Despite great progress in identifying genetic variants that influence human disease, most inherited risk remains unexplained. A more complete understanding requires genome-wide studies that fully examine less common alleles in populations with a wide range of ancestry. To inform the design and interpretation of such studies, we genotyped 1.6 million common single nucleotide polymorphisms (SNPs) in 1,184 reference individuals from 11 global populations, and sequenced ten 100-kilobase regions in 692 of these individuals. This integrated data set of common and rare alleles, called ‘HapMap 3’, includes both SNPs and copy number polymorphisms (CNPs). We characterized population-specific differences among low-frequency variants, measured the improvement in imputation accuracy afforded by the larger reference panel, especially in imputing SNPs with a minor allele frequency of ≤5%, and demonstrated the feasibility of imputing newly discovered CNPs and SNPs. This expanded public resource of genome variants in global populations supports deeper interrogation of genomic variation and its role in human disease, and serves as a step towards a high-resolution map of the landscape of human genetic variation.

The genetic association of the major histocompatibility complex (MHC) to rheumatoid arthritis risk has commonly been attributed to HLA-DRB1 alleles. Yet controversy persists about the causal variants in HLA-DRB1 and the presence of independent effects elsewhere in the MHC. Using existing genome-wide SNP data in 5,018 seropositive cases and 14,974 controls, we imputed and tested classical alleles and amino acid polymorphisms for HLA-A, B, C, DPA1, DPB1, DQA1, DQB1, and DRB1 along with 3,117 SNPs across the MHC. Conditional and haplotype analyses reveal that three amino acid positions (11, 71 and 74) in HLA-DRβ1, and single amino acid polymorphisms in HLA-B (position 9) and HLA-DPβ1 (position 9), all located in the peptide-binding grooves, almost completely explain the MHC association to disease risk. This study illustrates how imputation of functional variation from large reference panels can help fine-map association signals in the MHC.