Quantitative trait loci associated with traits determining grain and stover yield in pearl millet under terminal drought-stress conditions

Yadav, Rattan; Hash, C. T.; Bidinger, F. R.; Cavan, G.; Howarth, Catherine

doi:10.1007/s001220200008

Cited by 145 publications

(202 citation statements)

References 30 publications

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“…Better maintenance of HI may have contributed to high GY under drought stress. In pearl millet, QTL that contribute to increased drought tolerance through an ability to maintain HI and BY was also reported (Yadav et al, 2002). Six QTL for HI were detected on chromosomes 1 Table 1.…”

Section: Harvest Indexmentioning

confidence: 98%

Quantitative Trait Loci Associated with Drought Tolerance at Reproductive Stage in Rice

et al. 2004

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Drought is a major constraint to rice (Oryza sativa) yield and its stability in rainfed and poorly irrigated environments. Identifying genomic regions influencing the response of yield and its components to water deficits will aid in our understanding of the genetics of drought tolerance and development of more drought tolerant cultivars. Quantitative trait loci (QTL) for grain yield and its components and other agronomic traits were identified using a subset of 154 doubled haploid lines derived from a cross between two rice cultivars, CT9993-510 to 1-M and IR62266-42 to 6-2. Drought stress treatments were managed by use of a line source sprinkler irrigation system, which provided a linearly decreasing level of irrigation coinciding with the sensitive reproductive growth stages. The research was conducted at the Ubon Rice Research Center, Ubon, Thailand. A total of 77 QTL were identified for grain yield and its components under varying levels of water stress. Out of the total of 77 QTL, the number of QTL per trait were: 7-grain yield (GY); 8-biological yield (BY); 6-harvest index (HI); 5-d to flowering after initiation of irrigation gradient (DFAIG); 10-total spikelet number (TSN); 7-percent spikelet sterility (PSS); 23-panicle number (PN); and 11-plant height (PH). The phenotypic variation explained by individual QTL ranged from 7.5% to 55.7%. Under wellwatered conditions, we observed a high genetic association for BY, HI, DFAIG, PSS, TSN, PH, and GY. However, only BY and HI were found to be significantly associated with GY under drought treatments. QTL flanked by markers RG104 to RM231, EMP2_2 to RM127, and G2132 to RZ598 on chromosomes 3, 4, and 8 were associated with GY, HI, DFAIG, BY, PSS, and PN under drought treatments. The aggregate effects of these QTL on chromosomes 3, 4, and 8 resulted in higher grain yield. These QTL will be useful for rainfed rice improvement, and will also contribute to our understanding of the genetic control of GY under drought conditions at the sensitive reproductive stage. Close linkage or pleiotropy may be responsible for the coincidence of QTL detected in this experiment. Digenic interactions between QTL main effects for GY, BY, HI, and PSS were observed under irrigation treatments. Most (but not all) DH lines have the same response in measure of productivity when the intensity of water deficit was increased, but no QTL by irrigation treatment interaction was detected. The identification of genomic regions associated with GY and its components under drought stress will be useful for marker-based approaches to improve GY and its stability for farmers in drought-prone rice environments.In rainfed lowland areas, one of the major abiotic constraints depressing rice (Oryza sativa) production is water stress, including deficit (drought) or excess water (flood; O' Toole and Chang, 1979;Herdt, 1991;Lin and Shen, 1993). Drought stress is a serious limiting factor to rice production and yield stability in rainfed rice areas (Dey and Upadhyaya, 1996). The occurrence of drought can be as...

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Section: Harvest Indexmentioning

confidence: 98%

Quantitative Trait Loci Associated with Drought Tolerance at Reproductive Stage in Rice

et al. 2004

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“…Two mapping populations based on hybrid parental lines with known differential responses to terminal stress were created, genotyped and phenotyped under managed fi eld stress conditions (Yadav et al, 2002(Yadav et al, , 2004. Both exercises consistently detected a major putatitve QTL for grain yield under terminal drought stress conditions (and/or the ability to maintain grain yield under such stress conditions) on pearl millet linkage group 2 (LG2), along with a small number of minor QTLs for both grain and stover yield and their components, under stress, that were identifi ed in some but not all phenotyping environments.…”

Section: Characterization and Molecular Mapping Of Terminal Drought Tmentioning

confidence: 99%

Recent Advances in Marker-Assisted Selection for Drought Tolerance in Pearl Millet

Serraj¹,

Hash²,

Rizvi³

et al. 2005

Plant Production Science

105

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“…These determinants are characterized in terms of their effects, modes of action and interactions with other genetic loci (QTL epistasis) and the environment (QTL Â E) (Jansen et al, 1995;Korol et al, 1998). Two basic patterns of QTL Â E interaction are recognized, they are: (i) inversion in the ranking of allelic effects across environments (the crossover effect, CO) and (ii) variation in the magnitude of allelic effects without inversion of ranks (the scale effect, SC) (Yadav et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Genetics of phenotypic plasticity: QTL analysis in barley, Hordeum vulgare

2008

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Phenotypic plasticity is the variation in phenotypic traits produced by a genotype in different environments. In contrast, environmental canalization is defined as the insensitivity of a genotype's phenotype to variation in environments. Despite the extensive literature on the evolutionary significance and potential genetic mechanisms driving plasticity and canalization, few studies tried to unravel the genetic basis of this phenomenon. Using both simulations and real data from barley (Hordeum vulgare), we used QTL mapping to obtain insights into the genetics of phenotypic plasticity. We explored two ways of quantifying phenotypic plasticity, namely the phenotypic variance across environments and the Finlay-Wilkinson's regression slope. Each relates to a different concept of stability. Through QTL detection with real and simulated data, we show that each measure of plasticity detects specific types of plasticity QTL. Most of the plasticity QTLs were detected in the data set with the lowest number of environments. All plasticity QTL co-located with loci showing QTL Â E interaction and there were no QTL that only affected plasticity. The number of environments that are considered and their homogeneity is a key to interpret the genetic control of phenotypic plasticity. Regulatory pathways of plasticity may vary from one set of environments to another due to unique features of each environment. Therefore, with an increasing number of environments, it may become impossible to detect a single 'consistent' regulatory pathway for all environments.

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Quantitative trait loci associated with traits determining grain and stover yield in pearl millet under terminal drought-stress conditions

Cited by 145 publications

References 30 publications

Quantitative Trait Loci Associated with Drought Tolerance at Reproductive Stage in Rice

Quantitative Trait Loci Associated with Drought Tolerance at Reproductive Stage in Rice

Recent Advances in Marker-Assisted Selection for Drought Tolerance in Pearl Millet

Genetics of phenotypic plasticity: QTL analysis in barley, Hordeum vulgare

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