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Part 1: Document Description
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Citation |
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Title: |
Leaf ion content in pearl millet (Senegal, 2021-2022) |
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Identification Number: |
doi:10.23708/DWGEAJ |
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Distributor: |
DataSuds |
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Date of Distribution: |
2025-01-30 |
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Version: |
1 |
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Bibliographic Citation: |
Nakombo-Gbassault, Princia; Arenas, Sebastian; Affortit, Pablo; Faye, Awa; Flis, Paulina; Sine, Bassirou; Moukouanga, Daniel; Kane, Ndjido Ardo; Bennett, Malcolm; Wells, Darren; Cubry, Philippe; Bailey, Liz; Grondin, Alexandre; Vigouroux, Yves; Laplaze, Laurent, 2025, "Leaf ion content in pearl millet (Senegal, 2021-2022)", https://doi.org/10.23708/DWGEAJ, DataSuds, V1, UNF:6:eg+VmV/nauULROuvgkx5Ig== [fileUNF] |
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Citation |
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Title: |
Leaf ion content in pearl millet (Senegal, 2021-2022) |
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Identification Number: |
doi:10.23708/DWGEAJ |
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Authoring Entity: |
Nakombo-Gbassault, Princia (UMR DIADE - IRD, University of Montpellier, Cirad - France) |
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Arenas, Sebastian (UMR DIADE - IRD, University of Montpellier, Cirad - France) |
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Affortit, Pablo (UMR DIADE - IRD, University of Montpellier, Cirad - France) |
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Faye, Awa (CERAAS - Institut Sénégalais de Recherches Agricoles - Senegal) |
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Flis, Paulina (Plant Sciences - University of Nottingham - United Kingdom) |
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Sine, Bassirou (CERAAS - Institut Sénégalais de Recherches Agricoles - Senegal) |
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Moukouanga, Daniel (UMR DIADE - IRD, University of Montpellier, Cirad - France) |
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Kane, Ndjido Ardo (CERAAS - Institut Sénégalais de Recherches Agricoles - Senegal) |
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Bennett, Malcolm (Plant Sciences - University of Nottingham - United Kingdom) |
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Wells, Darren (Plant Sciences - University of Nottingham - United Kingdom) |
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Cubry, Philippe (UMR DIADE - IRD, University of Montpellier, Cirad - France) |
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Bailey, Liz (Plant Sciences - University of Nottingham - United Kingdom) |
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Grondin, Alexandre (UMR DIADE - IRD, University of Montpellier, Cirad - France) |
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Vigouroux, Yves (UMR DIADE - IRD, University of Montpellier, Cirad - France) |
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Laplaze, Laurent (UMR DIADE - IRD, University of Montpellier, Cirad - France) |
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Grant Number: |
Ionomil n°386 |
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Grant Number: |
ValoRoot n°2202-002 |
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Grant Number: |
Anatomics ICA-R1-180356 |
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Grant Number: |
PhD grant to PNG |
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Grant Number: |
PhD grant to PNG |
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Distributor: |
DataSuds |
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Access Authority: |
Grondin, Alexandre |
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Depositor: |
Grondin, Alexandre |
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Date of Deposit: |
2025-01-21 |
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Holdings Information: |
https://doi.org/10.23708/DWGEAJ |
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Study Scope |
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Keywords: |
Agricultural Sciences, Pearl millet, Ion, Leaf, Soil, GWAS |
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Topic Classification: |
Plant sciences |
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Abstract: |
