We conducted the first comprehensive association analysis of a coronary artery disease (CAD) cohort within the recently released UK Biobank whole genome sequencing dataset. We employed fine mapping tool PolyFun1 and pinpoint rs10757274 as the most likely causal SNP within the 9p21.3 CAD risk locus. Notably, we show that machine-learning (ML) approaches, REGENIE, and VariantSpark, exhibited greater sensitivity compared to traditional single-SNP logistic regression, uncovering rs28451064 in the 21q22.11 risk locus. Our findings underscore the utility of leveraging advanced computational techniques and cloud-based resources for mega-biobank analyses. Aligning with the paradigm shift of bringing compute to data, we demonstrate substantial resource optimisation (44% reduction) and performance gains (40% speedup) through compute architecture optimisation on UK Biobank's Research Analysis Platform and discuss three considerations for researchers implementing novel workflows to such datasets hosted on cloud-platforms. This study paves the way for harnessing mega-biobank-sized data through scalable, cost-effective cloud computing solutions.