signal for the Kirov_ARC gene-set was DLGAP2 (gene-level nominal p-value 0.01, CADD >15 missense variants).

DISCUSSION

The results of this study provide new data relevant to our understanding of the genetic architecture underlying the exceptionally increased risk for developing schizophrenia and related psychotic illnesses imparted by the 22q11.2 deletion (1). We demonstrate for the first time that individuals with 22q11.2DS who develop schizophrenia have a significantly greater schizophrenia polygenic risk score than those with no psychotic illness, suggesting that both the 22q11.2 deletion and the aggregate risk conferred by common genetic risk factors outside the deleted region contribute to schizophrenia in 22q11.2DS. There was no evidence for contributions from common or rare variants at the individual variant or gene-based level. There was however some suggestive evidence that rare coding variants in genes involved in ARC synaptic gene complexes, previously reported to be involved in large population-based schizophrenia samples [START_REF] Genovese | Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia[END_REF][START_REF] Purcell | A polygenic burden of rare disruptive mutations in schizophrenia[END_REF][START_REF] Marshall | Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects[END_REF][START_REF] Kirov | De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia[END_REF][START_REF] Fromer | De novo mutations in schizophrenia implicate synaptic networks[END_REF], may contribute to modifying risk of schizophrenia in 22q11.2DS.

Contribution of genome-wide polygenic risk

The PRS results showing that collective common genetic risk factors independent of the 22q11.2 deletion have a substantive role in increasing risk of schizophrenia in 22q11.2DS are consistent with research indicating the importance of polygenic risk in developing schizophrenia in large general population studies [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF][START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF]. They are also consistent with findings for individuals with other CNVs that increase risk for neuropsychiatric and neurodevelopmental disorders [START_REF] Bergen | Joint Contributions of Rare Copy Number Variants and Common SNPs to Risk for Schizophrenia[END_REF][START_REF] Tansey | Common alleles contribute to schizophrenia in CNV carriers[END_REF][START_REF] Niemi | Common genetic variants contribute to risk of rare severe neurodevelopmental disorders[END_REF]. They differ somewhat from those of a study examining CNVs ascertained as part of the PGC that reported small PRS effects in 56 individuals with schizophrenia and a 22q11.2 deletion, in contrast to greater PRS effects for CNVs with low risk for schizophrenia that were also present in control samples [START_REF] Bergen | Joint Contributions of Rare Copy Number Variants and Common SNPs to Risk for Schizophrenia[END_REF]. Availability of non-psychotic control groups and the sample sizes available could have contributed to the strong effect of schizophrenia PRS observed in our study.

The ability to directly compare PRS between individuals with 22q11.2DS and cohorts of schizophrenia and controls with no 22q11.2 deletion allowed us to determine that the contribution of polygenic risk to schizophrenia was slightly but significantly lower in those with, than in those without a 22q11.2 deletion ( 6). This suggests that when our results are considered according to the liability threshold model, the presence of the 22q11.2 deletion may lower to some extent the degree of background polygenic risk involved in developing schizophrenia.

Broad confidence intervals precluded our ability to determine additive or other relationships between the PRS and 22q11.2 deletion. Nonetheless, assuming an additive liability model, the proportion of variance liability for schizophrenia in 22q11.2DS attributable to the PRS was estimated to be 5.2% (range 4.9%-5.5%), which is in line with the estimates of 7% and 5.7% reported previously for idiopathic schizophrenia [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF][START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF]. Based on this model there were wide ranges for the estimated shift in liability attributed to the 22q11.2 deletion (from the expected 1% population prevalence of schizophrenia) (Table S7) and estimated schizophrenia prevalence in 22q11.2DS (1.7% to 33.7%). The latter values would therefore embrace the low estimates reported in other studies using statistical models [START_REF] Kirov | The penetrance of copy number variations for schizophrenia and developmental delay[END_REF][START_REF] Hoeffding | Risk of Psychiatric Disorders Among Individuals With the 22q11.2 Deletion or Duplication: A Danish Nationwide, Register-Based Study[END_REF] and the actual observed prevalence of psychotic illness in 22q11.2DS of >20% [START_REF] Mcdonald-Mcginn | 22q11.2 deletion syndrome[END_REF][START_REF] Fung | Lack of change in markers of presynaptic terminal abundance alongside subtle reductions in markers of presynaptic terminal plasticity in prefrontal cortex of schizophrenia patients[END_REF][START_REF] Schneider | Psychiatric disorders from childhood to adulthood in 22q11.2 deletion syndrome: results from the International Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome[END_REF].

