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BMC Psychiatry

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Open Access 01-12-2020 | Schizophrenia | Research article

Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia

Authors: Sameer Sardaar, Bill Qi, Alexandre Dionne-Laporte, Guy. A. Rouleau, Reihaneh Rabbany, Yannis J. Trakadis

Published in: BMC Psychiatry | Issue 1/2020

Abstract

Background

Machine learning (ML) algorithms and methods offer great tools to analyze large complex genomic datasets. Our goal was to compare the genomic architecture of schizophrenia (SCZ) and autism spectrum disorder (ASD) using ML.

Methods

In this paper, we used regularized gradient boosted machines to analyze whole-exome sequencing (WES) data from individuals SCZ and ASD in order to identify important distinguishing genetic features. We further demonstrated a method of gene clustering to highlight which subsets of genes identified by the ML algorithm are mutated concurrently in affected individuals and are central to each disease (i.e., ASD vs. SCZ “hub” genes).

Results

In summary, after correcting for population structure, we found that SCZ and ASD cases could be successfully separated based on genetic information, with 86–88% accuracy on the testing dataset. Through bioinformatic analysis, we explored if combinations of genes concurrently mutated in patients with the same condition (“hub” genes) belong to specific pathways. Several themes were found to be associated with ASD, including calcium ion transmembrane transport, immune system/inflammation, synapse organization, and retinoid metabolic process. Moreover, ion transmembrane transport, neurotransmitter transport, and microtubule/cytoskeleton processes were highlighted for SCZ.

Conclusions

Our manuscript introduces a novel comparative approach for studying the genetic architecture of genetically related diseases with complex inheritance and highlights genetic similarities and differences between ASD and SCZ.

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Schaefer GB. Clinical genetic aspects of ASD spectrum disorders. Int J Mol Sci. 2016;17(2):180.PubMedCentralCrossRef

Griswold AJ, Dueker ND, Van Booven D, Rantus JA, Jaworski JM, Slifer SH, et al. Targeted massively parallel sequencing of autism spectrum disorder-associated genes in a case control cohort reveals rare loss-of-function risk variants. Mol Autism. 2015;6:43.PubMedPubMedCentralCrossRef

Iossifov I, O'Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515(7526):216–21.PubMedPubMedCentralCrossRef

Iossifov I, Ronemus M, Levy D, Wang Z, Hakker I, Rosenbaum J, et al. De novo gene disruptions in children on the autistic spectrum. Neuron. 2012;74(2):285–99.PubMedPubMedCentralCrossRef

Awadalla P, Gauthier J, Myers RA, Casals F, Hamdan FF, Griffing AR, et al. Direct measure of the de novo mutation rate in autism and schizophrenia cohorts. Am J Hum Genet. 2010;87(3):316–24.PubMedPubMedCentralCrossRef

Ronemus M, Iossifov I, Levy D, Wigler M. The role of de novo mutations in the genetics of autism spectrum disorders. Nat Rev Genet. 2014;15:133.PubMedCrossRef

Turner TN, Coe BP, Dickel DE, Hoekzema K, Nelson BJ, Zody MC, et al. Genomic patterns of de novo mutation in simplex autism. Cell. 2017;171(3):710–22.e12.PubMedPubMedCentralCrossRef

Sullivan PF, Kendler KS, Neale MC. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry. 2003;60(12):1187–92.PubMedCrossRef

Ripke S, O'Dushlaine C, Chambert K, Moran JL, Kahler AK, Akterin S, et al. Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat Genet. 2013;45(10):1150–9.PubMedPubMedCentralCrossRef

10.

Consortium SPG-WASG. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011;43(10):969–76.CrossRef

11.

Shi J, Levinson DF, Duan J, Sanders AR, Zheng Y, Pe'er I, et al. Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature. 2009;460(7256):753–7.PubMedPubMedCentralCrossRef

12.

Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D, et al. Common variants conferring risk of schizophrenia. Nature. 2009;460(7256):744–7.PubMedPubMedCentralCrossRef

13.

McCarthy SE, Gillis J, Kramer M, Lihm J, Yoon S, Berstein Y, et al. De novo mutations in schizophrenia implicate chromatin remodeling and support a genetic overlap with autism and intellectual disability. Mol Psychiatry. 2014;19:652.PubMedPubMedCentralCrossRef

14.

Xu B, Roos JL, Levy S, van Rensburg EJ, Gogos JA, Karayiorgou M. Strong association of de novo copy number mutations with sporadic schizophrenia. Nat Genet. 2008;40(7):880–5.PubMedCrossRef

15.

