Additional file 12 of Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Additional file 12. Supplemental Acknowledgements. Members of the Psychiatric Genomics Consortium who contributed to this work

Additional file 5 of Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Additional file 5: Table S4: Identifying 16p11.2 and 22q11.2 cases from electronic health records (EHR). Keyword searches across all documents within the Vanderbilt EHR were performed to identify individuals carrying 16p11.2 or 22q11.2 CNVs. Individuals with documents containing matching keywords were reviewed manually to confirm the presence of 16p11.2 or 22q11.2 CNV. Individuals were excluded from case groups if their records included a mention of additional CNVs. Individuals within the 16p11.2 case groups were also excluded if the size of the reported CNV was 200-250 kb. Individuals within the 22q11.2 case group were excluded if the size of the CNV was smaller than 500 kb or if there was a mention of “distal” when referring to the deletion or duplication. Confirmed case numbers are listed, with the non-genotyped counts in parentheses. Non-genotyped individuals were used for downstream phenome-wide analyses

Additional file 1 of Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Additional file 1: Table S1. List of genotyped discovery and replication cohorts used in the study. List of datasets used for discovery and replication of association results with sample sizes. The specific cohorts from the Psychiatric Genomics Consortium that were used for this analysis are listed. All variables from the UK Biobank that were used for replication are shown

Additional file 11 of Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Additional file 11: Table S10. Enrichment of clinical categories among the top PheWAS associations. The top 15 traits (codes) for each gene analyzed (n = 1470 gene-trait pairs) were divided into 17 clinical categories (observed counts column). The values in the expected counts column are calculated as 1470 * {the proportion of traits of that category tested}. For example, 159 out of 1531 codes tested were from the “circulatory system” category, so the expected counts for “circulatory system” are calculated as 1470*159/1531. The last column contains the p-value from a binomial test comparing whether the observed proportion of clinical categories is more extreme than expected

Additional file 6 of Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Additional file 6: Table S5. Results of MultiXcan and S-MultiXcan associations between CNV genes and autism, schizophrenia, bipolar disorder, BMI, and IQ. For autism, bipolar disorder, and schizophrenia, z-scores and p-values come from a METAL meta-analysis across PGC cohorts. For BMI and IQ, mean z-scores and p-values come directly from S-MultiXcan output. Genes in each CNV are sorted by chromosomal position

Additional file 4 of Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Additional file 4: Table S3. Mendelian phenotypes annotated to 16p11.2 and 22q11.2 genes in PheWAS results. We compare Mendelian phenotypes annotated to 16p11.2 and 22q11.2 genes (as catalogued in OMIM) with our imputed gene expression PheWAS results. For each of the Mendelian traits, we list one or more related traits that were tested in PheWAS along with the p-value, selecting the trait(s) with the best p-value to represent. Traits that are in the top 1% of associations for individual genes are marked. This table is a proof-of-concept that our PheWAS approach can pick up known gene-phenotype associations but has not been quantified for enrichment due to the subjective nature of identifying related traits

Additional file 7 of Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Additional file 7: Table S6. Comparison of association results to independent data. For each gene-trait pair, we list the original p-value, the GWAS trait(s) that we classified as most similar to a PheWAS trait, its best p-value in an independent dataset, the number of GWAS datasets that were used for this trait, and the rank of this gene within that dataset. For UK Biobank summary statistics, we have genome-wide data; for datasets with individual-level data, only 16p11.2 and 22q11.2 genes were calculated. See Table S2 for more information on datasets used

Additional file 9 of Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Additional file 9: Table S8. Traits over-represented in CNV carriers. The four categories of CNV carrier – 16p11.2 duplication, 16p11.2 deletion, 22q11.2 duplication, 22q11.2 deletion – were tested separately. The results for all clinical traits tested are provided. The number of cases and controls for each trait is given, as well as whether the p-value meets either Bonferroni or FDR correction. Traits in bold were represented in over 5% of carriers

Additional file 8 of Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Additional file 8: Table S7. Conditional analysis for independence of associations. Conditional analysis was performed on the PGC schizophrenia data, the UK Biobank BMI data, as well as two BioVU clinical trait associations (16p11.2 genes and psychosis, 22q11.2 genes and morbid obesity). For each trait, we performed MultiXcan first adjusting for a specific gene, then by leaving a gene in and adjusting all the other genes associated with that trait out. The Pcond reported in the text is the p-value of this gene-trait pair when adjusting for all other genes considered for conditioning for this trait, unless otherwise stated

Additional file 3 of Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes

Additional file 3: Fig. S1-S6. Supplementary figures