Estimation of Genetic Parameters and Identification of Leaf Blast-Resistant Rice RILs Using Cluster Analysis and MGIDI
Reza Jalalifar,
Atefeh Sabouri,
Sedigheh Mousanejad
et al.
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… Show more
“…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.…”
Rice (Oryza sativa L.) stands as a important cereal sustaining over half of the world's population. This study delves into the challenges confronting breeders in the realm of crop improvement, specifically focusing on the intricate task of designing an ideotype-a genotype amalgamating diverse attributes for optimal performance. Traditional methodologies, exemplified by the Smith-Hazel (SH) index, grapple with issues such as multicollinearity and the complexities of economic weighting decisions. In response to these challenges, the Multi-Trait Genotype-Ideotype Distance Index (MGIDI), conceptualized by Olivoto and Nardino (2021), emerges as a ground breaking approach. Principal Component Analysis (PCA) aids in the reduction of trait dimensionality, revealing four key factors that collectively contribute to 79.444% of total variability. The Scree plot guides factor selection, ensuring a targeted analysis. The MGIDI index computation yields a total genetic gain of 273.025%, with specific traits like spikelet fertility and seedling dry weight exhibiting significant gains. Six high-performing rice accessions-SM227, NLR33892, MTU3626, 239(3), SMB3, and 405C3 were identified through MGIDI. These identified genotypes serve as valuable resources for developing recombinant populations, aligning with sustainable and effective crop improvement strategies. Additionally, these promising varieties exhibit strengths across various traits, offering potential for simultaneous trait improvement in future breeding programmes. The efficiency of MGIDI is highlighted through its innovative application in simultaneous trait selection, underscoring its significance across a wide range of crops.
“…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.…”
Rice (Oryza sativa L.) stands as a important cereal sustaining over half of the world's population. This study delves into the challenges confronting breeders in the realm of crop improvement, specifically focusing on the intricate task of designing an ideotype-a genotype amalgamating diverse attributes for optimal performance. Traditional methodologies, exemplified by the Smith-Hazel (SH) index, grapple with issues such as multicollinearity and the complexities of economic weighting decisions. In response to these challenges, the Multi-Trait Genotype-Ideotype Distance Index (MGIDI), conceptualized by Olivoto and Nardino (2021), emerges as a ground breaking approach. Principal Component Analysis (PCA) aids in the reduction of trait dimensionality, revealing four key factors that collectively contribute to 79.444% of total variability. The Scree plot guides factor selection, ensuring a targeted analysis. The MGIDI index computation yields a total genetic gain of 273.025%, with specific traits like spikelet fertility and seedling dry weight exhibiting significant gains. Six high-performing rice accessions-SM227, NLR33892, MTU3626, 239(3), SMB3, and 405C3 were identified through MGIDI. These identified genotypes serve as valuable resources for developing recombinant populations, aligning with sustainable and effective crop improvement strategies. Additionally, these promising varieties exhibit strengths across various traits, offering potential for simultaneous trait improvement in future breeding programmes. The efficiency of MGIDI is highlighted through its innovative application in simultaneous trait selection, underscoring its significance across a wide range of crops.
“…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.…”
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