The Role of Big Data in Personalizing the Healthcare Experience: Genomics Part 2

Human Genome: Then and Now 

Research in the field of genomics has come a long way in the past 60 years.  The pioneering effort in studying the human genome and its effect on disease was the Human Genome Project (1990-2003), which changed sequencing from a manual process to an automated computer based one.

Driven by advances in technology that have dramatically reduced costs, Genome Wide Association Studies (GWAS) are expanding on the Human Genome Project in discovering connections between genes and diseases.  GWAS tests single nucleotide polymorphisms (SNPs) for association between genes and diseases.  (A single nucleotide polymorphism is where there is one nucleotide difference between two genes.  For example two sequence DNA fragments, AAGCCTA vs AAGCTTA have one differing nucleotide.)

More than 1600 GWAS publications have connected 2000 gene associations with more than 300 common human disease traits.

GWAS hasn’t yet proven directly useful for guiding individual health, but we may be on the brink of changing this. There are three near future clinical applications for GWAS: 1)Predictive models to identify high risk patients, i.e. Type 1 Diabetes patients; 2) Classifying disease subtypes of potential use for more precisely guided clinical trials and targeted treatments; 3)Provide better information for screening drug candidates for toxicity and efficacy before clinical trials.

The Cost to Sequence the Human Genome

By the time the Human Genome Project was completed the cost to sequence the human genome was $40 million, down from $95 million just two years before.  Academics and companies have been working hard to make sequencing affordable and therefore available to the public.  Today an individual human genome can be sequenced for around $5000 consistently and accurately.

To learn more, and about the companies that are using genomics and other data to improve human health, join us on Sept 18th at 10am PST for the Webinar, using Big Data to Personalize the Healthcare Experience.

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