Illumina whole-genome sequencing provides greater insight into genetic signals behind common diseases--according to Nature study

Rhea-AI Summary

Illumina (NASDAQ: ILMN) and collaborators published a Nature study on Nov 12, 2025 showing whole-genome sequencing (WGS) with DRAGEN capture nearly 90% of genetic signal across 34 common diseases and traits, addressing the "missing heritability" gap. The analysis used 347,630 WGS samples from UK Biobank and replication in the Alliance for Genomic Discovery.

Key findings: WGS outperformed whole-exome sequencing and arrays, with WES explaining 17.5% of genetic variance, arrays missing 20–40% of some rare alleles, and WGS recovering >30% of rare-variant heritability for HDL and LDL. Advanced AI tools (PrimateAI-3D, DRAGEN) improved rare-variant interpretation and drug-target discovery.

Positive

• WGS captured nearly 90% genetic signal across 34 traits
• Analysis of 347,630 WGS samples from UK Biobank
• WES explained only 17.5% of genetic variance (WGS superior)
• Array methods missed 20–40% of subset rare alleles
• WGS recovered >b30% of rare-variant heritability for HDL and LDL
• Findings replicated in Alliance for Genomic Discovery

Negative

• None.

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.

SAN DIEGO, Nov. 12, 2025 /PRNewswire/ -- A study 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

 

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SOURCE Illumina, Inc.

FAQ

What did Illumina (ILMN) announce in the Nov 12, 2025 Nature study about WGS?

The study reported that Illumina WGS with DRAGEN captured nearly 90% of genetic signal across 34 traits, using 347,630 WGS samples and replication in the Alliance.

How does Illumina WGS (ILMN) compare with whole-exome sequencing in the study?

WGS outperformed WES; the study found WES explained only 17.5% of total genetic variance versus much higher capture by WGS.

What percentage of rare alleles did array-based methods miss compared with Illumina WGS (ILMN)?

For a subset of rare alleles, array-based methods missed between 20% and 40% of variants detected by WGS.

What investor-relevant sample size supported Illumina's (ILMN) WGS findings?

The primary analysis used 347,630 WGS samples from UK Biobank, with replication in the Alliance for Genomic Discovery.

Did the study identify implications for drug-target discovery using Illumina tools (ILMN)?

Yes; the study says WGS plus AI tools like PrimateAI-3D helped identify rare and common variant associations that can inform drug target candidates.