Justin’s Notes

Aggregation as Microscope: Why Both Selection and Structure Hide at the Single Locus

2026-08-07 · AI-generated insight

These two notes describe the same statistical move applied to two different questions, and the parallel is worth making explicit: signals that are invisible at any single genetic locus become unmistakable when you aggregate correlated small effects.

Winegard and Carl's rebuttal of Lewontin turns on this. At any one locus, between-population variation is dwarfed by within-population variation — just as nose size alone can't sex a face. But correlated differences across thousands of loci make population structure legible, the way a whole face makes sex obvious. The information was always there; it just doesn't live in any single variable.

The ancient DNA study faces the mirror-image problem in the time dimension. A single allele's frequency shift over millennia is ambiguous — it could be drift, migration, or population structure. The method works by testing for consistent trends over time, and the headline findings involve combinations of alleles: standard-deviation-scale shifts in polygenic predictors of body fat, schizophrenia, and cognitive performance. Again, aggregation across many weak, correlated signals is what turns noise into detection.

But there's a deeper layer: in the selection case, aggregation isn't just our epistemic trick — it's apparently how evolution itself operated. The study confirms that classic hard sweeps (one big mutation driven to fixation) were rare. Recent human evolution instead worked through polygenic selection: nudging hundreds of alleles simultaneously, each by a little. Evolution distributed its signal across many loci for the same reason Lewontin could hide population structure there — individual loci are cheap, noisy, and low-stakes, while the aggregate carries the phenotype.

The synthesis: the genetic architecture of complex traits and the statistics needed to study them are the same shape. Lewontin's error was treating loci as independent witnesses rather than a correlated chorus. The ancient DNA method succeeds precisely by refusing that error in the temporal domain. Any future debate about recent human evolution — including uncomfortable ones about cognition, which the paper explicitly flags with its caveat about past versus present phenotypes — will be fought on this terrain: not "is there a gene for X?" but "do the small correlated shifts point the same direction?"

Woven from these notes

Ancient DNA has transformed our understanding of population history, but its potential to reveal as much about human evolutionary biology has not been realized because of limited sample sizes and the difficulty of distinguishing sustained rises in allele frequency increasing fitness—directional selection—from shifts due to migrations, population structure, or non-adaptive purifying or stabilizing selection. Here we present a method for detecting directional selection in ancient DNA time-series data that tests for consistent trends in allele frequency change over time, and apply it to 15,836 West Eurasians (10,016 with new data). Previous work has shown that classic hard sweeps driving advantageous mutations to fixation have been rare over the broad span of human evolution8,9. By contrast, in the past ten millennia, we find that many hundreds of alleles have been affected by strong directional selection. We also document one-standard-deviation changes on the scale of modern variation in combinations of alleles that today predict complex traits. This includes decreases in predicted body fat and schizophrenia, and increases in measures of cognitive performance. These effects were measured in industrialized societies, and it remains unclear how these relate to phenotypes that were adaptive in the past. We estimate selection coefficients at 9.7 million variants, enabling study of how Darwinian forces couple to allelic effects and shape the genetic architecture of complex traits.
Ancient DNA reveals pervasive directional selection across West Eurasia

In brief, Lewontin ignored the fact that differences among human populations are correlated. Although population variation at any one genetic locus tends to be small, global population structure becomes clear if one examines correlated differences across loci. For a simple analogy, consider men and women’s faces. If one takes any particular characteristic, say nose size, there is likely to be more variation within a sex than between the sexes (and it would be nearly impossible to classify faces by sex with any accuracy using only nose size). However, if one considers all facial characteristics together (which is, after all, how we actually experience human faces), then sex differences become sufficiently clear that an observer can guess the correct sex more than 95 percent of the time.

— Bo Winegard, Noah Carl Superior: The Return of Race Science—A Review