<p>Pearl millet (Pennisetum glaucum) is a nutrient-rich cereal crop cultivated in arid and semi-arid regions, particularly in sub-Saharan Africa, where it serves as a vital source of grain and fodder for millions of smallholder farmers. This crop is known to produce in hot, dry climates and nutrient-poor soils, making it a strategic crop for enhancing agricultural resilience in the face of climate change. Despite its impressive adaptability, pearl millet yield in sub-Saharan Africa remains low. In order to enhance productivity and nutritional quality, it is important to better understand the physiological and genetic mechanisms that regulate the uptake, accumulation, transport and utilisation of nutrients because they are critical for sustaining growth, development, and resistance to biotic and abiotic stresses.</p> <p>In this project, we aimed at studying the diversity for leaf ion content in pearl millet and its genetic control. For this, a diverse panel of 165 pearl millet inbred lines from the pearl millet association panel (PMiGAP, Sehgal et al., 2015) was analyzed for leaf ion content under irrigated and vegetative drought stress conditions during the 2021 and 2022 growing seasons in the field in Senegal (Affortit et al., in preparation).</p> <p>The last ligulated leaf from the main tiller was sampled from three plants at 49 days after sowing in 2021 and 42 days after sowing in 2022. Leaves were washed in a 0.1% Triton X-100 solution, rinsed with deionized water, and stored in paper bags before drying at 60°C in an oven for three days. Leaf disks were sampled from dry leaves harvested from the field at around 5 cm from the ligule. Three leaf disks (5 cm diameter) from the three plants harvested in the same plot were pooled. Ion content was also studied in soils sampled at different locations and depths in the field at the beginning of the experiment. Plants remaining in the field were subjected to agro-morphological measurements (Day to flowering in particular; Affortit et al., in preparation).</p> Ion content in leaves and soils were measured at the ionomic platform of the University of Nottingham using Inductively Coupled Plasma Mass Spectrometers (ICP-MS) following a similar procedure to Danku et al (2013). Samples were analyzed for the content of a number of ions including As, Cr, Li, Na, Mg, P, Pb, Se, S, K, Ca, Mn, Fe, Co, Ni, Cu, Zn, Rb, Sr, Mo and Cd. For ion content in leaves, Best Linear Unbiased Estimators (BLUEs) were calculated for each ion using StatgenSTA package considering a resolvable incomplete block design for the analysis (Rossum, 2023).</p> <p>Inbred lines were genotyped using tGBS Genotyping by Sequencing technology conducted with the restriction enzyme Bsp1286I (Freedom Markers, USA). Samples were sequenced using an Illumina HiSeq X instrument, and reads were aligned to the Cenchrus Americanus ASM217483v2 (Varshney et al., 2017) reference genome after debarcoding and trimming of reads. Lines with high missing data were removed and SNPs were filtered based on missing percentage (< 50%) and minor allele frequency (MAF > 5%). A total of 269,248 SNP were used in GWAS. The missing data were imputed based on a matrix factorization approach using the "impute" function of the LEA package (Frichot and François 2015).</p> <p>GWAS was performed on BLUEs, Box-Cox transformed BLUEs to achieve normality and residues from the linear regression between individual ion concentrations and flowering time. Residuals of BLUEs were calculated to correct for potential confounding effect of phenology on ion content, as correlation between flowering time and ion content was observed. GWAS was performed using four mixed models: Latent Factor Mixed Models (LFMM, Frichot et al., 2013), Efficient Mixed-Model Association (EMMA, Kang et al., 2008), Bayesian information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK, Huang et al., 2019), and Compressed Mixed Linear Model (CMLM, Zhang et al., 2010). GWAS analyses were initially conducted separately for each year using BLUEs, and p-values for each method were then combined across two years independently using the Fisher's method (Cubry et al., 2020). False Discovery Rate (FDR) estimation was performed for each trait to correct for multiple testing. To calculate a p-values threshold based on the number of independent SNPs, a pruning process was implemented with Plink v1.9 to exclude highly correlated SNPs. These thresholds were used to select significant SNP-trait associations. Only associations identified by at least two methods in each individual GWAS and further validated by the Fisher's combined method were considered for further analyses.</p> <p>In total, 161 significant associations were identified and delimited into 73 QTL regions associated with beneficial ions such as magnesium or potassium and toxic heavy metals such as cadmium.</p> <p>The findings offer novel QTLs associated with grain ion content and candidate genes that are potentially valuable for breeding programs aiming at improving pearl millet leaf ion content and ultimately grain biofortification.</p> |
