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Edinburgh Research Explorer Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data Citation for published version: 23 and Me Research Team 2018, 'Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data', American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, vol. 177, no. 7, pp. 641-657. https://doi.org/10.1002/ajmg.b.32652 Digital Object Identifier (DOI): 10.1002/ajmg.b.32652 Link: Link to publication record in Edinburgh Research Explorer Document Version: Peer reviewed version Published In: American Journal of Medical Genetics Part B: Neuropsychiatric Genetics General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 11. Oct. 2021 American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Genetic correlations among psychiatric and immune-related phenotypes based on genome-wide association data. Journal: American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Manuscript ForID NPG-18-0024 Peer Review Wiley - Manuscript type: Research Article Date Submitted by the Author: 20-Feb-2018 Complete List of Authors: Tylee, Daniel; SUNY Upstate Medical University, Deprtment of Psychiatry and Behavioral Sciences Sun, Jiayin; SUNY Upstate Medical University, Deprtment of Psychiatry and Behavioral Sciences Hess, Jonathan; SUNY Upstate Medical University, Neuroscience & Physiology Tahir, Muhammad; SUNY Upstate Medical University, Neuroscience & Physiology Sharma, Esha; SUNY Upstate Medical University, Neuroscience & Physiology Malik, Rainer; Klinikum der Universität München, Ludwig-Maximilians- University Worrall, Bradford; University of Virginia School of Medicine Levine, Andrew; University of California Los Angeles, Neurology Martinson, Jeremy; University of Pittsburgh Nejentsev, Sergey; University of Cambridge Speed, Doug; University College London Fischer, Annegret; Kiel University Mick, Erick; University of Massachusetts Medical School Walker, Brian; University of Arkansas for Medical Sciences Crawford, Andrew; University of Bristol Grant, Struan; University of Pennsylvania Perelman School of Medicine Polychronakos, Constantin; The McGill University Health Center, Departments of Pediatrics and Human Genetics Bradfield, Jonathan; Children's Hospital of Philadelphia, Sleiman, Patrick; University of Pennsylvania Perelman School of Medicine Hakonarson, Hakon; Children's Hospital of Philadelphia, Ellinghaus, Eva; Kiel University Elder, James; University of Michigan Tsoi, Lam; University of Michigan Trembath, Richard; King's College London Barker, Jonathan; King's College London Franke, Andre; University of Kiel Dehghan, Abbas; Erasmus University Medical Center Faraone, Stephen; SUNY Upstate Medical University, Psychaitry Glatt, Stephen; Neuropsychiatric Genetics, SUNY Keywords: genome-wide association, genetic correlation, pleiotropy John Wiley & Sons, Inc. Page 1 of 414 American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Peer Review 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 John Wiley & Sons, Inc. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Page 2 of 414 1 2 3 4 5 6 7 Reviewer Comments to Author: 8 9 Reviewer: 1 10 11 Reviewer Comment 1: “..the datasets used to obtain several of the key findings (ulcerative colitis and Crohn's disease, 12 from Liu et al) is a trans-ethnic, multi-platform expansion of the dataset from the IBD consortium. Correctly, the authors 13 (based on the sample numbers) appear to have removed the Immunochip and non-European samples, to avoid bias in the 14 correlations. However, the remaining GWAS samples form a subset of the samples in a previous publication, Jostins L et 15 al, 2012, and which have been previously analyzed in Bulik-Sullivan B et al, 2015 (and shown to not correlate with the 16 same bipolar and schizophrenia datasets which are reported as significantly correlated here). To their credit, the authors 17 discuss the issue (in relation to the different filtering scheme used in this paper), but nevertheless using a less-stringent 18 filtering approach, to analyze a subset of a previously non-correlating dataset, to arrive at significant correlations, raises 19 serious questions regarding the reliabilityFor of those Peer results. At Review a minimum, it would be required to see either a similarly 20 significant result using the larger dataset from Jostins L et al 2012, and preferably also a demonstration that the Liu et al 21 2015 dataset produces this significant correlation without extra filtering.” 