Estimation of Genetic Parameters and Identification of Leaf Blast-Resistant Rice RILs Using Cluster Analysis and MGIDI

Abstract

Rice blast disease, caused by the fungus Magnaporthe oryzae, poses a significant threat to rice cultivation. One effective way to deal with this disease is to identify and introduce resistant varieties using different breeding methods. This study utilized a population of 153 recombinant inbred lines (RILs) derived from the crossing of the Shahpasand (SH) and IR28 varieties, characterized by susceptibility and resistance to leaf blast, respectively. In combination with 12 control varieties, these genotypes were subjected to an extensive evaluation of disease severity (5 stages), the area under the disease progress curve (AUDPC), type, and the infection rate in 2021 and 2022. Analysis of variance revealed significant genetic variation, highlighting the potential of the RIL population for identifying and selecting resistant lines. Employing cluster analysis and the multi-trait genotype-ideotype distance index (MGIDI), 17 lines were identified as the most resistant over a two-year evaluation period. The average AUDPC for these resistant lines was estimated at 2.435 ± 0.114, and lines 17 and 111 had the lowest AUDPC (1.526 and 1.630, respectively) and showed the least infection in two years. Conversely, lines 42 and 43 showed the highest AUDPC values (255.312 and 248.209) along with heightened sensitivity. The use of MGIDI yielded a substantial selection differential (SD) of −59.12% for traits related to leaf blast disease resistance, demonstrating the effectiveness of this method. Furthermore, new recombinant populations are expected to be developed in future plant breeding projects by crossing the most susceptible and resistant lines, which will be new sources of resistance to this disease.

Multi trait genotype- ideotype distance index (MGIDI) for early seedling vigour and yield related traits to identify elite lines in rice (Oryza sativa L.)

“…Moreover, Olivoto and Nardino (2021) have indicated that MGIDI stands out as the most efficient index for selecting genotypes with desired characteristics, further reinforcing its applicability and effectiveness in crop improvement strategies. Jalalifar et al (2023) shed light on the promising prospects of selected rice genotypes through MGIDI in their study, emphasizing their careful selection as a valuable resource for breeding programs. These chosen genotypes serve as a foundation for creating recombinant populations through strategic crosses, fostering maximum genetic diversity for the development of new rice lines.…”

Effective multidimensional treatment identification of different chemical fertilizers: Response of insect dynamics and rice production

“…This technique has been successfully applied in various crop breeding programs for genotype selection, such as wheat [ 30 ], rice [ 31 ] and maize [ 32 ]. Jalalifar et al [ 33 ] have recently introduced an advanced tool that enables breeders to efficiently select the most desirable genotypes with resistance to leaf blast in rice considering the interaction between outcome across multiple years. Consequently, in present study, EMT is anticipated to contribute to increased rice yields and has the potential to alleviate insect pest pressures, thereby reducing the incidence of pest outbreaks and minimizing yield losses.…”