Genetic Relationships Among Panicle Characteristics of Rice (Oryza sativa L.) Using Unconditional and Conditional QTL Analyses

Abstract

Using mixed-model-based composite interval mapping and conditional statistical methods, we studied quantitative trait loci (QTLs) with epistatic effects and QTLs by environment interaction effects for rice seed set percent (SSP), filled grain number per plant (FGP), and panicle length (PL). A population of 241 recombinant inbred lines was used which was derived from a cross between "Zhenshan 97" and "Minghui 63." Its linkage map included 221 molecular markers. Our QTL analysis detected 28, 25, and 32 QTLs for SSP, FGP, and PL, respectively. Each QTL explained 1.37%∼13.19% of the mean phenotypic variation. A comparison of conventional and conditional mapping provided information about the genetic control system involved in the synthetic process of SSP, FGP, and PL at the level of individual QTLs. Conditional QTLs with reduced (or increased) effects were identified for SSP, which were significantly influenced by FGP or PL. Some QTLs could express independently for the given traits, thereby providing possibilities for simultaneous improvement of SSR and PL, and SSR and FGP. Epistasis was more sensitive to environmental conditions than were additive effects.

Unconditional and conditional QTL underlying the genetic interrelationships between soybean seed isoflavone, and protein or oil contents

“…) and rice (Ye et al. ). By early 2014, no work has focused on QTL analyses of the relationships between seed isoflavones and other seed compositions in soybean.…”

“…In fact, these QTL were significantly influenced by filled grain numbers per plant and panicle length via conditional analysis (Ye et al. ). Zhao et al.…”

“…Through conventional QTL analysis, about twenty QTL controlled seed set in rice, which were not related to other panicle traits. In fact, these QTL were significantly influenced by filled grain numbers per plant and panicle length via conditional analysis (Ye et al 2009). Zhao et al (2006) analysed genetic relations among oil, protein content and yield components in rapeseed by conditional QTL and found two QTL underlying seed oil content that were not affected by seed protein content and yield.…”

Genetic analysis of kernel weight and kernel size in wheat (Triticum aestivum L.) using unconditional and conditional QTL mapping

“…In the present study, conditional QTL mapping was employed to investigate the genetic contribution of kernel size on QTL expression of TKW. Four outcomes can be obtained upon comparison of the effects of unconditional and conditional QTL mapping (Zhao et al 2006;Ye et al 2009): (a) a QTL detected with a similar or equal effect, indicating that this QTL for TKW is independent of a given trait; (b) a QTL detected with either a greatly reduced or enhanced effect, indicating that this QTL for TKW is partially influenced by related traits; (c) a QTL cannot be identified, meaning that this QTL is completely controlled by conditional traits; and (d) an additional QTL can be detected, suggesting that this QTL is entirely suppressed by conditional traits.…”

Statistical approaches in QTL mapping and molecular breeding for complex traits

“…These QTLs may be potential candidates to increase PEV in popcorn without decreasing the GWP/100 GW value. Ye et al [69] studied the genetic relationships in the panicle characteristics of rice using a similar approach.…”