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Time Period: |
2019-01-01-2022-12-31 |
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Country: |
Senegal |
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Geographic Coverage: |
Diourbel Region, Bambey |
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Geographic Bounding Box: |
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Notes: |
Data Type: Experimental data |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Studies |
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Grondin, Alexandre; Affortit, Pablo; Jones, Dylan; Faye, Awa; Sine, Bassirou; Kane, Ndjido; Bennett, Malcolm; Wells, Darren; Laplaze, Laurent; Atkinson, Jonathan, 2024, "Root anatomical diversity in pearl millet under irrigated and early drought stress (Senegal, 2021-2022)", <a href="https://doi.org/10.23708/PPN9BQ">https://doi.org/10.23708/PPN9BQ</a>, DataSuds. |
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Related Publications |
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Citation |
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Title: |
Princia Nakombo-Gbassault, Sebastian Arenas, Pablo Affortit, Awa Faye, Paulina Flis, Bassirou Sine, Daniel Moukouanga, Pascal Gantet, Ephrem Kosh Komba, Ndjido Kane, Malcolm Bennett, Darren Wells, Philippe Cubry, Liz Bailey, Alexandre Grondin, Yves Vigouroux, Laurent Laplaze. Genome wide association study of the leaf ionize in pearl millet. [in preparation] |
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Bibliographic Citation: |
Princia Nakombo-Gbassault, Sebastian Arenas, Pablo Affortit, Awa Faye, Paulina Flis, Bassirou Sine, Daniel Moukouanga, Pascal Gantet, Ephrem Kosh Komba, Ndjido Kane, Malcolm Bennett, Darren Wells, Philippe Cubry, Liz Bailey, Alexandre Grondin, Yves Vigouroux, Laurent Laplaze. Genome wide association study of the leaf ionize in pearl millet. [in preparation] |
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Other Reference Note(s) |
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Pablo Affortit, Awa Faye, Dylan Jones, Ezenwoko Benson, Bassirou Sine, James Burridge, Mame Sokhatil Ndoye, Luke Barry, Daniel Moukouanga, Rahul Bhosale, Tony Pridmore, Jonathan P. Lynch, Pascal Gantet, Vincent Vadez, Philippe Cubry, Ndjido Kane, Malcolm Bennett, Jonathan A. Atkinson, Laurent Laplaze, Darren M. Wells, Alexandre Grondin. Metaxylem size in pearl millet roots correlates to early drought stress tolerance and transpiration restriction in response to increasing evaporative demand in a soil type dependent manner. [in preparation] |
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File Description--f45134 |
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File: IonoMil_2021_Raw_Data.tab |
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Notes: |
UNF:6:kQqY9krIhjhHel4jvtuOag== |
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File Description--f45135 |
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File: IonoMil_2022_Raw_Data.tab |
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Notes: |
UNF:6:JIyi0lCYwZZexIXMR0Bn5A== |
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File Description--f45133 |
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File: Soil_Data_IonoMil.tab |
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Notes: |
UNF:6:aQhYLUxEvS8gzYHzKlaUXg== |
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File Description--f45637 |
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File: Pearl_Millet_IonoMil_Data_Dictionary.tab |
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Notes: |
UNF:6:6gtf1TyKWj5epK5wWgpcUQ== |
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File Description--f45152 |
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File: Table_sig_snp.tab |
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Notes: |
UNF:6:UyVPQ9FB7/NPKtyfMw02eA== |
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List of Variables: |
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Variables |
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f45134 Location: |
