Summary: Using whole-genome sequencing from 347,630 people, researchers quantified how much genetic variation explains human traits such as height, body mass index (BMI), fertility and disease risk. The study shows genes account for roughly 30% of variation on average, with higher heritability for traits like height and lower for traits such as fertility. By leveraging high-resolution genomic data rather than family relationships, the work provides clearer estimates of genetic contribution and advances prospects for earlier disease risk prediction.
This large-scale analysis of UK Biobank participants isolates genetic influence more precisely than twin or family studies, which can conflate shared genetics with shared environment. The results refine our understanding of how common and rare variants, coding and non-coding regions, contribute to complex human phenotypes and disease susceptibility.
Key Facts
- Large-scale sequencing: Whole-genome data from 347,630 individuals of European ancestry (UK Biobank).
- Heritability range: Across 34 traits, genetic factors explain between 12% and 74% of variation; average roughly 30%.
- Variant contributions: On average, whole-genome sequencing captures about 88% of pedigree-based narrow-sense heritability — ~20% from rare variants (MAF < 1%) and ~68% from common variants (MAF ≥ 1%).
- Functional split: Among rare-variant heritability, 21% comes from coding variants and 79% from non-coding variants.
- Association discoveries: Genome-wide analyses identified 11,243 common-variant associations and 886 rare-variant associations across the 34 phenotypes studied.
Source: University of Queensland
Study overview
Researchers, co-led by the University of Queensland and collaborators at Illumina, analyzed whole-genome sequence (WGS) data from 347,630 UK Biobank participants to estimate heritability for 34 complex traits and diseases. The traits included height, BMI, blood lipids, hypertension, fertility, smoking initiation and heart disease. By measuring roughly 40 million single-nucleotide and short indel variants (MAF > 0.01%), the team quantified how much of trait variability can be explained directly from genetic variation observed in unrelated individuals.
Professor Loic Yengo from UQ’s Institute for Molecular Bioscience highlighted that WGS now makes it possible to measure most genetic variants directly, avoiding the confounding that arises when relatives share both genes and environment. The study tested whether genomic estimates from unrelated individuals reproduce classic pedigree- or twin-based heritability estimates, and found that WGS can in many cases recover the majority of pedigree-based heritability.
Across the traits studied, genetic factors explained on average about 30% of inter-individual differences, with a broad range: for example, height showed a heritability estimate near 74%, while fertility showed an estimate near 12%. For BMI, the genomic estimate (35%) was lower than many family-based estimates (often around 50%), illustrating how family studies can overestimate genetic influence when shared environment is not fully accounted for.
Mapping and implications
Beyond estimating overall heritability, the researchers performed genome-wide association analyses and mapped many specific loci. They found that WGS accounts for most of the heritability for several phenotypes — for 15 traits there was no significant difference between WGS-based and pedigree-based estimates, implying that the genetic contribution is largely captured by the sequencing data. For lipid traits, the study demonstrated that a substantial portion of rare-variant heritability (over 25%) can be localized to specific loci using fewer than 500,000 fully sequenced genomes.
These findings refine the roadmap for identifying genetic risk factors that contribute to disease. Accurately estimating and mapping genetic effects helps prioritize loci for functional follow-up and improves risk prediction models. In turn, that could enable earlier identification of individuals at higher genetic risk and support preventive strategies before disease onset.
Funding and contributions
The research was funded by the Australian Research Council and the Snow Medical Research Foundation. Co-authors include investigators from the University of Queensland and technology partners at Illumina; Illumina’s Vice President of Artificial Intelligence, Kyle Farh, noted the value of large population-level genomic resources such as the UK Biobank in enabling this work.
Key Questions Answered:
A: They analyzed whole-genome sequences from over 347,000 people to quantify how much genetic variation contributes to traits like height, weight and disease risk.
A: On average, genetic variation explains about 30% of differences between individuals across the traits analyzed, with a range from roughly 74% for height down to about 12% for fertility.
A: The study confirms that whole-genome sequencing can provide precise heritability estimates without relying on relatives, helps map genetic contributions to specific loci, and supports improved disease risk prediction and preventive strategies.
About this genetics research news
Author: Dea Clark
Source: University of Queensland
Contact: Dea Clark – University of Queensland
Image: Image credited to Neuroscience News
Original Research: Open access. “Estimation and mapping of the missing heritability of human phenotypes” by Loic Yengo et al. Nature
Abstract (summary)
Rare coding variants shape inter-individual differences in human phenotypes, but the role of rare non-coding variants has been less well characterized. This study analyzes whole-genome sequence data from 347,630 individuals of European ancestry in the UK Biobank to quantify contributions from approximately 40 million single-nucleotide and short indel variants (MAF > 0.01%) to the heritability of 34 complex traits and diseases.
On average, WGS captures about 88% of pedigree-based narrow-sense heritability: approximately 20% of heritability arises from rare variants (MAF < 1%) and about 68% from common variants (MAF ≥ 1%). Among rare-variant heritability, 21% is attributable to coding variants and 79% to non-coding variants. The study found 15 traits where WGS-based and pedigree-based heritability estimates did not differ significantly, indicating that sequencing data largely explains their genetic contribution. Genome-wide association analyses identified 11,243 common-variant and 886 rare-variant associations across the 34 phenotypes. Overall, the results provide precise estimates of rare-variant heritability, explain heritability for many phenotypes, and demonstrate that for lipid traits a significant portion of rare-variant heritability can be mapped to specific loci using fewer than 500,000 fully sequenced genomes.