Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping
Min LiuXiaolong TanYan YangPeng LiuXiaoxiang ZhangYinchao ZhangLei WangYu HuLanglang MaZhaoling LiYanling ZhangChaoying ZouHaijian LinShibin GaoMichael LeeThomas LübberstedtGuangtang PanYaou Shen
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Abstract:
Kernel size-related traits are the most direct traits correlating with grain yield. The genetic basis of three kernel traits of maize, kernel length (KL), kernel width (KW) and kernel thickness (KT), was investigated in an association panel and a biparental population. A total of 21 single nucleotide polymorphisms (SNPs) were detected to be most significantly (P < 2.25 × 10-6 ) associated with these three traits in the association panel under four environments. Furthermore, 50 quantitative trait loci (QTL) controlling these traits were detected in seven environments in the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population, of which eight were repetitively identified in at least three environments. Combining the two mapping populations revealed that 56 SNPs (P < 1 × 10-3 ) fell within 18 of the QTL confidence intervals. According to the top significant SNPs, stable-effect SNPs and the co-localized SNPs by association analysis and linkage mapping, a total of 73 candidate genes were identified, regulating seed development. Additionally, seven miRNAs were found to situate within the linkage disequilibrium (LD) regions of the co-localized SNPs, of which zma-miR164e was demonstrated to cleave the mRNAs of Arabidopsis CUC1, CUC2 and NAC6 in vitro. Overexpression of zma-miR164e resulted in the down-regulation of these genes above and the failure of seed formation in Arabidopsis pods, with the increased branch number. These findings provide insights into the mechanism of seed development and the improvement of molecular marker-assisted selection (MAS) for high-yield breeding in maize.Keywords:
Genetic architecture
Association mapping
Linkage (software)
Association (psychology)
Genetic Association
Genetic linkage
Kernel (algebra)
Genetic architecture
Trait
Family-based QTL mapping
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Abstract Morphological traits for ear leaf are determinant traits influencing plant architecture and drought tolerance in maize. However, the genetic controls of ear leaf architecture traits remain poorly understood under drought stress. Here, we identified 100 quantitative trait loci (QTLs) for leaf angle, leaf orientation value, leaf length, leaf width, leaf size and leaf shape value of ear leaf across four populations under drought‐stressed and unstressed conditions, which explained 0.71%–20.62% of phenotypic variation in single watering condition. Forty‐five of the 100 QTL s were identified under water‐stressed conditions, and 29 stable QTL s ( sQTL s) were identified under water‐stressed conditions, which could be useful for the genetic improvement of maize drought tolerance via QTL pyramiding. We further integrated 27 independent QTL studies in a meta‐analysis to identify 21 meta‐ QTL s ( mQTL s). Then, 24 candidate genes controlling leaf architecture traits coincided with 20 corresponding mQTL s. Thus, new/valuable information on quantitative traits has shed some light on the molecular mechanisms responsible for leaf architecture traits affected by watering conditions. Furthermore, alleles for leaf architecture traits provide useful targets for marker‐assisted selection to generate high‐yielding maize varieties.
