Wild relatives of wheat are known for their high levels of genetic diversity for resilience to various biotic and abiotic stresses, micronutrient content, phenology, plant architecture, and many other important traits. This is in stark contrast to the narrow genetic diversity of cultivated wheat. One way to mine wild wheat diversity would be through association genetics on a sequence-configured panel. The huge genetic diversity of such a panel would need multiple reference genomes for a near-complete representation. Therefore, the traditional SNP-based association method, which necessitates read alignment to every reference assembly followed by association check for every SNP, is a computationally-intensive, unscalable, and impractical, approach to association genetics in this context. We developed a computationally-efficient, and scalable, k-mer—based association mapping approach, which requires the association algorithm to be run only once independent of the reference assembly. These association results can then be mapped to multiple assemblies in a computationally cheap step. We previously demonstrated the power of this k-mer–based Association Genetics approach applied to a Resistance gene ENrichment SEQuenced Ae. tauschii panel (AgRenSeq; Arora et al. 2019, Nature Biotechnology 37:139-143). We now illustrate how this pipeline can be scaled to whole genome shotgun data of the Ae. tauschii panel, available under the aegis of the Open Wild Wheat Consortium (http://www.openwildwheat.org/), for unbiased high-resolution trait-genotype correlation studies.