Variable Format: character Notes: UNF:6:AzGH0LzYdhvQcWFVswb3yQ== |
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f45134 Location: |
Variable Format: character Notes: UNF:6:0+qXUsDQasOBm2sgO+s48A== |
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f45135 Location: |
Variable Format: character Notes: UNF:6:wViDRONuWi+nOLwl49qnEw== |
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f45135 Location: |
Variable Format: character Notes: UNF:6:0+qXUsDQasOBm2sgO+s48A== |
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f45133 Location: |
Summary Statistics: Min. 2021.0; Valid 206.0; Max. 2022.0; Mean 2021.504854368932; StDev 0.5011944057415983; Variable Format: numeric Notes: UNF:6:5NaeXtzoel+z7MANSa99jw== |
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f45133 Location: |
Variable Format: character Notes: UNF:6:p0DU7VX//A0nYrN5FXv6KQ== |
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f45133 Location: |
Variable Format: character Notes: UNF:6:RJTdUcAOitO9+CLQwEa+2g== |
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f45133 Location: |
Variable Format: character Notes: UNF:6:wAQb+3dz50/7sfKfzSxvKw== |
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f45133 Location: |
Summary Statistics: Valid 206.0; Min. 0.022119031; StDev 0.1828249204215963; Mean 0.18896395702427182; Max. 1.045 Variable Format: numeric Notes: UNF:6:+yoHyJIMVgPC3j8G7sB5dA== |
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f45133 Location: |
Summary Statistics: Mean 503.88876161650484; Valid 206.0; Max. 827.986; Min. 130.178; StDev 134.19827750490487 Variable Format: numeric Notes: UNF:6:MVSTghaaadNPs+kYP9ShLQ== |
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f45133 Location: |
Summary Statistics: Mean 0.08264566190291263; StDev 0.045323800817113874; Max. 0.381; Valid 206.0; Min. 0.016 Variable Format: numeric Notes: UNF:6:DPGPYh0w+LAnkj+znSLSDA== |
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f45133 Location: |
Summary Statistics: StDev 0.02978474888999717; Valid 206.0; Min. 0.005; Max. 0.149; Mean 0.038747361995145634 Variable Format: numeric Notes: UNF:6:u8X5DE5ERkGu6fGwqLZyOQ== |
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f45133 Location: |
Summary Statistics: Min. 0.229685732; Valid 206.0; Max. 1.398; StDev 0.15689161466447105; Mean 0.4368662365291262 Variable Format: numeric Notes: UNF:6:cJb44FuqLuD6ddNl8vPAFA== |
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f45133 Location: |
Summary Statistics: StDev 7.496106475798103; Mean 11.241983602825243; Max. 65.589; Valid 206.0; Min. 4.358195211; Variable Format: numeric Notes: UNF:6:MqdxjPVmodHwzopvp9BDJg== |
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f45133 Location: |
Summary Statistics: Min. 9.76; Valid 206.0; Max. 56.3610602; StDev 8.038089467114334; Mean 23.882030773592234; Variable Format: numeric Notes: UNF:6:90YT3vNFMbmg/4pi+6bQFQ== |
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f45133 Location: |
Summary Statistics: Valid 206.0; Max. 0.013; StDev 0.0028480869566962458; Min. -4.85416E-4; Mean 0.004398004150485437; Variable Format: numeric Notes: UNF:6:NPoLdhW2QqO6Frj22qieTw== |
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f45133 Location: |
Summary Statistics: StDev 33.74678205359554; Valid 206.0; Mean 103.5902971345631; Min. 39.937; Max. 201.927 Variable Format: numeric Notes: UNF:6:q/S0YRVvU3l1NkhU2roxug== |
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f45133 Location: |
Summary Statistics: StDev 3.010947532070534; Min. 2.420968086; Max. 27.566; Valid 206.0; Mean 6.648892770082524; Variable Format: numeric Notes: UNF:6:TSPc/84Lernmrp167WxaPQ== |
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f45133 Location: |
Summary Statistics: Min. 8.32623E-4; Mean 0.011393091572815536; Valid 206.0; Max. 1.355105227; StDev 0.09427006823464891; Variable Format: numeric Notes: UNF:6:ZpEyGnZh0fzFILk5nmqY5Q== |
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f45133 Location: |
Summary Statistics: Min. 14.38589509; Valid 206.0; StDev 29.37335424758829; Max. 188.027; Mean 60.22332923582525 Variable Format: numeric Notes: UNF:6:K9m3dSkYSlBs+lyEyru0eg== |
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f45133 Location: |
Summary Statistics: Max. 0.871; Mean 0.17669158071359223; Valid 206.0; Min. 0.044628727; StDev 0.11785507873178012 Variable Format: numeric Notes: UNF:6:mVIwKvYu2Ml5H0j5CxGNqg== |
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f45133 Location: |
Summary Statistics: Valid 206.0; Max. 27.52829233; Mean 6.48877627165534; StDev 5.974185717938028; Min. 0.736 Variable Format: numeric Notes: UNF:6:EaC3ppWf5cHE2Regjal7aQ== |
