Future Directions

  • Deborah FinkelEmail author
  • Chandra A. Reynolds
Part of the Advances in Behavior Genetics book series (AIBG, volume 1)


In this future directions chapter, we look back at how the field of behavior genetics of cognition has changed in the transition from the twentieth century and acknowledge the gains that have been made. Then, we look forward to identify issues that are still in need of attention or resolution and new directions that we feel the field is prepared to explore. Since Waldman’s (1997) review of the field, we have made considerable progress in the following areas: focus on specific cognitive abilities; incorporating extremes of cognitive function and environmental background in our studies; searching for specific genetic loci associated with specific cognitive abilities; incorporating measures of environments in genetically informative studies; investigating gene by environment correlation and interaction; using developmental behavioral genetic methods to examine cognitive change; and using behavior genetics as a tool for examining the construct validity of intelligence. The fact that each issue is represented in more than one chapter in this volume, usually many chapters, highlights the fundamental integration of issues and approaches that characterizes the field of behavior genetics today. Common threads in the calls for future work include molecular genetics, environmental specificity, cognitive ability phenotypes, a broadening of the concept of interplay between genes and environments, and the continuing need for both quantitative and molecular approaches.


Intelligence Quotient Cognitive Aging Behavior Genetic Intraindividual Variability Behavioral Genetic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors gratefully acknowledge that a portion of this work was supported by the National Institutes of Aging (R01 AG037985). We thank Irwin Waldman for his input on this chapter.


