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Genetic Analysis

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Systems Modeling

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Abstract

Analysis of complex relationships between genotype and phenotype is imperative for crop improvement and better production. Genetic analysis started when humans practiced selective breeding for crop improvement and reorganized with the advent of the Mendelian genetic principles. Genetic analysis requires phenotyping and genotyping followed by application of statistical principles. Advances in the field of automation and informatics lead to high-throughput phenotyping and genotyping which eventually revolutionized the field of genetic analysis. Massive parallel sequencing (MPS) based on genotyping by sequencing (GBS) is one of the best high-throughput genotyping techniques utilized for discovering single-nucleotide polymorphism (SNP) in crop genomes and provides the insight into the genome, epigenome, and transcriptome on an extraordinary scale. Estimation of the type and extent of gene action controlling the inheritance of quantitative traits is made possible through genetic analysis. Genotype by genotype by environment (GGE) interaction is useful for evaluation of genotypes in mega-environment. Mapping of quantitative trait loci (QTL) is made through association between genotypic and phenotypic data and reveals the genetic basis of variation of multifactor traits in crop plants. The identified QTLs could be utilized as marker-assisted selection tool to enhance the efficiency of a breeding program dealing with the improvement of quantitative traits in a crop breeding program.

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Abbreviations

AFLP:

Amplified fragment length polymorphism

AMMI:

Additive main effects and multiplicative interaction

ANOVA:

Analysis of variance

BC:

Backcross

CIMMYT:

International Maize and Wheat Improvement Center

COI:

Crossover interaction

CSSLs:

Chromosome segment substitution lines

DH:

Double haploid

DNA:

Deoxyribonucleic acid

EDTA:

Ethylenediaminetetraacetic acid

GBS:

Genotyping by sequencing

GEI:

Genotype by environment interaction

GGE:

Genotype by genotype by environment

IL:

Introgressive lines

MPS:

Massive parallel sequencing

NGS:

Next-generation sequencing

NILs:

Near-isogenic lines

PCA:

Principal component analysis

QEI:

QTL-by-environment interactions

QTL:

Quantitative trait analysis

RAPD:

Random amplified polymorphic DNA

RFLP:

Restriction fragment length polymorphism

RHLs:

Residual heterozygous lines

RIL:

Recombinant inbred line

SNP:

Single-nucleotide polymorphism

SSLs:

Single-segment lines

SSR:

Simple sequence repeats

SVD:

Singular value decomposition

References

  • Ahmed K, Shabbir G, Ahmed M, Shah KN (2020) Phenotyping for drought resistance in bread wheat using physiological and biochemical traits. Sci Total Environ 729:139082. https://doi.org/10.1016/j.scitotenv.2020.139082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Aslam MU, 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 CO (eds) Quantification of climate variability, adaptation and mitigation for agricultural sustainability. Springer International Publishing, Cham, pp 113–136. https://doi.org/10.1007/978-3-319-32059-5_6

    Chapter  Google Scholar 

  • Canforaa L, Rosb M (2018) 2. Deoxyribonucleic acid (DNA) extraction. In: Crop diversification and low-input farming across Europe: from practitioners’ engagement and ecosystems services to increased revenues and value chain organisation: 22–25

    Google Scholar 

  • Courtois B, Ahmadi N, Khowaja F, Price AH, Rami J-F, Frouin J, Hamelin C, Ruiz M (2009) Rice root genetic architecture: meta-analysis from a drought QTL database. Rice 2(2):115

    Article  Google Scholar 

  • Das CK, Bastia D, Naik B, Kabat B, Mohanty M, Mahapatra S (2018) GGEBiplot and AMMI analysis of grain yield stability & adaptability behaviour of paddy (Oryza sativa L.) genotypes under different agro-ecological zones of Odisha. Oryza 55(4):528–542

    Article  Google Scholar 

  • Dean AM (1995) A molecular investigation of genotype by environment interactions. Genetics 139(1):19–33

    CAS  PubMed  PubMed Central  Google Scholar 

  • Eisemann R (1981) Two methods of ordination and their application in analysing genotype environment interactions. In: Byth DE, Mungomery VE (eds) Interpretation of plant response and adaptation to agricultural environments. Australian Institute of agricultural Sciences, Brisbane, pp 293–307

    Google Scholar 

  • Furbank RT, Tester M (2011) Phenomics–technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16(12):635–644

    Article  CAS  Google Scholar 

  • Gabriel KR (1971) The biplot graphic display of matrices with application to principal component analysis. Biometrika 58(3):453–467