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f45133 Location: |
Summary Statistics: Valid 206.0; StDev 0.1692307177182983; Mean 0.2626990640048544; Max. 1.902; Min. 0.105163148; Variable Format: numeric Notes: UNF:6:VrcTiBf55jly2I3xbv3zHw== |
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f45133 Location: |
Summary Statistics: StDev 0.17517937950891246; Min. 0.110044134; Mean 0.42360834435436895; Valid 206.0; Max. 1.082 Variable Format: numeric Notes: UNF:6:WiFwwFypDM/+kTYjbSGIxw== |
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f45133 Location: |
Summary Statistics: Max. 6.637; StDev 1.2118861459342827; Mean 1.0253433115291264; Min. 0.005086025; Valid 206.0 Variable Format: numeric Notes: UNF:6:3hLHNWPikd4CiSpnF061kg== |
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f45133 Location: |
Summary Statistics: StDev 7.210678993194806; Min. 2.348; Valid 206.0; Mean 12.078200340203884; Max. 33.803 Variable Format: numeric Notes: UNF:6:EWWH/wAqoey7QWaNgnE+Aw== |
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f45133 Location: |
Summary Statistics: StDev 15.735469405458922; Valid 206.0; Min. 0.010508238; Mean 3.2919033479514566; Max. 129.255 Variable Format: numeric Notes: UNF:6:2HVX25+KbKohdfghJ7Jnvw== |
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f45637 Location: |
Variable Format: character Notes: UNF:6:E8d0aVQHGM3FWvWRkLCHhw== |
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f45637 Location: |
Variable Format: character Notes: UNF:6:SujNXjQPLc9rZRZaRNwvXw== |
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f45637 Location: |
Variable Format: character Notes: UNF:6:Dcmg+/OxmhG+0gD9yMlB2g== |
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f45152 Location: |
Variable Format: character Notes: UNF:6:rkGWHNpplnVfb3tK53yKtA== |
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f45152 Location: |
Variable Format: character Notes: UNF:6:LgHkrjmfIzCuXInUxqzjoA== |
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f45152 Location: |
Variable Format: character Notes: UNF:6:u/NjEFSdn1ONxbmPgXOj6g== |
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f45152 Location: |
Variable Format: character Notes: UNF:6:nhrgR6jxrIvF2A4kz80piw== |
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f45152 Location: |
Summary Statistics: Mean 9.527959404968943E7; Max. 2.57782515E8; Valid 161.0; Min. 201.0; StDev 7.077993718826923E7 Variable Format: numeric Notes: UNF:6:8N1vM0anjDcIS6puLKJr5Q== |
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f45152 Location: |
Summary Statistics: Max. 2.97E-5; StDev 5.707419041616693E-6; Min. 1.69E-11; Valid 161.0; Mean 4.9184392521739135E-6; Variable Format: numeric Notes: UNF:6:nMuYRMIpNJdlAQNr7d7GHQ== |
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f45152 Location: |
Variable Format: character Notes: UNF:6:Hu4bgUhMYHeUsYopBTZomA== |
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f45152 Location: |
Variable Format: character Notes: UNF:6:BXPmb01lq9fkT06sG4rhAw== |
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Label: |
Bash_fisher_combining.sh |
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Text: |
Bash script used to perform the Fisher combining analysis |
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Notes: |
application/x-sh |
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Label: |
Bash_GWAS.sh |
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Text: |
Bash script used to perform the association analysis |
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Notes: |
application/x-sh |
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Label: |
Bash_Manhattan_plot.sh |
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Text: |
Bash script used to produce Manhattan plots |
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Notes: |
application/x-sh |
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Label: |
BLUEs_IonoMil_WW_2021.txt |
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Text: |
BLUEs for leaf ion content measured under the well-watered treatment in the 2021 field experiment (with day to flowering) |
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Notes: |
text/plain |
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Label: |
BLUEs_IonoMil_WW_2022.txt |
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Text: |
BLUEs for leaf ion content measured under well-watered content in the 2022 field experiment (with day to flowering) |
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Notes: |
text/plain |
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Label: |
filtered_PearlMillet.all.snps_maf0.05_miss0.5_imputed_with_metadata.txt |
