An investigation into DNA methylation patterns associated with risk preference in older individuals

References

• Reiss D, Leve LD, Neiderhiser JM. How genes and the social environment moderate each other. Am J Public Heal. 2013;103(S1):S111–S121.
   Google Scholar
• Savage JE, et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat Gen. 2018;50(7):912–919.
   Google Scholar
• Uddin M, Ratanatharathorn A, Armstrong D, et al. Epigenetic meta-analysis across three civilian cohorts identifies NRG1 and HGS as blood-based biomarkers for post-traumatic stress disorder. Epigenomics. 2018;10(12):1585–1601.
   Google Scholar
• Agha G, Mendelson MM, Ward-Caviness CK, et al. Blood leukocyte DNA methylation predicts risk of future myocardial infarction and coronary heart disease. Circulation. 2019;140(8):645–657.
   Google Scholar
• Rietveld CA, Cesarini D, Benjamin DJ, et al. Molecular genetics and subjective well-being. Proc Natl Acad Sci. 2013;110(24):9692–9697.
   Google Scholar
• Benjamin DJ, Cesarini D, van der Loos MJHM, et al. The genetic architecture of economic and political preferences. Proc Natl Acad Sci. 2012;109(21):8026–8031.
   Google Scholar
• Horvath P, Zuckerman M. Sensation seeking, risk appraisal, and risky behavior. Pers Individ Differ. 1993;14(1):41–52.
   Google Scholar
• Ekpenyong NS, Aakpege YN. Alcohol consumption pattern and risky behaviour: a study of University Of Port Harcourt. IOSR-JHSS. 2014;19(3):25–32.
   Google Scholar
• Lundborg P, Lindgren B. Do they know what they are doing? Risk perceptions and smoking behaviour among Swedish teenagers. J Risk Uncertain. 2004;28(3):261–286.
   Google Scholar
• Szyf M. The dialogue between social environments and the genome. Am J Public Heal. 2013;103:S1–S11.
   Google Scholar
• Boardman JD, Daw J, Freese J. Defining the environment in gene-environment research: lessons from social epidemiology. Am J Public Heal. 2013;103:S63–S72.
   Google Scholar
• Portela A, Esteller M. Epigenetic modifications and human disease. Nat. Biotechnol. 2010;28(10):1057–1068.
   Google Scholar
• Notterman D, Mitchell. C. Epigenetics and understanding the impact of social determinants of health. Pediatr Clin North Am. 2015;62(5):1227–1240.
   Google Scholar
• Bush NR, Edgar RD, Park M, et al. The biological embedding of early-life socioeconomic status and family adversity in children’s genome-wide DNA methylation. Epigenomics. 2018;10(11):1445–1461.
   Google Scholar
• Cunliffe VT. The epigenetic impacts of social stress: how does social adversity become biologically embedded? Epigenomics. 2016;8(12):1653–1669.
   Google Scholar
• Moore LD, Le T, Fan G. DNA methylation and its basic function. Neuropsychopharmacology. 2013;38(1):22–38.
   Google Scholar
• Belsky DW. Life-course longitudinal studies are needed to advance integration of genomics and social epidemiology. Am J Epidemiol. 2018;187(1337):1337–1338.
   Google Scholar
• Burns F, Kee F. Methodology. In: Early Key Findings from a Study of Older People in Northern Ireland The NICOLA Study. 2017. p. 89–93. https://www.qub.ac.uk/sites/NICOLA/FileStore/Filetoupload,783215,en.pdf
   Google Scholar
• Barsky RB, Juster FT, Kimball MS, et al. Behavioral an experimental approach in the health and retirement study. Q J Econ. 1997;112(2):537–579.
   Google Scholar
• Charness G, Gneezy U, Imas A. Experimental methods: eliciting risk preferences. J Econ Behav Organ. 2013;87:43–51.
   Google Scholar
• Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics. 2012;13:86.
   Google Scholar
• Mansell G, Gorrie-Stone TJ, Bao Y, et al. Guidance for DNA methylation studies: statistical insights from the Illumina EPIC array. BMC Genomics. 2019;20(366). DOI:10.1186/s12864-019-5761-7.
