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

G deletions or insertions of an entire genetic region

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not be able to identify rare or structural genetic variants (e.g., deletions or insertions of an entire genetic region) that are causal, but they may identify SNPs that are correlated with these unobserved variants. Second, the frequencies of many genetic variants vary systematically across environments. If those environmental factors are not accounted for in the association analyses, some of the associations found may be spurious. To use a well-known example (Lander & Schork 1994), any genetic variants common in people of Asian ancestries will be associated statistically with chopstick use, but these variants would not cause chopstick use; rather, these genetic variants and the outcome of chopstick use are both distributed unevenly among people with different ancestries. This is the problem of “population stratification” discussed in Appendix 1. GWAS researchers have a number of strategies for addressing the challenges posed by population stratification (see FAQs 2.4 & 3.5 and Appendix 1). Even in studies such as ours that attempt to address and correct for heterogeneity in genetic ancestry, allele frequencies may nonetheless vary systematically with environmental factors. For example, a genetic variant that is associated with improved educational outcomes in the parental generation may have downstream effects on parental income and other factors known to influence children’s educational outcomes (such as neighborhood characteristics). This same genetic variant is likely to be inherited by the children of these parents, creating a correlation between the presence of the genetic variant in a child’s genome and the extent to which the child was reared in an environment with specific characteristics. A recent study of Icelandic families showed that the parental allele that is not passed on to the parent’s offspring is still associated with the child’s educational attainment, suggesting that GWAS results for educational attainment partly
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6 represent these intergenerational pathways (Kong et al. 2018). Our sibling analyses yield results that are consistent with this conclusion (see FAQ 2.4). Third, variants’ effects on an outcome may be indirect, so a variant that may be “causal” in one environment may have a diminished effect or no effect at all in other environments. For example, the nicotinic acetylcholine receptor gene cluster on chromosome 15 is associated with lung cancer (Thorgeirsson et al. 2008; Amos et al. 2008; Hung et al. 2008). From this observation alone we cannot conclude that these genetic variants cause lung cancer through some direct biological mechanism. In fact, it is likely that these genetic variants increase lung cancer risk through their effects on smoking behavior. In a tobacco-free environment, it is plausible that many of the associations would be substantially weaker and perhaps disappear altogether. Thus, even if we have credible evidence that a specific association is not spurious, it is entirely possible that the genetic variant in question influences the outcome through channels that we, in common parlance, would label environmental (e.g., smoking). Nearly forty years ago, the sociologist
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