This project will investigate several issues arising from the statistical and computational analysis of whole genome sequencing (WGS) based genomics studies. In the area of data management in WGS studies, we address the rapidly increasing cost associated with the transfer and storage of the massive files for the sequence reads and their associated quality scores. We will develop data compression methods to achieve a further compression of several folds beyond current standards, with minimal incurred errors. In the area of secondary analysis, we will develop new statistical learning methods to improve variant quality score recalibration and to filter out unreliable calls. This will improve te reliability of the key information provided by the WGS data, which are the variants calls indicating the locations where the genome differs from the reference and the nature of the differences. We will study methods for case-control studies based on WGS. In particular, we will develop statistical models to enable the integrating of information from multiple types of variants to obtain more powerful tests of association. We will apply the methods developed in this aim to the analysis of WGS data from a study on abdominal aortic aneurysm. Finally, we will address selected new questions associated with population scale WGS projects. Several national programs have recently been initiated to generate WGS data for hundreds of thousands of individuals with longitudinal medical records. The availability of this comprehensive data on a population scale will open up a rich frontier for genome medicine and will pose many new challenges for statistical analysis. We will formulate some of these new challenges and develop the statistical methods needed to meet these challenges.

Public Health Relevance

The research in this project concerns the design and implementation of statistical and computational methods for the analysis of data from whole genome sequencing studies. Methods will be developed for sequence quality score compression, variant call filtering, and methods for case-control association analysis and mega-cohort analysis based on whole genome sequencing.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
1R01HG007834-01
Application #
8750827
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Brooks, Lisa
Project Start
2014-09-22
Project End
2017-06-30
Budget Start
2014-09-22
Budget End
2015-06-30
Support Year
1
Fiscal Year
2014
Total Cost
$300,000
Indirect Cost
$94,741
Name
Stanford University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305