Hierarchical probabilistic models for multiple gene/variant associations based on next-generation sequencing data.

ABSTRACT:

Motivation

The identification of genetic variants influencing gene expression (known as expression quantitative trait loci or eQTLs) is important in unravelling the genetic basis of complex traits. Detecting multiple eQTLs simultaneously in a population based on paired DNA-seq and RNA-seq assays employs two competing types of models: models which rely on appropriate transformations of RNA-seq data (and are powered by a mature mathematical theory), or count-based models, which represent digital gene expression explicitly, thus rendering such transformations unnecessary. The latter constitutes an immensely popular methodology, which is however plagued by mathematical intractability.

Results

We develop tractable count-based models, which are amenable to efficient estimation thr

SUBMITTER: Vavoulis DV 

PROVIDER: S-EPMC5637939 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature