Though our previous candidate gene analysis [16] demonstrated that some genes were linked to both TNFα and TB, very few regions were linked to multiple traits in this genome scan.
Multiple traits shared this QTL, and it was detected with: count, location IN2 (count_IN2); leaf area, location CA (LA_GT); leaf area, location IN (LA_IN); location IN (length and width PCA value; LW_IN); and length and width for all locations (LW_all).
This mapping population was chosen for the first map due to the diverse genetic backgrounds of the parents: one is a small red lentil and the other a large green lentil and the population segregates for multiple traits for which this map will be used in the future to elucidate genetic control of these traits.
The use of site-averages, single-variable proxies, or multiple traits will not remove this underlying signal or reconcile the inherent uncertainties from non-independent data, and would mask the true uncertainty in prediction error.
Through the analysis of multiple traits over numerous years, this work contributes to our understanding of the genetic basis underlying phenotypic variation in hop, an essential prerequisite for future genetic improvement programs in hop.
Literature shows that the selected index is one of the helpful tools in this process, because it allows multiple traits to be selected simultaneously.
This might be achieved by integrating multiple traits that share underlying genetic and epigenetic architecture.
This improved the chance of combining multiple traits into a single variety.
This can occur if the same genes influence multiple traits, if genes for different traits are closely linked on chromosomes, or if environmental effects (e.g., temperature or diet) simultaneously influence multiple traits.
QTL mapping strategies adopted in this study revealed complementary results for single and multiple traits with high accuracy.