De Rubeis S, Buxbaum JD. Genetics and genomics of autism spectrum disorder: embracing complexity. Hum Mol Genet. 2015;24(R1):R24–31.PubMedPubMedCentralCrossRef

16.

Altmuller J, Palmer LJ, Fischer G, Scherb H, Wjst M. Genomewide scans of complex human diseases: true linkage is hard to find. Am J Hum Genet. 2001;69(5):936–50.PubMedPubMedCentralCrossRef

17.

Henriksen MG, Nordgaard J, Jansson LB. Genetics of schizophrenia: overview of methods, findings and limitations. Front Hum Neurosci. 2017;11:322.PubMedPubMedCentralCrossRef

18.

Ramaswami G, Geschwind DH. Genetics of autism spectrum disorder. Handb Clin Neurol. 2018;147:321–9. https://doi.org/10.1016/B978-0-444-63233-3.00021-X.CrossRef

19.

Risch NJ. Searching for genetic determinants in the new millennium. Nature. 2000;405:847.PubMedCrossRef

20.

Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30.PubMedPubMedCentralCrossRef

21.

Shalev-Shwartz S, Ben-David S. Understanding machine learning: from theory to algorithms. Cambridge: Cambridge University Press; 2014.CrossRef

22.

Friedman J, Hastie T, Tibshirani R. The elements of statistical learning: Springer series in statistics. New York; 2001. https://link.springer.com/book/10.1007/978-0-387-84858-7.

23.

Trakadis YJ, Sardaar S, Chen A, Fulginiti V, Krishnan A. Machine learning in schizophrenia genomics, a case-control study using 5,090 exomes. Am J Med Genet B Neuropsychiatr Genet. 2019;180(2):103–12. https://doi.org/10.1002/ajmg.b.32638. Epub 2018 Apr 28. .CrossRef

24.

Yu C, Arcos-Burgos M, Licinio J, Wong ML. A latent genetic subtype of major depression identified by whole-exome genotyping data in a Mexican-American cohort. Transl Psychiatry. 2017;7(5):e1134.PubMedPubMedCentralCrossRef

25.

Er F, Iscen P, Sahin S, Cinar N, Karsidag S, Goularas D. Distinguishing age-related cognitive decline from dementias: a study based on machine learning algorithms. J Clin Neurosci. 2017;42:186–92.PubMedCrossRef

26.

Gao F, Wang W, Tan M, Zhu L, Zhang Y, Fessler E, et al. DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis. 2019;8(9):44.PubMedPubMedCentralCrossRef

27.

Sun Y, Zhu S, Ma K, Liu W, Yue Y, Hu G, et al. Identification of 12 cancer types through genome deep learning. Sci Rep. 2019;9(1):17256.PubMedPubMedCentralCrossRef

28.

Jurmeister P, Schöler A, Arnold A, Klauschen F, Lenze D, Hummel M, et al. DNA methylation profiling reliably distinguishes pulmonary enteric adenocarcinoma from metastatic colorectal cancer. Mod Pathol. 2019;32(6):855–65.PubMedCrossRef

29.

Carroll LS, Owen MJ. Genetic overlap between autism, schizophrenia and bipolar disorder. Genome Med. 2009;1(10):102.PubMedPubMedCentralCrossRef

30.

Fischbach GD, Lord C. The Simons simplex collection: a resource for identification of autism genetic risk factors. Neuron. 2010;68(2):192–5.CrossRefPubMed

31.

Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–9.CrossRefPubMed

32.

Chen T, Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. San Francisco: ACM; 2016. p. 785–94.CrossRef

33.

Ward JH Jr. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963;58(301):236–44.CrossRef

34.

Chen T, He T, Benesty M, Khotilovich V. Package ‘xgboost’. R version 090; 2019.

35.

Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0--fundamental algorithms for scientific computing in Python. 2019. arXiv preprint arXiv:190710121.

36.

Hirokawa N, Tanaka Y. Kinesin superfamily proteins (KIFs): various functions and their relevance for important phenomena in life and diseases. Exp Cell Res. 2015;334(1):16–25.CrossRefPubMed

37.

Zhou R, Niwa S, Guillaud L, Tong Y, Hirokawa N. A molecular motor, KIF13A, controls anxiety by transporting the serotonin type 1A receptor. Cell Rep. 2013;3(2):509–19.CrossRefPubMed

38.

Delevoye C, Heiligenstein X, Ripoll L, Gilles-Marsens F, Dennis MK, Linares RA, et al. BLOC-1 brings together the actin and microtubule cytoskeletons to generate recycling endosomes. Curr Biol. 2016;26(1):1–13.CrossRefPubMed

39.

