Discovery of genomic regions associated with resistance to late wilt disease caused by Harpophora maydis (Samra, Sabet and Hing) in maize (Zea mays L.).

Late wilt disease (LWD) caused by Harpophora maydis (Samra, Sabet and Hing) is emerging as major production constraint in maize across the world. As a prelude to develop maize hybrid resistance to LWD, genetic basis of resistance was investigated. Two F2:3 mapping populations (derived from CV156670 × 414-33 (P-1) and CV156670 × CV143587 (P-2)) were challenged with LWD at two locations (Kallinayakanahalli and Muppadighatta) during 2017 post-rainy season. A wider range of LWD scores was observed at both locations in both the populations. LWD response was influenced by significant genotype × location interaction. Six and 56 F2:3 progeny families showed resistance level better than resistant parent. A total of 150 and 199 polymorphic single nucleotide polymorphism markers were used to genotype P-1 and P-2, respectively. Inclusive composite interval mapping was performed to detect significant Quantitative Trait Loci (QTL), QTL × QTL, QTL × location interaction effects. Three major and four minor QTL controlling LWD resistance were detected on chromosome-1. The position and effect of the QTL varied with the location. Significant di-QTL interactions involving QTL (with significant and/or non-significant effects) located within and between all the ten chromosomes were detected. Five of the seven detected QTL showed significant QTL × location interaction. Though two major QTL (q-lw-1.5 and q-lw-1.6) with lower Q×L interaction effects could be considered as stable, their phenotypic variance is not large enough to deploy them in Marker Assisted Selection (MAS). However, these QTL are of paramount importance in accumulating positive alleles for LWD resistance breeding.

Mapping genomic regions of moisture deficit stress tolerance using backcross inbred lines in wheat (Triticum aestivum L.).

Identification of markers associated with major physiological and yield component traits under moisture deficit stress conditions in preferred donor lines paves the way for marker-assisted selection (MAS). In the present study, a set of 183 backcross inbred lines (BILs) derived from the cross HD2733/2*C306 were genotyped using 35K Axiom genotyping array and SSR markers. The multi-trait, multi-location field phenotyping of BILs was done at three locations covering two major wheat growing zones of India, north-western plains zone (NWPZ) and central zone (CZ) under varying moisture regimes. A linkage map was constructed using 705 SNPs and 86 SSR polymorphic markers. A total of 43 genomic regions and QTL × QTL epistatic interactions were identified for 14 physiological and yield component traits, including NDVI, chlorophyll content, CT, CL, PH, GWPS, TGW and GY. Chromosomes 2A, 5D, 5A and 4B harbors greater number of QTLs for these traits. Seven Stable QTLs were identified across environment for DH (QDh.iari_6D), GWPS (QGWPS.iari_5B), PH (QPh.iari_4B-2, QPh.iari_4B-3) and NDVI (QNdvi1.iari_5D, QNdvi3.iari_5A). Nine genomic regions identified carrying major QTLs for CL, NDVI, RWC, FLA, PH, TGW and biomass explaining 10.32-28.35% of the phenotypic variance. The co-segregation of QTLs of physiological traits with yield component traits indicate the pleiotropic effects and their usefulness in the breeding programme. Our findings will be useful in dissecting genetic nature and marker-assisted selection for moisture deficit stress tolerance in wheat.

Analysis on diagnosing diabetic retinopathy by segmenting blood vessels, optic disc and retinal abnormalities.

The main intention of mass screening programmes for Diabetic Retinopathy (DR) is to detect and diagnose the disorder earlier than it leads to vision loss. Automated analysis of retinal images has the likelihood to improve the efficacy of screening programmes when compared over the manual image analysis. This article plans to develop a framework for the detection of DR from the retinal fundus images using three evaluations based on optic disc, blood vessels and retinal abnormalities. Initially, the pre-processing steps like green channel conversion and Contrast Limited Adaptive Histogram Equalisation is done. Further, the segmentation procedure starts with optic disc segmentation by open-close watershed transform, blood vessel segmentation by grey level thresholding and abnormality segmentation (hard exudates, haemorrhages, Microaneurysm and soft exudates) by top hat transform and Gabor filtering mechanisms. From the three segmented images, the feature like local binary pattern, texture energy measurement, Shanon's and Kapur's entropy are extracted, which is subjected to optimal feature selection process using the new hybrid optimisation algorithm termed as Trial-based Bypass Improved Dragonfly Algorithm (TB - DA). These features are given to hybrid machine learning algorithm with the combination of NN and DBN. As a modification, the same hybrid TB - DA is used to enhance the training of hybrid classifier, which outputs the categorisation as normal, mild, moderate or severe images based on three components.

Determination of genetic relationships among elite thermosensitive genic male sterile lines (TGMS) of rice (Oryza sativa L.) employing morphological and simple sequence repeat (SSR) markers.

A set of morphological traits and SSR markers were used to determine the genetic relationship among 12 elite thermosensitive genic male sterile (TGMS) lines developed at three different research institutions of India. Agro-morphological data recorded on 20 morphological traits revealed a wide base of genetic variation and a set of four morphological traits could distinguish most of the TGMS lines. Analysis with 30 SSR markers (20 EST-SSRs and 10 genomic SSRs) revealed 27 markers to be polymorphic, amplifying a total of 83 alleles. Each SSR marker amplified 2-6 alleles with an average of 2.76 alleles per marker and a PIC value varying from 0.54 to 0.96. Cluster analysis based on SSR and morphological data clearly differentiated the lines according to their source of origin. Correlation analysis between morphological and molecular data revealed a very poor association (r = 0.06), which could be attributed to selection pressure, genetic drift, sampling error and unknown relationship among related lines. The SSR markers discriminated the genotypes distinctly and quantified the genetic diversity precisely among the TGMS lines. Data on the yield per plant indicated that the genotypes grouping under a similar cluster showed same heterotic behaviour as compared to the genotypes from different clusters when crossed to similar pollinators.