# Characterization of adaptation mechanisms in sorghum using a multi-reference back-cross nested association mapping design and envirotyping

Vincent Garin, Chiaka Diallo, Mohamed Lamine Tekete, Korothimi Thera, Baptiste Guitton, Karim Dagno, Abdoulaye G. Diallo, Mamoutou Kouressy, Willmar L. Leiser, Fred Rattunde, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Vincent Garin, Chiaka Diallo, Mohamed Lamine Tekete, Korothimi Thera, Baptiste Guitton, et al. Characterization of adaptation mechanisms in sorghum using a multi-reference back-cross nested association mapping design and envirotyping. Sorghum in the 21st Century, Global Sorghum Conference, CIRAD; Kansas State University; Sorghum International Development; IRD; CERAAS, Jun 2023, Montpellier, France. pp.114. [hal-05181939](https://hal.science/hal-05181939v1)

HAL Id: hal-05181939

https://hal.science/hal-05181939v1

Submitted on 23 Jul 2025

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<figure><img src="image_2.png" /></figure>

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# Characterization of adaptation mechanisms in sorghum using a multi-reference back-cross nested association mapping design and envirotyping

Vincent Garin, Chiaka Diallo, Mohamed Lamine Tekete, Korotimi Thera, Baptiste Guitton, Karim Dagno, Abdoulaye G. Diallo, Mamoutou Kouressy, Willmar Leiser, Fred Rattunde, Ibrahima Sissoko, Aboubacar Toure, Baloua Nebie, Moussa Samake, Jana Kholova, Julien Frouin, David Pot, Michel Vaksmann, Eva Weltzien, Niaba Teme, and Jean-François Rami

<figure><img src="image_3.png" /></figure>

**Sorghum in the 21st Century**
**Global Sorghum Conference**
Resiliency and Sustainability in the Face of Climate Change

**June 5-9** The Corum Event Center, 2023 Montpellier, France
<figure><img src="image_4.png" /></figure>

## IER

Niaba Teme, Mohamed Lamine Tekete, Korotimi Thera, Karim Dagno, Abdoulaye G. Diallo, Mamoutou Kouressy

## CIRAD

Baptiste Guitton, David Pot, Julien Frouin, Michel Vaksmann, Jean-François Rami

## ICRISAT (Mali)

Eva Weltzien (Wisconcin University), Chiaka Diallo (Intitut polytechnique rural), Willmar Leiser (KWS), Fred Rattunde (Wisconcin University), Ibrahima Sissoko, Aboubacar Toure, Baloua Nebie (CIMMYT)

## ICRISAT (Inde)

Jana Kholova

## USTTB

Moussa Samake
# The challenge of the genotype by environment (GxE) interaction

<figure><img src="image_5.png" /></figure>

Plants, especially sorghum are experiencing strong GxE interaction.

Sorghum shows a wide range of adaptation (plasticity).

GxE is a challenge for breeders, especially in the context of marker assisted selection

<figure><img src="image_6.png" /></figure>

**How to meet the demand of farmers that experience extremely diverse cultivation conditions?**

-> Advanced genetic resources + Envirotyping + statistical methodology + decision support tools
Advanced genetic resources: West and Central Africa (WCA) BCNAM population

<figure><img src="image_7.png" /></figure>

WCA-BCNAM populations: 41 crosses between central parent Grinkan, Kenin-Keni, and Lata3 (N=3901) and 24 donors parents with a wide diversity of traits.
## Envirotyping: Multi-environment phenotyping

<figure><img src="image_8.png" /></figure>

Multi-environments characteristics:

Four testing sites: Sotuba, Cinzana, Samanko, Kolombada

Two sowing dates (Sot, Cin): End June, End July

<table><thead><tr><td>Location</td><td>Cumulated rain season [mm]</td><td>Temperature [°C]</td><td>Photoperiod (June) [h]</td><td>Photoperiod (July) [h]</td></tr></thead><tbody><tr><td>Sotuba</td><td>570 – 851</td><td>27</td><td>12.3</td><td>12</td></tr><tr><td>Cinzana</td><td>338 – 708</td><td>28.5</td><td>12.15</td><td>11.95</td></tr><tr><td>Samanko</td><td>967</td><td>27.5</td><td>12.3</td><td></td></tr><tr><td>Kolombada</td><td>438</td><td>26.7</td><td>12.3</td><td></td></tr></tbody></table>