Contribution of genome-wide rare variant functional gene-sets

Complementary to the polygenic risk results, and in innovative analyses that accounted for PRS effects, the results also indicated a possible role for rare variants in the gene set representing the ARC postsynaptic protein complex [START_REF] Genovese | Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia[END_REF][START_REF] Purcell | A polygenic burden of rare disruptive mutations in schizophrenia[END_REF][START_REF] Marshall | Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects[END_REF][START_REF] Kirov | De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia[END_REF][START_REF] Fromer | De novo mutations in schizophrenia implicate synaptic networks[END_REF][START_REF] Rees | Targeted Sequencing of 10,198 Samples Confirms Abnormalities in Neuronal Activity and Implicates Voltage-Gated Sodium Channels in Schizophrenia Pathogenesis[END_REF]. Consistent with our results in 22q11.2DS, effect sizes are expected to be modest for individual rare SNVs and related gene-set results in schizophrenia [START_REF] Rees | Targeted Sequencing of 10,198 Samples Confirms Abnormalities in Neuronal Activity and Implicates Voltage-Gated Sodium Channels in Schizophrenia Pathogenesis[END_REF]. It is therefore encouraging that our results for 22q11.2DS provided suggestive evidence in line with multiple studies that have reported rare variants, both SNVs and CNVs, affecting coding sequence of genes from the ARC and closely related synaptic gene-sets to be significantly associated with idiopathic schizophrenia [START_REF] Genovese | Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia[END_REF][START_REF] Purcell | A polygenic burden of rare disruptive mutations in schizophrenia[END_REF][START_REF] Marshall | Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects[END_REF][START_REF] Kirov | De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia[END_REF][START_REF] Fromer | De novo mutations in schizophrenia implicate synaptic networks[END_REF][START_REF] Kirov | The penetrance of copy number variations for schizophrenia and developmental delay[END_REF]. Higher powered studies will be needed to evaluate the precise genomic architecture, including direction of effect patterns for rare variants [START_REF] Kirov | De novo CNV analysis implicates specific abnormalities of postsynaptic signalling complexes in the pathogenesis of schizophrenia[END_REF][START_REF] Rees | Targeted Sequencing of 10,198 Samples Confirms Abnormalities in Neuronal Activity and Implicates Voltage-Gated Sodium Channels in Schizophrenia Pathogenesis[END_REF][START_REF]Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk[END_REF][START_REF] Ganna | Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum[END_REF].

Role of the 22q11.2 deletion and possible genetic mechanisms

We have proposed that the presence of the 22q11.2 deletion may lower an individual's tolerance for the expression of multiple biological mechanisms and/or pathways involved in developing schizophrenia [START_REF] Gur | A neurogenetic model for the study of schizophrenia spectrum disorders: the International 22q11.2 Deletion Syndrome Brain Behavior Consortium[END_REF][START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF], and anticipate that this is likely to be related to the collective effect of lowered multi-gene dosage at the 22q11.2 locus. Importantly, we did not find evidence for risk attributable to the 22q11.2 deletion to be mediated through common or rare variants of genes located on the intact 22q11.2 allele, suggesting that it is unlikely that such variants have a large effect on schizophrenia risk. This included the few LOF (in effect null) variants that we identified (Fig. 4) and are now the focus of an in-depth reverse phenotyping effort. These negative results appear consistent with a recent study of idiopathic schizophrenia reporting the absence of detectable individual gene effects for 22q11.2 deletion genes, in contrast to genes in other CNV regions [START_REF] Coe | Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity[END_REF].

Establishing the exact biological mechanisms through which the 22q11.2 deletion confers increased risk to schizophrenia is beyond the scope of this study. However, given the profile of the genes in this region, including those encoding transcription factors, mitochondrial proteins, and others, and the fact that most are expressed in brain (Fig. 4), the 22q11.2 deletion region may be particularly prone to general neurodevelopmental perturbation, and impaired regulatory mechanisms. For example, haploinsufficiency of DGCR8 affects miRNA buffering genome-wide, thus could reveal or enhance effects of common and rare variants elsewhere in the genome [START_REF] Stark | Altered brain microRNA biogenesis contributes to phenotypic deficits in a 22q11-deletion mouse model[END_REF][START_REF] Forstner | MicroRNAs as the cause of schizophrenia in 22q11.2 deletion carriers, and possible implications for idiopathic disease: a mini-review[END_REF][START_REF] Schofield | Monoallelic deletion of the microRNA biogenesis gene Dgcr8 produces deficits in the development of excitatory synaptic transmission in the prefrontal cortex[END_REF][START_REF] Brzustowicz | miRNA-mediated risk for schizophrenia in 22q11.2 deletion syndrome[END_REF][START_REF] Merico | MicroRNA Dysregulation, Gene Networks, and Risk for Schizophrenia in 22q11.2 Deletion Syndrome[END_REF][START_REF] Merico | Whole-Genome Sequencing Suggests Schizophrenia Risk Mechanisms in Humans with 22q11.2 Deletion Syndrome[END_REF]. Many of the genes in the ARC gene-set, for example, are predicted to be affected by DGCR8 haploinsufficiency [START_REF] Stark | Altered brain microRNA biogenesis contributes to phenotypic deficits in a 22q11-deletion mouse model[END_REF][START_REF] Merico | Whole-Genome Sequencing Suggests Schizophrenia Risk Mechanisms in Humans with 22q11.2 Deletion Syndrome[END_REF].

