A second generation human haplotype map of over 3.1 million SNPs

By Kelly A. Frazer, D. Ballinger, D. Cox, David A. Hinds, L. Stuve, Richard A. Gibbs, J. Belmont, A. Boudreau, P. Hardenbol, Suzanne M. Leal and 227 more. Nature, 2007

4,677 citations recorded by the source corpus.

We describe the Phase II HapMap, which characterizes over 3.1 million human single nucleotide polymorphisms (SNPs) genotyped in 270 individuals from four geographically diverse populations and includes 25–35% of common SNP variation in the populations surveyed. The map is estimated to capture untyped common variation with an average maximum r2 of between 0.9 and 0.96 depending on population. We demonstrate that the current generation of commercial genome-wide genotyping products captures common Phase II SNPs with an average maximum r2 of up to 0.8 in African and up to 0.95 in non-African populations, and that potential gains in power in association studies can be obtained through imputation. These data also reveal novel aspects of the structure of linkage disequilibrium. We show that 10–30% of pairs of individuals within a population share at least one region of extended genetic identity arising from recent ancestry and that up to 1% of all common variants are untaggable, primarily because they lie within recombination hotspots. We show that recombination rates vary systematically around genes and between genes of different function. Finally, we demonstrate increased differentiation at non-synonymous, compared to synonymous, SNPs, resulting from systematic differences in the strength or efficacy of natural selection between populations.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d68bb77f29ce1b19e13166f16c81e3841f03d8db

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Citations in the corpus

Whole population, genome-wide mapping of hidden relatedness. (2009, methodology): “5% of their genome through recent IBD (Frazer et al. 2007).”
BioMart – biological queries made easy (2009, methodology): “These include model organism databases such as Gramene [11], Dicty-base [12], Wormbase [13] and RGD (Rat Genome Data-base) [14] as well as HapMap variation [15], pancreatic expression database [16], Reactome pathways [17] and PRIDE proteomic [18] databases (see Table 1 for the full list).”
The Impact of Genetic Architecture on Genome-Wide Evaluation Methods (2010, background): “Haplotype blocks are physical segments of the genome within which haplotype diversity is low, bounded by areas where evidence for historical recombination exists (e.g., Goldstein 2001; Gabriel et al. 2002; Frazer et al. 2007).”
Fast and flexible simulation of DNA sequence data. (2008, methodology): “…using a model that incorporates a parameter f that reflects the ratio of the rate of gene conversion events and the rate of crossover events (Frisse et al. 2001) and then assumes a geometric distribution of gene conversion tract lengths L: where n is the number of nucleotides and (cid:5) is…”
An efficient and scalable analysis framework for variant extraction and refinement from population-scale DNA sequence data (2015, background): “After these steps, several quality control metrics (such as the number of mapped reads, base-quality distribution, insert size distribution, GC bias profile, sample identity checks, and estimated DNA sample contamination) are produced and stored into summary files (Jun et al. 2012; Li et al. 2013).”