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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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Comparison among GWAS results using three phenotyping methods for shoot fresh weight, shoot dry weight and green leaf area.The three phenotyping methods included manual measurement, RAP measurement and raw measurement. The RAP measurement is the predicted value calculated by the raw features and the selected models (shown in Supplementary Fig. 3). The raw measurement is the projected area calculated by the number of foreground pixels, which is easily extracted without modelling. Manhattan plots for shoot fresh weight (a), shoot dry weight (b) and green leaf area (c) using manual measurement (left), RAP measurement (middle) and raw measurement (right; the projected area was calculated by the number of foreground pixels) at the late booting stage. (d) Blue bars indicate associated loci detected by manual measurement. Red bars and green bars indicate specific loci detected by RAP measurement and raw measurement, respectively. The sample sizes of all the three traits are 402. 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. For Manhattan plots, −log10P values from a genome-wide scan are plotted against the position of the SNPs on each of 12 chromosomes, and the horizontal grey dashed line indicates the genome-wide suggestive threshold (P=1.21 × 10−6).
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f4: Comparison among GWAS results using three phenotyping methods for shoot fresh weight, shoot dry weight and green leaf area.The three phenotyping methods included manual measurement, RAP measurement and raw measurement. The RAP measurement is the predicted value calculated by the raw features and the selected models (shown in Supplementary Fig. 3). The raw measurement is the projected area calculated by the number of foreground pixels, which is easily extracted without modelling. Manhattan plots for shoot fresh weight (a), shoot dry weight (b) and green leaf area (c) using manual measurement (left), RAP measurement (middle) and raw measurement (right; the projected area was calculated by the number of foreground pixels) at the late booting stage. (d) Blue bars indicate associated loci detected by manual measurement. Red bars and green bars indicate specific loci detected by RAP measurement and raw measurement, respectively. The sample sizes of all the three traits are 402. 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. For Manhattan plots, −log10P values from a genome-wide scan are plotted against the position of the SNPs on each of 12 chromosomes, and the horizontal grey dashed line indicates the genome-wide suggestive threshold (P=1.21 × 10−6).

Mentions: In the RAP measurements, after the raw features were extracted, optimized models were chosen to infer shoot fresh weight, shoot dry weight and green leaf area (Supplementary Fig. 3). To evaluate the performance of the RAP with regard to loci identification for the three traits, we compared the RAP measurement, the manual measurement and the raw measurement. The raw measurement is the projected area calculated by the number of foreground pixels, which is easily extracted without modelling. We conducted GWAS for the three traits using these different measurement methods (Fig. 4d; Supplementary Table 15). With the suggestive P value thresholds adopted, 12 and 15 associated loci were detected by manual and RAP measurements, respectively. For the raw measurements, however, only two associated loci were detected. For the three traits, 8 of 12 loci detected by manual measurement were also detected by the RAP, whereas only one locus was detected by the raw measurement. We used the GWAS results for the three traits at the late booting stage as an illustration to provide a detailed comparison (Fig. 4). On the basis of Manhattan plots, the GWAS results of the three traits measured by the RAP were consistent with those obtained by manual measurement, whereas the raw measurements of shoot fresh weight and green leaf area failed to detect any associated loci. As shown in Supplementary Table 15, among the three associated loci detected by manual measurement, two were also detected by the RAP, whereas no loci were detected by raw measurement. Detailed information comparing Manhattan and quantile-quantile plots of the four traits at other stages is provided in Supplementary Figs 4–8.


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 among GWAS results using three phenotyping methods for shoot fresh weight, shoot dry weight and green leaf area.The three phenotyping methods included manual measurement, RAP measurement and raw measurement. The RAP measurement is the predicted value calculated by the raw features and the selected models (shown in Supplementary Fig. 3). The raw measurement is the projected area calculated by the number of foreground pixels, which is easily extracted without modelling. Manhattan plots for shoot fresh weight (a), shoot dry weight (b) and green leaf area (c) using manual measurement (left), RAP measurement (middle) and raw measurement (right; the projected area was calculated by the number of foreground pixels) at the late booting stage. (d) Blue bars indicate associated loci detected by manual measurement. Red bars and green bars indicate specific loci detected by RAP measurement and raw measurement, respectively. The sample sizes of all the three traits are 402. 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. For Manhattan plots, −log10P values from a genome-wide scan are plotted against the position of the SNPs on each of 12 chromosomes, and the horizontal grey dashed line indicates the genome-wide suggestive threshold (P=1.21 × 10−6).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Comparison among GWAS results using three phenotyping methods for shoot fresh weight, shoot dry weight and green leaf area.The three phenotyping methods included manual measurement, RAP measurement and raw measurement. The RAP measurement is the predicted value calculated by the raw features and the selected models (shown in Supplementary Fig. 3). The raw measurement is the projected area calculated by the number of foreground pixels, which is easily extracted without modelling. Manhattan plots for shoot fresh weight (a), shoot dry weight (b) and green leaf area (c) using manual measurement (left), RAP measurement (middle) and raw measurement (right; the projected area was calculated by the number of foreground pixels) at the late booting stage. (d) Blue bars indicate associated loci detected by manual measurement. Red bars and green bars indicate specific loci detected by RAP measurement and raw measurement, respectively. The sample sizes of all the three traits are 402. 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. For Manhattan plots, −log10P values from a genome-wide scan are plotted against the position of the SNPs on each of 12 chromosomes, and the horizontal grey dashed line indicates the genome-wide suggestive threshold (P=1.21 × 10−6).
Mentions: In the RAP measurements, after the raw features were extracted, optimized models were chosen to infer shoot fresh weight, shoot dry weight and green leaf area (Supplementary Fig. 3). To evaluate the performance of the RAP with regard to loci identification for the three traits, we compared the RAP measurement, the manual measurement and the raw measurement. The raw measurement is the projected area calculated by the number of foreground pixels, which is easily extracted without modelling. We conducted GWAS for the three traits using these different measurement methods (Fig. 4d; Supplementary Table 15). With the suggestive P value thresholds adopted, 12 and 15 associated loci were detected by manual and RAP measurements, respectively. For the raw measurements, however, only two associated loci were detected. For the three traits, 8 of 12 loci detected by manual measurement were also detected by the RAP, whereas only one locus was detected by the raw measurement. We used the GWAS results for the three traits at the late booting stage as an illustration to provide a detailed comparison (Fig. 4). On the basis of Manhattan plots, the GWAS results of the three traits measured by the RAP were consistent with those obtained by manual measurement, whereas the raw measurements of shoot fresh weight and green leaf area failed to detect any associated loci. As shown in Supplementary Table 15, among the three associated loci detected by manual measurement, two were also detected by the RAP, whereas no loci were detected by raw measurement. Detailed information comparing Manhattan and quantile-quantile plots of the four traits at other stages is provided in Supplementary Figs 4–8.

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