Large GWAS of schizophrenia indicate that the multiple SNPs involved at each significant locus are mostly intergenic or intronic, tag protein-coding and miRNA genes, and increase or decrease risk in about equal proportions, each with a very small effect size [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF][START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF]. Thus the common variant signal measured by polygenic risk may be consistent with a role for non-coding and/or regulatory mechanisms in schizophrenia, including in 22q11.2DS [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF][START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF][START_REF] Forstner | MicroRNAs as the cause of schizophrenia in 22q11.2 deletion carriers, and possible implications for idiopathic disease: a mini-review[END_REF][START_REF] Merico | Whole-Genome Sequencing Suggests Schizophrenia Risk Mechanisms in Humans with 22q11.2 Deletion Syndrome[END_REF][START_REF] Warnica | Copy number variable microRNAs in schizophrenia and their neurodevelopmental gene targets[END_REF]. The precise complement of common and rare variants leading to schizophrenia may be personal to the individual but nonetheless observable in aggregate across individuals [START_REF] Costain | Prenatal genetic testing with chromosomal microarray analysis identifies major risk variants for schizophrenia and other later-onset disorders[END_REF][START_REF] Harrison | Recent genetic findings in schizophrenia and their therapeutic relevance[END_REF]. Further investigations designed to determine the exact nature of the primary 22q11.2 deletion-related biological insult therefore promises to improve our understanding of the mechanisms underlying schizophrenia in both 22q11.2DS and the general population.

Advantages and limitations

We had available the largest sample of 22q11.2DS subjects with and without a psychotic disorder ever brought together to study the impact of common and rare SNVs to schizophrenia risk. The signal imparted by additional polygenic common variant risk, similar to previous studies using idiopathic schizophrenia case-control samples several orders of magnitude larger [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF][START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF], produced clearly significant results. The results support our design, using the non-psychotic 22q11.2DS subgroup as the main comparison for 22q11.2DS cases with schizophrenia subgroup within 22q11.2DS. A power analysis indicated that our primary analyses of polygenic risk using the 22q11.2DS schizophrenia group were adequately powered for comparisons with 22q11.2DS without psychotic illness and with the large population-based case-control samples. Also, to our knowledge, this was the first study of schizophrenia genetics to account for polygenic risk while analyzing rare variant burden. Despite these advantages, power was limited by the available cohort size (Fig. SF12) and would be affected by the possibility that some 22q11.2DS subjects in the non psychotic group may go on to develop schizophrenia. It is probable that some of the genome-wide polygenic risk associated with schizophrenia may also be involved in other neurodevelopmental disorders [START_REF] Smeland | Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence[END_REF]. This may make the current results all the more remarkable, given the presence of one or more neurodevelopmental phenotypes in most individuals with 22q11.2DS [START_REF] Swillen | Neurodevelopmental outcome in 22q11.2 deletion syndrome and management[END_REF].

With respect to rare variants, the current study focused primarily on the signal derived from the exome where findings are more likely to be biologically interpretable and potentially clinically relevant than that from non-coding variants [START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF][START_REF]Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk[END_REF]. Our conservative design, e.g., for each filtered variant set, considering only those genes with 5 or more rare variants observed in the 22q11.2DS sample of European ancestry, and using 30 gene-sets thus correcting for 300 tests, may have affected power. Also, as in other sequencing studies [START_REF] Rees | Targeted Sequencing of 10,198 Samples Confirms Abnormalities in Neuronal Activity and Implicates Voltage-Gated Sodium Channels in Schizophrenia Pathogenesis[END_REF], certain variant types, e.g., indels, were imperfectly captured [START_REF] Johnston | PEMapper and PECaller provide a simplified approach to whole-genome sequencing[END_REF]. Recruiting larger samples of individuals with schizophrenia and older adults with no psychotic illness from the 22q11.2DS population could provide the means to further delineate the strength of the relationship between the factors identified, including rare variants in the small ARC gene-set, and the risk for schizophrenia. This would also be essential to enable investigation of other mechanisms with lower effect sizes. In future studies, larger samples of 22q11.2DS may also assist in determining the role of X chromosome variants in the context of a 22q11.2 deletion, and provide sufficient data to assess those of non-European ancestry [START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF].

Although we were unable to use these WGS data to simultaneously assess CNVs or other structural variants [START_REF] Johnston | PEMapper and PECaller provide a simplified approach to whole-genome sequencing[END_REF], we have previously demonstrated in a smaller but overlapping sample that additional rare CNVs overlapping genes outside of the 22q11.2 deletion region contribute to schizophrenia risk in 22q11.2DS [START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF]. Also, the IBBC proband-only 22q11.2DS sample provides no capability of examining inheritance, parent-of-origin, or segregation data for the 22q11.2 deletion or any other variants, or the family history of schizophrenia, all of which are factors of interest in disease expression [START_REF] Mcdonald-Mcginn | 22q11.2 deletion syndrome[END_REF][START_REF] Gur | A neurogenetic model for the study of schizophrenia spectrum disorders: the International 22q11.2 Deletion Syndrome Brain Behavior Consortium[END_REF][START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF]. In smaller samples, the parental origin and inheritance status of the 22q11.2 deletion have not appeared to play a role in schizophrenia, however [START_REF] Bassett | Copy number variations and risk for schizophrenia in 22q11.2 deletion syndrome[END_REF], and one can expect that >90% of the 22q11.2 deletions would arise as de novo mutations [START_REF] Mcdonald-Mcginn | 22q11.2 deletion syndrome[END_REF].

