The paper investigates the genetic and statistical properties of nested association mapping (NAM) in maize, a strategy that combines linkage analysis and association mapping to dissect complex quantitative traits. NAM involves selecting diverse founders, developing related mapping progenies (preferably recombinant inbred lines), genotyping founders and progenies with common-parent-specific (CPS) markers, and projecting high-density marker information from founders to progenies. The authors demonstrate the power of NAM through computer simulations, showing that with 5000 genotypes, 30–79% of simulated quantitative trait loci (QTL) were precisely identified. They also discuss the population and quantitative genetics aspects of the design and examine the statistical power of NAM for different genetic architectures of complex traits. The study highlights the potential of NAM to facilitate the integration of molecular variation with phenotypic variation for various complex traits.The paper investigates the genetic and statistical properties of nested association mapping (NAM) in maize, a strategy that combines linkage analysis and association mapping to dissect complex quantitative traits. NAM involves selecting diverse founders, developing related mapping progenies (preferably recombinant inbred lines), genotyping founders and progenies with common-parent-specific (CPS) markers, and projecting high-density marker information from founders to progenies. The authors demonstrate the power of NAM through computer simulations, showing that with 5000 genotypes, 30–79% of simulated quantitative trait loci (QTL) were precisely identified. They also discuss the population and quantitative genetics aspects of the design and examine the statistical power of NAM for different genetic architectures of complex traits. The study highlights the potential of NAM to facilitate the integration of molecular variation with phenotypic variation for various complex traits.