1. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ. Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet (London, England). 2006;367(9524):1747-57.
2. Wittchen HU, Jacobi F, Rehm J, Gustavsson A, Svensson M, Jonsson B, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol. 2011;21(9):655-79.
3. Sullivan PF, Neale MC, Kendler KS. Genetic epidemiology of major depression: review and meta-analysis. The American journal of psychiatry. 2000;157(10):1552-62.
4. Health WFfM. DEPRESSION: A Global Crisis. 2012.
5. Lopresti AL, Maker GL, Hood SD, Drummond PD. A review of peripheral biomarkers in major depression: the potential of inflammatory and oxidative stress biomarkers. Prog Neuropsychopharmacol Biol Psychiatry. 2014;48:102-11.
6. Zheng H, Zheng P, Zhao LC, Jia JM, Tang SL, Xu PT, et al. Predictive diagnosis of major depression using NMR-based metabolomics and least-squares support vector machine. Clinica Chimica Acta. 2017;464:223-7.
7. Flint J, Kendler KS. The genetics of major depression. Neuron. 2014;81(3):484-503.
8. Howard DM, Adams MJ, Clarke TK, Hafferty JD, Gibson J, Shirali M, et al. Genome- wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nature neuroscience. 2019;22(3):343-52.
9. Hyde CL, Nagle MW, Tian C, Chen X, Paciga SA, Wendland JR, et al. Identification of 15 genetic loci associated with risk of major depression in individuals of European descent. Nature genetics. 2016;48(9):1031-6.
10. Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome- wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nature genetics. 2018;50(5):668-81.
11. Dudbridge F. Power and predictive accuracy of polygenic risk scores. PLoS genetics. 2013;9(3):e1003348.
12. Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748-52.
13. Colodro-Conde L, Couvy-Duchesne B, Zhu G, Coventry WL, Byrne EM, Gordon S, et al. A direct test of the diathesis–stress model for depression. Molecular psychiatry. 2018;23(7):1590.
14. Maier R, Moser G, Chen G-B, Ripke S, Absher D, Agartz I, et al. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. The American Journal of Human Genetics. 2015;96(2):283-94.
15. Ueki M, Tamiya G, Alzheimer's Disease Neuroimaging I. Smooth-Threshold Multivariate Genetic Prediction with Unbiased Model Selection. Genet Epidemiol. 2016;40(3):233-43.
16. Robinson MR, Kleinman A, Graff M, Vinkhuyzen AA, Couper D, Miller MB, et al. Genetic evidence of assortative mating in humans. Nature Human Behaviour. 2017;1(1):0016.
17. Moser G, Lee SH, Hayes BJ, Goddard ME, Wray NR, Visscher PM. Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS genetics. 2015;11(4):e1004969.
18. Kuriyama S, Yaegashi N, Nagami F, Arai T, Kawaguchi Y, Osumi N, et al. The Tohoku Medical Megabank Project: Design and Mission. Journal of epidemiology. 2016;26(9):493-511.
19. Zhang W, Ng HW, Shu M, Luo H, Su Z, Ge W, et al. Comparing genetic variants detected in the 1000 genomes project with SNPs determined by the International HapMap Consortium. Journal of genetics. 2015;94(4):731-40.
20. Delaneau O, Howie B, Cox AJ, Zagury JF, Marchini J. Haplotype estimation using sequencing reads. American journal of human genetics. 2013;93(4):687-96.
21. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS genetics. 2009;5(6):e1000529.
22. Nagasaki M, Yasuda J, Katsuoka F, Nariai N, Kojima K, Kawai Y, et al. Rare variant discovery by deep whole-genome sequencing of 1,070 Japanese individuals. Nature communications. 2015;6:8018.
23. Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68-74.
24. Radloff L. The CES-D scale: A self-report depression scale for research in the general population. Applied psychological measurement. 1977;1(3):385-401.
25. Wada K, Tanaka K, Theriault G, Satoh T, Mimura M, Miyaoka H, et al. Validity of the Center for Epidemiologic Studies Depression Scale as a screening instrument of major depressive disorder among Japanese workers. American journal of industrial medicine. 2007;50(1):8-12.