CONCLUSIONS

Our results indicate that, despite its high impact, the risk conferred by the 22q11.2 deletion is even greater when present with the genome-wide risk factors that also have a key role in schizophrenia risk in the general population [START_REF] Pardinas | Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection[END_REF][START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF][START_REF] Rees | Targeted Sequencing of 10,198 Samples Confirms Abnormalities in Neuronal Activity and Implicates Voltage-Gated Sodium Channels in Schizophrenia Pathogenesis[END_REF]. Given the magnitude of the risk of the 22q11.2 deletion relative to common genetic risk factors, including polygenic risk, understanding the biological consequences of this deletion will be an important next step in appreciating the underlying pathology of schizophrenia. The overall results further illustrate the value of using 22q11.2DS as a model for delineating the genetic architecture of schizophrenia. The direction of effect, and the type and precise position of the additional variants will be important factors to consider. Lessons learned from 22q11.2DS may be applicable to other recurrent CNVs that contribute to risk for schizophrenia in the general population [START_REF] Marshall | Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects[END_REF]. Future analyses of raw whole-genome sequencing data will allow for more detailed assessment of variants and pathways implicated, and the overall genetic architecture and proposed mechanisms of schizophrenia, incorporating those related to structural variants [START_REF] Bassett | Rare Genome Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome[END_REF]. These observations have important implications for research, including functional genomic studies using induced pluripotent stem cells from individuals with 22q11.2DS to model schizophrenia, and potentially for clinical genetic practice. Given the magnitude of the effect size of the 22q11.2 deletion, prospective studies using algorithms generated from such sequence data together with phenotypic data relevant to schizophrenia risk could lead to findings of clinical relevance to the individual [START_REF] Gur | A neurogenetic model for the study of schizophrenia spectrum disorders: the International 22q11.2 Deletion Syndrome Brain Behavior Consortium[END_REF]48). Analysis of schizophrenia PRS in 432 individuals of European ancestry with 22q11.2DS, with (n=212) or without (n=220) schizophrenia. Variants from the 22q11.2 deletion, MHC regions and X chromosome were excluded and results adjusted for sex, top 3 ancestry PCs. P-values were adjusted using P-ACT for the 13 p-value thresholds (pT) used for PRS construction [START_REF]Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci[END_REF]. Details on the number of SNPs used (maximum 68,966 SNPs for polygenic risk scoring at pT=1, minimum 202 SNPs at pT=1×10 -6 ) are shown in Table S9. Figure 3a shows the proportion of variance explained by PRS (Nagelkerke's R2) for each of the six comparisons (clockwise: i to vi). Figure 3b shows the odds ratio and 95% confidence interval bars per PRS standard deviation constructed for these six PRS analyses (left to right: i to vi). Taken as a whole, these results demonstrate that the PRS in the CLOZUK-schizophrenia is significantly greater than that seen in 22q11.2DS individuals with schizophrenia, which itself is significantly greater than that seen in the WTCCC-controls and the 22q11.2DS non-psychotic groups.

Figure 1 :

 1 Figure 1: IBBC 22q11.2 DS cohort overview and schematic of data generation and analyses performed.The full IBBC cohort and European subset are illustrated as yellow circles, broken down by numbers with or without schizophrenia, as well as sex and 22q11.2 deletion extent for individuals of European Ancestry. Whole-genome sequencing methods and data are represented by blue boxes and microarray data represented by green boxes (lighter for IBBC 22q11.2DS data, darker for schizophrenia case-control study data[START_REF] Rees | Analysis of Intellectual Disability Copy Number Variants for Association With Schizophrenia[END_REF]). i to vi represent the comparisons presented in Fig.3for novel reciprocal case-control comparisons of schizophrenia PRS between the 22q11.2DS and population-based cohorts.

Figure 2 .

 2 Figure 2. Polygenic risk score analyses of schizophrenia in 22q11.2DS.

Figure 2a .

 2a The proportion of variance in schizophrenia explained by PRS (Nagelkerke's R2) across different pT thresholds. At pT=0.05, Nagelkerke's pseudo-R2= 0.077 (adjusted p=6.73×10-6 ).

Figure 2b .

 2b Odds ratio and 95% confidence interval bars per one standard deviation increase in PRS across different pT thresholds. At pT=0.05 a one standard deviation increase in PRS corresponded to a 1.77-fold higher odds for schizophrenia in 22q11.2DS.

Figure 3 .

 3 Figure 3. Polygenic risk score analyses in 22q11.2DS individuals with or without schizophrenia, and a case-control cohort without 22q11.2DS. PRS analyses were performed for 322 individuals with 22q11.2DS (147 with schizophrenia, 175 without) and 35,182 individuals with no 22q11.2 deletion (10,791 schizophrenia CLOZUK cases, 24,391 WTCCC controls) (6); all subjects were of European ancestry. Six comparisons of schizophrenia PRS were considered (Figure 1) between: (i) individuals with 22q11.2DS, with and without schizophrenia (ii) 22q11.2DS-schizophrenia and WTCCC controls (iii) 22q11.2DS-non-psychotic and WTCCC controls (iv) the CLOZUK schizophrenia cohort and 22q11.2DS-schizophrenia, (v) the CLOZUK schizophrenia cohort and 22q11.2DS-non-psychotic, and (vi) the CLOZUK schizophrenia cohort and WTCCC controls.Figure3ashows the proportion of variance explained by PRS (Nagelkerke's R2) for each of the six comparisons (clockwise: i to vi). Figure3bshows the odds ratio and 95% confidence interval bars per PRS standard deviation constructed for these six PRS analyses (left to right: i to vi). Taken as a whole, these results demonstrate that the PRS in the CLOZUK-schizophrenia is significantly greater than that seen in 22q11.2DS individuals with schizophrenia, which itself is significantly greater than that seen in the WTCCC-controls and the 22q11.2DS non-psychotic groups.

