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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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Genome-wide association studies of five traits at the late booting stage measured by the RAP and five yield-related traits measured by the YTS.Manhattan plots (left) and quantile-quantile plots (right) for shoot fresh weight (a), plant height (b), tiller number (c), green leaf area (d) and plant compactness (e) measured by the RAP, and grain length (f), grain width (g), grain length/width ratio (h), 1,000-grain weight (i) and grain-projected area (j) measured by the YTS. The sample sizes are 402 for the five traits measured by RAP (a–e), and the sample sizes are 514 for five yield traits measured by YTS (f–j). 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). For quantile-quantile plots, the horizontal axis shows −log10-transformed expected P values, and the vertical axis indicates −log10-transformed observed P values. The names of known related genes are shown above the corresponding association peaks.
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f3: Genome-wide association studies of five traits at the late booting stage measured by the RAP and five yield-related traits measured by the YTS.Manhattan plots (left) and quantile-quantile plots (right) for shoot fresh weight (a), plant height (b), tiller number (c), green leaf area (d) and plant compactness (e) measured by the RAP, and grain length (f), grain width (g), grain length/width ratio (h), 1,000-grain weight (i) and grain-projected area (j) measured by the YTS. The sample sizes are 402 for the five traits measured by RAP (a–e), and the sample sizes are 514 for five yield traits measured by YTS (f–j). 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). For quantile-quantile plots, the horizontal axis shows −log10-transformed expected P values, and the vertical axis indicates −log10-transformed observed P values. The names of known related genes are shown above the corresponding association peaks.

Mentions: After establishment of the phenotyping platform, we performed GWAS across 529 diverse O. sativa accessions for 15 traits. In contrast to previous related studies, these traits were measured automatically by the RAP and YTS instead of performing manual measurements89. Using a Bonferroni correction based on the effective numbers of independent markers17, the P value thresholds were 1.21E–06 and 6.03E–08 (suggestive and significant, respectively) for the entire population18. In our study, only the associations that exceeded the P value thresholds with clear peak-like signals were considered. With the significance threshold set, we identified 57 loci, including 15 loci associated with four traits measured by the RAP and 42 loci with five traits measured by the YTS (Supplementary Data 1). According to the suggestive threshold, 138 associated loci were identified; of these, 49 were associated with six traits measured by the RAP and 89 were associated with five yield-related traits (Supplementary Data 1). Manhattan plots and quantile-quantile plots for the 15 traits at different stages are shown in Fig. 3 and 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)

Genome-wide association studies of five traits at the late booting stage measured by the RAP and five yield-related traits measured by the YTS.Manhattan plots (left) and quantile-quantile plots (right) for shoot fresh weight (a), plant height (b), tiller number (c), green leaf area (d) and plant compactness (e) measured by the RAP, and grain length (f), grain width (g), grain length/width ratio (h), 1,000-grain weight (i) and grain-projected area (j) measured by the YTS. The sample sizes are 402 for the five traits measured by RAP (a–e), and the sample sizes are 514 for five yield traits measured by YTS (f–j). 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). For quantile-quantile plots, the horizontal axis shows −log10-transformed expected P values, and the vertical axis indicates −log10-transformed observed P values. The names of known related genes are shown above the corresponding association peaks.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: Genome-wide association studies of five traits at the late booting stage measured by the RAP and five yield-related traits measured by the YTS.Manhattan plots (left) and quantile-quantile plots (right) for shoot fresh weight (a), plant height (b), tiller number (c), green leaf area (d) and plant compactness (e) measured by the RAP, and grain length (f), grain width (g), grain length/width ratio (h), 1,000-grain weight (i) and grain-projected area (j) measured by the YTS. The sample sizes are 402 for the five traits measured by RAP (a–e), and the sample sizes are 514 for five yield traits measured by YTS (f–j). 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). For quantile-quantile plots, the horizontal axis shows −log10-transformed expected P values, and the vertical axis indicates −log10-transformed observed P values. The names of known related genes are shown above the corresponding association peaks.
Mentions: After establishment of the phenotyping platform, we performed GWAS across 529 diverse O. sativa accessions for 15 traits. In contrast to previous related studies, these traits were measured automatically by the RAP and YTS instead of performing manual measurements89. Using a Bonferroni correction based on the effective numbers of independent markers17, the P value thresholds were 1.21E–06 and 6.03E–08 (suggestive and significant, respectively) for the entire population18. In our study, only the associations that exceeded the P value thresholds with clear peak-like signals were considered. With the significance threshold set, we identified 57 loci, including 15 loci associated with four traits measured by the RAP and 42 loci with five traits measured by the YTS (Supplementary Data 1). According to the suggestive threshold, 138 associated loci were identified; of these, 49 were associated with six traits measured by the RAP and 89 were associated with five yield-related traits (Supplementary Data 1). Manhattan plots and quantile-quantile plots for the 15 traits at different stages are shown in Fig. 3 and 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