Integrating common and rare genetic variation in diverse human populations

By David Altshuler, Richard A. Gibbs, L. Peltonen, Emmanouil T. Dermitzakis, S. Schaffner, F. Yu, P. Bonnen, P. D. de Bakker, P. Deloukas, Stacey Gabriel and 59 more. Nature, 2010

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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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cb59554e76f45f344bc60b97a6144dbb4cb0de05

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Citations in the corpus

The critical needs and challenges for genetic architecture studies in Africa (2018, neutral): “14 populations relative to other continental populations [42,66] creates greater challenges 1 when attempting to balance case/control collections at the outset of many studies due to 2”
Scaling accurate genetic variant discovery to tens of thousands of samples (2017, methodology): “SNPs and indels were called with each of the four variant calling algorithms from whole genome and exome-capture sequencing data from the well characterized CEU HapMap trio [2].”
Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ethnic meta-analysis (2020, result): “We did not replicate the association of the AGTR2 variant (rs146662075, chrX:115408811) as reported by Bonas-Guarch et al.10, which might be the result of poor imputation of the 1000 Genomes reference panel for this variant.”
Estimating Individual Admixture Proportions from Next Generation Sequencing Data (2013, methodology): “Overlap with the HapMap 3 genotype data: The HapMap 3 (Altshuler et al. 2010b) data set contains genotypes for 1.6 million SNPs in 1184 reference individuals from 11 populations.”
RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data (2017, background): “5; Supplementary Table S2) with reported disease-related loci that were selected in at least one of the four populations genotype data (CEU, CHB, JPT and YRI) from the HapMap project (release 28) (42).”