Tarabeux J, Champagne N, Brustein E, Hamdan FF, Gauthier J, Lapointe M, et al. De novo truncating mutation in Kinesin 17 associated with schizophrenia. Biol Psychiatry. 2010;68(7):649–56.CrossRefPubMed

40.

Forsingdal A, Fejgin K, Nielsen V, Werge T, Nielsen J. 15q13.3 homozygous knockout mouse model display epilepsy-, autism- and schizophrenia-related phenotypes. Transl Psychiatry. 2016;6(7):e860.PubMedPubMedCentralCrossRef

41.

Ionita-Laza I, Xu B, Makarov V, Buxbaum JD, Roos JL, Gogos JA, et al. Scan statistic-based analysis of exome sequencing data identifies FAN1 at 15q13.3 as a susceptibility gene for schizophrenia and autism. Proc Natl Acad Sci U S A. 2014;111(1):343–8.PubMedCrossRef

42.

Ge SX, Jung D. ShinyGO: a graphical enrichment tool for ani-mals and plants: bioRxiv; 2018.

43.

Breitenkamp AF, Matthes J, Herzig S. Voltage-gated calcium channels and autism Spectrum disorders. Curr Mol Pharmacol. 2015;8(2):123–32.PubMedCrossRef

44.

Eyles DW, Burne TH, McGrath JJ. Vitamin D, effects on brain development, adult brain function and the links between low levels of vitamin D and neuropsychiatric disease. Front Neuroendocrinol. 2013;34(1):47–64.PubMedCrossRef

45.

Pasca SP, Portmann T, Voineagu I, Yazawa M, Shcheglovitov A, Pasca AM, et al. Using iPSC-derived neurons to uncover cellular phenotypes associated with Timothy syndrome. Nat Med. 2011;17(12):1657–62.PubMedPubMedCentralCrossRef

46.

Gladysz D, Krzywdzinska A, Hozyasz KK. Immune abnormalities in autism spectrum disorder-could they hold promise for causative treatment? Mol Neurobiol. 2018;55(8):6387–435.PubMedPubMedCentralCrossRef

47.

Hughes HK, Mills Ko E, Rose D, Ashwood P. Immune dysfunction and autoimmunity as pathological mechanisms in autism spectrum disorders. Front Cell Neurosci. 2018;12:405.PubMedPubMedCentralCrossRef

48.

Kocovska E, Gaughran F, Krivoy A, Meier UC. Vitamin-D deficiency as a potential environmental risk factor in multiple sclerosis, schizophrenia, and autism. Front Psychiatry. 2017;8:47.PubMedPubMedCentralCrossRef

49.

Guang S, Pang N, Deng X, Yang L, He F, Wu L, et al. Synaptopathology involved in autism spectrum disorder. Front Cell Neurosci. 2018;12:470.PubMedPubMedCentralCrossRef

50.

Hutsler JJ, Zhang H. Increased dendritic spine densities on cortical projection neurons in autism spectrum disorders. Brain Res. 2010;1309:83–94.PubMedCrossRef

51.

Tang G, Gudsnuk K, Kuo SH, Cotrina ML, Rosoklija G, Sosunov A, et al. Loss of mTOR-dependent macroautophagy causes autistic-like synaptic pruning deficits. Neuron. 2014;83(5):1131–43.PubMedPubMedCentralCrossRef

52.

Zoghbi HY, Bear MF. Synaptic dysfunction in neurodevelopmental disorders associated with autism and intellectual disabilities. Cold Spring Harb Perspect Biol. 2012;4(3). https://doi.org/10.1101/cshperspect.a009886.PubMedPubMedCentralCrossRef

53.

Gupta S, Ellis SE, Ashar FN, Moes A, Bader JS, Zhan J, et al. Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat Commun. 2014;5:5748.PubMedCrossRef

54.

Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011;474(7351):380–4.PubMedPubMedCentralCrossRef

55.

Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science. 2018;359(6376):693–7.PubMedPubMedCentralCrossRef

56.

Lin YC, Frei JA, Kilander MB, Shen W, Blatt GJ. A subset of autism-associated genes regulate the structural stability of neurons. Front Cell Neurosci. 2016;10:263.PubMedPubMedCentral

57.

Borovac J, Bosch M, Okamoto K. Regulation of actin dynamics during structural plasticity of dendritic spines: signaling messengers and actin-binding proteins. Mol Cell Neurosci. 2018;91:122–30.PubMedCrossRef

58.

