# Sorghum back-cross nested association mapping populations and multiparental multi-environment statistical methodology to support breeding in West-Africa

Vincent Garin, Mohamed Tekete, Chiaka Diallo, Korothimi Thera, Niaba Teme, Mamoutou Kouressy, Karim Dagno, Abdoulaye Diallo, Baloua Nebie, Aboubacar Touré, et al.

► To cite this version:

Vincent Garin, Mohamed Tekete, Chiaka Diallo, Korothimi Thera, Niaba Teme, et al. Sorghum back-cross nested association mapping populations and multiparental multi-environment statistical methodology to support breeding in West-Africa. Interdrought Conference (Plant production in water limited environments), Nov 2022, Dakar, Senegal. ISRA, 1 p., 2022. [hal-05176543](https://hal.science/hal-05176543v1)

HAL Id: hal-05176543

https://hal.science/hal-05176543v1

Submitted on 22 Jul 2025

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# Sorghum back-cross nested association mapping populations and multiparental multi-environment statistical methodology to support breeding in West-Africa

Garin Vincent (V.Garin@cgiar.org), Tekete Mohamed, Diallo Chiaka, Thera Korotimi, Teme Niaba, Kouressy Mamoutou, Dagno Karim, Diallo Abdoulaye, Nebie Baloua, Toure Aboubacar, Weltzien Eva, Rattunde Fred, Sissoko Ibrahima, Kholova Jana, Guitton Baptiste, Pot David, Rami Jean-François, Vaksman Michel

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

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<table><thead><tr><th>Parents</th><th>Race</th><th>Origin</th><th>Kp3</th><th>PH</th><th>Maturity</th><th>Specific advantage</th></tr></thead><tbody><tr><td>Fara-Fara</td><td>Guinea</td><td>Nigeria</td><td>+</td><td>+</td><td>+</td><td>Geographic and intra-racial diversity</td></tr><tr><td>E36-1</td><td>Caudatum</td><td>Ethiopia</td><td>-</td><td>-</td><td>av</td><td>Drought tolerance</td></tr><tr><td>IS15401</td><td>Guinea</td><td>Cameroon</td><td>+</td><td>+</td><td>+</td><td>Striga, low-P, midge res., grain quality</td></tr><tr><td>IS23540</td><td>Caudatum</td><td>Ethiopia</td><td>-</td><td>-</td><td>av</td><td>Sweet stem</td></tr><tr><td>B35</td><td>Durra/Caud.</td><td>Ethiopia</td><td>-</td><td>-</td><td>-</td><td>Drought tolerance</td></tr><tr><td>Konotene</td><td>Durra</td><td>Mali</td><td>-</td><td>+</td><td>+</td><td>Grain weight</td></tr><tr><td>SC566-14</td><td>Caudatum</td><td>Brazil</td><td>-</td><td>-</td><td>-</td><td>Aluminium tolerance</td></tr><tr><td>Framida</td><td>Caudatum</td><td>Burkina F.</td><td>-</td><td>-</td><td>+</td><td>Striga tolerance</td></tr><tr><td>CSM417</td><td>Guinea</td><td>Mali</td><td>+</td><td>+</td><td>+</td><td>Grain quality and adaptation</td></tr><tr><td>CSM63</td><td>Guinea</td><td>Mali</td><td>-</td><td>av</td><td>-</td><td>Precocity</td></tr><tr><td>CSM388</td><td>Guinea</td><td>Mali</td><td>+</td><td>+</td><td>+</td><td>Grain quality and adaptation</td></tr><tr><td>Gadiaba</td><td>Durra</td><td>Mali</td><td>+</td><td>+</td><td>+</td><td>Grain weight</td></tr><tr><td>W. Kaura</td><td>Durra/Caud.</td><td>Nigeria</td><td>+</td><td>-</td><td>+</td><td>Racial diversity</td></tr><tr><td>V33/08</td><td>Caud./Guinea</td><td>Mali</td><td>av</td><td>av</td><td>+</td><td>Productivity and Grain quality</td></tr><tr><td>Kalaban</td><td>Caudatum</td><td>Mali</td><td>-</td><td>-</td><td>av</td><td>Head bug resistance</td></tr><tr><td>Malisor</td><td>Caudatum</td><td>Mali</td><td>+</td><td>-</td><td>-</td><td>Head bug resistance</td></tr><tr><td>BimbG</td><td>Guinea</td><td>Guinea</td><td>+</td><td>+</td><td>+</td><td>Grain quality, photoperiod sensitivity</td></tr><tr><td>Hafijeka</td><td>Guinea</td><td>Gambia</td><td>+</td><td>+</td><td>+</td><td>Grain quality</td></tr><tr><td>S. Kaura</td><td>Durra/Caud.</td><td>Nigeria</td><td>+</td><td>+</td><td>+</td><td>Productivity, Stover quality</td></tr><tr><td>Sangatigui</td><td>Guinea</td><td>Mali</td><td>av</td><td>av</td><td>av</td><td>Intra-racial diversity and vitreous grain</td></tr><tr><td>Grinkan</td><td>Caud./Guinea</td><td>Mali</td><td>av</td><td>av</td><td>av</td><td>Racial diversity</td></tr><tr><td>DouaG</td><td>Guinea</td><td>Mali</td><td>+</td><td>+</td><td>+</td><td>Low-P adaptation</td></tr><tr><td>Gnossiconi</td><td>Guinea</td><td>Burkina F.</td><td>av</td><td>av</td><td>av</td><td>Grain and panicle traits</td></tr><tr><td>Ngolofing</td><td>Guinea</td><td>Mali</td><td>+</td><td>+</td><td>av</td><td>Grain and panicle traits</td></tr><tr><td>Sambalma</td><td>Caud./Guinea</td><td>Nigeria</td><td>+</td><td>+</td><td>+</td><td>Grain and panicle traits, Al tolerance</td></tr></tbody></table>

## Genotyping data

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GBS data -> 51'545 SNPs -> 1 map

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## Phenotyping data

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19 env (Loc x sowing D x P trt)

## Envirotyping data

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15 environmental covariates

## Statistical models

$$ \underline{y}_{icj} = \mu + e_j + c_{cj} + x_{ia} * \beta_{aj} + \underline{g}e_{icj} + \underline{\epsilon}_{icj} $$

$$ V \begin{bmatrix} y_{i11} \\ y_{i12} \\ y_{i22} \\ y_{i13} \\ y_{i23} \end{bmatrix} = \begin{bmatrix} \sigma_g^2 + \sigma_\epsilon^2 & 0 & \sigma_g^2 & 0 & \sigma_g^2 & 0 \\ 0 & \sigma_g^2 + \sigma_\epsilon^2 & 0 & \sigma_g^2 & 0 & \sigma_g^2 \\ \sigma_g^2 & 0 & \sigma_g^2 + \sigma_\epsilon^2 & 0 & \sigma_g^2 & 0 \\ 0 & \sigma_g^2 & 0 & \sigma_g^2 + \sigma_\epsilon^2 & 0 & \sigma_g^2 \\ \sigma_g^2 & 0 & \sigma_g^2 & 0 & \sigma_g^2 + \sigma_\epsilon^2 & 0 \\ 0 & \sigma_g^2 & 0 & \sigma_g^2 & 0 & \sigma_g^2 + \sigma_\epsilon^2 \end{bmatrix} $$

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

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Multi-parent multi-environment QTL detection with extension to estimate the QTLxEC effect.

## QTL parental effects projections in the environment

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