The human genome project and its offshoots have dramatically increased the amount of genetic data available to researchers. Interdisciplinary studies that bring together biologists and quantitative scientists are critical to mine the vast amount of public data and understand the genetic basis of human disease and variation. New quantitative methods have revolutionized the analysis of high dimensional genomic data sets. We have over 18 years of NIH-funded experience developing these leading-edge methods, and over ten years experience teaching these methods to genetic researchers in intensive summer workshops. Our continuing goal is to create tools and workshops to train the next generation of genomic scientists to mine, analyze, and share data, which will advance the understanding, diagnosis, and treatment of disease, and promote public health. As part of this educational endeavor, we propose to offer an intensive week-long workshop in the theory and practice of statistical genomics and systems biology (with an emphasis on network methods). The participants will spend much of their time performing hands-on exercises with widely used, state-of-the-art applications our group has developed for discovering genes that influence disease traits. We will use real and simulated data sets that will journey from classic linkage and association tests to the analysis of high-density SNP genotypes, RNA-seq transcript counts, rare variants from deep sequencing, and network analysis methods,. The participants will range from graduate students to senior researchers, both quantitative and non-quantitative, and from many fields, including biology, genetics, clinical and biomedical research, interdisciplinary and translational sciences, bioinformatics, statistics, computational biology, computer science, applied mathematics, physics, and social science. We plan to bring together quantitative, biological, and interdisciplinary researchers and give them an understanding of the issues and the vocabulary of quantitative genomics so that they can communicate and collaborate effectively and productively. Without understanding and close collaboration between quantitative and qualitative scientists, solutions to the challenges of modern genomics will elude us. The workshops will be offered on the campus of the University of California, Los Angeles, and all materials will also be offered remotely, through our Distance Learning Initiative. In successive years the emphasis will alternate between statistical genomics and systems biology, but each year both topics will be addressed. The focal topic each year will be covered in an intensive five-day course, with computer exercises. The secondary topic will be covered in an optional two-day module either before or after the primary topic. All course materials, plus additional resources, will be available throughout the year to course alumni and any interested researcher. Feedback and interaction with the course instructors will also be available throughout the year in the Distance Learning environment.

Public Health Relevance

The human genome project and its offshoots have dramatically increased the amount of genetic data available to researchers. Interdisciplinary studies that bring together biological and quantitative scientists are critical for mining this vas amount of public data in order to understand the genetic basis of human disease and variation. Our goal is to train current and upcoming generations of interdisciplinary scientists, and to develop and distribute innovative educational software tools that can be used to train researchers to share, mine, and analyze data, which in turn will advance the understanding, diagnosis, and treatment of disease, and promote public health.

Agency
National Institute of Health (NIH)
Type
Education Projects (R25)
Project #
5R25GM103774-02
Application #
8657457
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Krasnewich, Donna M
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California Los Angeles
Department
Genetics
Type
Schools of Medicine
DUNS #
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Lange, Kenneth; Papp, Jeanette C; Sinsheimer, Janet S et al. (2014) Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data. Annu Rev Stat Appl 1:279-300
Lange, Kenneth; Papp, Jeanette C; Sinsheimer, Janet S et al. (2013) Mendel: the Swiss army knife of genetic analysis programs. Bioinformatics 29:1568-70