Maes M, Anderson G, Betancort Medina SR, Seo M, Ojala JO. Integrating autism spectrum disorder pathophysiology: mitochondria, vitamin A, CD38, oxytocin, serotonin and melatonergic alterations in the placenta and gut. Curr Pharm Des. 2019;25(41):4405–20. https://doi.org/10.2174/1381612825666191102165459.CrossRef

59.

Zhou W, Li S. Decreased levels of serum retinoic acid in chinese children with autism spectrum disorder. Psychiatry Res. 2018;269:469–73.PubMedCrossRef

60.

Carter MD, Shah CR, Muller CL, Crawley JN, Carneiro AM, Veenstra-VanderWeele J. Absence of preference for social novelty and increased grooming in integrin beta3 knockout mice: initial studies and future directions. Autism Res. 2011;4(1):57–67.PubMedPubMedCentralCrossRef

61.

Padmakumar M, Van Raes E, Van Geet C, Freson K. Blood platelet research in autism spectrum disorders: in search of biomarkers. Res Pract Thromb Haemost. 2019;3(4):566–77.PubMedPubMedCentralCrossRef

62.

Ritvo ER, Yuwiler A, Geller E, Ornitz EM, Saeger K, Plotkin S. Increased blood serotonin and platelets in early infantile autism. Arch Gen Psychiatry. 1970;23(6):566–72.PubMedCrossRef

63.

Bagni C, Zukin RS. A synaptic perspective of fragile X syndrome and autism spectrum disorders. Neuron. 2019;101(6):1070–88.PubMedCrossRef

64.

Danesi C, Keinanen K, Castren ML. Dysregulated Ca(2+)-permeable AMPA receptor signaling in neural progenitors modeling fragile X syndrome. Front Synaptic Neurosci. 2019;11:2.PubMedPubMedCentralCrossRef

65.

Herman AB, Vrakas CN, Ray M, Kelemen SE, Sweredoski MJ, Moradian A, et al. FXR1 is an IL-19-responsive RNA-binding protein that destabilizes pro-inflammatory transcripts in vascular smooth muscle cells. Cell Rep. 2018;24(5):1176–89.PubMedCrossRef

66.

Martinez-Cerdeno V. Dendrite and spine modifications in autism and related neurodevelopmental disorders in patients and animal models. Dev Neurobiol. 2017;77(4):393–404.PubMedCrossRef

67.

Telias M. Molecular mechanisms of synaptic dysregulation in fragile X syndrome and autism spectrum disorders. Front Mol Neurosci. 2019;12:51.PubMedPubMedCentralCrossRef

68.

Zhang Z, Marro SG, Zhang Y, Arendt KL, Patzke C, Zhou B, et al. The fragile X mutation impairs homeostatic plasticity in human neurons by blocking synaptic retinoic acid signaling. Sci Transl Med. 2018;10(452). https://doi.org/10.1126/scitranslmed.aar4338.PubMedPubMedCentralCrossRef

69.

Zhao X, Wang Y, Meng C, Fang N. FMRP regulates endothelial cell proliferation and angiogenesis via the miR-181a-CaM-CaMKII pathway. Cell Biol Int. 2018;42(10):1432–44.PubMedCrossRef

70.

Dean B. Understanding the pathology of schizophrenia: recent advances from the study of the molecular architecture of postmortem CNS tissue. Postgrad Med J. 2002;78(917):142.PubMedPubMedCentralCrossRef

71.

Kathryn MG, Anthony AG. The role of neurotransmitters in schizophrenia. Schizophrenia and psychotic spectrum disorders. Oxford: Oxford University Press; 2016. https://dx.doi.org/10.1093/med/9780199378067.003.0010.

72.

Brown AS, Borgmann-Winter K, Hahn CG, Role L, Talmage D, Gur R, et al. Increased stability of microtubules in cultured olfactory neuroepithelial cells from individuals with schizophrenia. Prog Neuro-Psychopharmacol Biol Psychiatry. 2014;48:252–8.CrossRef

73.

Datta SR, McQuillin A, Rizig M, Blaveri E, Thirumalai S, Kalsi G, et al. A threonine to isoleucine missense mutation in the pericentriolar material 1 gene is strongly associated with schizophrenia. Mol Psychiatry. 2010;15(6):615–28.CrossRefPubMed

74.

Gurling HM, Critchley H, Datta SR, McQuillin A, Blaveri E, Thirumalai S, et al. Genetic association and brain morphology studies and the chromosome 8p22 pericentriolar material 1 (PCM1) gene in susceptibility to schizophrenia. Arch Gen Psychiatry. 2006;63(8):844–54.PubMedPubMedCentralCrossRef

75.

