<p></p>Comprehensive information of breeding germplasm is a necessity to develop effective strategies for accelerated breeding. I characterized Purdue University soft red winter wheat breeding population that was subjectof intensive germplasm introduction and introgression from exotic germplasm. Using genotyping-by-sequences (GBS) approach, I developed ~15,000 single nucleotide polymorphisms (SNPs) and studied extent of linkage disequilibrium (LD)and hidden population structure in the population.The extent of LD and its decay varied among chromosomes with chromosomes 2B and 7D showing the most extended islands of high-LDandslow rates of decay. Four sub-populations, two with North American origin and two with Australian and Chinese origins, were identified. Genome-wide scans for signatures of selection using FSTand hapFLK identified 13 genomic regions under selection, of which six loci (<i>LT, Ppd-B1, Fr-A2, Vrn-A1, Vrn-B1, Vrn3</i>) were associated with environmental adaptation and two loci were associated with disease resistance genes (<i>Sr36 </i>and <i>Fhb1</i>).<br><p></p><div><br></div><div>The population was evaluated for agronomic performance in field conditions across two years in two locations. Genome-wide association studies identified major loci controlling yield and yield related traits. For days to heading and plant height, large effects loci were identified on chromosome 6A and 7B. For test weight, number of spikes per square meter, and number of kernels per square meter, large effect loci were identified on chromosomes 1A, 4B, and 5A, respectively. However, for grain yield<i> per se</i>, no major loci were detected. A combination of selection for other large effect loci for yield components and genomic prediction could be a promising approach for yield improvement.<br></div><div><br></div><div>In addition, the population was evaluated for FHB resistance under misted FHB nurseries inoculated with scabby corn across 2017-18 (Y1) and 2018-19 (Y2) seasons at Purdue Agronomy Farm, West Lafayette,in randomized incomplete block designs. Phenotypic data included disease incidence (INC), disease severity (SEV), <i>Fusarium</i> damaged kernels (FDK), FHB index (FHBdx), and deoxynivalenol concentration (DON). Twenty-five loci were identified at -<i>log</i>P ≥ 4.0 to be associated with five FHB-related traits. Of these 25, eighteen explained more than 1% of the phenotypic variations. A major QTL on chromosome 2Bi.e., Q2B.1 that explained 36% of variation in FDK was also associated with INC, FHBdx, and DON. The marker-trait associations that explained more than 5% phenotypic variation were identified on chromosomes 1A, 2B, 3B, 5A, 7A, 7B,and 7D. To investigate the applicability of other QTL with less signal intensity, the threshold criterion was lowered to -<i>log</i>P ≥ 3.0, which resulted in the identification of 67 unique regions for all traits. This study showed that the FHB-related traits have significant correlations with the number of favorable alleles at these loci, suggesting their utility in improving FHB resistance in this population by marker-assisted selection.The genotype and phenotype data produced in this study will be valuable to train genomic prediction models and study the optimal design of genomic selection training sets. This study laid foundation for the design and breeding decisions to increase the efficiency of pyramiding strategies and achieving transgressive segregation for economically important traits such as yield and FHB resistance.<br></div>