    Article  Google Scholar 

  • Hayward AC, Tollenaere R, Dalton-Morgan J, Batley J (2015) Molecular marker applications in plants. In: Plant genotyping. Springer, New York, pp 13–27

    Chapter  Google Scholar 

  • He J, Zhao X, Laroche A, Lu Z-X, Liu H, Li Z (2014) Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 5:484

    Article  Google Scholar 

  • Kang MS (2002) Chapter 15: Genotype–environment interaction: progress and prospects. In: Quantitative genetics, genomics, and plant breeding. CAB International, Wallingford, p 219

    Google Scholar 

  • Kaya Y, Turkoz M (2016) Evaluation of genotype by environment interaction for grain yield in durum wheat using non-parametric stability statistics. Turk J Field Crops 21(1):51–59

    Google Scholar 

  • Mohammed MI (2009) Genotype × environment interaction in bread wheat in northern Sudan using AMMI analysis. Am Eurasian J Agric Environ Sci 6:427–433

    Google Scholar 

  • Normanly J (2012) High-throughput phenotyping in plants: methods and protocols. Springer, Totowa

    Book  Google Scholar 

  • Pieruschka R, Poorter H (2012) Phenotyping plants: genes, phenes and machines. Funct Plant Biol 39(11):813–820

    Article  Google Scholar 

  • Poehlman JM (2013) Breeding field crops. Springer, New York

    Google Scholar 

  • Rodrigues PC (2018) An overview of statistical methods to detect and understand genotype-by-environment interaction and QTL-by-environment interaction. Biometr Lett 55(2):123–138

    Article  Google Scholar 

  • Saltz JB, Bell AM, Flint J, Gomulkiewicz R, Hughes KA, Keagy J (2018) Why does the magnitude of genotype-by-environment interaction vary? Ecol Evol 8(12):6342–6353

    Article  Google Scholar 

  • Sanders MF, Bowman JL (2014) Genetic analysis: an integrated approach. Pearson Education, New York

    Google Scholar 

  • Shah SH, Shah SM, Khan MI, Ahmed M, Hussain I, Eskridge KM (2009) Nonparametric methods in combined heteroscedastic experiments for assessing stability of wheat genotypes in Pakistan. Pak J Bot 41(2):711–730

    Google Scholar 

  • Tian J, Deng Z, Zhang K, Yu H, Jiang X, Li C (2015a) Genetic analysis methods of quantitative traits in wheat. In: Genetic analyses of wheat and molecular marker-assisted breeding, vol 1. Springer, Dordrecht, pp 13–40

    Chapter  Google Scholar 

  • Tian J, Zhiying D, Zhang K, Yu H, Jiang X, Li C (2015b) Genetic analyses of wheat and molecular marker-assisted breeding, volume 1: genetics map and QTL mapping. Springer, Dordrecht

    Book  Google Scholar 

  • Wilstermann AM (2019) THE GENE: from genetics to postgenomics. Perspect Sci Christ Faith 71(3):184–187

    Google Scholar 

  • Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000) Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci 40(3):597–605

    Google Scholar 

  • Yan W, Kang MS (2002) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC press, Boca Raton, FL

    Google Scholar 

  • Yan W, Tinker NA (2006) Biplot analysis of multi-environment trial data: principles and applications. Can J Plant Sci 86(3):623–645

    Google Scholar 

  • Yu Z, Wang X, Zhang L (2018) Structural and functional dynamics of dehydrins: a plant protector protein under abiotic stress. Int J Mol Sci 19(11):3420

    Article  Google Scholar 

  • Yu D, Zhang J, Tan G, Yu N, Wang Q, Duan Q, Qi X, Cheng M, Yan C, Wei Z (2019) An easily-performed high-throughput method for plant genomic DNA extraction. Anal Biochem 569:28–30

    Article  CAS  Google Scholar 

  • Zaidi PH (2019) Management of drought stress in field phenotyping. CIMMYT, Mexico

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Plant Breeding and Genetics, PMAS-Arid Agriculture University, Rawalpindi, Pakistan

    Munir Ahmad & Rashid Mehmood Rana

Authors
  1. Munir Ahmad
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  2. Rashid Mehmood Rana
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Corresponding author

Correspondence to Munir Ahmad .

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Editors and Affiliations

  1. Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, Umeå, Sweden, Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan

    Mukhtar Ahmed

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Ahmad, M., Rana, R.M. (2020). Genetic Analysis. In: Ahmed, M. (eds) Systems Modeling. Springer, Singapore. https://doi.org/10.1007/978-981-15-4728-7_7

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