In a groundbreaking study leveraging whole-genome sequencing (WGS) data from an unprecedented cohort of 452,618 individuals, researchers have taken a significant leap towards resolving the enduring mystery of missing heritability in human phenotypes. This extensive genome-wide association study (GWAS) spanning 34 distinct traits unmasks the hidden contributions of rare genetic variants to complex traits, markedly advancing our understanding of genetic architecture beyond common variant analysis.
The study detected a total of 12,129 independent associations with phenotypic traits, distinguishing 11,243 linked to common variants and unveiling 886 rare-variant associations (RVAs). These findings illuminate how much of the elusive rare-variant heritability can now be directly mapped to single loci, particularly through the lens of large-scale WGS, a technique offering unprecedented resolution across the entire genome compared to previous array-based genotyping or imputation methods.
Intriguingly, rare variants associated with traits were only found in 30 of the 34 studied phenotypes, revealing an inherent challenge: the limited statistical power to detect associations involving variants with very low minor allele frequencies (MAF). Most RVAs (64%) had a MAF exceeding 0.1%, indicating that while WGS exponentially expands variant discovery, the rarer spectrum remains difficult to dissect with current cohort sizes.
One of the study’s critical insights pertains to pleiotropy, the phenomenon where a single genetic variant impacts multiple traits. Approximately 8% of genome-wide significant variants influenced more than one phenotype. Notably, the SLC39A8 missense variant rs13107325, with a MAF of 7.5%, exhibited associations with an impressive 14 different traits. Equally compelling, a rare insertion-deletion variant within the ASGR1 gene intron showed pleiotropic effects across nine distinct traits, underscoring that rare variants can exert broad biological influence.
Quantitatively, after adjusting for statistical biases such as the winner’s curse, the average phenotypic variance explained by RVAs was slightly higher (0.027%) than that explained by common variant associations (0.023%). Yet, when considering the overall heritability, common variants account for approximately 31% of the common-variant heritability portion, whereas rare variants explained about 11% of rare-variant heritability. This remarkable data reflects the complementary roles both variant classes play within the genomic landscape of human traits.
Lipid traits featured prominently in the rare variant analysis, with 18% of RVAs linked to dyslipidemia, triglycerides, LDL, or HDL cholesterol, despite these phenotypes comprising only 12% of the total traits examined. This 1.5-fold enrichment suggests that rare variants impacting lipid metabolism are more likely to have substantial effect sizes, emphasizing the biological significance of rare genetic architecture in cardiovascular-related traits.
Further validation emerged from an independent European ancestry cohort of approximately 67,000 individuals from the Alliance for Genomic Discovery (AGD). Here, RVAs identified in the UK Biobank explained roughly a third of the rare-variant heritability for LDL and HDL, reinforcing the robustness and replicability of findings across populations. Moreover, alkaline phosphatase (ALK) stood out as the sole non-lipid phenotype for which rare variants explained more than one-third of its estimated rare variant heritability, highlighting unique rare variant effects beyond lipid metabolism.
The study also revealed that 41% of RVAs were situated within genomic loci accessible to whole-exome sequencing (WES), predominantly in coding regions. Nonetheless, many of the most impactful RVAs were located outside these WES target zones, underscoring the limitation of exome-focused studies and reinforcing the importance of comprehensive WGS approaches for capturing the full spectrum of functionally significant variants.
A striking example includes the rare indel rs754165241 in ASGR1, associated with a 1.43 standard deviation increase in ALK levels and explaining about 3% of phenotypic variance—the largest variance explained among all associations detected. This variant has been validated in several large population cohorts, further proving the power of WGS to uncover rare variants with outsized biological effects.
Technological insights from the study highlight the immense advantages of WGS over imputation-based approaches for locus detection and fine-mapping. The authors document improved fine-mapping resolution, revealing previously undetectable haplotypes in European ancestry populations that are absent from existing imputation reference panels. This technological advancement promises to sharpen precision medicine strategies by offering a more comprehensive inventory of genetic variation.
Collectively, this expansive GWAS using WGS data exemplifies the evolving landscape of genetic discovery, where integration of rare and common variants brings researchers closer to the elusive goal of fully characterizing heritable contributions to complex traits. The results hold significant implications for genetic epidemiology, risk prediction, and the future design of genomic studies aimed at personalized health interventions.
The study also underscores the persistent gaps in rare variant detection and heritability mapping, advocating for even larger cohorts and diverse ancestries to enhance power and generalizability. Such efforts will be critical to unravel the full genetic underpinnings of human health and disease, potentially revealing novel therapeutic targets.
By demonstrating that significant portions of rare-variant heritability are now accessible via WGS-based GWAS and corroborating these associations across independent datasets, the research sets a new gold standard. This heralds a new era in human genetics research, where the intricate fabric of rare and common variation can be more fully appreciated and leveraged.
Researchers and clinicians alike can anticipate a paradigm shift as these findings illustrate the essential need for whole-genome approaches in capturing the genetic complexity of human traits—pushing beyond the lists of common SNPs and spotlighting the critical impact of rare genetic variation in shaping biology.
Strongly rooted in rigorous data and innovative methodology, this landmark study charts a path toward a comprehensive genetic map of human traits and diseases, bringing the scientific community steps closer to decoding the extensive yet hidden architecture of heritability.
Subject of Research:
Deciphering the contributions of rare and common genetic variants to human phenotypes using extensive whole-genome sequencing in a large population cohort.
Article Title:
Estimation and mapping of the missing heritability of human phenotypes.
Article References:
Wainschtein, P., Zhang, Y., Schwartzentruber, J. et al. Estimation and mapping of the missing heritability of human phenotypes. Nature (2025). https://doi.org/10.1038/s41586-025-09720-6
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DOI: https://doi.org/10.1038/s41586-025-09720-6
Tags: advancements in genomic analysis methodschallenges in detecting rare variantsgenome-wide association study findingslarge-scale genetic researchmapping heritability to single lociminor allele frequency implicationsmissing heritability in human traitsphenotypic trait associationspleiotropy in genetic studiesrare genetic variants and complex traitsunderstanding genetic architecturewhole-genome sequencing in genetics
