Genetics and Genomics Commons^™

The Role Of An Explicit Causal Framework In Affected Sib Pair Designs With Covariates , Constantine E. Frangakis, Fan Li, Betty Q. Doan

Johns Hopkins University, Dept. of Biostatistics Working Papers

The affected sib/relative pair (ASP/ARP) design is often used with covariates to find genes that can cause a disease in pathways other than through those covariates. However, such "covariates" can themselves have genetic determinants, and the validity of existing methods has so far only been argued under implicit assumptions. We propose an explicit causal formulation of the problem using potential outcomes and principal stratification. The general role of this formulation is to identify and separate the meaning of the different assumptions that can provide valid causal inference in linkage analysis. This separation helps to (a) develop better methods under explicit …

New Statistical Paradigms Leading To Web-Based Tools For Clinical/Translational Science, Knut M. Wittkowski

COBRA Preprint Series

As the field of functional genetics and genomics is beginning to mature, we become confronted with new challenges. The constant drop in price for sequencing and gene expression profiling as well as the increasing number of genetic and genomic variables that can be measured makes it feasible to address more complex questions. The success with rare diseases caused by single loci or genes has provided us with a proof-of-concept that new therapies can be developed based on functional genomics and genetics.

Common diseases, however, typically involve genetic epistasis, genomic pathways, and proteomic pattern. Moreover, to better understand the underlying biologi-cal …

Searching For Differentially Expressed Gene Combinations, Marcel Dettling, Edward Gabrielson, Giovanni Parmigiani

Johns Hopkins University, Dept. of Biostatistics Working Papers

Background: Comparison of mRNA expression levels across biological samples is a widely used approach in genomics. Available data-analytic tools for deriving comprehensive lists of differentially expressed genes rely on data summaries formed using each gene in isolation from others. These approaches ignore biological relationships among genes and may miss important biological insight provided by genomics data.

Methods: We propose a fast, easily interpretable and scalable approach for identifying pairs of genes that are differentially expressed across phenotypes or experimental conditions. These are defined as pairs for which there is detectable phenotype discrimination using the joint distribution, but not from either …