Written by:
Vice President, Centre for Genomics Research (CGR), AstraZeneca
Senior Director, Genome Analytics, Centre for Genomics Research, AstraZeneca
Associate Principal Scientist, Genome Analytics, Centre for Genomics Research, AstraZeneca
Our R&D is anchored in a clear belief: Designing the best medicines begins with the best understanding of human biology. We’ve invested heavily in large‑scale genomics, multi‑omics, and advanced computation because these tools help us understand disease with greater accuracy and depth than ever before. Much of this work centred on single‑letter changes in our genetic code. Today, however, we can now view a broader, more interconnected picture of how the genome shapes health and disease.
Our latest research, published in Nature, takes a major leap forward to more accurately characterise large DNA deletions or duplications—called copy number variants (CNVs)—in more than 470,000 individuals to create one of the largest CNV analyses to date. And we’re making these results openly accessible through our AZPheWAS portal, so researchers everywhere can explore, hypothesize and innovate.
Why CNVs matter for the future of genomics research
Since founding our Centre for Genomic Research nearly ten years ago, we've known that understanding human biology is essential to delivering better medicines. That promise is already reflected in industry success rates, with therapeutic targets with human genetic insights being up to seven times more likely to be approved as medicines. If we can understand how changes in our DNA influence disease, we can learn from it to design better medicines.
Until recently, most insights came from studying small nucleotide variants (SNVs), which are small, single letter differences in our genetic code. These tiny changes can help reveal how biology can go wrong in disease.1 In 2023, we showed that combining genetic data with protein measurements can uncover how these SNVs affect proteins circulating in our blood.2
But DNA can change in much bigger ways than a single letter. In our latest research, we analyse copy number variants (CNVs)—larger stretches of DNA that are duplicated or deleted.
- When a region is duplicated, a gene could produce too much protein, which may contribute to disease.
- When a region is deleted, less protein is made. Often, insufficient levels of a protein are detrimental to human health. But, in some special cases, they may lower disease risk revealing natural protective mechanisms - that we call genetic resilience.
By studying larger DNA changes found among hundreds of thousands of people, we’re now gaining a clearer, more comprehensive picture of how genetics shapes health, which is helping us uncover new ways to design better medicines.
A deeper dive: key findings from the study
Our multi-ancestry analysis also revealed several ways that CNVs shape human health across populations. One example comes from a duplication involving the HNF1B gene. Individuals who carry this variant show not only higher levels of the HNF1B protein in the system but also an increased likelihood of developing chronic kidney disease in their lifetime.
Protective variants can help preserve human health, often functioning like genetic “off switches”; not only safeguarding certain individuals from disease, but also offering a playbook for designing new medicines that mimic the same biological effect. For example, the IGHE gene deletion is found to significantly lower the individual’s IgE levels, but the data also shows that the same deletion offers natural protection against developing asthma and allergic disease, pointing to a pathway that could support new therapeutic approaches.
Open-access genomic data to accelerate research and innovation
We know that science is more impactful when researchers everywhere can leverage the findings. That’s why we created AZPheWAS, an open platform where researchers can explore this vast, multi-omic dataset, including insights from this CNV analysis. Researchers around the world can query a gene to see how its changes affect protein levels or disease risk and analyse results across a large range of health traits.
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