A bioinformatic and transcriptomic approach to identifying positional candidate genes without fine mapping: an example using rice root-growth QTLs

Fine mapping can accurately identify positional candidate genes for quantitative trait loci (QTLs) but can be time consuming, costly, and, for small-effect QTLs with low heritability, difficult in practice. We propose an alternative approach, which uses meta-analysis of original mapping data to produce a relatively small confidence interval for target QTLs, lists the underlying positional candidates, and then eliminates them using whole-genome transcriptomics. Finally, sequencing is conducted on the remaining candidate genes allowing identification of allelic variation in either expression or protein sequence. We demonstrate the approach using root-growth QTLs on chromosomes 2, 5, and 9 of the Bala × Azucena rice mapping population. Confidence intervals of 10.5, 9.6, and 5.4 cM containing 189, 322, and 81 genes, respectively, were produced. Transcriptomics eliminated 40% of candidate genes and identified nine expression polymorphisms. Sequencing of 30 genes revealed that 57% of the predicted proteins were polymorphic. The limitations of this approach are discussed.