Molecular Dissection of Developmental Behavior of Plant Height in Rice (Oryza sativa L.)

Yan, Jun; Zhu, Jun; He, Chi; Benmoussa, Mebrouk; Wu, Ping

doi:10.1093/genetics/150.3.1257

Cited by 143 publications

(10 citation statements)

References 30 publications

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“…Only a series of conditional QTLs truly reflected the expression periods and quantities of a QTL throughout the whole developmental stage. Conditional QTL mapping has widely been applied to reveal dynamic gene patterns for developmental traits 8 , 9 , 15 , 16 , 23 , 30 , 31 . There were four representative patterns for the genetic control of growth trajectories, permanent QTLs, early QTLs, late QTLs and inverse QTLs 21 .…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, plant height is a relatively easily investigated trait that can be measured at a series of stages to allow us to dynamically explore the genetic mechanism of development. Thus, plant height is often used as a model trait for the study of developmental behaviors 8 – 10 .…”

Section: Introductionmentioning

confidence: 99%

“…Unconditional QTL mapping usually directly analyzes the phenotypic values measured at various growth stages and then infers the dynamic genetic architecture of a developmental trait by longitudinal comparison of the mapping results 12 . Conditional QTL mapping needs to first estimate the conditional effects for the phenotypic values at time given the phenotypes at time 13 , 14 and then to conduct QTL mapping based on these estimations 8 , 9 , 15 , 16 . Conditional QTL mapping can effectively measure the net expression of genes from time to time since is independent of (the phenotypic value at time ) 17 .…”

Section: Introductionmentioning

confidence: 99%

“…Studies using these strategies have provided a wealth of information about QTLs controlling the developmental behavior of traits, such as QTL accumulated effects at a point in time, QTL net effects in a period of time, and functional QTLs changing the process of development. Over the past 30 years, great efforts have been made to dissect the genetic basis of plant height 8 – 10 , 16 , 23 – 25 . Studies have indicated that plant height in rice is generally controlled by both qualitative and quantitative genes, more than 1000 QTLs have been mapped on rice chromosomes, and their action mechanisms manifest mainly accumulation and interaction of QTL effects, i.e., additive effect and dominant or epistatic effect with the features of spatio-temporal expression ( http://www.gramene.org/qtl ).…”

Section: Introductionmentioning

confidence: 99%

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Dynamic analysis of QTLs on plant height with single segment substitution lines in rice

Zhao

Huang

et al. 2022

Sci Rep

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Dynamic regulation of QTLs remains mysterious. Single segment substitution lines (SSSLs) and conditional QTL mapping and functional QTL mappings are ideal materials and methods to explore dynamics of QTLs for complex traits. This paper analyzed the dynamics of QTLs on plant height with SSSLs in rice. Five SSSLs were verified with plant height QTLs first. All five QTLs had significant positive effects at one or more developmental stages except QTL1. They interacted each other, with negative effects before 49 d after transplanting and positive effects since then. The five QTLs selectively expressed in specific periods, mainly in the periods from 35 to 42 d and from 49 to 56 d after transplanting. Expressions of epistasis were dispersedly in various periods, negative effects appearing mainly before 35 d. The five QTLs brought the inflexion point ahead of schedule, accelerated growth and degradation, and changed the peak plant height, while their interactions had the opposite effects. The information will be helpful to understand the genetic mechanism for developmental traits.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic analysis of QTLs on plant height with single segment substitution lines in rice

Zhao

Huang

et al. 2022

Sci Rep

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“…A seemingly straightforward approach for assessing dynamic genetic effects underlying longitudinal traits is performing linkage or association analysis at each time point independently (Wu et al, 1999;Yan et al, 1998;Würschum et al, 2014;Moore et al, 2013). In one of the first applications of HTP for genetic studies in plants, Moore et al (2013) used an imagebased platform to quantify root gravitropic responses in Arabidopsis biparental mapping populations.…”

Section: Single Time Point Genetic Inferencementioning

confidence: 99%

Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data

Morota¹,

Jarquín²,

Campbell³

et al. 2019

Preprint

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The advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Section 1, Section 2 discusses field-based HTP, including the use of unmanned aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal GS. The chapter concludes with a discussion of some standing issues.

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Regional heritability mapping and genome‐wide association identify loci for rice traits

et al. 2022

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Although genome‐wide association studies (GWAS) based on single‐marker analysis have been widely applied in plant breeding programs, the effectivity of the methodology is still undermined by high false‐positive rates and the limited power to detect associations. Bayesian methods, which estimate marker effects simultaneously, proved to be efficient, indicating genes with important effects. Regional heritability mapping (RHM), on the other hand, determines the genome region (group of markers) associated with the phenotype, considers population structure and familial relatedness, and is more powerful to detect quantitative trait loci (QTL) and reduced false‐positive rates than single‐marker methodologies. A single‐marker mixed model (SM‐MM) Bayesian approach and RHM were used for 11 traits in 413 rice (Oryza sativa L.) accessions genotyped for 44,100 single‐nucleotide polymorphism (SNP) markers. Using RHM in regions of 0.21 and 0.69 Mb, respectively, detected five and seven associated regions with 163 and 569 SNPs. Bayesian method with regions of 0.21 and 0.69 Mb detected regions for all traits, whereas SM‐MM detected four single SNP–trait associations. For the 11 traits, RHM explained approximately 25–40 and 25–76% using genome regions of 0.21 and 0.69 Mb, respectively, and SM‐MM using single markers explained 1–7% of the genomic heritability. Regional heritability mapping was more effective than SM‐MM in capturing major proportions of genomic heritability. The regions found in this study were within or close to the QTL noted in the Q‐TARO and Gramene QTL databases.

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Molecular Dissection of Developmental Behavior of Plant Height in Rice (Oryza sativa L.)

Cited by 143 publications

References 30 publications

Dynamic analysis of QTLs on plant height with single segment substitution lines in rice

Dynamic analysis of QTLs on plant height with single segment substitution lines in rice

Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data

Regional heritability mapping and genome‐wide association identify loci for rice traits

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