The genetics of vigour-related traits in chickpea (Cicer arietinum L.): insights from genomic data.

KEY MESSAGE: QTL controlling vigour and related traits were identified in a chickpea RIL population and validated in diverse sets of germplasm. Robust KASP markers were developed for marker-assisted selection. To understand the genetic constitution of vigour in chickpea (Cicer arietinum L.), genomic data from a bi-parental population and multiple diversity panels were used to identify QTL, sequence-level haplotypes and genetic markers associated with vigour-related traits in Australian environments. Using 182 Recombinant Inbred Lines (RILs) derived from a cross between two desi varieties, Rupali and Genesis836, vigour QTL independent of flowering time were identified on chromosomes (Ca) 1, 3 and 4 with genotypic variance explained (GVE) ranging from 7.1 to 28.8%. Haplotype analysis, association analysis and graphical genotyping of whole-genome re-sequencing data of two diversity panels consisting of Australian and Indian genotypes and an ICRISAT Chickpea Reference Set revealed a deletion in the FTa1-FTa2-FTc gene cluster of Ca3 significantly associated with vigour and flowering time. Across the RIL population and diversity panels, the impact of the deletion was consistent for vigour but not flowering time. Vigour-related QTL on Ca4 co-located with a QTL for seed size in Rupali/Genesis836 (GVE = 61.3%). Using SNPs from this region, we developed and validated gene-based KASP markers across different panels. Two markers were developed for a gene on Ca1, myo -inositol monophosphatase (CaIMP), previously proposed to control seed size, seed germination and seedling growth in chickpea. While associated with vigour in the diversity panels, neither the markers nor broader haplotype linked to CaIMP was polymorphic in Rupali/Genesis836. Importantly, vigour appears to be controlled by different sets of QTL across time and with components which are independent from phenology.

Novel Salinity Tolerance Loci in Chickpea Identified in Glasshouse and Field Environments.

A better understanding of the genetics of salinity tolerance in chickpea would enable breeding of salt tolerant varieties, offering potential to expand chickpea production to marginal, salinity-affected areas. A Recombinant Inbred Line population was developed using accelerated-Single Seed Descent of progeny from a cross between two chickpea varieties, Rupali (salt-sensitive) and Genesis836 (salt-tolerant). The population was screened for salinity tolerance using high-throughput image-based phenotyping in the glasshouse, in hydroponics, and across 2 years of field trials at Merredin, Western Australia. A genetic map was constructed from 628 unique in-silico DArT and SNP markers, spanning 963.5 cM. Markers linked to two flowering loci identified on linkage groups CaLG03 and CaLG05 were used as cofactors during genetic analysis to remove the confounding effects of flowering on salinity response. Forty-two QTL were linked to growth rate, yield, and yield component traits under both control and saline conditions, and leaf tissue ion accumulation under salt stress. Residuals from regressions fitting best linear unbiased predictions from saline conditions onto best linear unbiased predictions from control conditions provided a measure of salinity tolerance per se, independent of yield potential. Six QTL on CaLG04, CaLG05, and CaLG06 were associated with tolerance per se. In total, 21 QTL mapped to two distinct regions on CaLG04. The first distinct region controlled the number of filled pods, leaf necrosis, seed number, and seed yield specifically under salinity, and co-located with four QTL linked to salt tolerance per se. The second distinct region controlled 100-seed weight and growth-related traits, independent of salinity treatment. Positional cloning of the salinity tolerance-specific loci on CaLG04, CaLG05, and CaLG06 will improve our understanding of the key determinants of salinity tolerance in chickpea.

Exploring genetic variation for salinity tolerance in chickpea using image-based phenotyping.

Soil salinity results in reduced productivity in chickpea. However, breeding for salinity tolerance is challenging because of limited knowledge of the key traits affecting performance under elevated salt and the difficulty of high-throughput phenotyping for large, diverse germplasm collections. This study utilised image-based phenotyping to study genetic variation in chickpea for salinity tolerance in 245 diverse accessions. On average salinity reduced plant growth rate (obtained from tracking leaf expansion through time) by 20%, plant height by 15% and shoot biomass by 28%. Additionally, salinity induced pod abortion and inhibited pod filling, which consequently reduced seed number and seed yield by 16% and 32%, respectively. Importantly, moderate to strong correlation was observed for different traits measured between glasshouse and two field sites indicating that the glasshouse assays are relevant to field performance. Using image-based phenotyping, we measured plant growth rate under salinity and subsequently elucidated the role of shoot ion independent stress (resulting from hydraulic resistance and osmotic stress) in chickpea. Broad genetic variation for salinity tolerance was observed in the diversity panel with seed number being the major determinant for salinity tolerance measured as yield. This study proposes seed number as a selection trait in breeding salt tolerant chickpea cultivars.

Quantifying the Onset and Progression of Plant Senescence by Color Image Analysis for High Throughput Applications.

Leaf senescence, an indicator of plant age and ill health, is an important phenotypic trait for the assessment of a plant's response to stress. Manual inspection of senescence, however, is time consuming, inaccurate and subjective. In this paper we propose an objective evaluation of plant senescence by color image analysis for use in a high throughput plant phenotyping pipeline. As high throughput phenotyping platforms are designed to capture whole-of-plant features, camera lenses and camera settings are inappropriate for the capture of fine detail. Specifically, plant colors in images may not represent true plant colors, leading to errors in senescence estimation. Our algorithm features a color distortion correction and image restoration step prior to a senescence analysis. We apply our algorithm to two time series of images of wheat and chickpea plants to quantify the onset and progression of senescence. We compare our results with senescence scores resulting from manual inspection. We demonstrate that our procedure is able to process images in an automated way for an accurate estimation of plant senescence even from color distorted and blurred images obtained under high throughput conditions.

Quantitative profiling of polar primary metabolites of two chickpea cultivars with contrasting responses to salinity.

This study reports a GC-QqQ-MS method for the quantification of forty-eight primary metabolites from four major classes (sugars, sugar acids, sugar phosphates, and organic acids) which can be applied to a number of biological systems. The method was validated in terms of linearity, reproducibility and recovery, using both calibration standards and real samples. Additionally, twenty-eight biogenic amines and amino acids were quantified using an established LC-QqQ-MS method. Both GC-QqQ-MS and LC-QqQ-MS quantitative methods were applied to plant extracts from flower and pod tissue of two chickpea (Cicer arietinum L.) cultivars differing in their ability to tolerate salinity, which were grown under control and salt-treated conditions. Statistical analysis was applied to the data sets using the absolute concentrations of metabolites to investigate the differences in metabolite profiles between the different cultivars, plant tissues, and treatments. The method is a significant improvement of present methodology for quantitative GC-MS metabolite profiling of organic acids and sugars, and provides new insights of chickpea metabolic responses to salinity stress. It is applicable to the analysis of dynamic changes in endogenous concentrations of polar primary metabolites to study metabolic responses to environmental stresses in complex biological tissues.