How Genetics Methods Can Answer Intelligence Questions
Aimed at integrating cutting-edge psychological science into the classroom, columns about teaching Current Directions in Psychological Science offer advice and how-to guidance about teaching a particular area of research or topic in psychological science that has been the focus of an article in the APS journal Current Directions in Psychological Science.
Few research topics generate as much debate as intelligence. Some students wonder whether intelligence matters, whereas others view intelligence as the ultimate test of human value. But psychological scientists set aside their personal beliefs and opinions, focus on the evidence, and try to build new insights that can help us better understand ourselves, our fellows, and our community.
Lee and Morris (2025) show how breakthroughs in genetic research have three promising directions for the future of intelligence research. First, widespread adoption of genome-wide association study (GWAS) approaches has enabled researchers to establish strong causal inferences without using experimental procedures, by examining within- and between-family variation in specific sites within the genome (Tan et al., 2024). Second, Lee and Morris describe how GWAS approaches have helped to establish a causal relationship between brain volume and intelligence. Finally, Lee and Morris argue that GWAS analyses have shown that intelligence (g) is a valid and reliable psychological phenomenon rather than a statistical artifact.
To bring this cutting-edge research into the classroom, instructors can use the following activities. The activities illustrate concepts that all psychology students should understand: reliability and the distinction between correlation and causation. Instructors can use the activities in virtual or face-to-face classes.
Student Activity #1: To Predict Intelligence, Use Single Genes or Millions of Genes?
Introduce this activity as an imaginary role-playing challenge. Tell the class, “My job as a researcher is to get to know you. My conclusions will have consequences for your future, including your health, wealth, and educational opportunities.”
Next, divide the class into two large sections (Group A and Group B). Instructors say, “OK, now I have divided you into two sections. For members of Group A (point to them), I will base my entire impression of you on a single, 30-minute interview. That’s all you get.”
Instructors continue by saying, “Group B (point to them) will have a different assessment experience. I will base my impression of you on millions of observations. With your permission, I will assess you in as many different situations and with as many different people as possible.”
As a class, discuss how the different research procedures used to assess Group A and Group B members would affect the researchers’ impressions. Which approach would produce the best reliability, defined as the consistency or replicability of the measurement? Also, ask students to consider that they were placed into the two groups according to where they sat that particular day in class. How might something as random as their location relate to whether they received a basic (30-minute) or extensive (millions of observations) evaluation?
Using Lee and Morris (2025) as a framework, tell students that this activity illustrates breakthroughs in genetics research. In early research, scientists investigated how single genes predicted outcomes, including health, wealth, and educational achievements. Much as political parties identify single candidates to run for office, behavioral genetics researchers would attempt to identify a “candidate gene” to predict complex outcomes. Unfortunately, the traditional candidate-gene approach produced unreliable results (Munafò, 2006). Today’s researchers employ the GWAS design, which examines millions of sites in the human genome and their relationship to intelligence and psychopathology (Savage et al., 2018; Trubetskoy et al., 2022).
Focusing on intelligence research, ask students to consider whether candidate-gene or GWAS approaches would influence the research’s reliability. Also, ask students to consider how some people may not have access to GWAS measurements because they live in a poor (vs. rich) country. How might something as random as access to GWAS measurements affect intelligence research?
Student Activity #2: Might Your Hat Size May Predict Your Intelligence?
Have students consider whether their hat size—or, more formally, their head circumference—would predict their intelligence. Why might researchers hypothesize that head circumference predicts intelligence levels? And how might researchers determine whether such a relationship is merely a correlation or if it is causal? Using a free artificial intelligence large language model (such as ChatGPT, Perplexity, or Gemini) have students review the main reasons why correlation does not imply causation.
Lee and Morris (2025) summarize research that has shown that greater brain volume and head circumference predict greater intelligence. This research has also addressed the main drawbacks of correlation research, namely, third variable and directionality problems. For example, GWAS analyses have shown that third variables (such as the tendency for people with high intelligence to mate with taller people) explain the relationship between greater brain volume and higher intelligence. Although it’s impossible to manipulate brain volume experimentally, GWAS analyses offer the closest next step in establishing a causal relationship between brain volume and intelligence.
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Additional References
Munafò, M. R. (2006). Candidate gene studies in the 21st century: A meta-analysis, mediation, and moderation. Genes, Brain, and Behavior, 5, 3–8.
Savage, J. E., Jansen, P. R., Stringer, S., Watanabe, K., Bryois, J., de Leeuw, C. A., Nagel, M., Awasthi, S., Barr, P. B., Coleman, J. R. I., Grasby, K. L., Hammerschlag, A. R., Kaminski, J. A., Karlsson, R., Krapohl, E., Lam, M., Nygaard, M., Reynolds, C. A., Trampush, J. W., . . . Posthuma, D. (2018). Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nature Genetics, 50, 912–919.
Tan, T., Jayashankar, H., Guan, J., Nehzati, S. M., Mir, M., Bennett, M., Agerbo, E., Ahlskog, R., Anapaz, V. P. d. A., Åsvold, B. O., Benonisdottir, S., Bhatta, L., Boomsma, D. I., Brumpton, B., Campbell, A., Chabris, C. F., Cheesman, R., Chen, Z., China Kadoorie Biobank Collaborative Group, . . . Young, A. S. (2024). Family-GWAS reveals effects of environment and mating on genetic associations. medRxiv.
Trubetskoy, V., Pardiñas, A. F., Qi, T., Panagiotaropoulou, G., Awasthi, S., Bigdeli, T. B., Bryois, J., Chen, C.-Y., Dennison, C. A., Hall, L. S., Lam, M., Watanabe, K., Frei, O., Ge, T., Harwood, J. C., Koopmans, F., Magnusson, S., Richards, A. L., Sidorenko, J., . . . Schizophrenia Working Group of the Psychiatric Genomics Consortium. (2022). Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature, 604, 502–508.
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