The Future of Genomic Sequencing: Trends That Will Define the Next Decade

In an exclusive interaction with India Pharma Outlook, Rami Mehio, VP and Head of Software and Informatics Development at Illumina, discusses how advanced genetic sequencing is transforming healthcare. He highlights the role of evolving technology, data sharing, inclusivity in research, and privacy safeguards in enabling precise diagnoses, personalized treatments, and better clinical outcomes while reducing healthcare costs. A global leader in genomic software innovation, Rami Mehio brings over two decades of experience in software, bioinformatics, and signal processing. He excels in leading cross-functional teams, driving innovation, and scaling genomic informatics for global healthcare impact.

NGS adoption is accelerating globally, enhancing clinical diagnostics and outcomes. How is this trend transforming precision medicine across public and private healthcare sectors?

The development of more mature, end-to-end technologies has enabled the adoption of genetic tests in numerous geographic locations and across expanding clinical indications. With the growth of real-world evidence and the establishment of clinical utilities across multiple disease conditions, genetic testing is now becoming more accessible and utilized in clinics. Diagnostic genetic tests are now applied across multiple disease areas, including undiagnosed disease, comprehensive genomic profiling, therapy selection, and disease monitoring. This widespread use has led to the expansion of clinical guidelines to incorporate Next-Generation sequencing testing alongside coverage by private insurers and Medicare. Having access to clear molecular diagnosis will result in more effective therapeutic decisions and an overall reduction in healthcare costs.

Genome sequencing costs are steadily declining, enabling broader access. How are global startups leveraging this to build scalable, affordable models for preventive genomics?

Declining sequencing costs are enabling startups to offer cheaper preventative or screening tests, most of which still require private coverage. Examples include screening for early cancer or polygenic risk score evaluations. The price point spurs more demand and supports the startups’ viability. It is important to note that reducing the cost of tests requires not only reducing the sequencing cost but also reducing the cost of library preparation and informatics, which are crucial for wider adoption.

Interoperability is essential for genomic data utility. How are global initiatives like GA4GH and the Global Alliance tackling integration challenges for large-scale research collaboration?

In some specific streams, initiatives like the Beacon Project have been impactful, facilitating the federated discovery of genomic data in biomedical research and clinical applications. In other areas, commercial offerings are much faster moving than a non-profit standard body is able to keep up with. While the success has been limited, the direction remains promising.

Global genomic databases often underrepresent diverse populations. How are research bodies worldwide working to ensure inclusivity in population genomics and close representation gaps?

Recently, there have been a lot of initiatives to address this gap, primarily driven by national population projects or biobanks. In the Middle East, Africa, Latin America, and Asia, several large-scale initiatives are underway, sequencing hundreds of thousands of genomes of the local populations. Some of these projects’ databases are not made public and will mostly benefit the nation’s health system and big pharma, while others will contribute to global databases of genomic variations. Initiatives to open-source more databases, under the right consent and privacy rules, can play a major role in helping the development of precision medicine. Incorporating the genotypes across multiple ancestries will improve our ability to interpret rare genomic variants and will provide more equitable access and results.

Direct-to-consumer genomics is expanding rapidly, raising global privacy concerns. How are global regulations like GDPR and HIPAA protecting genetic data from misuse by third parties?

The field and regulations are evolving and becoming more sophisticated. Efforts initially started from the need to de-identify data and limit its movement and use. More recently, regulations have extended to the usage of AI in diagnostics, such as the AI ACT in Europe. Additionally, there are many certifications required by different countries aimed at making the data secure from illegal access. Overall, the regulations are improving and limiting the exposure of end-user data obtained through direct-to-consumer genomics. However, intrusions and errors are still possible. Technologies like homomorphic encryption may come to the rescue; since analysis is done on encrypted data, the data is never revealed.

Long-read sequencing is gaining global traction in complex genome analysis. How will this advance rare disease research and the development of personalized therapeutics by 2035?

Rare Disease analysis has advanced with genomic sequencing very significantly over the past few years. While standard whole-genome sequencing has shown great results, improving the diagnostic yield relies on being able to detect complex variants, sometimes in difficult regions of the genome. The addition of long-read sequencing has improved the diagnostic yield marginally but comes at a higher cost, lower scalability, and more stringent DNA quality and input requirements. Alternatively, innovative bioinformatics methods with Standard Sequencing by Synthesis (SBS) sequencing, like the ones developed on Illumina’s DRAGEN germline analysis, can accurately identify difficult variants generally without the extra cost and complex extraction. Additionally, Constellation Mapped Reads, Illumina’s new on-flow cell library prep technology, provides additional long-range information and insights previously only seen with native long-read technologies. These new methods show great promise to improve diagnostic yield through interrogating the entire human genome.

While detecting difficult variants is of great importance, understanding the pathogenicity of a variant in general, and particularly in the context of the phenotype, is a significant remaining challenge. Most variants in a human sample are of unknown significance (VUS), and recent algorithm development like Illumina’s Primate-AI 3D has shown great accuracy in predicting variant pathogenicity, allowing interpretation tools to surface those variants as potentially causative. Tools such as Illumina’s Emedgene software significantly reduce the burden of variant interpretation through the use of multiple annotation sources, prediction tools, and explainable AI. Other advancements in multi-omics, such as methylation and transcriptomic sequencing analysis, are also credited with improved diagnostic yield, so we expect the combination of all these advancements to further the field significantly.

Precision therapies begin with an accurate molecular diagnosis. Diseases that were once considered fatal and without treatment options, such as spinal muscular atrophy, now have targeted antisense oligonucleotide and gene therapies available. With the widespread availability of genetic testing, it is anticipated that more therapeutics will be developed for conditions previously considered untreatable and too rare to invest in.