Haile, T. A. | Search bioRxiv

bioRxiv2021

Genetic Basis for Lentil Adaptation to Summer Cropping in Northern Temperate Environments

The continued success of lentil (Lens culinaris Medik.) genetic improvement relies on the availability of broad genetic diversity and new alleles need to be identified and incorporated into the cultivated gene pool. Availability of robust and predictive markers greatly enhances the precise transfer of genomic regions from unadapted germplasm. Quantitative trait loci (QTLs) for key phenological traits in lentil were located using a recombinant inbreed line (RIL) population derived from a cross between an Ethiopian landrace (ILL 1704) and a northern temperate cultivar (CDC Robin). Field experiments were conducted at Sutherland research farm in Saskatoon and at Rosthern, Saskatchewan, Canada during 2018 and 2019. A linkage map was constructed using 21,634 SNPs located on seven linkage groups (LGs) which correspond to the seven haploid chromosomes of lentil. Eight QTL were identified for six phenological traits. Flowering related QTL were identified at two regions on LG6. FLOWERING LOCUS T (FT) genes were annotated within the flowering time QTL interval based on the lentil reference genome. Similarly, a major QTL for post-flowering developmental processes was located on LG5 with several senescence-associated genes annotated within the QTL interval. The flowering time QTL was validated in a different genetic background indicating the potential use of the identified markers for marker-assisted selection to precisely transfer genomic regions from exotic germplasm into elite crop cultivars without disrupting adaptation. Core IdeasO_LIStable QTL were located for key phenological traits in lentil that lead to regional adaptation. C_LIO_LIFT genes are candidates for controlling flowering time in lentil grown in temperate environments. C_LIO_LIA major locus controlling post-flowering developmental processes was located on lentil LG5 with several senescence-associated genes annotated within the QTL interval. C_LIO_LIMarkers identified in this study can be useful for marker-assisted selection to precisely transfer genomic regions from exotic germplasm into elite lentil cultivars without disrupting adaptation. C_LI

molecular biology↗

bioRxiv2019

Genomic selection for lentil breeding: empirical evidence

Genomic selection (GS) is a type of marker-based selection which was initially suggested for livestock breeding and is being encouraged for crop breeding. Several statistical models and approaches have been developed to implement GS; however, none of these methods have been tested for use in lentil breeding. This study was conducted to evaluate different GS models and prediction scenarios based on empirical data and to make recommendations for designing genomic selection strategies for lentil breeding. We evaluated nine single-trait models, two multiple-trait models, and models that account for population structure and genotype-by-environment interaction (GEI) using a lentil diversity panel and two recombinant inbred lines (RIL) populations that were genotyped using a custom exome capture assay. Within-population, across-population and across-environment predictions were made for five phenology traits. Prediction accuracy varied among the evaluated models, populations, prediction scenarios, traits, and statistical models. Single-trait models showed similar accuracy for each trait in the absence of large effect QTL but BayesB outperformed all models when there were QTL with relatively large effects. Models that accounted for GEI and multiple-trait (MT) models increased prediction accuracy for a low heritability trait by up to 66% and 14% but accuracy did not improve for traits of high heritability. Moderate to high accuracies were obtained for within-population and across-environment predictions but across-population prediction accuracy was very low. This suggests that GS can be implemented in lentil to make predictions within populations and across environments, but across-population prediction should not be considered when the population size is small.

genomics↗