Background and Purpose: The genetic contribution to ischemic stroke may include rare- or low-frequency variants of high-penetrance and large-effect sizes. Analyses focusing on early-onset disease, an extreme-phenotype, and on the exome, the protein-coding portion of genes, may increase the likelihood of identifying such rare functional variants. To evaluate this hypothesis, we implemented a 2-stage discovery and replication design, and then addressed whether the identified variants also associated with older-onset disease. Methods: Discovery was performed in UMD-GEOS Study (University of Maryland-Genetics of Early-Onset Stroke), a biracial population-based study of first-ever ischemic stroke cases 15 to 49 years of age (n=723) and nonstroke controls (n=726). All participants had prior GWAS (Genome Wide Association Study) and underwent Illumina exome-chip genotyping. Logistic-regression was performed to test single-variant associations with all-ischemic stroke and TOAST (Trial of ORG 10172 in Acute Stroke Treatment) subtypes in Whites and Blacks. Population level results were combined using meta-analysis. Gene-based aggregation testing and meta-analysis were performed using seqMeta. Covariates included age and gender, and principal-components for population structure. Pathway analyses were performed across all nominally associated genes for each stroke outcome. Replication was attempted through lookups in a previously reported meta-analysis of early-onset stroke and a large-scale stroke genetics study consisting of primarily older-onset cases. Results: Gene burden tests identified a significant association with NAT10 in small-vessel stroke ( P =3.79×10 ^{− 6} ). Pathway analysis of the top 517 genes ( P <0.05) from the gene-based analysis of small-vessel stroke identified several signaling and metabolism-related pathways related to neurotransmitter, neurodevelopmental notch-signaling, and lipid/glucose metabolism. While no individual SNPs reached chip-wide significance ( P <2.05×10 ⁻⁷ ), several were near, including an intronic variant in LEXM (rs7549251; P =4.08×10 ^{− 7} ) and an exonic variant in TRAPPC11 (rs67383011; P =5.19×10 ^{− 6} ). Conclusions: Exome-based analysis in the setting of early-onset stroke is a promising strategy for identifying novel genetic risk variants, loci, and pathways.