22 23 Author Response 1: The reviewer raises an excellent point; our findings appear to be discrepant with other 24 reports, and that deserves a more thorough investigation. We’ve made major revisions to address this concern. 25 We obtained multiple versions of each data set and processed them in various ways to explore the effects on LD- 26 27 score regression findings. Specifically, we examined the following data sets: 28 29 Bipolar Sklar et al., 2011, N=16k, 1KGv3_MAF>0.05 30 Bipolar Sklar et al., 2011, N=16k, INFO > 0.9 31 Bipolar Hou et al., 2016, N= 40k, 1KGv3_MAF>0.05 32 Bipolar Hou et al., 2016, N= 40k, All SNPS (No INFO available) 33 PGC Schizophrenia European = 75k, 1KGv3_MAF>0.05 34 PGC Schizophrenia European = 75k, INFO > 0.9 35 Crohn’s Disease, Frank, N=24K, 1KGv3_MAF>0.05 36 Crohn’s Disease, Frank, N=24K, 1KGv3_MAF>0.05 37 Crohn’s Disease, Liu, N=21K, 1KGv3_MAF>0.05 38 Crohn’s Disease, Liu, N=21K, INFO > 0.9 39 [Jostins et al., does not provide full summary data, so is not analyzed in our study] 40 Ulcerative Colitis, Andersen, N=21K, 1KGv3_MAF>0.05 41 Ulcerative Colitis, Andersen, N=21K, All SNPS (No INFO available) 42 Ulcerative Colitis, Liu, N=27K, 1KGv3_MAF>0.05 43 Ulcerative Colitis, Liu, N=27K, All SNPS (No INFO available) 44 45 We compared the correlations among these different versions of the data. The findings are depicted in 46 47 Supplementary Figures 3 and 4 [embedded below]. We describe these analyses and the pattern of results in the 48 Discussion section of the paper [excerpted below]. We demonstrate that different patterns of pre-filtering alter the 49 results, such that correlations with schizophrenia and bipolar (for Sklar et al.,) generally become less significant 50 when relatively rare variants (below MAF 5%) are included in the analyses. There is evidence from another 51 genetic correlation study, using a similar method, that differences in magnitude and significance (and in rare 52 cases, even the directionality) of the correlation can change when the analyses are stratified by MAF 53 (https://www.ncbi.nlm.nih.gov/pubmed/29220677). For our study, we chose to exclude relatively rare variation. 54 We discuss this as a limitation as follows: 55 56 57 58 Stephen J. Glatt, Ph.D. 750 East Adams Street 59 Syracuse, NY 13210, U.S.A. Director, Psychiatric Genetic Epidemiology & Neurobiology LaboratoryJohn (PsychGENe Wiley & Sons, Lab) Inc. E-mail: [email protected] 60 Associate Professor, Departments of Psychiatry and Behavioral Sciences & Neuroscience and Physiology Facsimile: (315) 464-7744 Associate Director, Medical Genetics Research Center Telephone: (315) 464-7742 Page 3 of 414 American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Peer Review 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 John Wiley & Sons, Inc. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Page 4 of 414 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 For Peer Review 20 21 22 23 24 25 26 27 28 29 Response 1 Continued [Excerpt from Discussion]: “The LDSC approach featured here attempts to quantitate 30 similarities and differences in association signals across the entire genome. Some of our phenotype pairs have 31 been examined previously using genome-wide assessment methods, yielding apparently contradictory 32 findings.24,25,71 For example, a previous study implementing a REML-based approach did not find significant 33 SNP-based co-heritabilities between CD and the major psychiatric phenotypes.105 Additionally, the first study 34 implementing the LDSC method found no significant correlation (rg = 0.08 ± 0.08, uncorrected p = 0.33) between 35 BD and UC;22 this study used a smaller data set for BD (Sklar et al., 2011; N = 16,731) and a different version of 36 the UC data set (reported as Jostins et al.,2012; N = 27,432). A similar non-correlation is also reported in LD- 37 Hub (http://ldsc.broadinstitute.org/), using what appears to be the same data sets, although referencing a related 38 article (Liu et al., 2015; N = 27,432). The analyses portrayed in our main text utilized a larger BD data set (Hou 39 et al., N = 40,225), the same data set for UC (Liu et al., 2015; N = 27,432), and uniform criteria for SNP retention 40 based on inclusion in the HapMap3 panel and MAF > 5 % within the 1000 Genomes Project Phase 3 European 41 samples.
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  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase