Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another.

Lark, Karl G.; Chase, Kevin; Adler, Fred; Mansur, L. M.; Orf, J. H.

doi:10.1073/pnas.92.10.4656

Cited by 229 publications

(138 citation statements)

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“…The d 2 g , which cannot be explained by detected QTL, may be attributed to QTL with smaller effects, and/or due to QTL 3 QTL interactions referred to as epistatic interaction. Epistasis is the phenotypic effect of interaction among alleles at multiple loci (Xing et al, 2002) that play an important role in the genetic basis of quantitative traits (Lark et al, 1995;Li et al, 1997;Yu et al, 1997). We understand that the distances between marker loci and the QTL biased the estimated effects of epistasis.…”

Section: Qtl 3 Qtl Interactionsmentioning

confidence: 99%

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: Qtl 3 Qtl Interactionsmentioning

confidence: 99%

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

et al. 2004

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“…The interaction between genes is an important source of genetic variability and its effects are larger than the quantitative theory implied (Moreno 1994). In studying pairs of quantitative trait loci (QTL) in soybean, Lark et al (1995) reported that interactions between QTL are frequent and control large effects. They also found that an allele at one locus with little or no variation by itself exerted a large effect on height via interaction with other loci.…”

Section: Introductionmentioning

confidence: 99%

Genetic Architecture of Secondary Yield Components in Mungbean (Vigna radiata (L.) Wilczek).

Khattak¹,

Haq

Ashraf³

et al. 2002

Breed. Sci.

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Triple Test Cross (TTC) analyses were carried out to detect epistasis, and to estimate additive and dominance components of genetic variation in two seasons (kharif and spring/summer) for branches per plant, pod clusters per plant, pods per cluster, pod length, biomass, and harvest index in mungbean. The results revealed the absence of epistasis for all the traits in the kharif season but in contrast the presence of epistasis for pod clusters per plant and biomass in the spring/summer season. The partitioning of total epistasis revealed that both i type (additive × additive), and j and l type (additive × dominance, and dominance × dominance) interactions were significant with predominance effect of i type for pod clusters per plant and biomass. The additive and dominance gene effects were highly significant for branches per plant, pod length, and harvest index in both seasons; for pod clusters per plant and biomass in the kharif season; and for pods per cluster in spring/ summer season. The magnitude of additive variance was higher for all these traits in both seasons except for pods per cluster and pod length in the spring/summer season, which exhibited a higher value of dominance than additive variance. The direction of dominance was observed towards fewer pod clusters per plant, small pod size, and high harvest index. The predominance of i type interaction and additive gene action for most of the traits in the present investigation suggests the occurrence of selection in the late generations in the segregating population for the improvement of seed yield through yield components in mungbean.

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“…Strong interactions between QTLs have also been observed in maize (Lukens and Doebley 1999) and soybean (Lark et al 1995). Recent genetic analyses using molecular markers in several plant species have clearly shown that, in addition to single locus QTLs, epistatic interactions play an important role on the genetic basis of quantitative traits (Lark et al 1995;Maughan et al 1996;Li et al 1997;Yu et al 1997;Poelwijk et al 2011;Krajewski et al 2012;Bocianowski 2012cBocianowski , 2013Bocianowski , 2014.…”

Section: Introductionmentioning

confidence: 99%

“…Epistasis or the additive-by-additive interaction between loci on a genome that controls a quantitative trait is of great interest to geneticists (Eta-Ndu and Openshaw 1999; Gao and Zhu 2007;Kang et al 2009;Zuo and Kang 2010;Binh et al 2011;He et al 2011). Strong interactions between QTLs have also been observed in maize (Lukens and Doebley 1999) and soybean (Lark et al 1995). Recent genetic analyses using molecular markers in several plant species have clearly shown that, in addition to single locus QTLs, epistatic interactions play an important role on the genetic basis of quantitative traits (Lark et al 1995;Maughan et al 1996;Li et al 1997;Yu et al 1997;Poelwijk et al 2011;Krajewski et al 2012;Bocianowski 2012cBocianowski , 2013Bocianowski , 2014.…”

Section: Introductionmentioning

confidence: 99%

Mixed linear model approaches in mapping QTLs with epistatic effects by a simulation study

Bocianowski

Nowosad

2014

Euphytica

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Epistasis refers to the phenotypic effects of interactions between alleles of different loci. Statistical detection of such interactions remains to be the subject of presented studies. A method based on mixed linear model was developed for mapping QTLs with digenic epistasis. Reliable estimates of QTL additive and epistasis effects can be obtained by the maximum-likelihood estimate methods. Likelihood ratio and t statistics were combined for testing hypotheses about QTL effects (additive and epistasis effects). Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for effects of QTLs. Additionally, the mixed-model approaches also showed high accuracy in mapping QTLs with epistasis effects. The information obtained in this study will be useful for manipulating the QTLs for plant breeding by marker assisted selection.

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Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another.

Cited by 229 publications

References 2 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

Genetic Architecture of Secondary Yield Components in Mungbean (Vigna radiata (L.) Wilczek).

Mixed linear model approaches in mapping QTLs with epistatic effects by a simulation study

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