Development of a Candidate Gene Selection Model Based on Multiple Species and Data Types

Although multiple large data sets have been generated for identification of candidate genes for disease or economically important phenotypic traits, many of the key genes involved remain unknown.  Previously, we have developed an algorithm that incorporates weights for genes quantified in multiple species and types of data into an aggregated gene weight to assess their relative importance.  The objective of this project was to develop methods for inclusion of other gene or phenotype specific data types to the weighting model.  A simulated data set of QTL and gene expression (GE) data were created from data available from the Animal QTL Database and NCBI GEO, which led to the identification of myostatin in muscle hypertrophy phenotypes.  Genes from QTL and GE projects were assigned weights, normalized and aggregated to determine a specific gene’s relative importance, as previously described.  Pathway data and protein-protein interactions were developed using the ranked gene weights from the QTL and GE data.  Gene weights were modified by the inclusion of weighting scores from significant pathways and interactions with proteins.  The inclusion of additional data types improved the prediction of myostatin in the simulated data set.  Generally, increased number of QTL within the simulated projects decreased the predictability of myostatin.  The predictability of myostatin in simulated projects with a low percentage of the myostatin QTL and larger simulated projects was improved by the inclusion of data from multiple species and data types.