Xuanjun Fang

Hainan Provincial Key Laboratory of Crop Molecular Breeding, Hainan Institute of Tropical Agricultural Resources (HITAR), Sanya, 572025, Hainan, China
Author    Correspondence author
Molecular Plant Breeding, 2026, Vol. 17, No. 1   
Received: 15 Apr., 2026    Accepted: 25 Apr., 2026    Published: 30 Apr., 2026

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advances in high-throughput sequencing and dense molecular markers have transformed the study of complex traits from phenotype- and pedigree-based inference to genome-scale, data-driven analysis. In this context, the relationship between statistical genetics and quantitative genetics has become increasingly important, yet conceptual ambiguity persists regarding their disciplinary roles. This study provides a systematic synthesis of their theoretical foundations, historical development, and conceptual distinctions.

Quantitative genetics is characterized as a problem- and theory-driven discipline focusing on the genetic architecture of complex traits and breeding strategies, whereas statistical genetics is defined as a methodology-driven field centered on model construction, inference, and analysis of high-dimensional genomic data. Through the examination of key paradigms such as QTL mapping, genome-wide association studies (GWAS), and genomic selection, we demonstrate that these two fields are not competing but highly complementary: quantitative genetics formulates biological questions and conceptual frameworks, while statistical genetics provides the inferential tools required to address them.

Building on this perspective, we propose an integrated framework based on the dimensions of “problem–method–data” and “theory–algorithm–application,” and further incorporate bioinformatics as a data-processing layer. This unified structure clarifies the roles of different disciplines within modern genetic research and highlights their coordinated interaction in the genomics era.

Finally, we discuss future directions in the context of molecular breeding, emphasizing the roles of multi-omics integration, artificial intelligence, and large-scale computation. We argue that deeper integration of theory and methodology is essential for improving the resolution and predictive power of complex trait analysis. This work provides a coherent conceptual framework for understanding the relationship between statistical and quantitative genetics and offers guidance for research design and interdisciplinary integration in modern genetics and breeding.

 

Statistical genetics; Quantitative genetics; QTL mapping; Genome-wide association studies (GWAS); Genomic selection; Molecular breeding; Multi-omics; Unified framework