Complex traits and common diseases are extremely polygenic, their heritability spread
across thousands of loci. One possible explanation is that thousands of genes and
loci have similarly important biological effects when mutated. However, we hypothesize
that for most complex traits, relatively few genes and loci are critical, and negative
selection—purging large-effect mutations in these regions—leaves behind common-variant
associations in thousands of less critical regions instead. We refer to this phenomenon
as flattening . To quantify its effects, we introduce a mathematical definition of
polygenicity, the effective number of independently associated SNPs ( M e ), which
describes how evenly the heritability of a trait is spread across the genome. We developed
a method, stratified LD fourth moments regression (S-LD4M), to estimate M e , validating
that it produces robust estimates in simulations. Analyzing 33 complex traits (average
N = 361k), we determined that heritability is spread ∼4× more evenly among common
SNPs than among low-frequency SNPs. This difference, together with evolutionary modeling
of new mutations, suggests that complex traits would be orders of magnitude less polygenic
if not for the influence of negative selection. We also determined that heritability
is spread more evenly within functionally important regions in proportion to their
heritability enrichment; functionally important regions do not harbor common SNPs
with greatly increased causal effect sizes, due to selective constraint. Our results
suggest that for most complex traits, the genes and loci with the most critical biological
effects often differ from those with the strongest common-variant associations.