26. Hubert M, Vandervieren E. An adjusted boxplot for skewed distributions. Computational statistics & data analysis. 2008;52(12):5186-201.
27. Chatterjee N, Wheeler B, Sampson J, Hartge P, Chanock SJ, Park J-H. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nature genetics. 2013;45(4):400.
28. Momen M, Mehrgardi AA, Sheikhy A, Esmailizadeh A, Fozi MA, Kranis A, et al. A predictive assessment of genetic correlations between traits in chickens using markers. Genetics, selection, evolution : GSE. 2017;49(1):16.
29. Williams EJ. Regression analysis: wiley; 1959.
30. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. 2017.
31. Ueki M. A note on automatic variable selection using smooth-threshold estimating equations. Biometrika. 2009:1005-11.
32. Zou H. The adaptive lasso and its oracle properties. Journal of the American statistical association. 2006;101(476):1418-29.
33. Bühlmann P, Van De Geer S. Statistics for high-dimensional data: methods, theory and applications: Springer Science & Business Media; 2011.
34. Gregory KB, Wang D, McMahan CS. Adaptive elastic net for group testing. Biometrics. 2018.
35. Huang J, Ma SG, Zhang CH. Adaptive Lasso for Sparse High-Dimensional Regression Models. Statistica Sinica. 2008;18(4):1603-18.
36. van de Geer S, Bühlmann P, Zhou S. The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso). Electronic Journal of Statistics. 2011;5:688-749.
37. Patterson N, Price AL, Reich D. Population structure and eigenanalysis. PLoS genetics. 2006;2(12):e190.
38. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nature genetics. 2006;38(8):904-9.
39. de Los Campos G, Vazquez AI, Fernando R, Klimentidis YC, Sorensen D. Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS genetics. 2013;9(7):e1003608.
40. Nakaya N, Nakamura T, Tsuchiya N, Tsuji I, Hozawa A, Tomita H. The Association Between Medical Treatment of Physical Diseases and Psychological Distress After the Great East Japan Earthquake: The Shichigahama Health Promotion Project. Disaster Med Public. 2015;9(4):374-81.
41. Tsuchiya N, Nakaya N, Nakamura T, Narita A, Kogure M, Aida J, et al. Impact of social capital on psychological distress and interaction with house destruction and displacement after the Great East Japan Earthquake of 2011. Psychiatry Clin Neurosci. 2017;71(1):52-60.
42. Yoshida H, Kobayashi N, Honda N, Matsuoka H, Yamaguchi T, Homma H, et al. Post‐ traumatic growth of children affected by the Great East Japan Earthquake and their attitudes to memorial services and media coverage. Psychiatry and clinical neurosciences. 2016;70(5):193-201.
43. Cherlin S, Plant D, Taylor JC, Colombo M, Spiliopoulou A, Tzanis E, et al. Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data. Genet Epidemiol. 2018;42(8):754-71.
44. Kooperberg C, LeBlanc M, Obenchain V. Risk prediction using genome‐wide association studies. Genetic epidemiology. 2010;34(7):643-52.
45. Subramanian J, Simon R. Overfitting in prediction models - is it a problem only in high dimensions? Contemp Clin Trials. 2013;36(2):636-41.
46. Direk N, Williams S, Smith JA, Ripke S, Air T, Amare AT, et al. An analysis of two genome-wide association meta-analyses identifies a new locus for broad depression phenotype. Biological psychiatry. 2017;82(5):322-9.
47. Oquendo MA, Baca-Garcia E, Artes-Rodriguez A, Perez-Cruz F, Galfalvy HC, Blasco- Fontecilla H, et al. Machine learning and data mining: strategies for hypothesis generation. Mol Psychiatry. 2012;17(10):956-9.
48. Dailly E, Chenu F, Renard CE, Bourin M. Dopamine, depression and antidepressants. Fundam Clin Pharmacol. 2004;18(6):601-7.
49. Krystal JH, Sanacora G, Blumberg H, Anand A, Charney DS, Marek G, et al. Glutamate and GABA systems as targets for novel antidepressant and mood-stabilizing treatments. Molecular Psychiatry. 2002;7(S1):S71-S80.
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