Project Description Recent investments in data collection have produced rich catalogs of information about genomic function, human variation, and mammalian evolution, but improved computational tools are needed to integrate and interpret these catalogs, in order to permit the acquisition of new biological knowledge and advances in medicine. Here we outline an ambitious project to develop computational methods that will integrate publicly available data catalogs to provide deep new insights about the evolution and function of sequences in the human genome. Our proposal focuses in particular on noncoding sequences, which remain the most sparsely annotated and poorly understood regions of the genome. The proposal addresses three fundamental and closely related problems: (1) inference of human demography, to provide improved """"""""null models"""""""" for statistical genetics and reveal local signatures of gene flow, natural selection, and other phenomena;(2) detection of natural selection on interspersed noncoding sequences such as transcription factor binding sites, to provide information about their function and the evolutionary processes that have shaped them;and (3) genome-wide prediction of """"""""functional potential"""""""" based on integrated data sets, to identify new functional elements and prioritize candidate disease loci for experimental follow-up. Our proposal includes innovative statistical modeling, new algorithms for inference, the development of freely available software tools and browser resources, and detailed analyses of the latest genomic data sets. To our knowledge this will be the most comprehensive effort yet undertaken to integrate comparative, population, and functional genomic data in addressing fundamental questions about the function and evolution of sequences in the human genome. Our software and the results of our data analysis will be freely available to the research community.

Public Health Relevance

We propose to develop new computational and statistical methods, and new publicly avail- able resources, to integrate catalogs of information about genomic function, human variation, and mammalian evolution. This work will help to convert the rich data that has been collected in recent years into knowledge about the biology of genomes and advances in medicine.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Krasnewich, Donna M
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Cornell University
Biostatistics & Other Math Sci
Schools of Earth Sciences/Natur
United States
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