  1. Alarcon, M., Plomin, R., Fulker, D. W., Corley, R., & DeFries, J. C. (1999). Molarity not modularity: Multivariate genetic analysis of specific cognitive abilities in parents and their 16-year-old children in the Colorado Adoption Project. Cognitive Development, 14(1), 175–193. doi: Scholar
  2. Colom, R., & Thompson, P. M. (2011). Understanding human intelligence by imaging the brain. In T. Chamorro-Premuzic, S. von Stumm, & A. Furnham (Eds.), The Wiley-Blackwell handbook of individual differences (pp. 330–352). New York: Wiley-Blackwell.Google Scholar
  3. Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., et al. (2011). Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular Psychiatry, 16(10), 996–1005. doi:10.1038/mp.2011.85.PubMedCrossRefGoogle Scholar
  4. Davies, G., Harris, S. E., Reynolds, C. A., Payton, A., Knight, H. M., Liewald, D. C., et al. (2012). A genome-wide association study implicates the APOE locus in nonpathological cognitive ageing. Molecular Psychiatry. doi:10.1038/mp.2012.159.Google Scholar
  5. Daviss, B. (2005). Growing pains for metabolomics. The Scientist, 19, 25–28.Google Scholar
  6. Deary, I. J. (2012). Intelligence. Annual Review of Psychology, 63, 453–482. doi:10.1146/annurev-psych-120710-100353.PubMedCrossRefGoogle Scholar
  7. Elder, G. H. (1975). Age differentiation and life course. Annual Review of Sociology, 1, 65–190.CrossRefGoogle Scholar
  8. Deary, I. J., Yang, J., Davies, G., Harris, S. E., Tenesa, A., Liewald, D., Luciano, M., Lopez, L. M., Gow, A. J., Corley, J., Redmond, P., Fox, H. C., Rowe, S. J., Haggarty, P., McNeill, G., Goddard, M. E., Porteous, D. J., Whalley, L. J., Starr, J. M., & Visscher, P. M. (2012). Genetic contributions to stability and change in intelligence from childhood to old age. Nature, 482(7384), 212–215. doi:10.1038/nature10781Google Scholar
  9. Elder, G. H. (1998). The life course as developmental theory. Child development, 69, 1–12.PubMedGoogle Scholar
  10. Emery, C. F., Finkel, D., & Pedersen, N. L. (2012). Pulmonary function as a cause of cognitive aging. Psychological Science, 23(9), 1024–1032. doi:10.1177/0956797612439422.PubMedCrossRefGoogle Scholar
  11. Finkel, D., & Pedersen, N. L. (2012). Intra-individual variability in reaction time shares genetic variance with cognitive functioning in late adulthood. Paper presented at the annual meeting of the Gerontological Society of America, San Diego, CA.Google Scholar
  12. Finkel, D., & Reynolds, C. A. (2009). Behavioral genetic investigations of cognitive aging. Y.-K. Kim (Ed.), Handbook of behavior genetics (pp. 101–112). New York: Springer.CrossRefGoogle Scholar
  13. Finkel, D., Pedersen, N. L., McGue, M., & McClearn, G. E. (1995). Heritability of cognitive abilities in adult twins: comparison of Minnesota and Swedish data. Behavior Genetics, 25(5), 421–431.PubMedCrossRefGoogle Scholar
  14. Finkel, D., Pedersen, N. L., Plomin, R., & McClearn, G. E. (1998). Longitudinal and cross-sectional twin data on cognitive abilities in adulthood: The Swedish Adoption/Twin Study of Aging. Developmental Psychology, 34(6), 1400–1413.PubMedCrossRefGoogle Scholar
  15. Finkel, D., Reynolds, C. A., McArdle, J. J., & Pedersen, N. L. (2005). The longitudinal relationship between processing speed and cognitive ability: Genetic and environmental influences. Behavior Genetics, 35(5), 535–549. doi:10.1007/s10519-005-3281-5.PubMedCrossRefGoogle Scholar
  16. Finkel, D., Reynolds, C. A., Berg, S., & Pedersen, N. L. (2006). Surprising lack of sex differences in normal cognitive aging in twins. International Journal of Aging and Human Development, 62(4), 335–357.PubMedCrossRefGoogle Scholar
  17. Finkel, D., Reynolds, C. A., McArdle, J. J., & Pedersen, N. L. (2007). Cohort differences in trajectories of cognitive aging. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 62(5), 286–294.CrossRefGoogle Scholar
  18. Fulker, D. W., Cardon, L. R., DeFried, J. C., Kimberling, J. W., Pennington, B. F., & Smith, S. D. (1991). Multiple regression analysis of sib-pair data on reading to detect quantitative trait loci. Reading and Writing: An Interdisciplinary Journal, 3, 299–313.CrossRefGoogle Scholar
  19. Genin, E., Hannequin, D., Wallon, D., Sleegers, K., Hiltunen, M., Combarros, O., et al. (2011). APOE and Alzheimer disease: A major gene with semi-dominant inheritance. Molecular Psychiatry, 16(9), 903–907. doi:10.1038/mp.2011.52.PubMedCrossRefGoogle Scholar