Trait: Flag leaf, plant height, number of internodes, average internode length, peduncle length, panicle length, yield
## Envirotyping: 15 environmental covariates (EC)

<table><thead><tr><th>Category</th><th>EC</th><th>Unit</th><th>Observed/<br>inferred</th><th>Sum/mean</th></tr></thead><tbody><tr><td rowspan="6">Atmospheric</td><td>cumulated rain</td><td>mm</td><td>obs</td><td>sum</td></tr><tr><td>humidity</td><td>%</td><td>obs</td><td>mean</td></tr><tr><td>vapour pressure deficit</td><td>kPa</td><td>inf</td><td>mean</td></tr><tr><td>slope of saturation VP curve</td><td>kPa/d</td><td>inf</td><td>mean</td></tr><tr><td>potential evapotranspiration</td><td>mm/day</td><td>inf</td><td>mean</td></tr><tr><td>deficit by precipitation</td><td>mm/day</td><td>inf</td><td>mean</td></tr><tr><td rowspan="5">Temperature</td><td>minimum temperature</td><td>d</td><td>obs</td><td>mean</td></tr><tr><td>maximum temperature</td><td>d</td><td>obs</td><td>mean</td></tr><tr><td>temperature range</td><td>d</td><td>obs</td><td>mean</td></tr><tr><td>cumulated degree day</td><td>dd</td><td>obs</td><td>sum</td></tr><tr><td>T effect on radiation use efficiency</td><td>0-1</td><td>inf</td><td>mean</td></tr><tr><td rowspan="3">Radiation</td><td>cumulated hour of sun</td><td>h</td><td>obs</td><td>sum</td></tr><tr><td>photoperiod</td><td>h</td><td>inf</td><td>mean</td></tr><tr><td>solar radiation</td><td>MJ/m^2/day</td><td>inf</td><td>sum</td></tr><tr><td>Photothermal</td><td>Photothermal (photoperiod * DD)</td><td>h*dd</td><td>obs</td><td>sum</td></tr></tbody></table>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

EnvRtype: Costa-Neto et al. (2020)
Nasapower: Sparks (2018)
$$ \underline{y}_{icj} = \mu + e_j + c_{cj} + x_{ia} * \beta_{aj} + \underline{g}e_{icj} + \underline{\epsilon}_{icj} \quad [1] $$

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

We assumed that each parent carries a different allele at the QTL position that can have a different effect in each environment (N_par x N_Env effect) estimated compared to reference (central parent).
$$ \underline{y}_{icj} = \mu + e_j + c_{cj} + x_{ia} * \beta_{aj} + \underline{g}e_{icj} + \underline{\epsilon}_{icj} \quad [1] $$

<figure><img src="image_13.png" /></figure>

$$ \mathbf{V}(g\mathbf{e}_{icj} + \boldsymbol{\epsilon}_{icj}) $$

<figure><img src="image_14.png" /></figure>

$$ \sigma_g^2 $$

Compound symmetry (CS)

$$ V \begin{bmatrix} y_{i11} \\ y_{i'21} \\ y_{i12} \\ y_{i'22} \\ y_{i13} \\ y_{i'23} \end{bmatrix} = \begin{bmatrix} \sigma_g^2 + \sigma_\epsilon^2 & 0 & \sigma_g^2 & 0 & \sigma_g^2 & 0 \\ 0 & \sigma_g^2 + \sigma_\epsilon^2 & 0 & \sigma_g^2 & 0 & \sigma_g^2 \\ \sigma_g^2 & 0 & \sigma_g^2 + \sigma_\epsilon^2 & 0 & \sigma_g^2 & 0 \\ 0 & \sigma_g^2 & 0 & \sigma_g^2 + \sigma_\epsilon^2 & 0 & \sigma_g^2 \\ \sigma_g^2 & 0 & \sigma_g^2 & 0 & \sigma_g^2 + \sigma_\epsilon^2 & 0 \\ 0 & \sigma_g^2 & 0 & \sigma_g^2 & 0 & \sigma_g^2 + \sigma_\epsilon^2 \end{bmatrix} $$

(uniform gene action)

