Quantitative Trait Loci (QTL) Mapping for Disease Resistance in Crop Species Using Advanced Biostatistical Approaches

Quantitative Trait Loci (QTL) However, continued mapping of the genetic region(s) in disease resistance in crop species is indispensable for the development of high-yielding, robust crop species. Phenotypic trait-based QTL mapping is a bridge between a specific genomic region and a genetic marker of resistance to disease on the population level or to a translated genetic marker of disease resistance. Using advanced biostatistical methods, such as mixed models and Bayesian inference, this study increases the accuracy and resolution of QTL identification (e.g., wheat, rice, and maize). It is found that a more robust analysis can furnish additional robustness that considers the interaction of environment and genetic variance and population structure, both significant yet frequently neglected facets in standard QTL mapping. By combining crop breeders with genomics-based cutting-edge biostatistical tools, we can choose plants resistant to disease.

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Publication Details

Journal
Global Drug Design & Development Review
Published
2024-09-30
DOI
https://doi.org/10.31703/gdddr.2024(ix-iii).01
Primary Topic
Genetics and Plant Breeding
Type
article
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article

Quantitative Trait Loci (QTL) Mapping for Disease Resistance in Crop Species Using Advanced Biostatistical Approaches

Nida Khanim, Shagufta Muhammad Hanif, Hafiza Sadia Rasool
Global Drug Design & Development Review
Genetics and Plant Breeding
article

Quantitative Trait Loci (QTL) Mapping for Disease Resistance in Crop Species Using Advanced Biostatistical Approaches

Nida Khanim, Shagufta Muhammad Hanif, Hafiza Sadia Rasool
article en

Abstract

Quantitative Trait Loci (QTL) However, continued mapping of the genetic region(s) in disease resistance in crop species is indispensable for the development of high-yielding, robust crop species. Phenotypic trait-based QTL mapping is a bridge between a specific genomic region and a genetic marker of resistance to disease on the population level or to a translated genetic marker of disease resistance. Using advanced biostatistical methods, such as mixed models and Bayesian inference, this study increases the accuracy and resolution of QTL identification (e.g., wheat, rice, and maize). It is found that a more robust analysis can furnish additional robustness that considers the interaction of environment and genetic variance and population structure, both significant yet frequently neglected facets in standard QTL mapping. By combining crop breeders with genomics-based cutting-edge biostatistical tools, we can choose plants resistant to disease.

Global Drug Design & Development ReviewVol. IX(III)
Lahore College for Women University (PK), Abbottabad University of Science and Technology (PK), Federal Urdu University (PK)
Openalex Percentile: Top 88%
Genetics and Plant Breeding
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