Figure 4 .

 4 Figure 4. Representation of 46 genes spanning the 22q11.2 LCR22A-LCR22D deletion region annotated for contextual information and results from the current study: Representation of 46 genes spanning the 22q11.2 low copy repeat (LCR) LCR22A-LCR22D deletion region annotated for contextual information and, on grey background, results from the current study. The sections numbered 1-11 indicate the following: 1) Gene expression in brain, ranging from none to low (white) to low-medium (yellow) to medium-high (orange) to very high (red) according to BrainSpan (BrainSpan: Atlas of the Developing Human Brain [[http://developinghumanbrain.org]]http:// developinghumanbrain.org]) and as previously processed by (10); 2 & 3) Exome Aggregation Consortium (ExAC) (48) calculated probability (ranging from 0 to 1.00, shown as a spectrum from white to deep violet) that the gene is intolerant to (2) two LOF variants (recessive; pRec), or (3) a single LOF variant (haploinsufficient; pLI); 4 & 5) Neuro-phenotype data availability, (4) from mouse in the form of mouse homologues from MGI (49) as a union of two MPO-based gene-sets [MP:0005386 behavior/neurological phenotype, MP:0003631 nervous system phenotype] as used in (10), and (5) for human neurologic disease genes from the Clinical Genomic Database (CGD) (CGD: Clinical Genomic Database. [https://research.nhgri.nih.gov/CGD/]); 6) Clusters of small rare deletions in the general population per the Database of Genomic Variants (DGV) (50), indicating presumed tolerance to hemizygosity, where an aqua circle/ oval represents a single deletion cluster of ≤1% frequency;

  schemes) ** Adjusted p-value based on multiple test correction using permutation-based FDR for 1000 iterations of 360 tests (30 gene-sets (10) x 2 variant sets x 3 frequency weighting schemes x 2 PRS and no PRS) *** Burden result based on a logistic regression model combines effects from different types of rare variants (LOF Indel, LOF SNV, Missense with CADD > 15 and Missense other) with PRS as one of the covariates Kirov_ARC = 28 genes in an activity-regulated cytoskeleton-associated protein (ARC) complex curated by George Kirov in 2012 (11).

Table 1 .

 1 Gene-set burden association results for rare variants in Kirov ARC pathway and schizophrenia in 432 European subjects with 22q11.2DS

	Burden (SKAT)
	Variant group and weight	P-value	Adjusted P-value (FWER) *
	Missense -any	0.00055	0.053
	Missense -any, weighted for rarity 0.0028	0.22
	Any with CADD > 15	0.0045	0.31
	Missense with CADD > 15	0.0059	0.37
	Burden (Logistic Regression) ***
	Variant group	Coefficient point estimate Coefficient Ward P-value	Global P-value Adjusted P-value
					(permutation-FDR) **
	LOF Indel	NA	NA	0.0012	0.25
	LOF SNV	0.17	0.12
	Missense with CADD > 15	-0.30	0.0065
	Missense other	-0.22	0.034

* Adjusted p-value based on multiple test correction using P_ACT for the 300 tests conducted (30 gene-sets

[START_REF] Marshall | Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects[END_REF]

x 5 variant filters x 2 weighting

Mol Psychiatry. Author manuscript; available in PMC 2021 August 03.

Acknowledgements

The IBBC is supported by the National Institute of Mental Health grants U01MH101719, U01MH0101720, U01MH0101723, U01MH101722, and U01MH101724. Other grant support includes: Brain and Behavior Foundation Young Researcher grant (formerly NARSAD, grant n. 21278) to Dr. Armando, Fondecyt-Chile Grant 1171014 to Drs. Fritsch, Ornstein, and Repetto, grant FWO G.0E11.17N to Dr. Vermeesch, MRC Centre grant MR/L010305/1, National Institute of Mental Health grant U01MH101724, and Welsh Government funded Research Centre 514032 to Dr. Owen, NARSAD (Brain and Behavior Research Foundation) to Dr. Ousley, National Institute of Mental Health grant MH064824 to Drs. Antshel, Fremont, Kates, and Simon, NIH grant R01GM117946 to Drs. Duncan and Voss, NIH grant U54NS091859 to Drs. Chopra, Epstein, Johnston, and Warren, NIH grants R01MH100917 and R01GM117946 to Drs. Cutler, Epstein, Kotlar, Mulle, and Zwick, NIMH K01 MH112774 to Dr. Jalbrzikowski, NIMH R01

