The objective of this project is to develop software for the analysis of data from large- scale genotyping and sequencing genetic studies, building on the existing software package PLINK. PLINK, a software tool to manipulate and analyze whole-genome SNP datasets that has been actively developed over the past four years and has a wide base of users.
The specific aims are to significantly upgrade core capacities, the interface, auxiliary resources and user-support: Core capacities: significantly adapt and upgrade data-storage capacities to handle a) order-of-magnitude larger datasets than can fit into memory and b) a more generic, unified representation of different types of genetic variation data and meta-information. Interface: extend the existing interface to provide a) a looser coupling between data storage and analysis components, via multiple interfaces in external languages, including standard bioinformatics tools such as R and Perl, and b) features designed to facilitate reproducible research and parallel processing. Auxiliary resources: package standard existing resources, including the functional annotation of variants, reference genome sequences and gene assemblies, pathways and ontologies, in a manner that allows seamless integration between genomic resources and user data. Support: create a high-quality collection resources to support users, via online documentation and tutorials, including user-generated wiki pages, e-mail support and an annual training course. Particular attention will be paid to ensure interoperability with other major software, file-formats and resources that are generated by the broader genetics community.

Public Health Relevance

This Project is to develop software for the analysis of large datasets from modern genetic studies. New high-throughput genotyping and sequencing technologies are capable of producing vast amounts of data, but there is a need for analytic tools that biomedical researchers can use. These studies have the potential to uncover genetic determinants for a large number of diseases and traits, which can be relevant for prediction of risk, and give insight into novel targets for treatments.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
1R01HG005827-01
Application #
7934359
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brooks, Lisa
Project Start
2010-09-27
Project End
2013-06-30
Budget Start
2010-09-27
Budget End
2011-06-30
Support Year
1
Fiscal Year
2010
Total Cost
$319,350
Indirect Cost
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
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