Hamshere ML, Walters JT, Smith R, Richards AL, Green E, Grozeva D, et al. Genome-wide significant associations in schizophrenia to ITIH3/4, CACNA1C and SDCCAG8, and extensive replication of associations reported by the schizophrenia PGC. Mol Psychiatry. 2013;18(6):708–12.CrossRefPubMed

76.

Moehle MS, Webber PJ, Tse T, Sukar N, Standaert DG, DeSilva TM, et al. LRRK2 inhibition attenuates microglial inflammatory responses. J Neurosci. 2012;32(5):1602–11.PubMedPubMedCentralCrossRef

77.

Prabakaran S, Swatton JE, Ryan MM, Huffaker SJ, Huang JT, Griffin JL, et al. Mitochondrial dysfunction in schizophrenia: evidence for compromised brain metabolism and oxidative stress. Mol Psychiatry. 2004;9(7):684–97, 43.CrossRefPubMed

78.

Shimizu S, Matsuzaki S, Hattori T, Kumamoto N, Miyoshi K, Katayama T, et al. DISC1-kendrin interaction is involved in centrosomal microtubule network formation. Biochem Biophys Res Commun. 2008;377(4):1051–6.CrossRefPubMed

79.

Tzeng NS, Hsu YH, Ho SY, Kuo YC, Lee HC, Yin YJ, et al. Is schizophrenia associated with an increased risk of chronic kidney disease? A nationwide matched-cohort study. BMJ Open. 2015;5(1):e006777.PubMedPubMedCentralCrossRef

80.

Tzur Bitan D, Krieger I, Berkovitch A, Comaneshter D, Cohen A. Chronic kidney disease in adults with schizophrenia: a nationwide population-based study. Gen Hosp Psychiatry. 2019;58:1–6.PubMedCrossRef

81.

Cao H, Chen J, Meyer-Lindenberg A, Schwarz E. A polygenic score for schizophrenia predicts glycemic control. Transl Psychiatry. 2017;7(12):1295.PubMedPubMedCentralCrossRef

82.

Pillinger T, Beck K, Gobjila C, Donocik JG, Jauhar S, Howes OD. Impaired glucose homeostasis in first-episode schizophrenia: a systematic review and meta-analysis. JAMA Psychiatry. 2017;74(3):261–9.PubMedPubMedCentralCrossRef

83.

Greenhalgh AM, Gonzalez-Blanco L, Garcia-Rizo C, Fernandez-Egea E, Miller B, Arroyo MB, et al. Meta-analysis of glucose tolerance, insulin, and insulin resistance in antipsychotic-naïve patients with nonaffective psychosis. Schizophr Res. 2017;179:57–63.PubMedCrossRef

84.

Anney R, Klei L, Pinto D, Almeida J, Bacchelli E, Baird G, et al. Individual common variants exert weak effects on the risk for autism spectrum disorders. Hum Mol Genet. 2012;21(21):4781–92.PubMedPubMedCentralCrossRef

85.

de la Torre-Ubieta L, Won H, Stein JL, Geschwind DH. Advancing the understanding of autism disease mechanisms through genetics. Nat Med. 2016;22:345.PubMedPubMedCentralCrossRef

86.

Krumm N, O'Roak BJ, Karakoc E, Mohajeri K, Nelson B, Vives L, et al. Transmission disequilibrium of small CNVs in simplex autism. Am J Hum Genet. 2013;93(4):595–606.PubMedPubMedCentralCrossRef

87.

Steinberg S, de Jong S, Mattheisen M, Costas J, Demontis D, Jamain S, et al. Common variant at 16p11.2 conferring risk of psychosis. Mol Psychiatry. 2014;19(1):108–14.PubMedCrossRef

88.

Chang H, Li L, Li M, Xiao X. Rare and common variants at 16p11.2 are associated with schizophrenia. Schizophr Res. 2017;184:105–8.PubMedCrossRef

Title: Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia
Authors: Sameer Sardaar
Bill Qi
Alexandre Dionne-Laporte
Guy. A. Rouleau
Reihaneh Rabbany
Yannis J. Trakadis
Publication date: 01-12-2020
Publisher: BioMed Central
Keywords: Schizophrenia
Autism Spectrum Disorder
Published in: BMC Psychiatry / Issue 1/2020
Electronic ISSN: 1471-244X
DOI: https://doi.org/10.1186/s12888-020-02503-5

2024 ASCO Annual Meeting

Springer Medicine

Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia

Abstract

Background

Methods

Results

Conclusions

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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