Digenic analysis confirms known and uncovers novel schizophrenia risk genes
Main Article Content
Abstract
Based on an Ashkenazy Jewish case-control cohort for schizophrenia, we carried out (1) genetic association analysis for one variant at a time (GWAS) and (2) digenic analysis by comparing frequencies of genotype pairs between cases and controls. To control for genetic heterogeneity between sexes, we analyzed males and females separately. After pruning of variants in each of males and females, single-variant allelic analysis furnished 9 and 8 statistically significant variants in males and females, respectively, with 3 of these variants being significant in both males and females. Of the 14 distinct variants in males and females, 5 (36%) reside in genes. For digenic analysis, we evaluated all pairs of variants and, for a given variant pair, all nine genotype pairs. For each genotype pair, we applied the Fisher exact test to evaluate whether the given genotype pair was more frequent in cases than controls. We found 76 significant genotype pairs, comprising 36 distinct variants, 20 (56%) of which reside in genes, with many of which being known risk genes, thus lending credence to our approach.
Article Details
How to Cite
NING, Jian; WANG, Xingyu; OTT, Jurg.
Digenic analysis confirms known and uncovers novel schizophrenia risk genes.
Medical Research Archives, [S.l.], v. 12, n. 9, sep. 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5723>. Date accessed: 04 oct. 2024.
doi: https://doi.org/10.18103/mra.v12i9.5723.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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5. Lencz T, Yu J, Khan RR, et al. Novel ultra-rare exonic variants identified in a founder population implicate cadherins in schizophrenia. Neuron. 2021/05/05/ 2021;109(9):1465-1478.e4. doi:10.1016/j.neuron.2021.03.004
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7. Uffelmann E, Huang QQ, Munung NS, et al. Genome-wide association studies. Nature Reviews Methods Primers. 2021/08/26 2021;1(1):59. doi:10.1038/s43586-021-00056-9
8. Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. Sep 2008;83(3):311-21. doi:10.1016/j.ajhg.2008.06.024
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10. Nakamura T, Takata A. The molecular pathology of schizophrenia: an overview of existing knowledge and new directions for future research. Molecular Psychiatry. 2023/05/01 2023;28(5):1868-1889. doi:10.1038/s41380-023-02005-2
11. Ott J, Sun D. Multilocus association analysis under polygenic models. International journal of data mining and bioinformatics. 2012;6(5):482-9. doi:10.1504/IJDMB.2012.049302
12. Okazaki A, Ott J. Machine learning approaches to explore digenic inheritance. Trends Genet. Oct 2022;38(10):1013-1018. doi:10.1016/j.tig.2022.04.009
13. Zhang Q, Long Q, Ott J. AprioriGWAS, a new pattern mining strategy for detecting genetic variants associated with disease through interaction effects. Research Support, Non-U.S. Gov't. PLoS Comput Biol. Jun 2014;10(6):e1003627. doi:10.1371/journal.pcbi.1003627
14. Zhang Q, Bhatia M, Park T, Ott J. A multi-threaded approach to genotype pattern mining for detecting digenic disease genes. Front Genet. 2023;14:1222517. doi:10.3389/fgene.2023.1222517
15. Lencz T, Guha S, Liu C, et al. Genome-wide association study implicates NDST3 in schizophrenia and bipolar disorder. Nature communications. 2013/11/19 2013;4(1):2739. doi:10.1038/ncomms3739
16. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience. 2015;4:7. doi:10.1186/s13742-015-0047-8
17. Duncan LE, Ratanatharathorn A, Aiello AE, et al. Largest GWAS of PTSD (N=20,070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol Psychiatry. Mar 2018;23(3):666-673. doi:10.1038/mp.2017.77
18. Judson O. Dr. Tatiana's Sex Advice to All Creation: The Definitive Guide to the Evolutionary Biology of Sex. Holt Paperbacks; 2003:320.
