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What Apple and DNA-apps could mean for researchers

By: Roche Life Science

Posted: May 13, 2015 | Lab Life

Apple is now collaborating with multiple U.S. academic research institutions to launch apps that would let iOS users have their DNA tested, according to a recent MIT Technology Review, citing unnamed individuals familiar with the work. While Apple would not collect or analyze the DNA themselves, iOS users who opt-in to the studies would provide DNA samples using "spit kits" to Apple-approved partner laboratories. The first institutions to partner with Apple for these DNA-app based studies include the advanced gene-sequencing centers operated by the University of California, San Francisco, and Mount Sinai Hospital in New York. These initial studies plan to analyze a gene panel of up to 100 medically relevant genes for studying various health topics, including causes of premature birth. The genetic data would be stored in the cloud for researchers to analyze, with the eventual goal of being available to iOS users as a means to receive and share health-relevant DNA information.

How does Apple's ResearchKit apply to genetic studies?

These endeavors follow close on the heel of Apple's ResearchKit release in March, an open source medical research platform designed to facilitate researchers and developers to create apps that could potentially collect data from more than 700 million iOS users worldwide. Health information collected from these apps is stored in the cloud and can be analyzed by medical researchers and Apple's partner healthcare institutions. Apps developed within the ResearchKit platform are transforming the way people are recruited for clinical studies, with consent to participate available at the touch of a button. Thus far, apps have been launched for asthma, Parkinson's disease, diabetes, cardiovascular disease, and breast cancer, with more in the pipeline and many having already amassed significant users/participants.

Similarly, Apple's HealthKit platform has shown early promise in helping physicians monitor patients with chronic conditions by storing and transmitting patient-generated health information such as blood pressure, weight and blood sugar. According to Reuters, 14 of 23 top hospitals contacted indicated they have - or are in the process of - implementing pilot programs using Apple's HealthKit service. Not surprisingly, other technology giants are joining suit. Google has launched the Google Fit open platform, with multiple partnering research developers to connect to apps and devices. While Samsung Electronics recently announced collaboration with Partners HealthCare, the Boston-based integrated healthcare system that includes Massachusetts General Hospital, to develop mobile health solutions for chronic condition management with a clinical trial set to launch in June 2015.

What the future holds for DNA-apps

The blossoming successes of these open source platforms for medical research, including ResearchKit and HealthKit, has set the precedent for the use of mobile devices to reach potential study subjects, and in fact, empowers users to participate in medical research with ease of information sharing. This provides the necessary scaffold for using these platforms to enter the DNA-world of medical research. Indeed, Apple is leading the charge with multiple DNA-apps in the pipeline that may potentially reach millions of users and swiftly accumulate large-scale genome banks. While the initial studies proposed by Apple plan to test a limited number of medically relevant genes, the almost certain next step is the development of a database containing thousands, if not millions, of whole human genomes.

Importantly, for these DNA-apps and research platforms to be successful, they must overcome two critical challenges:

  1. efficient, cost-effective processing of large volumes of next-generation sequencing data, and
  2. massive storage capabilities.

The former requires a robust bioinformatics pipeline for high-throughput analysis with supercomputer capabilities, while the latter is being addressed with cloud-based infrastructure technology. Companies like IBM have begun to address this issue on the larger scale with the development of the IBM Watson Health system; a secure, cloud-based platform that utilizes the extensive processing capabilities of the Watson supercomputer to store, aggregate, and analyze massive amounts of medical data from various apps and devices. IBM has also recently expanded its partnership with Apple to offer cloud platform and analytics for both HealthKit and ResearchKit apps. Expectedly, IBM is not alone in the race for commercial storage of genome mega-databases. Google Genomics has offered to store any genome in the cloud for $25 a year and has developed an interface that moves DNA data into Google's cloud infrastructure where experiments are performed using the same database technology that indexes the Web5. Even the National Cancer Institute is capitalizing on commercial storage of genetic data, when last October they offered to pay $19 million to store copies of their 2.6 petabyte Cancer Genome Atlas with data centers at both Google Genomics and Amazon.

Potential for bioinformatics technology

So what does this mean for the future of DNA-apps and medical research? For one thing, the development of DNA-apps to potentially reach millions of users changes the way we participate in medical research and obtain health information. Not only will many of our own DNA genomes be stored in the cloud for research, but also the development of apps that facilitate interpretation of our personal genetic data for health predictions and sharing of health-relevant DNA information will inevitably become mainstream technology.

Notably, the nearly $4 trillion healthcare market has piqued the interest of technology super-giants like Apple, Google, and IBM, transforming the way health and genetic information is stored and analyzed. It has been well established for quite some time that advancement in bioinformatics technology has lagged behind its genetic counterparts. We are finally on the cusp of a new era of bioinformatics that will enable researchers to analyze genetic data on the largest scale to date. Allowing researchers to answer questions long since unanswered, and to ask an entirely new questions not previously possible. The ability to do comparative analysis and computational modeling against thousands, or potentially millions, of genome samples will set in motion a wave of scientific and medical discoveries for years to come. This simply changes everything.


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