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Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice.

Yang W, Guo Z, Huang C, Duan L, Chen G, Jiang N, Fang W, Feng H, Xie W, Lian X, Wang G, Luo Q, Zhang Q, Liu Q, Xiong L - Nat Commun (2014)

Bottom Line: Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1.Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information.The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.

View Article: PubMed Central - PubMed

Affiliation: 1] Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China [2] National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, China [3] MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China [4] College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.

ABSTRACT
Even as the study of plant genomics rapidly develops through the use of high-throughput sequencing techniques, traditional plant phenotyping lags far behind. Here we develop a high-throughput rice phenotyping facility (HRPF) to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period. Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1. Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information. The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.

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Related in: MedlinePlus

Comparison of rice accessions exhibiting different plant compactness values and grain-projected areas.Representative rice accessions exhibiting different plant compactness values at late booting stage (a), different plant compactness values at milk grain stage (c), and the grain-projected area (e). Manhattan plots (left) and quantile-quantile plots (right) for plant compactness at late booting stage (sample size=402) (b), plant compactness at milk grain stage (sample size=269) (d), and grain-projected area (sample size=514) (f). The P values are computed from a likelihood ratio test with a mixed-model approach using the factored spectrally transformed linear mixed models (FaST-LMM) programme (P=1.21 × 10−6). *New traits are those that cannot be defined and extracted using traditional measurement techniques.
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f5: Comparison of rice accessions exhibiting different plant compactness values and grain-projected areas.Representative rice accessions exhibiting different plant compactness values at late booting stage (a), different plant compactness values at milk grain stage (c), and the grain-projected area (e). Manhattan plots (left) and quantile-quantile plots (right) for plant compactness at late booting stage (sample size=402) (b), plant compactness at milk grain stage (sample size=269) (d), and grain-projected area (sample size=514) (f). The P values are computed from a likelihood ratio test with a mixed-model approach using the factored spectrally transformed linear mixed models (FaST-LMM) programme (P=1.21 × 10−6). *New traits are those that cannot be defined and extracted using traditional measurement techniques.

Mentions: In addition to the traditional agronomic traits, new traits, including plant compactness and grain-projected area, can be extracted by the RAP and the YTS, respectively. Plant compactness reflects plant density and plant architecture, and a more detailed description of plant compactness is provided in the Supplementary Note 2. As shown in Fig. 5a,c, the plants became more compact and the leaves became more upright with increases in plant compactness. Plant compactness provided meaningful information on plant architecture in addition to the commonly recognized traits (such as plant height, tiller number and green leaf area) (Supplementary Fig. 9). This was also the reason that plant compactness was chosen to improve the biomass and leaf area prediction. Seven and four loci were associated with plant compactness at the late booting stage and the milk grain stage, respectively (shown in Fig. 5b,d; Supplementary Data 1). Grain-projected area can be effectively extracted by the YTS and overcomes the limitations inherent to the manual measurement of grain size (Fig. 5e). Traditionally, grain size, which is one of the key component traits for grain yield, is evaluated based on grain length and width. Grain-projected area is a 2D projected image of grain and is a composite trait reflecting both the grain length and width. Several known loci associated with grain size, such as GS32425, MADS2928 and TH127, and 24 new loci were detected with grain-projected area (shown in Fig. 5f; Supplementary Data 1).


Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice.

Yang W, Guo Z, Huang C, Duan L, Chen G, Jiang N, Fang W, Feng H, Xie W, Lian X, Wang G, Luo Q, Zhang Q, Liu Q, Xiong L - Nat Commun (2014)

Comparison of rice accessions exhibiting different plant compactness values and grain-projected areas.Representative rice accessions exhibiting different plant compactness values at late booting stage (a), different plant compactness values at milk grain stage (c), and the grain-projected area (e). Manhattan plots (left) and quantile-quantile plots (right) for plant compactness at late booting stage (sample size=402) (b), plant compactness at milk grain stage (sample size=269) (d), and grain-projected area (sample size=514) (f). The P values are computed from a likelihood ratio test with a mixed-model approach using the factored spectrally transformed linear mixed models (FaST-LMM) programme (P=1.21 × 10−6). *New traits are those that cannot be defined and extracted using traditional measurement techniques.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4214417&req=5

f5: Comparison of rice accessions exhibiting different plant compactness values and grain-projected areas.Representative rice accessions exhibiting different plant compactness values at late booting stage (a), different plant compactness values at milk grain stage (c), and the grain-projected area (e). Manhattan plots (left) and quantile-quantile plots (right) for plant compactness at late booting stage (sample size=402) (b), plant compactness at milk grain stage (sample size=269) (d), and grain-projected area (sample size=514) (f). The P values are computed from a likelihood ratio test with a mixed-model approach using the factored spectrally transformed linear mixed models (FaST-LMM) programme (P=1.21 × 10−6). *New traits are those that cannot be defined and extracted using traditional measurement techniques.
Mentions: In addition to the traditional agronomic traits, new traits, including plant compactness and grain-projected area, can be extracted by the RAP and the YTS, respectively. Plant compactness reflects plant density and plant architecture, and a more detailed description of plant compactness is provided in the Supplementary Note 2. As shown in Fig. 5a,c, the plants became more compact and the leaves became more upright with increases in plant compactness. Plant compactness provided meaningful information on plant architecture in addition to the commonly recognized traits (such as plant height, tiller number and green leaf area) (Supplementary Fig. 9). This was also the reason that plant compactness was chosen to improve the biomass and leaf area prediction. Seven and four loci were associated with plant compactness at the late booting stage and the milk grain stage, respectively (shown in Fig. 5b,d; Supplementary Data 1). Grain-projected area can be effectively extracted by the YTS and overcomes the limitations inherent to the manual measurement of grain size (Fig. 5e). Traditionally, grain size, which is one of the key component traits for grain yield, is evaluated based on grain length and width. Grain-projected area is a 2D projected image of grain and is a composite trait reflecting both the grain length and width. Several known loci associated with grain size, such as GS32425, MADS2928 and TH127, and 24 new loci were detected with grain-projected area (shown in Fig. 5f; Supplementary Data 1).

Bottom Line: Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1.Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information.The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.

View Article: PubMed Central - PubMed

Affiliation: 1] Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China [2] National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, China [3] MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China [4] College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.

ABSTRACT
Even as the study of plant genomics rapidly develops through the use of high-throughput sequencing techniques, traditional plant phenotyping lags far behind. Here we develop a high-throughput rice phenotyping facility (HRPF) to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period. Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1. Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information. The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.

Show MeSH
Related in: MedlinePlus