MH085953 and NIH U54 EB020403 to Drs. Bearden and Kushan, NIMH U01MH101724 to Dr. van den Bree, P01HD070454 to Drs. Crowley, Diacou, Emanuel, Guo, Jin, Lilley, McDonald-McGinn, McGinn, E.M. Moss, Morrow, Salmons, Sharkus, Tran, Unolt, Zackai, and Zhao, P50MH09689 and R01-MH-1072351 to Dr. R.E. Gur, P50MH096891 to Drs. Calkins and R.C. Gur, National Institute of Mental Health R01MH085953 to Drs. C. Murphy and D. Murphy, R01-MH-107235 to Dr. R.C. Gur, Swiss National Science Foundation (Grant number 324730_144260) and National Center of Competence in Research (NCCR) "Synapsy-The Synaptic Bases of Mental Diseases" (Grant number 51NF40-185897) to Dr. Eliez, T32 MH019112 to Dr. Tang, the Binational Science Foundation, grant 2017370 to Dr. Gothelf, U01 MH087626 to Drs. Calkins, Gallagher, R.C. Gur, and R.E. Gur, U54HD090260 to Drs. Diacou, Guo, Morrow, and Zhao, UO1-MH191719 and R01 MH087636-01A1 to Drs. Crowley, Emanuel, Jin, Lilley, McDonald-McGinn, McGinn, E.M. Moss, Salmons, Sharkus, Tran, Unolt, and Zackai, Van de Werf fund for cardiovascular research to Dr. Breckpot, IWT 131625 to Dr. Demaerel, and Wellcome Trust grant 102428/Z/13/Z to Drs. Monfeuga and Williams. The authors thank all individuals with 22q11.2 deletion syndrome and their families for their generous contributions to this and related research studies, and are grateful to support charities including Max Appeal, The 22Crew and Unique. This work included participants of the NCMH study, a collaboration between Cardiff, Swansea and Bangor Universities. We thank the following members of the Division of Psychological Medicine and Clinical Neurosciences at the Cardiff School of Medicine for technical assistance: S. Chawner, A. Cunningham, C. Eaton, A. Evans, S. Morrison, H. Moulding, P. Birch, M. Tong, S. Bowen, L. Benger, M. Thomas, L. Reed, C.T. Carmichael, L. Bates, B. Lugonja, J. Morgan, N. Vinh, A. Evans, L. Hopkins, L. Tram, S. Jacques, S. Lewis.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

Authors


Tomas Ekeberg
email: ekeberg@xray.bmc.uu.se

Martin Kauffmann

Marvin Seibert

Chantal Abergel

Filipe Maia

Virginie Seltzer

Daniel Deponte

Andrew Aquila

Andreas Andreasson

Bianca Iwan

Single-shot diffraction data from the Mimivirus particle using an X-ray free-electron laser

Background & Summary

Free-electron lasers (FEL) provide ultra short and extremely bright pulses of coherent X-rays [START_REF] Emma | First lasing and operation of an ångström-wavelength free-electron laser[END_REF] . It has been predicted that such pulses could enable structure determination without crystallization by outrunning radiation damage and thus capturing diffraction data before the particle has time to respond and eventually be destroyed by the deposited energy [START_REF] Neutze | Potential for biomolecular imaging with femtosecond X-ray pulses[END_REF] . Experimental verification of this 'diffraction-beforedestruction' principle has been demonstrated several times for resolution down to 10 nm (refs 3,4).

Using many such diffraction patterns from multiple copies of a reproducible sample, the patterns could be assembled into a 3D diffraction space from which the 3D structure could be derived 5,6 . This promise was a main part of the scientific case for building free-electron lasers [START_REF] Galayda | LCLS The First Experiments[END_REF] . Several examples of 2D reconstructions from biological samples at X-ray FEL have been demonstrated 4,[START_REF] Hantke | High-throughput imaging of heterogeneous cell organelles with an X-ray laser[END_REF][START_REF] Van Der Schot | Imaging single cells in a beam of live cyanobacteria with an X-ray laser[END_REF] but 3D reconstructions have remained elusive.

A single diffraction pattern represents a curved slice through the Fourier transform of the electron density of the object. For successful 3D reconstruction many diffraction patterns from identical particles need to be assembled into the complete 3D Fourier transform of the particle. This is difficult since the orientation of the injected particles is unknown and has to be recovered from the diffraction data alone. A recent paper 10 demonstrates this, using a modified version of the expand, maximize and compress algorithm 5 (EMC) on the Mimivirus particle. Here we describe the data collection, data preprocessing and the dataset that was used for this reconstruction.

Today, beam time at free-electron lasers is scarce as there are few facilities and they serve a multitude of scientific disciplines. Furthermore, applications such as 3D imaging require a large amount of effort in algorithm development and testing. Several groups around the world are active in this development but the majority of them don't have regular beam time access. This dataset can thus serve as a benchmark for algorithm testing and give many more groups access to experimental data.

For any new method, validation tools are of crucial importance. Therefore, together with the 3D reconstruction of the Mimivirus we also presented two new validation methods [START_REF] Ekeberg | Three-Dimensional Reconstruction of the Giant Mimivirus Particle with an X-Ray Free-Electron Laser[END_REF] . Further development of these methods, and the development of new ones will therefore benefit from being applied to this dataset in particular.

The sample in this dataset is the Mimivirus (Acanthamoeba polyphaga mimivirus) [START_REF] Raoult | The 1.2-megabase genome sequence of Mimivirus[END_REF]12 . Mimivirus is part of a recently discovered class of giant DNA viruses. Viral capsid is pseudo-icosahedral with a corner-to-corner diameter of 500 nm and a face-to-face diameter of 400 nm (ref. 13). The virus is covered by fibres with a length of 125 nm giving it a total diameter of 750 nm (ref. 14).

