The advent of advanced genome sequence technologies has revealed an enormous amount of genetic variation in major crops. Most important traits in wheat are quantitatively inherited and controlled by many minor effect genes. There is still a significant gap in connecting the genetic variation (genotypes) to phenotypes. Here, we reported using a structured multi-parent population (MPP) approach, an approach that can enrich genetic diversity, and have high statistical power and resolution, to bridge this gap and unlock the basis of complex traits in bread wheat. This large scale MPP of bread wheat was developed by the nested association mapping (NAM) approach with more than 5000 recombinant inbred lines (RILS) from 50 subpopulations. This NAM population is comprised not only of progeny derived from parents of an elite panel of Canadian cultivars from the nineteenth century to current, but also a synthetic hexaploid wheat (SHW) panel that captures divergent D genomes from Aegilops tauschii, the D genome progenitor. A high-quality haplotype map was assembled with more than 1.4 million SNPs uncovered by high depth genome-wide exome capture sequencing. With a population exome capture sequencing (Pop-ECS) approach and the haplotype-map as reference, missing data was imputed and finally, 159,011 high quality SNPs for 2,440 NAM RILs were generated, allowing the development of a high-resolution NAM based linkage map. Genomic analysis revealed an average 27.0% increase in polymorphism in the SHW panel over the elite panel D genomes, demonstrating the value of using SHWs to increase the sampled diversity of D genomes. Phenotypic analysis demonstrated that broad phenotypic variations were captured by this NAM population for disease and agronomic traits. We performed a joint linkage mapping analysis of these traits and identified 491 QTLs in total, with a few QTL precisely mapped to known genes for plant height, flowering time, rust and fusarium head blight resistance. These previously not reported new QTLs would be good targets to improve wheat performance by breeding. These findings demonstrate this NAM resource as a powerful tool to dissect complex traits, but more applications for wheat genetic, genomic and evolutionary research exist for this unique resource.