The Conversation (0)
Study shows that rare variants captured by Illumina WGS with DRAGEN™ variant calling explain most of the "missing heritability" in complex human diseases and traits, including those related to blood pressure and cholesterol
Results demonstrate the advantages of WGS over WES and genotyping arrays for assessing genetic risk and identifying drug target candidates.
A study XT-OQ!XzKw6hACMmFV-2CL1nKCObdIZKrQdyFYf378YHzPLnOUFqrBHuUg3xknaNjUjFnJ5_zd6Gn8Edslli3r%24&a=published+today+in+Nature" target="_blank" rel="nofollow">published today in Nature, authored by Illumina, Inc. (NASDAQ: ILMN) scientists and by collaborators from The University of Queensland demonstrates the importance of whole-genome sequencing (WGS) to more fully capture the genetics underlying complex human traits and diseases. Across all 34 diseases and traits studied, WGS captured nearly 90% of the genetic signal, based on heritability estimates from family studies. This marks a step toward solving the "missing heritability" problem, resolving the gap between family-based heritability and the heritability estimates made by genome wide association studies.
"This study shows how Illumina's whole-genome sequencing, powered by DRAGEN secondary analysis and cutting-edge statistical and deep-learning tools, get more out of large cohort studies," said Rami Mehio, senior vice president and general manager of BioInsight at Illumina. "Our top-performing WGS reveals much more of the genetic signals underlying common diseases, offering researchers AI driven insights that can predict disease risk and identify drug targets."
Solving for missing heritability
Heritability is a critical parameter for researchers, giving them an upper limit for genetics-based risk predictions and pointing them toward traits to target for drug discovery and development. Heritability estimates from large, biobank-based genetic studies typically do not match estimates from family-based genetic studies. The gap between these findings is known as "missing heritability." In the study, analysis of WGS data fully captures heritability for 25 of the 34 selected traits. This includes traits associated with clinical indicators like blood pressure and cholesterol levels as well as physical characteristics like waist-to-height ratio.
"Quantifying the relative contribution of rare and common variants behind this heritability gap gives researchers better strategies to identify genes to target for drug development and discovery," said Loic Yengo, professor of statistical genomics at The University of Queensland's Institute for Molecular Biosciences, who co-supervised the study.
The ambitious study, the largest of its kind to date, analyzed 347,630 WGS samples from the UK Biobank to examine how genetic variation contributes to physical and disease-related traits. Researchers also identified common and rare associations with genome-wide association studies (GWAS) of 452,618 individuals and demonstrated the superiority of WGS over whole-exome sequencing (WES) or array-based imputed genotypes for detecting and resolving rare variants. The study further revealed a significant correlation between scores from Illumina's PrimateAI-3D and variant effect sizes, underscoring the importance of advanced analysis tools for rare variant interpretation.
"Population-level genomic datasets like UK Biobank give researchers access to a wealth of data," said Kyle Farh, vice president of Artificial Intelligence at Illumina and co-author of the study, "Illumina's leading AI software and informatics capabilities enable greater insights from that data to drive precision health care and drug discovery."
WGS proves superior to other methods in detecting common and rare variants
The study shows WGS outperformed other genotyping methods in detecting common and rare disease variants, including those with downstream clinical implications. For a subset of rare alleles, array-based methods missed between 20% and 40% of the variants found by WGS.
WES, a powerful tool for analyzing the protein-coding regions of the genome, was shown to capture only a fraction of the genetic effects, explaining just 17.5% of total genetic variance in the study. WGS specifically identified impactful variants in noncoding regions, such as a rare deletion that is associated with hormone function and hematological traits.
WGS-based GWAS uncovered an outsized number of rare variant associations related to lipid traits, like cholesterol levels. Over 30% of the rare variant heritability for HDL and LDL was recovered by WGS. These variants, and others identified with GWAS, can give translational researchers novel targets for diagnostics and therapeutics. To validate these findings, the analysis was also replicated using data from the Alliance for Genomic Discovery.
Recognizing the growing importance of insights from large genomic datasets, Illumina recently launched BioInsight to leverage sequencing, data analysis, software, and AI. You can learn more about BioInsight, and how it can empower your research, here.
About Illumina
Illumina is improving human health by unlocking the power of the genome. Our focus on innovation has established us as a global leader in DNA sequencing and array-based technologies, serving customers in the research, clinical, and applied markets. Our products are used for applications in the life sciences, oncology, reproductive health, agriculture, and other emerging segments. To learn more, visit illumina.com and connect with us on X, Facebook, LinkedIn, Instagram, TikTok, and YouTube.
Contacts
Investors:
Illumina Investor Relations
858-291-6421
IR@illumina.com
Media:
Christine Douglass
PR@illumina.com
View original content to download multimedia:https://www.prnewswire.com/news-releases/illumina-whole-genome-sequencing-provides-greater-insight-into-genetic-signals-behind-common-diseasesaccording-to-nature-study-302612737.html
SOURCE Illumina, Inc.
