Genetic statistics don’t necessarily predict the effects of an individual’s genes

I curated this 2015 Howard Hughes Medical Institute rodent study of DNA methylation because of the reason driving the researchers’ efforts:

“Epigenomic analyses are limited by averaging of population-wide dynamics and do not inform behavior of single cells. We observe dynamics at the single-cell level not predicted by epigenomic analysis.”

This rationale was also the driving force behind the Is what’s true for a population what’s true for an individual? study and its companion Changing an individual’s future behavior even before they’re born. The methodology of genome-wide association studies (GWAS) usually:

“Focuses on the average effect of alternative alleles averaged in a population.”

What this methodology often missed was:

“When phenotypic variation results from alleles that modify phenotypic variance rather than the mean, this link between genotype and phenotype will not be detected.”

Population-wide epigenetic statistics don’t necessarily inform us about the epigenetic activities and attributes of an individual’s genes, even down at the single-cell level. “The Xist RNA-PRC2 complex at 20-nm resolution reveals a low Xist stoichiometry and suggests a hit-and-run mechanism in mouse cells”

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