Highly specific and sensitive detection of single-nucleotide variants (SNVs) is of central importance in disease diagnosis and pharmacogenomics. However, it remains a great challenge to successfully detect very low amounts of mutant SNV sequences in real samples in which a SNV sequence may be surrounded by high levels of closely related wild-type sequences. Herein, we propose an ultra-specific and highly sensitive SNV sensing strategy by combining the competitive sequestration with the nonlinear hybridization chain reaction (HCR) amplification. The rationally designed sequestration hairpin can effectively sequester the large amount of wild-type sequence and thus dramatically improve the hybridization specificity in recognizing SNVs. To improve the detection sensitivity, a new fluorescent signal probe is fabricated by intercalating SYBR Green I dye into the nonlinear HCR based DNA dendrimer to further bind with SNVs for signal amplification. The hyperbranched DNA dendrimer possesses large numbers of DNA duplexes for dye intercalation, thus the signal probe shows strong fluorescence intensity, leading to large fluorescence signal amplification. Taking advantage of the improved hybridization specificity of the competitive sequestration and the enhanced fluorescence response of the nonlinear HCR amplification, the developed sensing strategy enables ultra-specific and highly sensitive detection of SNVs. Taking human pancreatic cancers and colorectal carcinomas related KRAS gene mutations as models, the developed strategy shows remarkably high specificity against 17 SNVs (discrimination factors ranged from 126 to 1001 with a median of 310), and achieves high sensitivity for 6 KRAS mutations (the best resultant detection limit reached 15 pM for KRAS G13D (c.38G > A)). Notably, combined with PCR amplification, our SNV sensing strategy could detect KRAS G12D (c.35G > A) from extracted human genomic DNA samples at abundance as low as 0.05%. This work expands the rule set of designing specific and sensitive SNV sensing strategies and shows promising potential application in clinical diagnosis. © 2020 Elsevier B.V.