The Application of New GWAS Methods in the Genetic Dissection of Complex Traits in Plants

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Genetics, Genomics and Biotechnology".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 5077

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College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
Interests: genome-wide association studies; QTL mapping; comparative genomics and bioinformatics
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Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Interests: statistical genetics and genomics
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College of Forestry, Hainan University, Haikou 570228, China
Interests: genomics and bioinformatics
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Department of Genetics, School of Medicine, University of Noth Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
Interests: statistical genetics and genomics
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Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
Interests: statistical genomics; genetics; rice
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Special Issue Information

Dear Colleagues,

Genome-wide association studies are widely used in the genetic dissection of complex traits. However, there is still room to improve their methodologies. In QTN, environmental interaction (QEI) and QTN-by-QTN interaction (QQI) detection effects to be estimated are confounded, polygenic backgrounds to be controlled are imperfect, and there are limited approaches available for detecting these interactions. Thus, single environment analyses are frequently applied in multiple environment experiments, which are inefficient and suboptimal, and very few interaction studies have been reported in plant genetics. To address these issues, we established a new framework in which all the effects to be estimated are compressed into an effect-related vector, while all the polygenic backgrounds are compressed into one polygenic background. Specifically, the compressed mixed model was integrated with mrMLM to establish the 3VmrMLM method, which is powered up to detect QTNs, QEIs, and QQIs and estimate their effects. This method is readily available for genetics analysis in animals, plants, forest trees, and humans. It is necessary to test new GWAS methods with a wide range of real datasets across species.

Dr. Yuan-Ming Zhang
Dr. Zhenyu Jia
Dr. Shang-Qian Xie
Dr. Jia Wen
Dr. Shibo Wang
Guest Editor

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Keywords

  • compressed variance component mixed model
  • 3VmrMLM
  • plant
  • genome-wide association study
  • multi-omics traits
  • complex traits
  • QTN-by-environment interaction

Published Papers (2 papers)

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Research

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18 pages, 7509 KiB  
Article
Genome-Wide Association Studies Using 3VmrMLM Model Provide New Insights into Branched-Chain Amino Acid Contents in Rice Grains
by Yao Sui, Yanru Che, Yue Zhong and Liqiang He
Plants 2023, 12(16), 2970; https://doi.org/10.3390/plants12162970 - 17 Aug 2023
Cited by 1 | Viewed by 1056
Abstract
Rice (Oryza sativa L.) is a globally important food source providing carbohydrates, amino acids, and dietary fiber for humans and livestock. The branched-chain amino acid (BCAA) level is a complex trait related to the nutrient quality of rice. However, the genetic mechanism [...] Read more.
Rice (Oryza sativa L.) is a globally important food source providing carbohydrates, amino acids, and dietary fiber for humans and livestock. The branched-chain amino acid (BCAA) level is a complex trait related to the nutrient quality of rice. However, the genetic mechanism underlying the BCAA (valine, leucine, and isoleucine) accumulation in rice grains remains largely unclear. In this study, the grain BCAA contents and 239,055 SNPs of a diverse panel containing 422 rice accessions were adopted to perform a genome-wide association study (GWAS) using a recently proposed 3VmrMLM model. A total of 357 BCAA-content-associated main-effect quantitative trait nucleotides (QTNs) were identified from 15 datasets (12 BCAA content datasets and 3 BLUP datasets of BCAA). Furthermore, the allelic variation of two novel candidate genes, LOC_Os01g52530 and LOC_Os06g15420, responsible for the isoleucine (Ile) content alteration were identified. To reveal the genetic basis of the potential interactions between the gene and environmental factor, 53 QTN-by-environment interactions (QEIs) were detected using the 3VmrMLM model. The LOC_Os03g24460, LOC_Os01g55590, and LOC_Os12g31820 were considered as the candidate genes potentially contributing to the valine (Val), leucine (Leu), and isoleucine (Ile) accumulations, respectively. Additionally, 10 QTN-by-QTN interactions (QQIs) were detected using the 3VmrMLM model, which were putative gene-by-gene interactions related to the Leu and Ile contents. Taken together, these findings suggest that the implementation of the 3VmrMLM model in a GWAS may provide new insights into the deeper understanding of BCAA accumulation in rice grains. The identified QTNs/QEIs/QQIs serve as potential targets for the genetic improvement of rice with high BCAA levels. Full article
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Review

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47 pages, 1967 KiB  
Review
Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS
by Md. Alamin, Most. Humaira Sultana, Xiangyang Lou, Wenfei Jin and Haiming Xu
Plants 2022, 11(23), 3277; https://doi.org/10.3390/plants11233277 - 28 Nov 2022
Cited by 4 | Viewed by 2835
Abstract
Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used [...] Read more.
Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene–gene interaction, gene–environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society. Full article
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