Nested association mapping (NAM) has been an invaluable approach for plant genetics community and can dissect the genetic architecture of complex traits. As the most popular NAM analysis strategy, joint multifamily mapping can combine all information from diverse genetic backgrounds and increase population size. However, it is influenced by the genetic heterogeneity of quantitative trait locus (QTL) across various subpopulations. Multi-locus association mapping has been proven to be powerful in many cases of QTL mapping and genome-wide association studies. Therefore, we developed a multi-locus association model of multiple families in the NAM population, which could discriminate the effects of QTLs in all subpopulations. A series of simulations with a real maize NAM genomic data were implemented. The results demonstrated that the new method improves the statistical power in QTL detection and the accuracy in QTL effect estimation. The new approach, along with single-family linkage mapping, was used to identify QTLs for three flowering time traits in the maize NAM population. As a result, most QTLs detected in single family linkage mapping were identified by the new method. In addition, the new method also mapped some new QTLs with small effects, although their functions need to be identified in the future.

嵌套关联作图（NAM）对于植物遗传学界来说是一种非常宝贵的方法，可以剖析复杂性状的遗传结构。作为最流行的 NAM 分析策略，联合多科作图可以结合来自不同遗传背景的所有信息并增加种群规模。然而，它受到不同亚群体数量性状位点（QTL）遗传异质性的影响。多位点关联作图已在许多 QTL 作图和全基因组关联研究中被证明是强大的。因此，我们开发了NAM群体中多个家族的多位点关联模型，该模型可以区分QTL在所有亚群体中的影响。使用真实的玉米 NAM 基因组数据进行了一系列模拟。结果表明，新方法提高了QTL检测的统计能力和QTL效应估计的准确性。新方法与单科连锁图谱一起用于鉴定玉米 NAM 群体中三个开花时间性状的 QTL。结果，单科连锁作图中检测到的大多数QTL都被新方法鉴定了。此外，新方法还定位了一些影响较小的新QTL，尽管它们的功能需要在未来进行鉴定。