Nilanjan

Nilanjan Chatterjee, PhD

Bloomberg Distinguished Professor

Department of Biostatistics, Bloomberg School of Public Health Department of Oncology, School of Medicine

Johns Hopkins University

Contact:
Johns Hopkins Bloomberg School of Public Health
Department of Biostatistics
615 N Wolfe Street
Baltimore, MD 21205
Phone: 410-502-8971
Fax: 410-955-0958
E-Mail: nilanjan@jhu.edu
Administrative Coordinator:
Kim Clay
Phone:  410-955-3067
Email:  kphilpo3@jhmi.edu

ASSET

ASSET

DESCRIPTION

This package is for subset-based association analysis of heterogeneous but possibly related traits.

DETAILS

ASSET is a suite of statistical tools specifically designed to be powerful for pooling association signals across multiple studies when true effects may exist only in a subset of the studies and could be in opposite directions across studies. The method explores all possible subsets (or a restricted set if user specifies so) of studies and evaluates fixed-effect meta-analysis-type test-statistics for each subset. The final test-statistic is obtained by maximizing the subset-specific test-statistics over all possible subsets and then evaluating its significance after efficient adjustment for multiple-testing, taking into account the correlation between test-statistics across different subsets due to overlapping subjects. The method not only returns a p-value for significance for the overall evidence of association of a SNP across studies, but also outputs the "best subset" containing the studies that contributed to the overall association signal. For detection of association signals with effects in opposite directions, ASSET allows subset search separately for positively- and negatively- associated studies and then combines association signals from two directions using a chi-square test-statistic. The method can take into account correlation due to overlapping subjects across studies (e.g. shared controls). Although the method is originally developed for conducting genetic association scans, it can also be applied for analysis of non-genetic risk factors as well.

 

UPDATES

The new version includes option to pre-screen phenotypes based on significance of marginal association tests. It increases computational speed for ASSET when analyzing larger number of phenotypes.

AUTHOR

Samsiddhi Bhattacharjee, Nilanjan Chatterjee and Bill Wheeler.

REFERENCE

A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits.
Bhattacharjee S, Rajaraman P, Jacobs KB, Wheeler WA, Melin BS, Hartge P; GliomaScan Consortium, Yeager M, Chung CC, Chanock SJ, Chatterjee N.
Am J Hum Genet. 2012 May 4;90(5):821-35. doi: 10.1016/j.ajhg.2012.03.015.

SUPPORT

Questions? Contact: Dr. Nilanjan Chatterjee