This news story reports on this new article about smoking, obesity and genotype. The variant of interest, which causes smokers to smoke more heavily, was associated with increased BMI, but only in those who have never smoked. This seemed at first a surprising and intriguing new result, and the press release makes it sound really exciting as a paradigm for GxE interactions. All good.
Except that when I went to read the article I found that the estimated effect of this SNP in CHRNA5-A3-B4 is to reduce BMI by a whopping 0.74%. I'll bet that less than 1% of your BMI is around an amount of weight that you gain or lose over the course of a meal. The same SNP caused 0.35% higher BMI in never smokers, an effect half as large, and I seriously doubt that a third of a percent of a BMI point is even within the measurement error of the scales they used in the study (and certainly less than the normal weight variation across a typical day). The p-values here are small because the study is so enormous, but the effect sizes are so absurdly trivial that this could never have any observable impact on an individual level.
To be fair, if the estimated effect were huge, it would be implausible, so this is a finding that could really be true. By aside from what it might reveal about the underlying biology, it seems irrelevant. Moreover, when effects are this small, the tiniest violation (e.g. of their exclusion restriction assumption for their Mendelian randomization) could easily dwarf the claimed result. Not something that Sandro Galea could call “consequential epidemiology”, I don’t think.
I like the idea of GxE in principle, since the world must work this way. But if effects are in this range, which seems realistic, then it doesn’t seem that we can have a lot of confidence in the results (i.e. it wouldn’t take very much bias to knock you off center by a third of a percent), and even if true, the results wouldn’t have any direct implications for either population or individual health. I guess if we accumulated enough such examples, it could start to make a meaningful difference in phenotype. Maybe we’re just at the beginning of a long process of knowledge accumulation.
Except that when I went to read the article I found that the estimated effect of this SNP in CHRNA5-A3-B4 is to reduce BMI by a whopping 0.74%. I'll bet that less than 1% of your BMI is around an amount of weight that you gain or lose over the course of a meal. The same SNP caused 0.35% higher BMI in never smokers, an effect half as large, and I seriously doubt that a third of a percent of a BMI point is even within the measurement error of the scales they used in the study (and certainly less than the normal weight variation across a typical day). The p-values here are small because the study is so enormous, but the effect sizes are so absurdly trivial that this could never have any observable impact on an individual level.
To be fair, if the estimated effect were huge, it would be implausible, so this is a finding that could really be true. By aside from what it might reveal about the underlying biology, it seems irrelevant. Moreover, when effects are this small, the tiniest violation (e.g. of their exclusion restriction assumption for their Mendelian randomization) could easily dwarf the claimed result. Not something that Sandro Galea could call “consequential epidemiology”, I don’t think.
I like the idea of GxE in principle, since the world must work this way. But if effects are in this range, which seems realistic, then it doesn’t seem that we can have a lot of confidence in the results (i.e. it wouldn’t take very much bias to knock you off center by a third of a percent), and even if true, the results wouldn’t have any direct implications for either population or individual health. I guess if we accumulated enough such examples, it could start to make a meaningful difference in phenotype. Maybe we’re just at the beginning of a long process of knowledge accumulation.
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