Mapping consistent additive and epistatic QTLs for plant production traits under drought in target populations of environment using locally adapted landrace in rice (<i>Oryza sativa</i> L.)

Rajurkar, Ashish B.; Muthukumar, C.; Ayyenar, Bharathi; Thomas, Helen Baby; Babu, R. Chandra

doi:10.1080/1343943x.2020.1862680

Cited by 5 publications

(4 citation statements)

References 55 publications

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“…The findings should be interpreted with caution until further empirical validation is conducted. However, the parameter combinations used in the analyses presented here correspond to the cases most often encountered in actual QTL studies [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]. In simulation studies, the number of lines analyzed is most often assumed to be as high as 500 or 2000 [ 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

“…The same number of QTLs was presented in their studies by Wu [ 52 ] and Balestre et al [ 53 ]. The assumed number of five epistatic pairs was a result observed in many practical experiments [ 54 , 55 , 56 , 57 ]. Simulation studies based on 10,000 generations were also presented by Goutelle et al [ 58 ], Avery et al [ 59 ], Johnson et al [ 60 ], Sorojsrisom et al [ 61 ], and Wang et al [ 62 ], among others.…”

Section: Discussionmentioning

confidence: 99%

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A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies

Cyplik

Bocianowski

2023

IJMS

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The goal of the breeding process is to obtain new genotypes with traits improved over the parental forms. Parameters related to the additive effect of genes as well as their interactions (such as epistasis of gene–by–gene interaction effect and additive–by–additive–by–additive of gene–by–gene–by–gene interaction effect) can influence decisions on the suitability of breeding material for this purpose. Understanding the genetic architecture of complex traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL–by–QTL interactions and QTL–by–QTL–by–QTL interactions. With regards to the comparing methods for estimating additive–by–additive–by–additive of QTL×QTL×QTL interaction effects by Monte Carlo simulation studies, there are no publications in the open literature. The parameter combinations assumed in the presented simulation studies represented 84 different experimental situations. The use of weighted regression may be the preferred method for estimating additive–by–additive–by–additive of QTL–QTL–QTL triples interaction effects, as it provides results closer to the true values of total additive–by–additive–by–additive interaction effects than using unweighted regression. This is also indicated by the obtained values of the determination coefficients of the proposed models.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies

Cyplik

Bocianowski

2023

IJMS

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“…Norungan has high zinc and iron accumulation (Anuradha et al,2012). It is well known for its deep penetrating roots associated with plant production traits (Rajurkar et al,2021). Hence, exploring Norungan's endophyte as an inoculant for rice crop nutrition, drought mitigation, and disease management would be a viable approach.…”

Section: Introductionmentioning

confidence: 99%

Drought-Tolerant Landrace Rice Norungan Harbors Novel Putative Competitive Endophytes

Nunna¹,

Balachandar²

2022

MAJ

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The landraces and wild species wisely choose their microbiome and explored them as a second genome for nutrient acquisition from the limited resources of the soil ecosystem and can withstand drought and diseases. Hence, the present study aimed to explore the drought-tolerant rice landrace of Tamil Nadu for the putative competitive endophytes to develop a plant-growth-promoting endophytic bacterial inoculant. Twenty two putative competitive endophytes were authenticated from plant tissues and seeds of Norungan. The 16S rRNA gene sequencing revealed that Norungan rice was colonized predominantly by Firmicutes, including six species of Bacillus, four species of Priestia, and one species each of Micrococcus, Geobacillus, and Lysinibacillus, along with two Proteobacteria, Enterobacter cloacae and Kosakonia oryzae. These endophytes were screened for nutrient transformation, growth hormone production, drought mitigation, and antagonism against plant pathogens. Bacillus cereus NE07, B. paralicheniformis NE18, B. subtilis NE20, and Priestia flexa NE09 showed potential nutrient-transforming capability, while Bacillus cereus NE07 and Priestia flexa NE09 showed high plant growth hormones (indole-3-acetic acid and gibberellic acid production). Similarly, Bacillus cereus NE07 and Priestia endophytica (NE14) showed high ACC deaminase, proline accumulation, biofilm production, and potential candidates for drought mitigation. Bacillus hayneshii NE04 and Priestia aryabhattai NE10, and Bacillus cereus NE07 showed antagonism against fungal pathogen Rhizoctonia solani and bacterial leaf blight causing Xanthomonas oryzae. The results confirmed that no single strain with all plant-growth-promoting and stress-mitigating traits found in Norungan, and these strains were performed division of labor basis. Hence, developing a putative competitive endophytic bacterial consortium with compatible strains representing cumulative beneficial traits as inoculants would be a novel approach for improving rice productivity

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“…Norungan has high zinc and iron accumulation (Anuradha et al 2012) and possesses deep penetrating roots associated with plant production traits (Rajurkar et al 2021). On the other hand, the Co51 is a highyielding cultivar of Tamil Nadu, a derivative of a cross between ADT 43 and RR 272 -1745 characterized by short-duration and fertilizer-responsive (Robin et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Rhizosphere functioning and rhizo-microbiome of two contrasting genotypes of rice under drought-induced conditions

Nunna¹,

Balachandar

2022

Preprint

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Background and aim Rhizosphere function is the key determinant of crop growth and fitness under moisture-stress conditions. Human-centered breeding adversely affects microbiome recruitment of crops. Hence, during drought, wild types and landraces adapt to stress, while modern cultivars and hybrids fail to do so and are susceptible to drought. Understanding the rhizosphere difference between landraces and cultivars will help to improve microbiome-mediated drought mitigation. Methods We compared rhizospheres of rice landrace, Norungan, and high-yielding cultivar, Co51, grown under normal moisture and drought-induced conditions using soil biochemical and 16S rRNA gene sequencing approach. Results Soil carbon pools, enzymes, and respiration were adversely affected due to drought in both genotypes' rhizospheres. However, Norungan rhizosphere accounted for less harm to the soil attributes than Co51. Reduction in soil organic carbon (3.94%), microbial biomass carbon (14.26%), labile carbon (1.94%), dehydrogenase (10.1%), urease (21.27%), phosphatase (9.61%), and respiration rate (15.02%) was accounted in Co51 than Norungan, during drought. Alpha diversity of bacterial communities in rice's rhizosphere was significantly lower than in bulk soil, and drought further reduced the diversity in both genotypes. Moisture-stress reduced the abundance of Firmicutes (180%) and Bacteroidetes (57%) in Norungan's rhizosphere, while, Acidobacteria (51%), Actinobacteria (54%), Chlolorflexi (41%), and Proteobacteria (34%) got increased. On the other hand, Co51 rhizosphere acquired an enhanced abundance of Firmicutes (79%) and Bacteriodetes (170%) and lessened abundance of Acidobacteria (15%) and Proteobacteria (31%) due to drought. Conclusion These results suggest that landrace Norungan under moisture-stress conditions recruits less-diversified, specific groups of microorganisms to augment rhizosphere functioning.

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Mapping consistent additive and epistatic QTLs for plant production traits under drought in target populations of environment using locally adapted landrace in rice (Oryza sativa L.)

Cited by 5 publications

References 55 publications

A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies

A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies

Drought-Tolerant Landrace Rice Norungan Harbors Novel Putative Competitive Endophytes

Rhizosphere functioning and rhizo-microbiome of two contrasting genotypes of rice under drought-induced conditions

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