Summary: Using full genome sequencing data from more than 347,000 individuals, researchers quantified how much genetic differences explain human traits such as height, body mass index, fertility, and disease risk. The results show that genes account for roughly 30% of the variation between individuals, with higher estimates for traits like height and lower for fertility.
Unlike twin studies that mix genetic and environmental factors, genome sequencing isolates genetic influence more precisely. This breakthrough allows scientists to better identify disease risks early, advancing preventative medicine and genetic understanding.
Key Facts
- Large-Scale Sequencing: Used data from 347,630 people in the UK Biobank.
- Heritability Range: Genes explain 12–74% of variation across traits.
- Clinical Potential: Findings could help identify disease risks long before symptoms emerge.
Source: University of Queensland
Genome sequencing has been used to determine how much genes influence human characteristics including height and weight, and susceptibility to diseases like Type 2 diabetes, in a study co-led by University of Queensland researchers and collaborators at genomic technology company Illumina, Inc.
This study is the largest of its kind and used the DNA sequences of 347,630 people of European descent from the UK Biobank to quantify how much trait differences between people can be explained by genetic factors, known as heritability.
Professor Loic Yengo from UQ’s Institute for Molecular Bioscience said whole genome sequencing allows the accurate measurement of most genetic variants, unlike traditional methods using data from relatives and twin studies.
“An outstanding question in human genetics has been how much twin-based estimates of heritability could be replicated using modern genomic technologies when applied to unrelated individuals,” Professor Yengo said.
“Our study answers this question and demonstrates for the first time that this approach works.”
Among the 34 characteristics and diseases the researchers studied were height, body mass index (BMI), cholesterol, hypertension, fertility, smoking initiation and heart disease.
“Across the traits studied, we’ve estimated genetic factors can explain on average 30 per cent of differences between people, ranging from 74 per cent for height and 12 per cent for fertility,” Professor Yengo said.
“One of the limitations of the traditional approach is that relatives and twins share not only genes but also environmental factors.
“For example, family-based estimates put genetic influence on a person’s BMI at 50 per cent, but the genomic sequencing determined the influence was 35 per cent.”
The next step is to map the genes or genetic variants between individuals to explain why some people develop disease and others don’t.
“That would allow at-risk individuals to be identified early, and preventative measures taken well in advance of the disease developing,” Professor Yengo said.
The research was funded by the Australian Research Council and the Snow Medical Research Foundation.
Co-author and Illumina Vice President of Artificial Intelligence Kyle Farh said population-level genomic datasets like UK Biobank give researchers access to a wealth of data”.
Key Questions Answered:
A: They analyzed genome sequences from over 347,000 people to measure how much genes influence traits like height, weight, and disease risk.
A: Genes explain about 30% of individual differences overall — up to 74% for height and 12% for fertility.
A: It confirms that genome sequencing can accurately estimate heritability without relying on twin studies, paving the way for early disease risk prediction.
About this genetics research news
Author: Dea Clark
Source: University of Queensland
Contact: Dea Clark – University of Queensland
Image: The image is 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
Estimation and mapping of the missing heritability of human phenotypes
Rare coding variants shape inter-individual differences in human phenotypes. However, the contribution of rare non-coding variants to those differences remains poorly characterized.
Here we analyse whole-genome sequence (WGS) data from 347,630 individuals with European ancestry in the UK Biobank to quantify the relative contribution of 40 million single-nucleotide and short indel variants (with a minor allele frequency (MAF) larger than 0.01%) to the heritability of 34 complex traits and diseases.
On average across phenotypes, we find that WGS captures approximately 88% of the pedigree-based narrow sense heritability: that is, 20% from rare variants (MAF < 1%) and 68% from common variants (MAF ≥ 1%). We show that coding and non-coding genetic variants account for 21% and 79% of the rare-variant WGS-based heritability, respectively.
We identified 15 traits with no significant difference between WGS-based and pedigree-based heritability estimates, suggesting their heritability is fully accounted for by WGS data.
Finally, we performed genome-wide association analyses of all 34 phenotypes and, overall, identified 11,243 common-variant associations and 886 rare-variant associations.
Altogether, our study provides high-precision estimates of rare-variant heritability, explains the heritability of many phenotypes and demonstrates for lipid traits that more than 25% of rare-variant heritability can be mapped to specific loci using fewer than 500,000 fully sequenced genomes.
