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QTL Modelling: An Adaptation Option in Spring Wheat for Drought Stress

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Quantification of Climate Variability, Adaptation and Mitigation for Agricultural Sustainability

Abstract

A project was executed to study genotype to phenotype relationships through QTL analysis in a recombinant inbred population of 77 lines and its integration in crop simulation modeling. RILs were generated from a cross between wheat cultivar Opata and SH-349. At two leaves stage drought was imposed using gravimetric method for drought maintenance at 40 % of field capacity and control was maintained at 100 % field capacity. At three phenological stages viz. jointing, flag leaf and anthesis; photosynthetic rate, stomatal conductance, transpiration rate, stomatal resistance were determined and chlorophyll content was measured. The RILs under study exhibited high phenotypic variation under drought stress. The physiological and phenological data was used to parameterize and validate Agricultural Production Systems Simulator (APSIM); a crop growth and development modeling tool. It was noted that APSIM predicted the phenology of all the 77 RILs with R2 value ranging from 0.72 to 0.98. The same mapping population was used for QTL mapping using computational approaches with observed data and simulated data from crop simulation model APSIM. In linkage group 1 a single QTL controlling 13 physiological traits and another QTL controlling a single trait for phenology was found. In linkage group 2 one QTL controlling 7 phenological traits was mapped. The QTLs which were mapped with real data were the same as with simulated data. This indicated that the simulated data with crop models under different environmental scenarios could be efficiently used for QTL mapping reducing the environmental contribution in G x E complex and suggesting the QTLs with more precision. Photosynthetic attributes of these RILs under drought stress at different phenological stages suggests complex physiological aspects critical for coping moisture stress and provides a strong basis for their utilization in wheat cultivar improvement for drought stress adaptation under changing climatic scenarios.

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Author information

Authors and Affiliations

  1. National Agricultural Research Centre, PARC- Institute of Advanced Studies in Agriculture, Islamabad, Pakistan

    Muhammad Umair Aslam, Armghan Shehzad, Muhammad Iqbal & Muhammad Asim

  2. Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan

    Mukhtar Ahmed

  3. Department of Biological Systems Engineering, Washington State University, Pullman, WA, USA

    Mukhtar Ahmed

  4. Ministry of Food and Agriculture, Islamabad, Pakistan

    M. Aslam

Authors
  1. Muhammad Umair Aslam
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  2. Armghan Shehzad
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  4. Muhammad Iqbal
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  5. Muhammad Asim
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  6. M. Aslam
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Corresponding author

Correspondence to Muhammad Umair Aslam .

Editor information

Editors and Affiliations

  1. Department of Biological Systems Engineering, Washington State University, PULLMAN, Washington, USA

    Mukhtar Ahmed

  2. Biological System Engineering, Washington State University, Pullman, Washington, USA

    Claudio O. Stockle

Appendix 1: Modified Genetic Coefficients in APSIM Wheat -Module for Wheat Mapping Population

Appendix 1: Modified Genetic Coefficients in APSIM Wheat -Module for Wheat Mapping Population

