Abstract
A recent series of papers in Nature Genetics described several combined Genome Wide Association Studies (GWAS) of height. Although height is a highly heritable trait that can be measured with near-perfect reliability, and the studies employed the latest SNP technology in massive samples numbering in the tens of thousands, only a handful of variants related to height were identified. The SNPs that were shown to be related to height collectively accounted for under 5% of the variation. The reasons for this surprising result are explored, focusing on the analogy between GWAS and the long-standing search for environmental causes of behavior, which I call EWAS, for Environment Wide Association Study. The problems of non-experimental causal inference faced by GWAS researchers were confronted long ago by mainstream social science, and were never fully overcome; they almost certainly cannot be fully overcome. Likewise, the statistical and methodological procedures that are employed in the genome project to control for multiple statistical tests and population stratification have been used in social science for decades. Understanding their successes and failures in that domain helps frame a reasonable set of expectations for the genomics of complex human characteristics.
Keywords
- Propensity Score
- Genome Wide Association Study
- Delinquent Behavior
- Population Stratification
- Causal Process
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.
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- 1.
In which the required significance level, usually p<.05, is divided by the total number of tests to be conducted in the experiment.
- 2.
The greatest proponent of such ideas was the great theoretical psychologist Paul Meehl. The interested reader is directed to his many papers on the subject, most especially, Meehl, 1978, which should be required reading for GWAS researchers.
- 3.
A latent variable is a hypothetical process that cannot be observed directly, but which serves to explain relationships that can be observed among actual measurements. If one observes that many aspects of deprived environments—crime, poor schools, inadequate nutrition, unstimulating surroundings—tend to co-occur, the latent variable poverty can be invoked to explain why. The relevant statistical procedure is known as factor analysis. See MacCorquodale and Meehl (1948), or for an accessible statistical treatment, Loehlin (1992).
- 4.
As was the case for within-family studies of the environment, however, the existence of within sib-pair genetic associations still do not prove a causal relationship between the gene and the outcome. There still might be uncontrolled confounds within pairs (one member might be sent to a Japanese school where chopstick use is encouraged, while the other goes to an American school). The within-pair association controls for a class of confounds that vary between sibling pairs, which is a big help but not a panacea for the shortcomings of non-experimental science.
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Turkheimer, E. (2012). Genome Wide Association Studies of Behavior are Social Science. In: Plaisance, K., Reydon, T. (eds) Philosophy of Behavioral Biology. Boston Studies in the Philosophy of Science, vol 282. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1951-4_3
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