Trait mapping has been indispensable for enhancing crop plants by identifying genomic regions associated with key agronomic traits such as yield, quality, stress tolerance, and disease resistance. Various mapping techniques, each with distinct advantages and limitations, have significantly advanced crop breeding. Linkage mapping provides accuracy for simple traits but has low resolution, while QTL mapping can dissect complex quantitative traits, though it is limited to structured or biparental populations. Association mapping, or genome-wide association studies (GWAS), offers higher resolution and utilizes diverse populations, but requires extensive genotyping and is prone to false positives.

Recent advancements in trait mapping have markedly improved our ability to understand and enhance crop traits. Fine mapping and gene cloning have pinpointed specific genes within QTL regions, facilitating deeper studies of key agronomic traits. The outcomes of these mapping techniques have revolutionized molecular breeding. Marker-assisted selection (MAS) allows for the selection of plants based on genetic markers, while genomic selection (GS) uses genome-wide markers to predict complex traits, thereby accelerating breeding cycles and improving genetic gains. Additionally, these mapping approaches have enabled precision breeding and marker-assisted gene pyramiding, allowing for the stacking of desirable traits in crops. Collectively, these advancements have led to the development of improved crop varieties, enhancing food security and agricultural sustainability.

We invite submissions on the outcomes of QTL mapping, its integration into breeding programs, and its role in developing climate-resilient, high-yielding varieties. Specifically, we welcome Brief Research Reports, Data Reports, Methods, Mini Reviews, Opinions, Original Research, Reviews, and Systematic Reviews, related to the following sub-themes (but not limited to):

• Identification and mapping of QTLs associated with resistance, yield, and quality traits.
• Harnessing multi-trait QTL mapping for next-level crop improvement.
• Innovative population designs for enhanced QTL discovery
• Prediction of candidate genes linked to targeted traits.
• Integration of QTL and/or GWAS data into practical breeding programs for improved products.
• Integrating QTL discoveries with genomics, transcriptomics, and phenomics datasets
• Machine learning and AI-driven approaches for accurate QTL predictions
• Enhancing crop productivity and resilience through genetic prediction and selection.
• Application and challenges of QTL mapping and GWAS across diverse crop species and environments.
• Exploration of advanced techniques and tools for QTL mapping and GWAS, such as high-throughput sequencing, bioinformatics approaches, and statistical analyses.

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Frontiers in Genetics

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