The International HapMap 3 Consortium has developed a comprehensive resource of common and rare genetic variants across 11 diverse human populations. This study integrates genome-wide SNP genotyping and sequencing data with copy number polymorphism (CNP) analysis to improve the understanding of human genetic variation. The HapMap 3 dataset includes over 1.4 million SNPs and CNPs, providing a more detailed view of genetic variation than previous studies. The dataset was generated by genotyping 1.6 million SNPs in 1,184 individuals from 11 populations and sequencing 10 regions in 692 individuals. This resource supports deeper exploration of genomic variation and its role in human disease. The study highlights the importance of including diverse populations in genetic research, as common SNPs alone are insufficient to explain all genetic variation. Low-frequency and rare variants, which contribute significantly to hereditary risk, were analyzed to improve imputation accuracy. The HapMap 3 resource also includes CNPs, which were identified through analysis of probe-level intensity data from Affymetrix and Illumina arrays. The results show that CNPs have a similar distribution of allele frequencies as SNPs, with a higher proportion of low-frequency variants in African and admixed populations. The study also evaluated the effectiveness of imputation methods for low-frequency and rare variants, demonstrating that imputation accuracy improves with larger reference panels. The results indicate that imputation can be effective for both SNPs and CNPs, especially when using a diverse set of reference populations. The study further shows that imputation accuracy varies across populations, with African and admixed populations providing more informative data for non-African populations. The HapMap 3 resource is a valuable tool for genome-wide association studies and for understanding the genetic basis of complex diseases. It provides a more comprehensive view of human genetic variation, including rare and low-frequency variants, and supports the development of more accurate imputation methods. The study underscores the importance of including diverse populations in genetic research to better understand the genetic basis of human disease.The International HapMap 3 Consortium has developed a comprehensive resource of common and rare genetic variants across 11 diverse human populations. This study integrates genome-wide SNP genotyping and sequencing data with copy number polymorphism (CNP) analysis to improve the understanding of human genetic variation. The HapMap 3 dataset includes over 1.4 million SNPs and CNPs, providing a more detailed view of genetic variation than previous studies. The dataset was generated by genotyping 1.6 million SNPs in 1,184 individuals from 11 populations and sequencing 10 regions in 692 individuals. This resource supports deeper exploration of genomic variation and its role in human disease. The study highlights the importance of including diverse populations in genetic research, as common SNPs alone are insufficient to explain all genetic variation. Low-frequency and rare variants, which contribute significantly to hereditary risk, were analyzed to improve imputation accuracy. The HapMap 3 resource also includes CNPs, which were identified through analysis of probe-level intensity data from Affymetrix and Illumina arrays. The results show that CNPs have a similar distribution of allele frequencies as SNPs, with a higher proportion of low-frequency variants in African and admixed populations. The study also evaluated the effectiveness of imputation methods for low-frequency and rare variants, demonstrating that imputation accuracy improves with larger reference panels. The results indicate that imputation can be effective for both SNPs and CNPs, especially when using a diverse set of reference populations. The study further shows that imputation accuracy varies across populations, with African and admixed populations providing more informative data for non-African populations. The HapMap 3 resource is a valuable tool for genome-wide association studies and for understanding the genetic basis of complex diseases. It provides a more comprehensive view of human genetic variation, including rare and low-frequency variants, and supports the development of more accurate imputation methods. The study underscores the importance of including diverse populations in genetic research to better understand the genetic basis of human disease.