Detecting selection footprints offers valuable insight into evolutionary processes and the mechanisms underlying phenotypic diversity in selected traits. Domestication, natural and artificial selection, and breeding have produced indigenous goats well-adapted to their local environments, making them crucial genetic resources. Understanding the genetic foundation of these adaptations can guide the development of effective breeding strategies to preserve and improve local goat breeds. This study investigated selection signatures in Lao native goats using Illumina's Goat SNP50 BeadChip, analysing 420 Lao native goats, 87 goats from three Chinese breeds, and 51 Teddi goats from Pakistan as test populations. We applied the de-correlation composite multiple signals (DCMS) method, incorporating p values from nine statistical tests, including runs of homozygosity in the Lao goat population, and fixation index and cross-population extended haplotype homozygosity between Lao goats and test populations. Significant genomic regions were identified using a 0.05 threshold adjusted for multiple testing. Our results uncovered 24 genomic regions harbouring 68 unique-coding genes. Analysis revealed both annotated and novel candidate genes linked to a variety of characteristics, including adaptation to the tropical monsoon climate (e.g., ABHD6, GATA4 and MSRA) and economic traits like growth and status (e.g., CNTNAP5, FAM135B and GATA4), reproduction (e.g., NPHP3, ARSJ and GATA4), milk production (e.g., MRPL32, PRSS51 and EPHA7), and carcass characteristics (e.g., GNAI1, SOX7 and FAM135B). These results offered insightful information about genetic mechanisms driving economic traits and tropical climate adaptation of Lao native goats. Combining p values from various statistical tests into a single DCMS framework effectively assists in selecting and prioritising candidate genes for further analysis.