<figure><img src="image_15.png" /></figure>

$$ \sigma_{g_{jj'}}^2 $$

Unstructured (UN)

$$ V \begin{bmatrix} y_{i11} \\ y_{i'21} \\ y_{i12} \\ y_{i'22} \\ y_{i13} \\ y_{i'23} \end{bmatrix} = \begin{bmatrix} \sigma_{g_1}^2 + \sigma_\epsilon^2 & 0 & \sigma_{g_{12}} & 0 & \sigma_{g_{13}} & 0 \\ 0 & \sigma_{g_1}^2 + \sigma_\epsilon^2 & 0 & \sigma_{g_{12}} & 0 & \sigma_{g_{13}} \\ \sigma_{g_{12}} & 0 & \sigma_{g_2}^2 + \sigma_\epsilon^2 & 0 & \sigma_{g_{23}} & 0 \\ 0 & \sigma_{g_{12}} & 0 & \sigma_{g_2}^2 + \sigma_\epsilon^2 & 0 & \sigma_{g_{23}} \\ \sigma_{g_{13}} & 0 & \sigma_{g_{23}} & 0 & \sigma_{g_3}^2 + \sigma_\epsilon^2 & 0 \\ 0 & \sigma_{g_{13}} & 0 & \sigma_{g_{23}} & 0 & \sigma_{g_3}^2 + \sigma_\epsilon^2 \end{bmatrix} $$

(Set of gene act differently in each E)
$$ \underline{y_{icj}} = \underline{env_j} + \underline{cross_{cj}} + x_{ip} * \beta_{pj} + \underline{GE_{icj}} + \underline{e_{icj}} (2) $$

$$ x_{ip} * (\beta_p + EC_e * S_p + l_{pe}) $$

**Bp**: parental allele main effect across environment

**ECe**: value of the environmental covariate in environment E

**Sp**: Sensitivity of the parental QTL allelic effect to the EC

<figure><img src="image_16.png" /></figure>
a) Estimation of the QTL allele sensitivity to the EC

<figure><img src="image_17.png" /></figure>

$$Q_{eff} = \beta_{p} + EC_{e} * S_{p}$$
$$\hat{Q}_{eff} = 250 + EC_{e} * 0.05$$

b) Projection of the QTL effect given new EC values

<figure><img src="image_18.png" /></figure>
## mppR: Multi-Parent Population QTL Analysis

Analysis of experimental multi-parent populations to detect regions of the genome (called quantitative trait loci, QTLs) influencing phenotypic traits measured in unique and multiple environments. The population must be composed of crosses between a set of at least three parents (e.g. factorial design, 'diallel', or nested association mapping). The functions cover data processing, QTL detection, and results visualization. The implemented methodology is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017) <doi:10.1007/s00122-017-2923-3> as well as in Garin, Malosetti and van Eeuwijk (2020) <doi:10.1007/s00122-020-03621-0>.

**Version:** 1.4.0

**Depends:** R (≥ 3.1.0)

**Imports:** dplyr, ggplot2, graphics, grDevices, igraph, Matrix, methods, nlme, parallel, qtl, stats, utils

**Suggests:** knitr, r markdown, testthat

**Published:** 2023-01-05

**Author:** Vincent Garin [aut, cre], Valentin Wimmer [aut], Dietrich Borchardt [ctb, dtc], Fred van Eeuwijk [ctb, ths], Marcos Malosetti [ctb, ths]

