Status and prospects of genome‐wide association studies in plants

Tibbs‐Cortes, Laura E.; Zhang, Zhiwu; Yu, Jianming

doi:10.1002/tpg2.20077

Cited by 259 publications

(205 citation statements)

References 127 publications

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“…The first was an LMM that included only the additive main effect term and the random effect ‘ u G + u GE + u GT ’ (Table 1), and the second was an LMM that included GGE-type Q×E effect terms and the random effect ‘ u G + u GE + u GT ’ (Table 1). We focused on QTLs with FDR > 0.05, which are often used as genome-wide significant thresholds in GWAS (Cortes et al . 2020).…”

Section: Resultsmentioning

confidence: 99%

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Exploring Efficient Linear Mixed Models to Detect Quantitative Trait Locus-by-Environment Interactions

Yamamoto

Matsunaga

2020

Preprint

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Genotype-by-environment interactions (GEIs) are important for not only a precise understanding of genotype–phenotype relationships but also improving the environmental adaptability of crops. Although many formulae have been proposed to model GEI effects, a comprehensive comparison of their efficacy in genome-wide association studies (GWASs) has not been performed. Therefore, the advantages and disadvantages of the formulae are not well recognized. In this study, linear mixed models (LMMs) consisting of various combinations of foreground fixed genetic and background random genetic effect terms were constructed. Next, the power to detect quantitative trait loci (QTLs) with GEI effects was compared across the LMMs by using simulation. The fixed genetic effect terms of the genotype main effects and GEI (GGE) model were preferred over those based on the additive main effects and multiplicative interaction (AMMI) model because the latter showed p-value inflation, whereas the former yielded theoretically expected p-value distribution. With regard to the background random genetic effects, inclusion of genotype-by-trial interaction effects has been recommended to achieve high power and robustness when phenotype data are obtained from multiple environments and multiple trials. The recommended form of LMM was applied to GWASs performed using real agronomic trait data of tomato F1 varieties (Solanum lycopersicum L.) that were obtained from two different cropping seasons. The GWASs detected QTLs with not only persistent effects across the cropping seasons, but also cropping season-specific effects. Thus, the application of GWAS strategy to phenotypic data from multiple environments and trials allowed the detection of more QTLs with GEI effects.

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Section: Resultsmentioning

confidence: 99%

“…2017). For QTL detection, genetic mapping approaches such as the genome-wide association study (GWAS) are necessary (Cortes et al . 2020).…”

Section: Introductionmentioning

confidence: 99%

Exploring Efficient Linear Mixed Models to Detect Quantitative Trait Locus-by-Environment Interactions

Yamamoto

Matsunaga

2020

Preprint

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“…Among these loci, some were commonly shared by the five sub-traits and across 2 years in the studied population. GWAS has proven to be a successful approach in identifying the genetic cause of many complex traits in plant species (Tibbs Cortes et al, 2021). However, the traditional GWAS analysis examines marker-based associations and has shown difficulty in assessing rare variants or common variants of small effect size (Eichler et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

Integrating Genome-Wide Association Analysis With Transcriptome Sequencing to Identify Candidate Genes Related to Blooming Time in Prunus mume

et al. 2021

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Prunus mume is one of the most important woody perennials for edible and ornamental use. Despite a substantial variation in the flowering phenology among the P. mume germplasm resources, the genetic control for flowering time remains to be elucidated. In this study, we examined five blooming time-related traits of 235 P. mume landraces for 2 years. Based on the phenotypic data, we performed genome-wide association studies, which included a combination of marker- and gene-based association tests, and identified 1,445 candidate genes that are consistently linked with flowering time across multiple years. Furthermore, we assessed the global transcriptome change of floral buds from the two P. mume cultivars exhibiting contrasting bloom dates and detected 617 associated genes that were differentially expressed during the flowering process. By integrating a co-expression network analysis, we screened out 191 gene candidates of conserved transcriptional pattern during blooming across cultivars. Finally, we validated the temporal expression profiles of these candidates and highlighted their putative roles in regulating floral bud break and blooming time in P. mume. Our findings are important to expand the understanding of flowering time control in woody perennials and will boost the molecular breeding of novel varieties in P. mume.

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“…Genome-wide association studies (GWAS) have been conducted to understand the genetic architecture of complex traits (Lipka et al, 2015;Tibbs Cortes et al, 2021). In addition to the single trait mixed model approach (Yu et al, 2006;Kang et al, 2010;Zhang et al, 2010), the GWAS method has been advanced to identify pleiotropic loci (Grotzinger et al, 2018), gene-environment interactions (Moore et al, 2019), and gene expression effects across varying conditions and tissues (Urbut et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops

Guo

Wang

et al. 2021

Molecular Plant

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Identifying mechanisms and pathways involved in gene-environment interplay and phenotypic plasticity is a long-standing challenge. It is highly desirable to establish an integrated framework with an environmental dimension for complex trait dissection and prediction. A critical step is to identify an environmental index that is both biologically relevant and estimable for new environments. With extensive field-observed complex traits, environmental profiles, and genome-wide single nucleotide polymorphisms for three major crops (maize, wheat, and oat), we demonstrated that identifying such an environmental index (i.e., a combination of environmental parameter and growth window) enables genome-wide association studies and genomic selection of complex traits to be conducted with an explicit environmental dimension. Interestingly, genes identified for two reaction-norm parameters (i.e., intercept and slope) derived from flowering time values along the environmental index were less colocalized for a diverse maize panel than for wheat and oat breeding panels, agreeing with the different diversity levels and genetic constitutions of the panels. In addition, we showcased the usefulness of this framework for systematically forecasting the performance of diverse germplasm panels in new environments. This general framework and the companion CERIS-JGRA analytical package should facilitate biologically informed dissection of complex traits, enhanced performance prediction in breeding for future climates, and coordinated efforts to enrich our understanding of mechanisms underlying phenotypic variation.

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Status and prospects of genome‐wide association studies in plants

Cited by 259 publications

References 127 publications

Exploring Efficient Linear Mixed Models to Detect Quantitative Trait Locus-by-Environment Interactions

Exploring Efficient Linear Mixed Models to Detect Quantitative Trait Locus-by-Environment Interactions

Integrating Genome-Wide Association Analysis With Transcriptome Sequencing to Identify Candidate Genes Related to Blooming Time in Prunus mume

An integrated framework reinstating the environmental dimension for GWAS and genomic selection in crops

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