Legend: Boston Convention & Exhibition Center = CC, Westin Boston Waterfront = W, Seaport Boston Hotel = S
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
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| CE_10C | Sun, 8/3/2014, 8:30 AM - 5:00 PM | CC-160A | |
| Analysis of Genome-Wide Sequencing Association Studies — Professional Development Continuing Education Course | |||
| ASA , Biometrics Section | |||
| Rapid advances in next generation sequencing technology have culminated in the development of large scale sequencing association studies for identifying genetic variants associated with complex traits and disease risk. However, analyzing such studies is difficult due to the sheer size and cost of these data sets, the systemic variability from the sequencing technology, as well as the massive dimensionality and rarity of the sequence variants. To address these critical statistical gaps, new methods for designing and analyzing sequencing studies have emerged. This course provides an overview of statistical methods for analysis of genome-wide sequencing association studies and is appropriate for both methodological and applied researchers. Topics include a review of basic genetics and sequencing technology, statistical pipelines for preprocessing of whole exome sequencing data, quality control methods, imputation methods for sequencing data, statistical methods for detecting rare variant effects, study design considerations, as well as emerging statistical issues including meta-analysis and interaction testing. Data examples will be provided and software will be discussed. Prerequisites for the course are beginning graduate or senior undergraduate coursework in applied and mathematical statistics. Prior knowledge of genetics is not required but can be helpful. | |||
| Instructor(s): Yun Li, University of North Carolina at Chapel Hill, Xihong Lin, Harvard School of Public Health, Michael Wu, Fred Hutchinson Cancer Research Center | |||
2014 JSM Online Program Home
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