Methods

These methods were described in ref. 10. The description here is more detailed with regards to data collection and on-line data analysis.

Sample injection

Purified Mimivirus particles 15 were transferred into a volatile buffer (250 mM ammonium acetate, pH 7.5) and the suspension was aerosolized with helium in a gas dynamic nebulizer 16 . The aerosol of hydrated and adiabatically cooled particles entered a differentially pumped aerodynamic lens [START_REF] Bogan | Single particle X-ray diffractive imaging[END_REF] .

Data collection

Experiments were performed at the Atomic Molecular Optics (AMO) beam line [START_REF] Bostedt | Ultra-fast and ultra-intense X-ray sciences: first results from the Linac Coherent Light Source free-electron laser[END_REF] of the Linac Coherent Light Source (LCLS) hard X-ray laser 1 , using the CAMP [START_REF] Strüder | Large-format, high-speed, X-ray pnCCDs combined with electron and ion imaging spectrometers in a multipurpose chamber for experiments at 4th generation light sources[END_REF] instrument [START_REF] Yoon | Unsupervised classification of single-particle X-ray diffraction snapshots by spectral clustering[END_REF][START_REF] Kassemeyer | Femtosecond free-electron laser X-ray diffraction data sets for algorithm development[END_REF] . The experiment was part of an experiment running from June 17 to June 21 of 2010 with proposal number L150. Diffraction data were recorded on a pair of pnCCD detectors [START_REF] Strüder | Large-format, high-speed, X-ray pnCCDs combined with electron and ion imaging spectrometers in a multipurpose chamber for experiments at 4th generation light sources[END_REF] at a repetition rate of 60 Hz matching the repetition rate of LCLS. The two detectors were placed at a distance of 740 mm from the interaction region with a gap between them of 2.1 mm to let the direct beam through. The pixel size is 75 μm and each detector in the pair has 512 × 1,024 pixels giving the full setup a pixel count of 1,024 × 1,024 pixels.

The photon energy was 1.2 keV corresponding to a wavelength of 1.03 nm. At this wavelength the full-period resolution at the edge of the detector is 19.9 nm. The electron bunch used to create the X-ray pulse was 70 fs long (full duration at half maximum) and the X-ray pulse is believed to be shorter than this [START_REF] Young | Femtosecond electronic response of atoms to ultra-intense X-rays[END_REF] . The focus was ~10 μm (full width at half maximum) at the interaction point, giving a power density of ~3.4 10 15 W cm -2 or 10 12 photons per pulse.

The experiment was performed at a pressure of 10 -6 mbar to reduce background scattering. Some of the most important experiment parameters are summarized in Table 1.

On-line data analysis

On-line hit-identification provided real-time statistics that guided injector alignment and tuning. Hits with a high scattering strength were identified by counting the number of pixels that measured a value above a threshold. Diffraction patterns with more than 500 pixels with a value above 170 ADU were defined as a hit. See ref. 21 for a detailed description.

Data Records

Two datasets are provided: the full record of all collected data and a smaller preprocessed dataset. Both data sets are available in the same CXIDB entry (Data Citation 1).

Full data record

We provide all data collected from the Mimivirus at this beamtime before any preprocessing or sorting. This dataset is in the extended tagged container (XTC) file format. This can be converted to HDF5 format using programs such as CASS [START_REF] Foucar | CASS-CFEL-ASG software suite[END_REF] or Cheetah [START_REF] Barty | Cheetah: software for high-throughput reduction and analysis of serial femtosecond X-ray diffraction data[END_REF] . This conversion normally also involves preprocessing and the data is therefore provided in the untouched XTC format.

The record contains 19 LCLS runs. 14 of these had the sample injector and X-ray laser turned on while the remaining 5 runs only collected detector background noise. These so called 'dark runs' can be used for to better subtract the background from the actual data. Table 2 shows a list of all 19 runs. Initial hit finding showed that 0.3% of the frames contained diffraction that was stronger than the background. The rest were misses, i.e., frames that were read out when the pulse did not hit any particle. In addition to hits from single Mimivirus particles the hits also include droplets of buffer, clusters of viruses and a few particles that were injected earlier and had stayed in the injection system.

Preprocessed and filtered data

This dataset contains the 198 preprocessed (see ref. 19) diffraction patterns that were used in ref. 10 to recover the 3D structure of the Mimivirus particle. This data is in the CXIDB format described in ref. 25. The pixel values in the data are in arbitrary detector units (ADU). The conversion factor from ADU to number of photons is 7 ADU per photon. Some areas of the detector were unreadable and some scattering angles were not covered by the detector, such as the gap between the two detector halves that lets the direct beam through. These areas are identified by a mask entry in the CXIDB file format.

The data was filtered in three steps. (1) Hits were distinguished from blanks using methods described in refs 21 and 24. This yielded 1,600 hits. ( 2 Table 2. List of experimental runs. Runs labeled as dark had the X-ray beam turned off and are included to allow for detector calibration. The lack of good hits before run 90 was possibly fixed by a changed injection nozzle at this point.

particle were selected by hand. An icosahedral or pseudo-icosahedral particle will in most orientations yield a distinctive type of pattern showing six outwards-going streaks. This feature and particle size, determined from the fringe spacing, was used for this selection. (3) When the detector is exposed to high intensities, the intensity can spill over from a pixel to neighboring pixels. We start seeing these effects at intensities above 750 photons per pixel. In the final dataset, diffraction patterns suffering from this effect were filtered out, resulting in 198 unsaturated diffraction patterns. A subset of this dataset is shown in Fig. 1.

