Recombination rates in admixed individuals identified by ancestry-based inference

Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, USA.
Nature Genetics (Impact Factor: 29.35). 07/2011; 43(9):847-53. DOI: 10.1038/ng.894
Source: PubMed


Studies of recombination and how it varies depend crucially on accurate recombination maps. We propose a new approach for constructing high-resolution maps of relative recombination rates based on the observation of ancestry switch points among admixed individuals. We show the utility of this approach using simulations and by applying it to SNP genotype data from a sample of 2,565 African Americans and 299 African Caribbeans and detecting several hundred thousand recombination events. Comparison of the inferred map with high-resolution maps from non-admixed populations provides evidence of fine-scale differentiation in recombination rates between populations. Overall, the admixed map is well predicted by the average proportion of admixture and the recombination rate estimates from the source populations. The exceptions to this are in areas surrounding known large chromosomal structural variants, specifically inversions. These results suggest that outside of structurally variable regions, admixture does not substantially disrupt the factors controlling recombination rates in humans.

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Available from: Nicholas M Rafaels, Apr 09, 2014
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    • "Other crucial applications have included pharmacogenomics; for example, in a recent study, Native American ancestry was significantly associated with the risk of relapse in children suffering from acute lymphoblastic leukemia (Yang et al. 2011). In addition to these traditional applications, in the more recent years, local ancestry inference methods have also found applications in other settings such as localizing sequences of unknown location from the human reference genome (Genovese et al. 2013), studying recombination rate variation (Hinch et al. 2011; Wegmann et al. 2011), inferring natural selection (Tang et al 2007; Jin et al. 2012), making demographic inferences (Bryc et al. 2010; Johnson et al. 2011; Kidd et al. 2012) and in joint association and admixture mapping to boost the power to detect disease linked genes and variants (Pasaniuc et al. 2011; Shriner et al. 2011 ). "
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    ABSTRACT: Ancestry inference is a frequently encountered problem and has many applications such as forensic analyses, genetic association studies, and personal genomics. The main goal of ancestry inference is to identify an individual's population of origin based on our knowledge of natural populations. Because both self-reported ancestry in humans or the sampling location of an organism can be inaccurate for this purpose, the use of genetic markers can facilitate accurate and reliable inference of an individual's ancestral origins. At a higher level, there are two different paradigms in ancestry inference: global ancestry inference which tries to compute the genome-wide average of the population contributions and local ancestry inference which tries to identify the regional ancestry of a genomic segment. In this mini review, I describe the numerous approaches that are currently available for both kinds of ancestry inference from population genomic datasets. I first describe the general ideas underlying such inference methods and their relationship to one another. Then, I describe practical applications in which inference of ancestry has proven useful. Lastly, I discuss challenges and directions for future research work in this area.
    Frontiers in Genetics 06/2014; 5. DOI:10.3389/fgene.2014.00204
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    • "In many scenarios of biological interest, substantial evolutionary change has taken place in a small number of generations due to recombination and/or selection on standing variation, rather than mutational input. For example, one may be interested in the genome-wide haplotype patterns that emerge from admixture between historically isolated populations (Wegmann et al., 2011) or from artificial selection on a quantitative trait. Studying these haplotype patterns can be difficult with existing forward-in-time simulators because detailed information about the mosaic haplotype structure of individuals is not readily available, and must be inferred from the output sequences of the simulation and/or stored recombination event data. "
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    ABSTRACT: Summary: forqs is a forward-in-time simulation of recombination, quantitative traits and selection. It was designed to investigate haplotype patterns resulting from scenarios where substantial evolutionary change has taken place in a small number of generations due to recombination and/or selection on polygenic quantitative traits.Availability and implementation: forqs is implemented as a command-line C++ program. Source code and binary executables for Linux, OSX and Windows are freely available under a permissive BSD license: jnovembre@uchicago.eduSupplementary information: Supplementary data are available at Bioinformatics online.
    Bioinformatics 12/2013; 30(4). DOI:10.1093/bioinformatics/btt712 · 4.98 Impact Factor
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    • "Price et al. applied this modified copying model to detect chromosomal segments of distinct ancestry in admixed individuals and estimated admixture fractions in recently admixed populations. The same model was applied by Wegmann et al. (2011), who used the inferred ancestry switch-points to estimate relative recombination rates between different populations. As discussed above, ˆ π LS is a very useful CSD with a variety of applications, but it was not derived from, though was certainly motivated by, principles underlying the coalescent process. "
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    ABSTRACT: Conditional sampling distributions (CSDs), sometimes referred to as copying models, underlie numerous practical tools in population genomic analyses. Though an important application that has received much attention is the inference of population structure, the explicit exchange of migrants at specified rates has not hitherto been incorporated into the CSD in a principled framework. Recently, in the case of a single panmictic population, a sequentially Markov CSD has been developed as an accurate, efficient approximation to a principled CSD derived from the diffusion process dual to the coalescent with recombination. In this paper, the sequentially Markov CSD framework is extended to incorporate subdivided population structure, thus providing an efficiently computable CSD that admits a genealogical interpretation related to the structured coalescent with migration and recombination. As a concrete application, it is demonstrated empirically that the CSD developed here can be employed to yield accurate estimation of a wide range of migration rates.
    Theoretical Population Biology 09/2012; 87(1). DOI:10.1016/j.tpb.2012.08.004 · 1.70 Impact Factor
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