**Maintainer:** Vincent Garin <vincent.garin6 at gmail.com>

**BugReports:** https://github.com/vincentgarin/mppR/issues

**License:** GPL-3

**URL:** https://github.com/vincentgarin/mppR

**NeedsCompilation:** no

**Citation:** mppR citation info

**Materials:** README NEWS

**CRAN checks:** mppR results

## Documentation:

**Reference manual:** mppR.pdf

**Vignettes:** QTL detection in multiparental populations characterized in multiple environments mppR: An R Package for QTL Analysis in Multi-parent Populations
<table><thead><tr><th></th><th>FLAG</th><th>PH</th><th>NODE_N</th><th>NODE_L</th><th>PED</th><th>PAN</th><th>GWGH</th><th>YIELD</th></tr></thead><tbody><tr><td>Grinkan 2012</td><td>6 (48.9)</td><td>3 (48)</td><td>4 (17.6)</td><td>2 (47.1)</td><td>6 (31.2)</td><td>5 (8.5)</td><td>3 (13.5)</td><td>3 (5.4)</td></tr><tr><td>Grinkan 2013</td><td>4 (32.1)</td><td>4 (44.5)</td><td>1 (9.6)</td><td>2 (42.6)</td><td>2 (21.1)</td><td>1 (4.6)</td><td>0</td><td>2 (7.3)</td></tr><tr><td>Kenin-Keni 2012</td><td>6 (53.4)</td><td>2 (9.6)</td><td>2 (16.8)</td><td>3 (18.8)</td><td>3 (14.3)</td><td>2 (5.2)</td><td>2 (7.9)</td><td>1 (3.9)</td></tr><tr><td>Kenin-Keni 2013</td><td>2 (35.5)</td><td>2 (12.7)</td><td>2 (22.3)</td><td>0</td><td>2 (16.9)</td><td>1 (5.8)</td><td>0</td><td>1 (5.9)</td></tr><tr><td>Lata3</td><td>4 (50.3)</td><td>2 (20.1)</td><td>2 (13.7)</td><td>1 (11)</td><td>2 (10.2)</td><td>0</td><td>6 (30.4)</td><td>4 (14.3)</td></tr></tbody></table>

Good explanation (R2) of FLAG, PH, NODE_N and PED. Less QTL and lower R2 for PAN and YIELD.
Results – Large QTL effect with candidate genes

<table><thead><tr><th>QTL ID</th><th>trait</th><th>chr</th><th>range [cM]</th><th>range [Mbp]</th><th>R2</th><th>QxE range</th><th>Npar</th><th>Candidate genes</th></tr></thead><tbody><tr><td>Q FL 3 78</td><td>FLAG</td><td>3</td><td>77.34-78.36</td><td>5.11-5.15</td><td>17.1</td><td>[-123; 144] [dd]</td><td>24</td><td>Elf3, SbCN12</td></tr><tr><td>Q FL 6 3</td><td>FLAG</td><td>6</td><td>1.49-2.94</td><td>0.04-0.08</td><td>19.4</td><td>[-178; 130] [dd]</td><td>24</td><td>Ma6</td></tr><tr><td>Q FL 6 38</td><td>FLAG</td><td>6</td><td>36.32-39.4</td><td>4.04-4.12</td><td>6.3</td><td>[-192; -27] [dd]</td><td>19</td><td>Ma1</td></tr><tr><td>Q FL 9 105</td><td>FLAG</td><td>9</td><td>103.7-106.7</td><td>5.46-5.54</td><td>2.7</td><td>[-40;91] [dd]</td><td>23</td><td>SbFL9.1</td></tr><tr><td>Q PH 7 76</td><td>PH</td><td>7</td><td>74.28-76.69</td><td>5.47-5.52</td><td>21.9</td><td>[-37; 69] [cm]</td><td>20</td><td>qHT7.1, (Dw3)</td></tr><tr><td>Q PH 7 106</td><td>PH</td><td>7</td><td>102-108.3</td><td>5.94-6.07</td><td>7.6</td><td>[-14;58] [cm]</td><td>15</td><td>Dw3</td></tr><tr><td>Q NN 3 78</td><td>NODE_N</td><td>3</td><td>78.13-78.75</td><td>5.14-5.17</td><td>9.3</td><td>[-1.8;2.1] [n]</td><td>16</td><td>Elf3, SbCN12</td></tr><tr><td>Q NN 6 2</td><td>NODE_N</td><td>6</td><td>1.49-2.73</td><td>0.04-0.08</td><td>7.9</td><td>[-2.6;1.8] [n]</td><td>19</td><td>Ma6</td></tr><tr><td>Q NL 7 78</td><td>NODE_L</td><td>7</td><td>76.29-79.59</td><td>5.51-5.58</td><td>29.9</td><td>[-0.1;4.2] [cm]</td><td>16</td><td>qHT7.1, (Dw3)</td></tr><tr><td>Q NL 7 98</td><td>NODE_L</td><td>7</td><td>96.1-100.7</td><td>5.83-5.91</td><td>11.8</td><td>[-5.8-4.2] [cm]</td><td>8</td><td>Dw3</td></tr><tr><td>Q PED 7 78</td><td>PED</td><td>7</td><td>74.8-82.1</td><td>54.84-56.26</td><td>12.6</td><td>[-8; 10] [cm]</td><td>24</td><td>qHT7.1, (Dw3)</td></tr></tbody></table>