Parameters

Vernalization sensitivity

Photothermal sensitivity

Thermal time for grain filling

Radiation use efficiency at floral initiation/flowering

Default Value in APSIM -Wheat Module

1.5

3

610

1.24/1.24

DRMP-5-1

0

3.45

635

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DRMP-5-3

0

3.5

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DRMP-5-4

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3.46

636

3.00/2.24

DRMP-5-05

0

3.44

633

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DRMP-5-6

0

3.45

635

3.00/2.24

DRMP-5-8

0

3.5

644

3.00/2.24

DRMP-5-10

0

3.6

662

3.00/2.24

DRMP-5-12

0

3.5

644

3.00/2.24

DRMP-5-13

0

3.3

624

3.00/2.24

DRMP-5-14

0

3.4

626

3.00/2.24

DRMP-5-15

0

3.34

625

3.00/2.24

DRMP-5-16

0

3.45

635

3.00/2.24

DRMP-5-17

0

3.4

625

3.00/2.24

DRMP-5-19

0

3.4

625

3.00/2.24

DRMP-5-20

0

3.5

644

3.00/2.24

DRMP-5-21

0

3.6

662

3.00/2.24

DRMP-5-22

0

3.6

662

3.00/2.24

DRMP-5-24

0

3.6

662

3.00/2.24

DRMP-5-25

0

3.6

662

3.00/2.24

DRMP-5-26

0

3.5

644

3.00/2.24

DRMP-5-28

0

3.5

644

3.00/2.24

DRMP-5-29

0

3.6

662

3.00/2.24

DRMP-5-30

0

3.45

635

3.00/2.24

DRMP-5-31

0

3.2

621

3.00/2.24

DRMP-5-32

0

3.5

644

3.00/2.24

DRMP-5-33

0

3.6

662

3.00/2.24

DRMP-5-35

0

3.6

662

3.00/2.24

DRMP-5-36

0

3.5

644

3.00/2.24

DRMP-5-37

0

3.7

653

3.00/2.24

DRMP-5-38

0

3.6

661

3.00/2.24

DRMP-5-44

0

3.5

644

3.00/2.24

DRMP-5-45

0

3.5

644

3.00/2.24

DRMP-5-46

0

3.5

644

3.00/2.24

DRMP-5-47

0

3.6

662

3.00/2.24

DRMP-5-48

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3.6

662

3.00/2.24

DRMP-5-49

0

3.5

644

3.00/2.24

DRMP-5-50

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3.6

662

3.00/2.24

DRMP-5-51

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3.5

644

3.00/2.24

DRMP-5-53

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3.5

644

3.00/2.24

DRMP-5-54

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3.5

644

3.00/2.24

DRMP-5-56

0

3.6

645

3.00/2.24

DRMP-5-57

0

3.5

644

3.00/2.24

DRMP-5-58

0

3.5

644

3.00/2.24

DRMP-5-61

0

3.5

644

3.00/2.24

DRMP-5-62

0

3.4

626

3.00/2.24

DRMP-5-64

0

3.6

663

3.00/2.24

DRMP-5-65

0

3.4

626

3.00/2.24

DRMP-5-66

0

3.6

663

3.00/2.24

DRMP-5-71

0

3.4

626

3.00/2.24

DRMP-5-72

0

3.5

644

3.00/2.24

DRMP-5-73

0

3.6

663

3.00/2.24

DRMP-5-74

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3.5

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DRMP-5-76

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3.5

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DRMP-5-78

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3.6

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DRMP-5-80

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3.6

663

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DRMP-5-81

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3.5

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DRMP-5-83

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3.5

644

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DRMP-5-84

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3.6

663

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DRMP-5-85

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3.4

626

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DRMP-5-86

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3.5

644

3.00/2.24

DRMP-5-87

0

3.6

663

3.00/2.24

DRMP-5-88

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3.6

663

3.00/2.24

DRMP-5-89

0

3.5

644

3.00/2.24

DRMP-5-90

0

3.4

626

3.00/2.24

DRMP-5-91

0

3.6

663

3.00/2.24

DRMP-5-92

0

3.5

644

3.00/2.24

DRMP-5-94

0

3.6

663

3.00/2.24

DRMP-5-95

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3.4

626

3.00/2.24

DRMP-5-96

0

3.5

644

3.00/2.24

DRMP-5-97

0

3.5

644

3.00/2.24

DRMP-5-101

0

3.6

663

3.00/2.24

DRMP-5-102

0

3.6

663

3.00/2.24

DRMP-5-103

0

3.4

626

3.00/2.24

DRMP-5-104

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3.5

644

3.00/2.24

DRMP-5-105

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3.5

644

3.00/2.24

DRMP-5-107

0

3.6

663

3.00/2.24

DRMP-5-108

0

3.4

626

3.00/2.24

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Aslam, M.U., Shehzad, A., Ahmed, M., Iqbal, M., Asim, M., Aslam, M. (2017). QTL Modelling: An Adaptation Option in Spring Wheat for Drought Stress. In: Ahmed, M., Stockle, C. (eds) Quantification of Climate Variability, Adaptation and Mitigation for Agricultural Sustainability. Springer, Cham. https://doi.org/10.1007/978-3-319-32059-5_6

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