Meta-analysis of Mapping Studies: Integrating QTLs Towards Candidate Gene Discovery

Meta-analysis consists of pooling results from multiple studies, and allows prediction of more precise and meaningful data. A plethora of QTL mapping studies for multiple traits in different crop plants have generated vast and often redundant information. A meta-QTL analysis helps in collating available trait specific QTL data from multiple studies and purging the redundant data, thereby leading to identification of more robust genomic regions known as “Meta-QTLs”. The identified meta-QTL regions have shorter confidence intervals from the yielding QTLs of individual mapping studies, thereby helping in gaining a deeper insight into the genetic framework of complex traits. This chapter provides an overview of the available tools for meta-analysis and a user-friendly protocol for using “BioMercator”, the most widely used software for performing a meta–QTL analysis. A good meta-QTL analysis is dependent on the availability of multiple independent trait specific QTL studies and therefore involves an extensive literature survey as an essential primary step. The compiled QTL information is processed to generate map files and a consensus map followed by projection of QTLs on the consensus map ultimately leading to identification of meta-QTL regions. Together, the reduction in the confidence interval and integration of meta-QTL regions with additional functional genomics datasets helps in shortlisting potential candidate genes and reduce the overall efforts for crop improvement through marker-assisted breeding or genetic engineering.