Linkage disequilibrium (LD) is the nonrandom association of alleles at different loci and plays a crucial role in association mapping, determining the resolution of genetic studies. Understanding LD is essential for identifying the genetic basis of quantitative traits (QTL) in plants. While LD has been extensively studied in humans and animals, research in plants is limited. Factors such as mating systems, population structure, and recombination hotspots significantly influence LD patterns. In selfing species, LD tends to be higher due to reduced recombination, whereas outcrossing species show more rapid LD decay. Recombination hotspots can affect LD levels, with some regions showing high LD despite physical distance. In plants, LD varies among species, with maize and Arabidopsis being well-studied. In maize, LD decreases rapidly with genetic distance, while in Arabidopsis, LD extends further due to its selfing nature. LD in sugarcane is extensive due to its bottlenecked breeding history. Association mapping has become a powerful tool for identifying QTL in plants, offering higher resolution than traditional mapping methods. However, population structure can lead to spurious associations, requiring statistical methods to account for this. LD is measured using statistics such as r² and D', which reflect different aspects of genetic variation. r² is influenced by both recombination and mutation history, while D' is more accurate for estimating recombination differences. LD decay plots and disequilibrium matrices are used to visualize LD patterns. In humans, LD extends over large distances, with variations due to population history and genetic drift. In cattle and fruit flies, LD is also influenced by bottlenecks and population structure. For plant species, LD is critical for genome dissection, but challenges remain in understanding its structure and application. The mating system, population history, and recombination rates significantly affect LD. Selfing species exhibit higher LD, while outcrossing species show more rapid decay. Recombination hotspots can create regions of high LD, even at a distance. Association mapping is increasingly used in plants to identify QTL, but population structure and limited genetic diversity can complicate results. Statistical methods are needed to account for these factors. The future of genome dissection relies on understanding LD patterns and applying appropriate statistical techniques to improve resolution and accuracy.Linkage disequilibrium (LD) is the nonrandom association of alleles at different loci and plays a crucial role in association mapping, determining the resolution of genetic studies. Understanding LD is essential for identifying the genetic basis of quantitative traits (QTL) in plants. While LD has been extensively studied in humans and animals, research in plants is limited. Factors such as mating systems, population structure, and recombination hotspots significantly influence LD patterns. In selfing species, LD tends to be higher due to reduced recombination, whereas outcrossing species show more rapid LD decay. Recombination hotspots can affect LD levels, with some regions showing high LD despite physical distance. In plants, LD varies among species, with maize and Arabidopsis being well-studied. In maize, LD decreases rapidly with genetic distance, while in Arabidopsis, LD extends further due to its selfing nature. LD in sugarcane is extensive due to its bottlenecked breeding history. Association mapping has become a powerful tool for identifying QTL in plants, offering higher resolution than traditional mapping methods. However, population structure can lead to spurious associations, requiring statistical methods to account for this. LD is measured using statistics such as r² and D', which reflect different aspects of genetic variation. r² is influenced by both recombination and mutation history, while D' is more accurate for estimating recombination differences. LD decay plots and disequilibrium matrices are used to visualize LD patterns. In humans, LD extends over large distances, with variations due to population history and genetic drift. In cattle and fruit flies, LD is also influenced by bottlenecks and population structure. For plant species, LD is critical for genome dissection, but challenges remain in understanding its structure and application. The mating system, population history, and recombination rates significantly affect LD. Selfing species exhibit higher LD, while outcrossing species show more rapid decay. Recombination hotspots can create regions of high LD, even at a distance. Association mapping is increasingly used in plants to identify QTL, but population structure and limited genetic diversity can complicate results. Statistical methods are needed to account for these factors. The future of genome dissection relies on understanding LD patterns and applying appropriate statistical techniques to improve resolution and accuracy.