No CrossRef data available.
Article contents
Mechanistic understanding of individual outcomes: Challenges and alternatives to genetic designs
Published online by Cambridge University Press: 11 September 2023
Abstract
I argue that advancing “second-generation” or mechanistic causal knowledge of individual outcomes requires a comprehensive research programme that uses a variety of different methods in addition to the ones described in the paper under discussion. I also highlight that environment-focused approaches can be as instrumental in identifying potential phenotypic causes as gene-focused approaches.
- Type
- Open Peer Commentary
- Information
- Copyright
- Copyright © The Author(s), 2023. Published by Cambridge University Press
References
Akimova, E. T., Breen, R., Brazel, D. M., & Mills, M. C. (2021). Gene–environment dependencies lead to collider bias in models with polygenic scores. Scientific Reports, 11(1), 1–9.CrossRefGoogle ScholarPubMed
Belsky, D. W., & Harden, K. P. (2019). Phenotypic annotation: Using polygenic scores to translate discoveries from genome-wide association studies from the top down. Current Directions in Psychological Science, 28(1), 82–90.CrossRefGoogle Scholar
Briley, D. A., Livengood, J., & Derringer, J. (2018). Behaviour genetic frameworks of causal reasoning for personality psychology. European Journal of Personality, 32(3), 202–220.CrossRefGoogle Scholar
Cesarini, D., & Visscher, P. M. (2017). Genetics and educational attainment. Npj Science of Learning, 2(1), 4.CrossRefGoogle ScholarPubMed
Eriksson, K., Lindvall, J., Helenius, O., & Ryve, A. (2021). Socioeconomic status as a multidimensional predictor of student achievement in 77 societies. Frontiers in Education, 6(6), 1–10.CrossRefGoogle Scholar
Freese, J. (2018). The arrival of social science genomics. Contemporary Sociology, 47(5), 524–536.CrossRefGoogle Scholar
Hackman, D. A., Gallop, R., Evans, G. W., & Farah, M. J. (2015). Socioeconomic status and executive function: Developmental trajectories and mediation. Developmental Science, 18(5), 686–702.CrossRefGoogle ScholarPubMed
Harden, K. P. (2021). The genetic lottery: Why DNA matters for social equality. Princeton University Press.Google Scholar
Kendler, K. S., Turkheimer, E., Ohlsson, H., Sundquist, J., & Sundquist, K. (2015). Family environment and the malleability of cognitive ability: A Swedish national home-reared and adopted-away cosibling control study. Proceedings of the National Academy of Sciences, 112(15), 4612–4617.CrossRefGoogle ScholarPubMed
Liu, H., & Guo, G. (2016). Opportunities and challenges of big data for the social sciences: The case of genomic data. Social Science Research, 59, 13–22.CrossRefGoogle ScholarPubMed
Pingault, J.-B., O'Reilly, P. F., Schoeler, T., Ploubidis, G. B., Rijsdijk, F., & Dudbridge, F. (2018). Using genetic data to strengthen causal inference in observational research. Nature Reviews Genetics, 19(9), 566–580.CrossRefGoogle ScholarPubMed
Tabery, J. (2014). Beyond versus: The struggle to understand the interaction of nature and nurture. MIT Press.CrossRefGoogle Scholar
Target article
Building causal knowledge in behavior genetics
Related commentaries (23)
A disanalogy with RCTs and its implications for second-generation causal knowledge
Addressing genetic essentialism: Sharpening context in behavior genetics
All that glisters is not gold: Genetics and social science
Behavior genetics and randomized controlled trials: A misleading analogy
Behavior genetics: Causality as a dialectical pursuit
Benefits of hereditarian insights for mate choice and parenting
Building causal knowledge in behavior genetics without racial/ethnic diversity will result in weak causal knowledge
Causal dispositionalism in behaviour genetics
Drowning in shallow causality
Extensions of the causal framework to Mendelian randomisation and gene–environment interaction
Genes, genomes, and developmental process
Genetics can inform causation, but the concepts and language we use matters
Genome-wide association study and the randomized controlled trial: A false equivalence
Human genomic data have different statistical properties than the data of randomised controlled trials
Mechanistic understanding of individual outcomes: Challenges and alternatives to genetic designs
Meeting counterfactual causality criteria is not the problem
On the big list of causes
Polygene risk scores and randomized experiments
Shallow versus deep genetic causes
The providential randomisation of genotypes
Theory matters for identifying a causal role for genetic factors in socioeconomic outcomes
When local causes are more explanatorily useful
Where not to look for targets of social reforms and interventions, according to behavioral genetics
Author response
Causal complexity in human research: On the shared challenges of behavior genetics, medical genetics, and environmentally oriented social science