   Google Scholar
• Andrews SV, Sheppard B, Windham GC, et al. Case-control meta-analysis of blood DNA methylation and autism spectrum disorder. Mol Autism. 2018;9(1). DOI:10.1186/s13229-018-0224-6.
   Google Scholar
• Graw S, Henn R, Thompson JA, et al. PwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS). BMC Bioinformatics. 2019;20(1). DOI:10.1186/s12859-019-2804-7
   Google Scholar
• Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009;4(1):44–57.
   Google Scholar
• Jafari M, Ansari-Pour N. Why, when and how to adjust your P values? Cell J. 2019;20:604–607.
   Google Scholar
• Pålsson AM. Does the degree of relative risk aversion vary with household characteristics? J Econ Psychol. 1996;17(6):771–787.
   Google Scholar
• Wang H, Hanna SD. Does risk tolerance decrease with age? JFCP. 1998;8(2):27–32.
   Google Scholar
• Boyle PA, Yu L, Buchman AS, et al. Risk aversion is associated with decision making among community-based older persons. Front Psychol. 2012;3:1–6.
   Google Scholar
• Hanna SD, Lindamood S. Risk tolerance of married couples in The Academy of Financial Services Meeting. 2005. p. 1–28. https://www.academia.edu/28816365/Risk_tolerance_of_married_couples
   Google Scholar
• Arano K, Parker C, Terry R. Gender-based risk aversion and retirement asset allocation. Econ Inq. 2010;48(1):147–155.
   Google Scholar
• Grazier S, Sloane PJ. Accident risk, gender, family status and occupational choice in the UK. Labour Econ. 2008;15(5):938–957.
   Google Scholar
• Jung S. Does education affect risk aversion? Evidence from the British education reform. Appl. Econ. 2015;47(28):2924–2938.
   Google Scholar
• König AN. Domain-specific risk attitudes and aging—a systematic review. J Behav Dec Mak. 2021;34:359–378.
   Google Scholar
• Donkers B, Melenberg B, Van Soest A. Estimating risk attitudes using lotteries: a large sample approach. J. Risk Uncertain. 2001;22(2):165–195.
   Google Scholar
• Hallahan T, Faff R, McKenzie M. An exploratory investigation of the relation between risk tolerance scores and demographic characteristics. J Multi Fin Manag. 2003;13:483–502.
   Google Scholar
• Yao R, Hanna SD. The effect of gender and marital status on financial risk tolerance. J Pers Financ. 2005;4:66–85.
   Google Scholar
• Bajtelsmit V, Bernasek A, Jianakoplos N. Gender differences in defined contribution pension decisions. Financ. Serv. Rev. 1999;8(1):1–10.
   Google Scholar
• Anderson LR, Mellor JM. Predicting health behaviors with an experimental measure of risk preference. J. Health Econ. 2008;27(5):1260–1274.
   Google Scholar
• James LM, Strom TQ, Leskela J. Risk-taking behaviors and impulsivity among veterans with and without PTSD and Mild TBI. Mil. Med. 2014;179(4):357–363.
   Google Scholar
• Reddy LF, Lee J, Davis MC, et al. Impulsivity and risk taking in bipolar disorder and schizophrenia. Neuropsychopharmacology. 2014;39(2):456–463. doi:10.1038/npp.2013.218
   Google Scholar
• Sørensen L, Sonuga-Barke E, Eichele H, van Wageningen H, Wollschlaeger D, Plessen KJ. Suboptimal decision making by children with ADHD in the face of risk: poor risk adjustment and delay aversion rather than general proneness to taking risks. Neuropsychology. 2017;31(2):119–128. doi:10.1037/neu0000297
   Google Scholar
• Whitton L, Cosgrove D, Clarkson C, et al. Cognitive analysis of schizophrenia risk genes that function as epigenetic regulators of gene expression. Am J Med Genet Part B Neuropsychiatr Genet. 2016;171:1170–1179. doi:10.1002/ajmg.b.32503
   Google Scholar
• Wray NR, Pergadia ML, Blackwood DHR, et al. Genome-wide association study of major depressive disorder: new results, meta-analysis, and lessons learned. Mol Psychiatry. 2012;17(1):36–48. doi:10.1038/mp.2010.109
   Google Scholar
• Wirgenes KV, Tesli M, Inderhaug E, et al. ANK3 gene expression in bipolar disorder and schizophrenia. Br J Psychiatry. 2014;205(3):244–245.
