Reading material 2 - QA for the paper on polygenic prediction of educational attainment.pdf

Research like the current study using molecular

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Research (like the current study) using molecular genetic data—data that measures each person’s DNA and can be used to identify differences between people at the molecular level—has similarly found that common SNPs jointly predict up to 20% of variation across individuals (Rietveld et al. 2013). This predictive power may derive from many different types of mechanisms. For example, genetic variation may affect neural functions such as memory. Genetic variation may improve sleep quality (making it easier to subsequently stay awake in boring lectures). Genetic variation can affect personality traits, such as the willingness to listen politely to and follow the instructions of teachers (who aren’t always right but nevertheless dictate grades and other outcomes). There may also be even more convoluted pathways. For example, genetic variation can affect one’s sociability, which might draw someone into or drive someone out of the particular social environments that exist in higher education. In prior GWAS studies, researchers have observed that some genetic variants are associated with educational attainment. In the SSGAC’s first major publication (Rietveld et al. 2013), we conducted a GWAS in a sample of roughly 100,000 people and identified three genetic variants that were statistically associated with educational attainment. In 2016, the SSGAC conducted another GWAS of educational attainment, this time in a sample of around 300,000 people (Okbay, Beauchamp, et al. 2016). We found that 74 genetic variants were associated with educational attainment. These included the three genetic variants identified in our earlier study (Rietveld et al. 2013). Both of these studies involved, at the time they were conducted, the largest sample sizes ever studied for genetic associations with a social science outcome. There were three key takeaways from the SSGAC’s prior work: (1) A GWAS approach can identify specific genetic variants statistically associated with behavioral variables if the study is conducted in large enough samples (at least 100,000 people). (2) Genetic variants that are associated with a behavioral variable such as educational attainment are each likely to have less predictive power (i.e., a smaller effect size) than are genetic variants that are associated with a biomedical or other physical outcome (Chabris et al. 2015). For example, of the hundreds of genetics variants found to be associated with height to date (Wood et al., 2014), the genetic variant with the strongest association predicts 0.4% of the variation across individuals in height, whereas the genetic variant with the strongest association with educational attainment identified to date predicts less than one tenth (<0.04%) as much of the variation in educational attainment (Okbay, Beauchamp, et al. 2016). (The genetic variants that have not yet been identified will very likely explain less variance than those that are currently known, since statistical power is greatest for those that explain the most variance.) (3) In the samples studied, at least 20% of the variation in educational attainment is predicted by genetic variation (Rietveld et al. 2013), implying that the genetic associations with
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