19. Carlisle SM, Qin H, Hendrickson RC, et al. Sex-based differences in the activation of peripheral blood monocytes in early Parkinson disease. npj Parkinson's Disease. 2021/04/13 2021;7(1):36. doi:10.1038/s41531-021-00180-z
20. Koch E, Kauppi K, Chen C-H. Candidates for drug repurposing to address the cognitive symptoms in schizophrenia. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 2023/01/10/ 2023;120:110637. doi:10.1016/j.pnpbp.2022.110637
21. Hukic DS, Lavebratt C, Olsson E, et al. Troponin T levels associated with genetic variants in NOTCH2 and MTNR1B in women with psychosis. Psychiatry research. 2017/04// 2017;250:217-220. doi:10.1016/j.psychres.2017.01.030
22. Hajek C, Guo X, Yao J, et al. Coronary Heart Disease Genetic Risk Score Predicts Cardiovascular Disease Risk in Men, Not Women. Circulation: Genomic and Precision Medicine. 2018/10/01 2018;11(10):e002324. doi:10.1161/CIRCGEN.118.002324
23. Saha E, Ben Guebila M, Fanfani V, et al. Gene regulatory networks reveal sex difference in lung adenocarcinoma. Biology of Sex Differences. 2024/08/06 2024;15(1):62. doi:10.1186/s13293-024-00634-y
24. Hjorthøj C, Compton W, Starzer M, et al. Association between cannabis use disorder and schizophrenia stronger in young males than in females. Psychological Medicine. 2023;53(15):7322-7328. doi:10.1017/S0033291723000880
25. Zhao H, Mitra N, Kanetsky PA, Nathanson KL, Rebbeck TR. A practical approach to adjusting for population stratification in genome-wide association studies: principal components and propensity scores (PCAPS). Statistical Applications in Genetics and Molecular Biology. 2018;17(6)doi:10.1515/sagmb-2017-0054
26. Elhaik E. Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated. Scientific reports. 2022/08/29 2022;12(1):14683. doi:10.1038/s41598-022-14395-4
27. Grosjean I, Roméo B, Domdom M-A, et al. Autophagopathies: from autophagy gene polymorphisms to precision medicine for human diseases. Autophagy. 2022/11/02 2022;18(11):2519-2536. doi:10.1080/15548627.2022.2039994
28. Li R, Wang Q, Qiu Y, et al. A Potential Autophagy-Related Competing Endogenous RNA Network and Corresponding Diagnostic Efficacy in Schizophrenia. Front Psychiatry. 2021;12:628361. doi:10.3389/fpsyt.2021.628361
29. Sepúlveda D, Grunenwald F, Vidal A, et al. Insulin-like growth factor 2 and autophagy gene expression alteration arise as potential biomarkers in Parkinson’s disease. Scientific reports. 2022/02/07 2022;12(1):2038. doi:10.1038/s41598-022-05941-1
30. Tan Y, Zhu J, Hashimoto K. Autophagy-related gene model as a novel risk factor for schizophrenia. Translational Psychiatry. 2024/02/13 2024;14(1):94. doi:10.1038/s41398-024-02767-5
31. Huang C, Guo Y, Li W, et al. Association of the CDKAL1 gene polymorphism with gestational diabetes mellitus in Chinese women. BMJ Open Diabetes Res Care. Apr 2023;11(2). doi:10.1136/bmjdrc-2022-003164
32. Steinthorsdottir V, Thorleifsson G, Reynisdottir I, et al. A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nature Genetics. 2007/06/01 2007;39(6):770-775. doi:10.1038/ng2043
33. Yin XY, Chen P, Zhu HW, et al. The type 2 diabetes mellitus susceptibility gene CDKAL1 polymorphism is associated with depressive symptom in first-episode drug-naive schizophrenic patients. Human Psychopharmacology: Clinical and Experimental. 2021;36(5):e2790. doi:10.1002/hup.2790
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