  20. Gerstorf, D., Lovden, M., Rocke, C., Smith, J., & Lindenberger, U. (2007). Well-being affects changes in perceptual speed in advanced old age: Longitudinal evidence for a dynamic link. Developmental Psychology, 43(3), 705–718. doi:10.1037/0012-1649.43.3.705.PubMedCrossRefGoogle Scholar
  21. Gottlieb, G. (1991). Experiential canalization of behavioral development: Theory. Developmental Psychology, 27, 4–13.CrossRefGoogle Scholar
  22. Hanscombe, K. B., Trzaskowski, M., Haworth, C. M., Davis, O. S., Dale, P. S., & Plomin, R. (2012). Socioeconomic status (SES) and children’s intelligence (IQ): In a UK-representative sample SES moderates the environmental, not genetic, effect on IQ. PLoS ONE, 7(2), e30320. doi:10.1371/journal.pone.0030320.PubMedCrossRefGoogle Scholar
  23. Heath, A. C., Berg, K., Eaves, L. J., Solaas, M. H., Corey, L. A., Sundet, J., et al. (1985). Education policy and the heritability of educational attainment. Nature, 314(6013), 734–736.PubMedCrossRefGoogle Scholar
  24. Hebda-Bauer, E. K., Luo, J., Watson, S. J., & Akil, H. (2007). Female CREBalphadelta- deficient mice show earlier age-related cognitive deficits than males. Neuroscience, 150(2), 260–272. doi: 10.1016/j.neuroscience.2007.09.019.PubMedCrossRefGoogle Scholar
  25. Hultsch, D. F., et al. (2008). Intraindividual variability, cognition, and aging. F. I. M. Craik, & T. A. Salthouse (Eds.), The handbook of aging and cognition (pp. 491–556). New York: Psychology Press.Google Scholar
  26. Infurna, F. J., Gerstorf, D., Ryan, L. H., & Smith, J. (2011a). Dynamic links between memory and functional limitations in old age: Longitudinal evidence for age-based structural dynamics from the AHEAD study. Psychology and Aging, 26(3), 546–558. doi:10.1037/a0023023.CrossRefGoogle Scholar
  27. Infurna, F. J., Gerstorf, D., & Zarit, S. H. (2011b). Examining dynamic links between perceived control and health: Longitudinal evidence for differential effects in midlife and old age. Developmental Psychology, 47(1), 9–18. doi: 10.1037/a0021022.CrossRefGoogle Scholar
  28. Jarvik, L. F., & Bank, L. (1983). Aging twins: Longitudinal psychometric data. In K.W. Schaie (Ed.), Longitudinal twin studies of adult psychological development (pp. 40–63). New York: Guilford Press.Google Scholar
  29. Johnson, W., & Krueger, R. F. (2005). Higher perceived life control decreases genetic variance in physical health: Evidence from a national twin study. Journal of Personality and Social Psychology, 88(1), 165–173. doi:10.1037/0022-3514.88.1.165.PubMedCrossRefGoogle Scholar
  30. Li, W., van Tol, M. J., Li, M., Miao, W., Jiao, Y., Heinze, H. J., et al. (2012). Regional specificity of sex effects on subcortical volumes across the lifespan in healthy aging. Human Brain Mapping. doi:10.1002/hbm.22168.Google Scholar
  31. Ljungman, C. G. (1975). What is IQ? Intelligence, heredity, and environment. London: Gordon Cremonesi.Google Scholar
  32. Luo, D., Petrill, S. A., & Thompson, L. A. (1994). An exploration of genetic g: Hierarchical factor analysis of cognitive data from the Western Reserve Twin Project. Intelligence, 18(3), 335–347. doi: Scholar
  33. McArdle, J. J., & Hamagami, F. (2003). Structural equation models for evaluating dynamic concepts within longitudinal twin analyses. Behavior Genetics, 33(2), 137–159.PubMedCrossRefGoogle Scholar
  34. McArdle, J. J., Prescott, C. A., Hamagami, F., & Horn, J. L. (1998). A contemporary method for developmental-genetic analyses of age changes in intellectual abilities. Developmental Neuropsychology, 14, 69–114.CrossRefGoogle Scholar
  35. McGue, M. (1994).Why developmental psychology should find room for behavioral genetics. C. E. Nelson (Ed.), Threats to optimal development: Integrating biological, psychological, and social risk factors (pp. 105–149). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  36. Mizuno, K., & Giese, K. P. (2010). Towards a molecular understanding of sex differences in memory formation. Trends in Neurosciences, 33(6), 285–291. doi:10.1016/j.tins. 2010. 03.001.Google Scholar
  37. Neale, M. C., & Cardon, L. (1992). Methodology for genetic studies of twin and families. Boston: Kluwer.CrossRefGoogle Scholar
  38. Pedersen, N. L., Christensen, K., Dahl, A. K., Finkel, D., Franz, C. E., Gatz, M., et al. (2012). IGEMS: The Consortium on Interplay of Genes and Environment Across Multiple Studies. Twin Research and Human Genetics: The Official Journal of the International Society for Twin Studies, 16,1–9. doi:10.1017/thg.2012.110.Google Scholar
  39. Plomin, R. (1997). Identifying genes for cognitive abilities and disabilities. In R. J. Sternberg & E. Grigorenko (Eds.), Intelligence, heredity, and environment (pp. 89–104). Cambridge: Cambridge University Press.Google Scholar