Large QTL effect with underlying candidate genes for FLAG and PH
<figure><img src="image_19.png" /></figure>

Parental QTL allelic effects are modulated by genetic background and environment
Results – Environmental sensitivity of FLAG and PH large QTLs

<figure><img src="image_20.png" /></figure>

Flag leaf appearance [dd]

<figure><img src="image_21.png" /></figure>
Results – Projection of expected QTL allelic effect in the Malian environment

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>
# Decision support tools – R shiny application for interactive QTL effect database

**Description:** Search for QTL positions in the database using trait, chromosome, and position information. QTL effect can also be subsetted according to R2 value

Select a trait

Flag leaf appearance

<figure><img src="image_25.png" /></figure>

Select chromosome(s)

6

Select physical boundaries ...

Lower boundary [bp]

<figure><img src="image_26.png" /></figure>

Upper boundary [bp]

100000000

... or Genetic boundaries

Lower boundary [cM]

<figure><img src="image_27.png" /></figure>

Upper boundary [cM]

1000

Filter by

● Physical distance

○ Genetic distance

Optional filter by R2 value [0-100]

minimum R2 value

<figure><img src="image_28.png" /></figure>

0

Search

QTL effect significance [-log10(p-value)]

<table><thead><tr><th>QTL</th><th>chr</th><th>bp</th><th>cM</th><th>logp_av</th><th>logp_min</th><th>logp_max</th></tr></thead><tbody><tr><td>QTL_FLAG_6_3</td><td>6.00</td><td>758633.94</td><td>2.60</td><td>47.20</td><td>15.90</td><td>96.50</td></tr><tr><td>QTL_FLAG_6_38</td><td>6.00</td><td>40649927.66</td><td>37.30</td><td>28.90</td><td>7.50</td><td>65.70</td></tr></tbody></table>

QTL R2

<table><thead><tr><th>QTL</th><th>chr</th><th>bp</th><th>cM</th><th>R2_av</th><th>R2_min</th><th>R2_max</th></tr></thead><tbody><tr><td>QTL_FLAG_6_3</td><td>6.00</td><td>758633.94</td><td>2.60</td><td>19.40</td><td>14.40</td><td>24.00</td></tr><tr><td>QTL_FLAG_6_38</td><td>6.00</td><td>40649927.66</td><td>37.30</td><td>6.30</td><td>2.20</td><td>11.10</td></tr></tbody></table>

QTL range of additive effects

<table><thead><tr><th>QTL</th><th>chr</th><th>bp</th><th>cM</th><th>Eff_min</th><th>Eff_max</th></tr></thead><tbody><tr><td>QTL_FLAG_6_3</td><td>6.00</td><td>758633.94</td><td>2.60</td><td>-178.51</td><td>130.16</td></tr><tr><td>QTL_FLAG_6_38</td><td>6.00</td><td>40649927.66</td><td>37.30</td><td>-191.67</td><td>26.98</td></tr></tbody></table>

List of unique QTL position that can be copied to get extra information about the QTL effect and the C
QTL_FLAG_6_3 QTL_FLAG_6_38

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>
<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

Tricot experiment (van Etten et al. 2019) for sorghum participatory variety selection with 200 farmers from 20 villages of the Adilabad district (India)
## ExM constraint selection

<figure><img src="image_33.png" /></figure>

## ExM constraint selection

<figure><img src="image_34.png" /></figure>

## Farmer variety preference

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>
Conclusion – Better integration of developed technologies and farmers need

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>
Questions

<figure><img src="image_42.png" /></figure>
Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., & Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. *G3*, *11*(4), jkab040

Sparks, A. H. (2018). nasapower: a NASA POWER global meteorology, surface solar energy and climatology data client for R. *Journal of Open Source Software*, *3*(30), 1035.

Van Etten, J., Beza, E., Calderer, L., Van Duijvendijk, K., Fadda, C., Fantahun, B., ... & Zimmerer, K. S. (2019). First experiences with a novel farmer citizen science approach: crowdsourcing participatory variety selection through on-farm triadic comparisons of technologies (tricot). *Experimental Agriculture*, *55*(S1), 275-296.