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Text: |
Imputed vcf (used for association analysis) |
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Notes: |
text/plain |
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Label: |
PearlMillet.all.snps_maf0.05_miss0.5.vcf |
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Text: |
Filtered SNPs from GBS genotyping (file used for pruning) |
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Notes: |
text/vcard |
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Label: |
IonoMil_DataverseIRD_References.docx |
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Notes: |
application/vnd.openxmlformats-officedocument.wordprocessingml.document |
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Label: |
Pearl_millet_IonoMil_ReadMe.txt |
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Text: |
Dataset citation, description and terms of use |
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Notes: |
text/plain |
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Label: |
GWAS_ion21_ETM.txt |
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Text: |
Input file for GWAS on the dataset obtained in 2021 (contain BLUEs and residues following a Gaussian law) |
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Notes: |
text/plain |
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Label: |
GWAS_ion22_ETM.txt |
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Text: |
Input file for GWAS on the dataset obtained in 2022 (contain BLUEs and residues following a Gaussian law) |
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Notes: |
text/plain |
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Label: |
In_PRUNE.prune.in |
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Text: |
List of SNPs that are independent |
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Notes: |
text/plain |
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Label: |
Residus_IonoMil_WW_2021.txt |
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Text: |
Residuals of the linear regression between individual ion concentrations (BLUEs) and flowering time measured under the well-watered treatment in 2021 |
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Notes: |
text/plain |
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Label: |
Residus_IonoMil_WW_2022.txt |
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Text: |
Residuals of the linear regression between individual ion concentrations (BLUEs) and flowering time measured under the well-watered treatment in 2022 |
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Notes: |
text/plain |
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Label: |
emma.txt |
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Text: |
R script used to perform the EMMA analysis |
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Notes: |
text/plain |
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Label: |
Pruning_script.txt |
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Text: |
R script used to remove non-independent SNPs from the vcf |
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Notes: |
text/plain |
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Label: |
RUN_boxcox_transformation.r |
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Text: |
R script used to transform BLUEs and residues in order to achieve normality |
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Notes: |
type/x-r-syntax |
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Label: |
RUN_fisher_combining.R |
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Text: |
R script used to perform the Fisher combining analysis |
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Notes: |
type/x-r-syntax |
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Label: |
RUN_GWAS_ionomil.R |
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Text: |
R script used to perform the association analyses |
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Notes: |
type/x-r-syntax |
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Label: |
RUN_linear_regression.r |
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Text: |
R script used to calculate the residues |
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Notes: |
type/x-r-syntax |
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Label: |
RUN_Manhattan_plots.R |
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Text: |
R script used to perform the Manhattan plots |
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Notes: |
type/x-r-syntax |