Grant Number:	2R13CA124365-06 Interpret this number
Primary Investigator:	Lin, Xihong
Organization:	Harvard School Of Public Health
Project Title:	Conferences on Emerging Statistical Issues in Biomedical Research
Fiscal Year:	2011

DESCRIPTION (provided by applicant): This is a renewal of the R13 application to continue the very successful annual conference series on Emerging Statistical and Quantitative Issues in Genomic Research in Health Sciences, which has been hosted by the Program in Quantitative Genomics (PQG) at Harvard School of Public Health (HSPH) in the last five years and is open to the whole research community. Modern health sciences are evolving rapidly, driven in large part by the rapid advance of high- throughput biotechnology. This changing landscape has provided researchers in health sciences, especially in cancer and other chronic diseases, with rich opportunities for new discoveries using genetic information to better understand disease biology and etiology and interplay of genes and environment, and to enhance risk prediction and personalized medicine in patient care. While such an "omics" era presents many exciting research opportunities, the explosion of massive information about the human genome presents extraordinary challenges in data processing, integration, analysis and result interpretation. Traditional statistical and computational techniques cannot meet these new demands. There is a critical need to discuss emerging quantitative issues at the forefront of scientific exploration, and to promote development of innovative statistical and quantitative methods to deal with massive high- throughput genomic and 'omics data in basic, population and clinical sciences. In this renewal application, we propose to continue hosting this conference series, where each conference focuses on emerging quantitative research areas of most current genomic research interest and the particular focus will evolve over time. A key feature of the conference series is to provide a timely and interactive platform to engage cross-discipline senior and junior investigators, such as statistical genetists, computational biologists, genetic epidemiologists, molecular biologists, and clinical scientists in cancer and other chronic diseases to critique existing quantitative methods, discuss in-depth emerging statistical and quantitative issues, identify priorities for future research and disseminate results. The 2011 conference is entitled ''Emerging Statistical and Quantitative Issues in Genomic Medicine." The 2012 conference is on " Beyond the 1000 Genomes Project: Sequencing, Complex Traits, and Population." The Conference series is cosponsored by HSPH PQG, the Department of Biostatistics of the HSPH and the Department of Biostatics and Computational Biology of the Dana Farber Cancer Institute. Serious efforts will continue to engage junior researchers, women and minorities in Conference activities. PUBLIC HEALTH RELEVANCE: The explosion of massive information about the human genome presents extraordinary challenges in data processing, integration, analysis and result interpretation. An annual conference on Emerging Statistical and Quantitative Issues in Genomic Research in Health Sciences is proposed to continue engaging cross-disciplinary quantitative and subject-matter researchers to critique existing quantitative methods, discuss in-depth emerging statistical and quantitative issues, identify priorities for future research and disseminate results in genomic research in health sciences.




A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.
Authors: Li Z. , Li X. , Zhou H. , Gaynor S.M. , Selvaraj M.S. , Arapoglou T. , Quick C. , Liu Y. , Chen H. , Sun R. , et al. .
Source: Nature Methods, 2022-10-27 00:00:00.0; , .
EPub date: 2022-10-27 00:00:00.0.
PMID: 36303018
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Source: Nature Genetics, 2020-08-24 00:00:00.0; , .
EPub date: 2020-08-24 00:00:00.0.
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