Meta-analysis of QTL and ontology-based candidate gene prioritization for key agronomic agronomic traits in cereals
QTL mapping remains an unavoidable technique to decipher the genetic components of quantitative traits variation in plants and especially in cereals given its economic importance. However the efficiency of statistical methods and the advance made in conducting such experiments, There still exist some bottlenecks mainly represented in the lack of heterogeneity between different studies, lack of results validation, and also imprecise statistical QTL detection leads to huge confidence intervals which increases candidate gene mining complexity. However, data integration methods such as meta-analysis can provide an efficient framework to detect “real” QTL and narrow down their confidence intervals for better control of candidate gene mining. Therefore, the objective of the first part of this work is to provide an overview of current advances in QTL analysis and meta-analysis highlighting different statistical methods and computational tools employed. In the second part, a meta-analysis approach has been applied on published QTL associated with Fusarium head blight resistance in barley. And in last part, a meta-analysis of QTL associated with four flowering time related traits in maize was performed using public QTL datasets associated with flowering-time related traits in maize, also an ontology-based candidate gene prioritization approach has been executed on genes flaking detected metaQTL confidence intervals in order to detect GO terms that are most over-represented in these regions.