Genetic architecture
Drought Tolerance
Marker-Assisted Selection
Inbred strain
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AbstractQuantitative traits are defined as traits that have a continuous phenotypic distribution (1,2). Variances of these traits are often controlled by the segregation of many loci, called quantitative trait loci (QTL). Therefore, quantitative traits are often synonymously called polygenic traits. Another characteristic of quantitative traits is that environmental variates can play a large role in determining the phenotypic variance. The polygenic nature and the ability of being modified by the environment make the study of genetic basis for quantitative traits more difficult than that for monogenic traits. Traditional methods of quantitative genetics that use only the phenotypic and pedigree information cannot separate the effects of individual loci but examine the collective effect of all QTL. With the rapid development of molecular technology, a large number of molecular markers (DNA variants) can be generated with ease. Most molecular markers are functionally neutral, but they normally obey the laws of Mendelian inheritance. Therefore, the relative positions of the markers along the genome (called the marker map) can be reconstructed using observed recombin ant events. The joint segregating patterns of markers, in conjunction with phenotypic and pedigree information, provides additional information about the genetic basis of quantitative traits, including the number and chromosomal locations of QTL, the mode of gene action, and sizes (effects) of individual QTL. A complete description of the properties of QTL is called the genetic architecture. The study of the genetic architecture of quantitative traits using molecular markers is called QTL mapping.KeywordsQuantitative Trait LocusQuantitative Trait Locus AnalysisQuantitative Trait Locus MappingDominance EffectEpistatic EffectThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Family-based QTL mapping
Mendelian inheritance
Quantitative Genetics
Polygene
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Anxiety, like other psychiatric disorders, is a complex neurobehavioral trait, making identification of causal genes difficult. In this study, we examined anxiety-like behavior and fear conditioning (FC) in an F(2) intercross of C57BL/6J and DBA/2J mice. We identified numerous quantitative trait loci (QTL) influencing anxiety-like behavior in both open field (OF) and FC tests. Many of these QTL were mapped back to the same chromosomal regions, regardless of behavior or test. For example, highly significant overlapping QTL on chromosome 1 were found in all FC measures as well as in center time measures in the OF. Other QTL exhibited strong temporal profiles over testing, highlighting dynamic relationship between genotype, test and changes in behavior. Next, we implemented a factor analysis design to account for the correlated nature of the behaviors measured. OF and FC behaviors loaded onto four main factors representing both anxiety and fear behaviors. Using multiple QTL modeling, we calculated the percentage variance in anxiety and fear explained by multiple QTL using both additive and interactive terms. Quantitative trait loci modeling resulted in a broad description of the genetic architecture underlying anxiety and fear accounting for 14-37% of trait variance. Factor analysis and multiple QTL modeling showed both unique and shared QTL for anxiety and fear; suggesting a partially overlapping genetic architecture for these two different models of anxiety.
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Family-based QTL mapping
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Family-based QTL mapping
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Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment-induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part-whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta-Volterra ordinary differential equations (LVODE) into an R-based computing platform, np2 QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np2 QTL constructs a bidirectional, signed and weighted network of QTL-QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype-environment interactions over any range of environmental change. The utility of np2 QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np2 QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.
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Genetic architecture
Inbred strain
Maternal effect
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Family-based QTL mapping
Genetic architecture
Genome-wide Association Study
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Abstract Recent genome-wide association studies suggest that the human genetic architecture of complex traits may vary between males and females; however, traditional approaches for association mapping cannot fully account for these between-sex differences... Over the past few years, genome-wide association studies have identified many trait-associated loci that have different effects on females and males, which increased attention to the genetic architecture differences between the sexes. The between-sex differences in genetic architectures can cause a variety of phenomena such as differences in the effect sizes at trait-associated loci, differences in the magnitudes of polygenic background effects, and differences in the phenotypic variances. However, current association testing approaches for dealing with sex, such as including sex as a covariate, cannot fully account for these phenomena and can be suboptimal in statistical power. We present a novel association mapping framework, MetaSex, that can comprehensively account for the genetic architecture differences between the sexes. Through simulations and applications to real data, we show that our framework has superior performance than previous approaches in association mapping.
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Genetic Association
Genome-wide Association Study
Association mapping
Association (psychology)
Trait
Statistical power
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Abstract The effects of quantitative trait loci (QTL) on phenotypic development may depend on the environment (QTL × environment interaction), other QTL (genetic epistasis), or both. In this article, we present a new statistical model for characterizing specific QTL that display environment-dependent genetic expressions and genotype × environment interactions for developmental trajectories. Our model was derived within the maximum-likelihood-based mixture model framework, incorporated by biologically meaningful growth equations and environment-dependent genetic effects of QTL, and implemented with the EM algorithm. With this model, we can characterize the dynamic patterns of genetic effects of QTL governing growth curves and estimate the global effect of the underlying QTL during the course of growth and development. In a real example with rice, our model has successfully detected several QTL that produce differences in their genetic expression between two contrasting environments. These detected QTL cause significant genotype × environment interactions for some fundamental aspects of growth trajectories. The model provides the basis for deciphering the genetic architecture of trait expression adjusted to different biotic and abiotic environments and genetic relationships for growth rates and the timing of life-history events for any organism.
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Epistasis
Family-based QTL mapping
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Quantitative Genetics
genetic model
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