Technical Validation

3D reconstruction

The 198 diffraction patterns were successfully assembled in a 3D Fourier volume and subsequent phase retrieval provided the full 3D electron density of the virus with a full-period resolution of 125 nm [START_REF] Ekeberg | Three-Dimensional Reconstruction of the Giant Mimivirus Particle with an X-Ray Free-Electron Laser[END_REF] . This indicates that the Mimivirus is reproducible to at least this resolution.

Validation of the 3D reconstruction

In cryo-electron microscopy (cryo-EM), data is routinely split prior to analysis and the analysis is performed in parallel on the two sets [START_REF] Harauz | Exact filters for general geometry three dimensional reconstruction[END_REF] . Our first method for validating the 3D reconstruction is an adaptation of this technique. The diffraction patterns are randomly split in two sets of equal size. The recovery of the 3D alignment is performed independently on the two sets using the same parameters but independent random starting points. Phase retrieval is also performed independently using the same parameters. The standard practice in the field is to repeat the reconstruction at least 100 times and the results are then averaged to average out effects of the random starting point 4,[START_REF] Chapman | High-resolution ab initio three-dimensional X-ray diffraction microscopy[END_REF] .

The EMC algorithm recovers the relative orientation of the particles from the diffracted data alone but the arbitrary rotation of the entire system can be different comparing the two resulting 3D electron density maps. In order to compare the two recovered electron densities we therefore have to rotate one of the two data sets to best match the other. This is done using brute force by interpolating one of the maps at a regular array corresponding to the tested rotation. To compare the two maps, the Pearson correlation coefficient is calculated. The rotation with the highest Pearson correlation is assumed to correspond to the proper relative orientation.

The two aligned electron density maps are then compared using the Fourier shell correlation (FSC) [START_REF] Harauz | Exact filters for general geometry three dimensional reconstruction[END_REF] , which provides a measure of the similarity as a function of resolution. The threshold for what is regarded as an acceptable fit ranges between 0.14 and 0.5 in cryo-EM literature [START_REF] Rosenthal | Optimal Determination of Particle Orientation, Absolute Hand, and Contrast Loss in Single-particle Electron Cryomicroscopy[END_REF][START_REF] Böttcher | Determination of the fold of the core protein of hepatitis B virus by electron cryomicroscopy[END_REF] . In X-ray crystallography some Bragg spots are usually excluded from the analysis and instead that information is used to verify the recovered expected strength of the respective Bragg spot [START_REF] Brünger | Free R value: a novel statistical quantity for assessing the accuracy of crystal structures[END_REF] . Using the same idea, 10% of the diffraction patterns were selected to be used for validation only. In the EMC scheme these patterns are excluded from the analysis but are still compared to the recovered Fourier transform of the particle. The measure for determining whether the recovered model agrees with the excluded diffraction patterns is the likelihood function used internally in EMC.

The analogy with the R free value in crystallography should not be over emphasized. Bragg peaks are linearly independent parameters and ther is no suitable analogy in the continuous diffraction case. Furthermore, in the case described here, the process that is validated is that of pattern alignment and not phase retrieval. It is therefore natural to choose individual diffraction patterns as the information unit to exclude rather than i.e., individual pixels or regions of pixels.

These validation methods were previously described in ref. 10.

Usage Notes

Data was stored in the CXIDB [START_REF] Maia | The Coherent X-ray Imaging Data Bank[END_REF] data format which uses the HDF5 format. HDF5 files are readable in many computing environments including python using the h5py module and MATLAB using e.g., the h5read function. Convenient functions for accessing the CXIDB data file exist in the libspimage package for C and python [START_REF] Maia | the image reconstruction package for coherent X-ray diffractive imaging[END_REF] . For visualizing data the CXIDB file browser Owl (https://github.com/FilipeMaia/owl) is recommended.

820 1 Figure 1 .

 11 Figure 1. The first 24 of the 198 diffraction patterns in this dataset. The color scale is logarithmic and ranges from 1 to 820 photons per pixel. This is a modification of a figure previously presented in ref. 10.

Table 1 .

 1 ) 307 Diffraction patterns that correspond to a Mimivirus Summary of experimental parameters.

	Parameter

SCIENTIFIC DATA | 3:160060 | DOI: 10.1038/sdata.2016.60

Acknowledgements

This work was supported by the Swedish Research Council, the Knut and Alice Wallenberg Foundation, the Max Planck Society, the European Research Council, the Röntgen-Ångström Cluster, and Stiftelsen Olle Engkvist Byggmästare. Portions of this research were carried out at the Linac Coherent Light Source, a national user facility operated by Stanford University on behalf of the US Department of Energy, Office of Basic Energy Sciences. We are grateful to the scientific and technical staff of the LCLS for support.

Data Citation

1. Ekeberg, T. et al. Coherent X-ray Imaging Data Bank http://dx.doi.org/10.11577/1236752 (2015).

Author Contributions

Additional Information

Competing financial interests: The authors declare no competing financial interest.