Personal, omic, and phenotype data community aggregation platform

Abstract

A system and method are disclosed for the collection and aggregation of genomic, medical, and other data of interest for individuals and populations that may be of interest for analysis, research, pharmaceutical development, medical treatment, and so forth. Contributors become members of a community upon creation of an account and providing of data or files. The data is received and processed, such as to analyze, structure, perform quality control, and curate the data. Value or shares in one or more community databases are computed and attributed to each contributing member. The data is controlled to avoid identification or personalization. Third parties interested in the database information may contribute value (e.g., pay) for access and use. Value flows back to the members and to a system administrative entity.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority from and the benefit of U.S. Provisional Application Ser. No. 62 / 712,063, entitled “Genomic and Medical Data Aggregation System and Method,” filed Jul. 30, 2018; U.S. Provisional Application Ser. No. 62 / 647,572, entitled “OMIC Information Database and Management Systems,” filed Mar. 23, 2018; and U.S. Provisional Application Ser. No. 62 / 587,842, entitled “OMIC Information Database and Management Systems,” filed Nov. 17, 2017, all of which are hereby incorporated by reference in their entirety.BACKGROUND[0002]The invention relates generally to the aggregation of personal data, which may include omic and phenotype data. In particular, the techniques disclosed provide for aggregating contributed data from members of a community who share value by virtue of their contribution and consenting to the use of their aggregated data.[0003]In the present context, personal, omic, genomic, medical, health, environmental...

AI Technical Summary

Benefits of technology

[0017]One long term goal may be increasing the value of the “coins” or “value” attributed to data contributors (sometimes referred to in the present disclosure as “members”) by maximizing the value of the database. This goal will incentivize members or collaborators to partner will all

Problems solved by technology

However, when evaluating complex traits or disease assocations, GWAS requires obtaining and analyzing data from large numbers of samples.

Additionaly GWAS data does not comprehensively cover the human genome, and cover all varieties of genomic structural variation, and so it may not be sufficient to identify the genomic association.

However, in general such data have been of little interest to the pharmaceutical industry because of high variation in data quality, standards of data encoding, and information gaps in the data such as corresponding phenotypic information.

In some cases, the reason is inefficient database design, need to maintain a proprietary advantage over competitive entities, or poor data management practices.

Given these trends, it seems unlikely that the pace of future research will be limited by information technology problems.

A more serious problem is that many pharmaceutical and biotech companies forgo an open, collaborative approach to research and development for understandable strategic reasons, for instance because they estimate its financial or discovery benefits are outweighed by legal, regulatory, and intellectual property risks.

As a consequence, public trust in their research efforts is eroded by a lack of transparency and sense of common purpose, and a distrust of the companies ultimate motives, which discourages study participants from providing broad consent to use their data.

Decisions

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