  40. Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2013). Behavioral genetics (6th ed.). New York: Worth Publishers.Google Scholar
  41. Posthuma, D., de Geus, E. J., & Boomsma, D. I. (2001). Perceptual speed and IQ are associated through common genetic factors. Behavior Genetics, 31, 593–602.PubMedCrossRefGoogle Scholar
  42. Reynolds, C. A., Finkel, D., Gatz, M., & Pedersen, N. L. (2002). Sources of influence on rate of cognitive change over time in Swedish twins: An application of latent growth models. Experimental Aging Research, 28(4), 407–433.PubMedCrossRefGoogle Scholar
  43. Reynolds, C. A., Gatz, M., Prince, J. A., Berg, S., & Pedersen, N. L. (2010). Serum lipid levels and cognitive change in late life. Journal of the American Geriatrics Society, 58(3), 501–509. doi:10.1111/j.1532-5415.2010.02739.x.PubMedCrossRefGoogle Scholar
  44. Roberts, R. O., Geda, Y. E., Knopman, D. S., Cha, R. H., Pankratz, V. S., Boeve, B. F., et al. (2012). The incidence of MCI differs by subtype and is higher in men: The Mayo Clinic Study of Aging. Neurology, 78(5), 342–351. doi: 10.1212/WNL.0b013e3182452862.PubMedCrossRefGoogle Scholar
  45. Scarr, S., & McCartney, K. (1983). How people make their own environments: A theory of genotype-environment effects. Child Development, 54, 424–435.PubMedGoogle Scholar
  46. Schollgen, I., Huxhold, O., & Schmiedek, F. (2012). Emotions and physical health in the second half of life: Interindividual differences in age-related trajectories and dynamic associations according to socioeconomic status. Psychology and Aging, 27(2), 338–352. doi:10.1037/a0026115.PubMedCrossRefGoogle Scholar
  47. Shineman, D. W., Salthouse, T. A., Launer, L. J., Hof, P. R., Bartzokis, G., Kleiman, R., et al. (2010). Therapeutics for cognitive aging. Annals of the New York Academy of Sciences, 1191 (Suppl 1), E1–15. doi:10.1111/j.1749-6632.2010.05532.x.PubMedCrossRefGoogle Scholar
  48. Smith, S. D. (2010). Learning disabilities. In J. Nurnberger &W. Berrettini (Eds.), Psychiatric Genetics. Cambridge: Cambridge University Press.Google Scholar
  49. Sperling, R. A., Aisen, P. S., Beckett, L. A., Bennett, D. A., Craft, S., Fagan, A. M., et al. (2011). Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia: The Journal of the Alzheimer’s Association, 7(3), 280–292. doi:10.1016/j.jalz. 2011. 03.003.CrossRefGoogle Scholar
  50. Sternberg, R. J., & Grigorenko, E. (Eds.). (1997). Intelligence, heredity, and environment. Cambridge: Cambridge University Press.Google Scholar
  51. Tucker-Drob, E., Reynolds, C. A., Finkel, D., & Pedersen, N. L. (2013). Shared and unique genetic and environmental influences on changes in multiple cognitive abilities over 16 years of late adulthood. Developmental Psychology (in press).Google Scholar
  52. van der Sluis, S., Willemsen, G., de Geus, E. J., Boomsma, D. I., & Posthuma, D. (2008). Gene-environment interaction in adults’ IQ scores: Measures of past and present environment. Behavior Genetics, 38(4), 348–360. doi:10.1007/s10519-008-9212-5.PubMedCrossRefGoogle Scholar
  53. van Dongen, J., Slagboom, P. E., Draisma, H. H., Martin, N. G., & Boomsma, D. I. (2012). The continuing value of twin studies in the omics era. Nature Reviews. Genetics, 13(9), 640–653. doi:10.1038/nrg3243.PubMedCrossRefGoogle Scholar
  54. Vandenberg, S. G. (1968). The nature and nurture of intelligence. In D. C. Glass (Ed.), Biology and behavior: Genetics (pp. 3–58). New York: Russell Sage Foundation.Google Scholar
  55. Vernon, P. E. (1979). Intelligence: Heredity and environment. New York: W. H. Freeman.Google Scholar
  56. Waddington, C. H. (1942). Canalization of development and the inheritance of acquired characters. Nature, 150, 563–565.CrossRefGoogle Scholar
  57. Waldman, I. D. (1997). Unresolved questions and future directions in behavior-genetic studies of intelligence. In R. J. Sternberg & E. Grigorenko (Eds.), Intelligence, heredity, and environment (pp. 552–570). Cambridge: Cambridge University Press.Google Scholar
  58. Wilson, R. S. (1978). Synchronies in mental development: An epigenetic perspective. Science, 202, 939–948.PubMedCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC 2014

Authors and Affiliations

  1. 1.Department of Psychology, School of Social SciencesIndiana University SoutheastNew AlbanyUSA
  2. 2.Department of PsychologyUniversity of CaliforniaRiversideUSA

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