   Google Scholar
• Watanabe Y, Nunokawa A, Shibuya M, et al. Rare truncating variations and risk of schizophrenia: whole-exome sequencing in three families with affected siblings and a three-stage follow-up study in a Japanese population. Psychiatry Res. 2016;30(235):13–18.
   Google Scholar
• Ji W, Li T, Pan Y, et al. CNTNAP2 is significantly associated with schizophrenia and major depression in the Han Chinese population. Psychiatry Res. 2013;207(3):225–228.
   Google Scholar
• Vecchia ED, Mortimer N, Palladino VS, et al. Cross-species models of attention-deficit/hyperactivity disorder and autism spectrum disorder: lessons from CNTNAP2, ADGRL3, and PARK2. Psychiatr. Genet. 2019;29(1):1–17. doi:10.1016/j.psychres.2012.09.024
   Google Scholar
• Shinohara M, Tachibana M, Kanekiyo T, et al. Role of LRP1 in the pathogenesis of Alzheimer’s disease: evidence from clinical and preclinical studies. J. Lipid Res. 2017;58(7):1267–1281.
   Google Scholar
• Ram Murthy A, Purushottam M, Kiran Kumar HBG, et al. Gender-specific association of TSNAX/DISC1 locus for schizophrenia and bipolar affective disorder in South Indian population. J. Hum. Genet. 2012;57(8):523–530.
   Google Scholar
• Gelernter J, Kranzler HR, Sherva R, et al. Genome-wide association study of alcohol dependence: significant findings in African- and European-Americans including novel risk loci. Mol Psychiatry. 2014;77:493–503.
   Google Scholar
• Yu L, Chibnik LB, Yang J, et al. Methylation profiles in peripheral blood CD4+ lymphocytes versus brain: the relation to Alzheimer’s disease pathology. Alzheimer’s Dement. 2016;12(9):942–951.
   Google Scholar
• Walton E, Hass J, Liu J, et al. Correspondence of DNA methylation between blood and brain tissue and its application to schizophrenia research. Schizophr. Bull. 2016;42(2):406–414.
   Google Scholar
• Edgar RD, Jones MJ, Meaney MJ, et al. BECon: a tool for interpreting DNA methylation findings from blood in the context of brain.Transl Psychiatry. 2017;7(8):e1187.
   Google Scholar
• Abdullah M, Chai PS, Chong MY, et al. Gender effect on in vitro lymphocyte subset levels of healthy individuals. Cell. Immunol. 2012;272(2):214–219.
   Google Scholar
• Klein SL, Flanagan KL. Sex differences in immune responses. Nat. Rev. Immunol. 2016;16(10):626–638.
   Google Scholar
• Kaminsky Z, Petronis A, Wang SC, et al. Epigenetics of personality traits: an illustrative study of identical twins discordant for risk-taking behavior. Twin Res. Hum. Genet. 2008;11(1):1–11.
   Google Scholar
• Jung SE, Lim SM, Hong SR, Lee EH, Shin KJ, Lee HY. DNA methylation of the ELOVL2, FHL2, KLF14, C1orf132/MIR29B2C, and TRIM59 genes for age prediction from blood, saliva, and buccal swab samples. Forensic Sci Int Genet. 2019;38:1–8.
   Google Scholar
• Philibert R, Dogan M, Beach SRH, et al. AHRR methylation predicts smoking status and smoking intensity in both saliva and blood DNA. Am. J. Med. Genet. Part B Neuropsychiatr. Genet. 2020;183(1):51–60.
   Google Scholar
• Harrison S, Howe L, Davies AR (2020). Making sense of Mendelian randomisation and its use in public health research. Cardiff: Public Health Wales NHS Trust & Bristol University. I accessed the webpage on the 25th August 2021.
   Google Scholar
• Li Y, Woodberry R, Liu H, et al. Why are women more religious than men? Do risk preferences and genetic risk predispositions explain the gender gap? J. Sci. Study Relig. 2020;59(2):289–310.
   Google Scholar
• Chung E, Cromby J, Papadopoulos D, et al. Social epigenetics: a science of social science? Sociol Rev. 2016;64(1_suppl):168–185.
   Google Scholar