References
- Kraft P, Yen Y-C, Stram DO, et al. Exploiting gene-environment interaction to detect genetic associations. Hum Hered. 2007;63:111–119.
- Eley TC, Sugden K, Corsico A, et al. Gene–environment interaction analysis of serotonin system markers with adolescent depression. Mol Psychiatry. 2004;9(10):908–915.
- Chen R, Mias G, Li-Pook-Than J, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148(6):1293–1307.
- Yugi K, Kubota H, Hatano A, et al. How to reconstruct biochemical networks across multiple ‘omic’ layers. Trends Biotechnol. 2016;34(4):276–290.
- Baker SG. Identifying combinations of cancer markers for further study as triggers of early intervention. Biometrics. 2000;56(4):1082–1087.
- Hood L. Systems biology and p4 medicine: past, present, and future. Rambam Maimonides Med J. 2013;4(2):e0012–e0012.
- Brown SDM, Lad HV. The dark genome and pleiotropy: challenges for precision medicine. Mamm Genome. 2019;30(7–8):212–216.
- Braun E. The unforeseen challenge: from genotype-to-phenotype in cell populations. Rep Prog Phys. 2015;78:036602.
- The 1000 Genomes Project Consortium., Corresponding authors., Auton A, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.
- Otsuka F, Tarone RE, Cayeux S, et al. Use of lymphoblastoid cell lines to evaluate the hypersensitivity to ultraviolet radiation in Cockayne syndrome. J Invest Dermatol. 1984;82(5):480–484.
- Zhang X, Gierman HJ, Levy D, et al. Synthesis of 53 tissue and cell line expression QTL datasets reveals master eQTLs. BMC Genomics. 2014;15(1):532.
- York LM. Functional phenomics: an emerging field integrating high-throughput phenotyping, physiology, and bioinformatics. J Exp Bot. 2019;70(2):379–386.
- Houle D, Govindaraju DR, Omholt S. Phenomics: the next challenge. Nat Rev Genet. 2010;11(12):855–866.
- Tiwari P, Kutum R, Sethi T, et al. Recapitulation of ayurveda constitution types by machine learning of phenotypic traits. PLoS One. 2017;12(10):e0185380.
- Prasher B, Gibson G, Mukerji M. Genomic insights into ayurvedic and western approaches to personalized medicine. J Genet. 2016;95(1):209–228.
- Prasher B, Varma B, Kumar A, et al. Ayurgenomics for stratified medicine: TRISUTRA consortium initiative across ethnically and geographically diverse Indian populations. J Ethnopharmacol. 2017;197:274–293.
- Prasher B, Negi S, Aggarwal S, et al. Whole genome expression and biochemical correlates of extreme constitutional types defined in Ayurveda. J Transl Med. 2008;6(48):48.
- Govindaraj P, Nizamuddin S, Sharath A, et al. Genome-wide analysis correlates ayurveda prakriti. Sci Rep. 2015;5(15786). DOI:10.1038/srep15786.
- Chaudhari D, Dhotre D, Agarwal D, et al. Understanding the association between the human gut, oral and skin microbiome and the Ayurvedic concept of prakriti. J. Biosci. 2019;44(5). DOI:10.1007/s12038-019-9939-6.
- Rotti H, Mallya S, Kabekkodu SP, et al. DNA methylation analysis of phenotype specific stratified Indian population. J. Transl. Med. 2015;13(151). DOI:10.1186/s12967-015-0506-0.
- Aggarwal S, Negi S, Jha P, et al. EGLN1 involvement in high-altitude adaptation revealed through genetic analysis of extreme constitution types defined in Ayurveda. Proc Natl Acad Sci. 2010;107(44):18961.
- Aggarwal S, Gheware A, Agrawal A, et al. Combined genetic effects of EGLN1 and VWF modulate thrombotic outcome in hypoxia revealed by ayurgenomics approach. J. Transl. Med. 2015;13(184). DOI:10.1186/s12967-015-0542-9.
- B. AU–, A. AU–, F. AU-T. Chromosome preparation from cultured cells. J Vis Exp. 2014;e50203. DOI:10.3791/50203
- Quah BJC, Warren HS, Parish CR. Monitoring lymphocyte proliferation in vitro and in vivo with the intracellular fluorescent dye carboxyfluorescein diacetate succinimidyl ester. Nat. Protoc. 2007;2(9):2049–2056.
- Abbas T, Kutum R, Pandey R, et al. Genetic differences between extreme and composite constitution types from whole exome sequences reveal actionable variations. bioRxiv. 2020. DOI:10.1101/2020.04.24.059006. 04.24.059006.
- Mjelle R, Hegre SA, Aas PA, et al. Cell cycle regulation of human DNA repair and chromatin remodeling genes. DNA Repair (Amst). 2015;30:53–67.
- Bhattacharya S, Srinivasan K, Abdisalaam S, et al. RAD51 interconnects between DNA replication, DNA repair and immunity. Nucleic Acids Res. 2017;45(8):4590–4605.
- Zhivotovsky B, Orrenius S. Cell cycle and cell death in disease: past, present and future. J. Intern. Med. 2010;268(5):395–409.
- Boehm M, Nabel E. The cell cycle and cardiovascular diseases. Prog Cell Cycle Res. 2003;5:19–30.
- Balomenos D, Martínez-A C. Cell-cycle regulation in immunity, tolerance and autoimmunity. Immunol Today. 2000;21(11):551–555.
- Park DS, Morris EJ, Greene LA, et al. G1/S cell cycle blockers and inhibitors of cyclin-dependent kinases suppress camptothecin-induced neuronal apoptosis. J Neurosci. 1997;17(1256):1256–1270.
- Paul P, Iyer S, Nadella RK, et al. Lithium response in bipolar disorder correlates with improved cell viability of patient derived cell lines. Sci Rep. 2020;10(7428). DOI:10.1038/s41598-020-64202-1.
- Ashok A, Naaz S, Kota LN, et al. Does retinoic acid reverse cell cycle dysregulation in Alzheimer’s disease lymphocytes? Asian J Psychiatr. 2019;39:174–177.
- Sethi TP, Prasher B, Ayurgenomics: MM. Way of threading molecular variability for stratified medicine. ACS Chem. Biol. 2011;6(9):875–880.
- Giovanni A, Wirtz-Brugger F, Keramaris E, et al. Involvement of cell cycle elements, cyclin-dependent kinases, pRb, and E2F·DP, in B-amyloid-induced neuronal death. J Biol Chem. 1999;274(27):19011–19016.
- Schwarz T. UV light affects cell membrane and cytoplasmic targets. J Photochem Photobiol B. 1998;44(2):91–96.
- Wlaschek M, Tantcheva-Poór I, Naderi L et al. Solar UV irradiation and dermal photoaging. J Photochem Photobiol B . 2001;63:41–51.
- Ingram LO, Fisher WD. Stimulation of cell division by membrane-active agents. Biochem Biophys Res Commun. 1973;50:200–210.