Copy number variations (CNVs) are genomic structural variations that result from the deletion or duplication of large genomic segments. The characterization of CNVs is largely underrepresented, particularly those of indigenous populations, such as the Orang Asli in Peninsular Malaysia. In the present study, we first characterized the genome-wide CNVs of four major native populations from Peninsular Malaysia, including the Malays and three Orang Asli populations; namely, Proto-Malay, Senoi, and Negrito (collectively called PM). We subsequently assessed the distribution of CNVs across the four populations. The resulting global CNV map revealed 3102 CNVs, with an average of more than 100 CNVs per individual. We identified genes harboring CNVs that are highly differentiated between PM and global populations, indicating that these genes are predominantly enriched in immune responses and defense functions, including APOBEC3A_B, beta-defensin genes, and CCL3L1, followed by other biological functions, such as drug and toxin metabolism and responses to radiation, suggesting some attributions between CNV variations and adaptations of the PM groups to the local environmental conditions of tropical rainforests.
The incomplete identification of structural variants (SVs) from whole-genome sequencing data limits studies of human genetic diversity and disease association. Here, we apply a suite of long-read, short-read, strand-specific sequencing technologies, optical mapping, and variant discovery algorithms to comprehensively analyze three trios to define the full spectrum of human genetic variation in a haplotype-resolved manner. We identify 818,054 indel variants (<50 bp) and 27,622 SVs (≥50 bp) per genome. We also discover 156 inversions per genome and 58 of the inversions intersect with the critical regions of recurrent microdeletion and microduplication syndromes. Taken together, our SV callsets represent a three to sevenfold increase in SV detection compared to most standard high-throughput sequencing studies, including those from the 1000 Genomes Project. The methods and the dataset presented serve as a gold standard for the scientific community allowing us to make recommendations for maximizing structural variation sensitivity for future genome sequencing studies.