Most of the heritability captured by current genetic studies of complex traits comes from common variants acting in aggregate rather than from a few rare, large-effect mutations. Genome-wide association studies and SNP-based heritability analyses have repeatedly shown a polygenic architecture: thousands of loci each contribute a small fraction of variance, and their summed effects explain a substantial share of trait differences across populations. Jian Yang at the University of Queensland provided early empirical support that common single-nucleotide polymorphisms account for a large proportion of heritable variation in traits such as adult height. Brendan Bulik-Sullivan at the Broad Institute and colleagues developed LD Score regression methods that reinforced the conclusion that many common variants underlie most complex traits.
Evidence from GWAS and partitioning studies
Beyond the count of associated variants, functional partitioning reveals where heritability is concentrated. Hilary K. Finucane at the Broad Institute and Alkes L. Price at Harvard T.H. Chan School of Public Health used partitioned heritability methods to show that genetic signal is enriched in regulatory regions and in annotations marking tissue-specific activity relevant to the trait. For example, variants in gene regulatory elements active in the brain tend to explain disproportionately more heritability for psychiatric traits, while immune-cell regulatory regions are enriched for autoimmune disease heritability. These results indicate that many causal variants lie outside protein-coding regions and affect gene expression in particular cell types.
Causes: frequency, effect size, and evolutionary forces
The distribution of contributions follows well-understood population genetic principles. Common variants are frequent enough that, even with small individual effects, their combined impact on population variance is large. Rare variants and structural changes can produce larger effects in individual carriers, but because they occur infrequently their contribution to overall population heritability is limited. Negative or purifying selection tends to remove strongly deleterious alleles from the population, pushing many high-effect variants to low frequency and leaving a background of weakly selected common variants that shape complex traits.
Consequences and real-world nuances
These genetic patterns have practical consequences. Polygenic scores built from common-variant GWAS capture measurable predictive power for some traits but are limited by genetic architecture and study design. The heavy Eurocentric bias of GWAS cohorts means scores perform worse in underrepresented populations, introducing potential disparities in clinical or social applications. Local demographic history and territory-specific rare variants can produce meaningful differences in disease risk between populations, underlining the importance of diverse sampling. Environment, culture, and gene–environment interaction further modulate how the same variant expresses across contexts, so genetic contribution to trait variance is not fixed across settings.
Taken together, evidence from leading researchers and institutions supports a model where many common, small-effect regulatory variants are the principal contributors to complex trait heritability at the population level, while rare and structural variants matter for individual cases and specific populations. Understanding both scales is essential for responsible scientific interpretation and equitable application.