Recombination rates in admixed individuals identified by ancestry-based inference

Wegmann, Daniel; Kessner, Darren; Veeramah, Krishna R.; Mathias, Rasika A.; Nicolae, Dan L.; Yanek, Lisa R.; Sun, Yan V.; Torgerson, Dara G.; Rafaels, Nicholas; Mosley, Thomas H.; Becker, Lewis C.; Ruczinski, Ingo; Beaty, Terri H.; Kardia, Sharon L.R.; Meyers, Deborah A.; Barnes, Kathleen C.; Becker, Diane M.; Freimer, Nelson B.; Novembre, John

doi:10.1038/ng.894

Cited by 116 publications

(127 citation statements)

References 53 publications

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“…However, the recombination maps of the African American differ significantly from those of the European at fine-scale. In addition, Hinch et al [29] also identified about 2,500 active recombination hotspots in African Americans but not in European. The 17bp DNA motif enriched in African specific hotspots is well matched to a predicted PRDM9 binding alleles common in Africans.…”

Section: Estimating Recombination Rates Based On Population Genetic Mmentioning

confidence: 99%

“…Based on the observed ancestry switch points in admixed population, Hinch et al [29] and Wegmann et al [30] constructed a high-resolution recombination map of African American in 2000, respectively. It was novel to use ancestry-based approach to identify recombination events and recombination hotspots.…”

Section: Estimating Recombination Rates Based On Population Genetic Mmentioning

confidence: 99%

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Population Genetics in the Genomic Era

Xu¹,

Jin²

2012

Studies in Population Genetics

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Section: Estimating Recombination Rates Based On Population Genetic Mmentioning

confidence: 99%

Section: Estimating Recombination Rates Based On Population Genetic Mmentioning

confidence: 99%

Population Genetics in the Genomic Era

Xu¹,

Jin²

2012

Studies in Population Genetics

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“…Drawing on coalescent theory, in this model, a haplotype sampled from a population is viewed as a mosaic of segments of previously sampled haplotypes. This mosaic structure can be efficiently modeled within a hidden Markov model to achieve very accurate solutions to many genetic problems such as genotype imputation (Marchini et al, 2007;Howie et al, 2009Howie et al, , 2012a, ancestry inference (Pasaniuc et al, 2009;Price et al, 2009), quality control in genome-wide association studies (Han et al, 2009), detection of identity by descent (IBD) segments (Browning, 2006;Browning and Browning, 2010), estimating recombination rates (Wegmann et al, 2011), haplotype phasing (Delaneau et al, 2012), migration rates (Roychoudhury and Stephens, 2007) and calling of genotypes at low coverage sequencing (Pasaniuc et al, 2012;Li et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Spatial Haplotype Copying Model with Applications to Genotype Imputation

Yang

Hormozdiari

Eskin

et al. 2015

Journal of Computational Biology

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Ever since its introduction, the haplotype copy model has proven to be one of the most successful approaches for modeling genetic variation in human populations, with applications ranging from ancestry inference to genotype phasing and imputation. Motivated by coalescent theory, this approach assumes that any chromosome (haplotype) can be modeled as a mosaic of segments copied from a set of chromosomes sampled from the same population. At the core of the model is the assumption that any chromosome from the sample is equally likely to contribute a priori to the copying process. Motivated by recent works that model genetic variation in a geographic continuum, we propose a new spatial-aware haplotype copy model that jointly models geography and the haplotype copying process. We extend hidden Markov models of haplotype diversity such that at any given location, haplotypes that are closest in the genetic-geographic continuum map are a priori more likely to contribute to the copying process than distant ones. Through simulations starting from the 1000 Genomes data, we show that our model achieves superior accuracy in genotype imputation over the standard spatial-unaware haplotype copy model. In addition, we show the utility of our model in selecting a small personalized reference panel for imputation that leads to both improved accuracy as well as to a lower computational runtime than the standard approach. Finally, we show our proposed model can be used to localize individuals on the genetic-geographical map on the basis of their genotype data.

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“…However, the recombination rate that is estimated by LDhat exploits LD that is formed over many generations, while the suppression of recombination is a function of the present inverted sequence frequency; therefore the estimated rate may not reflect the contemporary rate at inversion loci. A recent study (Wegmann et al 2011) found that many of the largest discrepancies between admixture-based and LDhat-based recombination rate estimates correspond to inversion loci, which may be explained by inaccurate LDhat estimates at inversions.We now assess LDhat on the data simulated with an inversion, where the inverted sequence has reached a certain frequency (10-90%) in the sample. Figure 1 shows that LDhat tends to produce systematically lower recombination rate estimates at inversion loci compared to loci without inversions.…”

mentioning

confidence: 99%

The Effect of Genomic Inversions on Estimation of Population Genetic Parameters from SNP Data

et al. 2013

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In recent years it has emerged that structural variants have a substantial impact on genomic variation. Inversion polymorphisms represent a significant class of structural variant, and despite the challenges in their detection, data on inversions in the human genome are increasing rapidly. Statistical methods for inferring parameters such as the recombination rate and the selection coefficient have generally been developed without accounting for the presence of inversions. Here we exploit new software for simulating inversions in population genetic data, invertFREGENE, to assess the potential impact of inversions on such methods. Using data simulated by invertFREGENE, as well as real data from several sources, we test whether large inversions have a disruptive effect on widely applied population genetics methods for inferring recombination rates, for detecting selection, and for controlling for population structure in genome-wide association studies (GWAS). We find that recombination rates estimated by LDhat are biased downward at inversion loci relative to the true contemporary recombination rates at the loci but that recombination hotspots are not falsely inferred at inversion breakpoints as may have been expected. We find that the integrated haplotype score (iHS) method for detecting selection appears robust to the presence of inversions. Finally, we observe a strong bias in the genome-wide results of principal components analysis (PCA), used to control for population structure in GWAS, in the presence of even a single large inversion, confirming the necessity to thin SNPs by linkage disequilibrium at large physical distances to obtain unbiased results. INVERSION polymorphisms are a copy-neutral form of structural variant, which although largely uncataloged in human populations, are likely a common feature in the human genome (Kidd et al. 2008). The study of inversions in human populations is in its infancy but early indications suggest that their influence on genetic variation (Stefansson et al. 2005) and phenotypic traits (Tantisira et al. 2008) may be significant. One of the first large inversions (1.1 Mb) to have been discovered in the human genome is located at the MAPT locus on chromosome 17 (Boettger et al. 2012;Steinberg et al. 2012). The inverted sequence was found to be associated with elevated fertility in women and an increased recombination rate genome-wide (Stefansson et al. 2005). Another study found that three of five detected inversions were located at sites of recurrent microdeletion associated with human disease (Kidd et al. 2008), adding to several studies linking inversions with susceptibility to disease (Lakich et al. 1993;Osborne et al. 2001;Koolen et al. 2006;Tantisira et al. 2008;Antonacci et al. 2009). Supporting Information, Figure S1 shows the distribution of known human inversions according to the TCAG database.To complement the investigation of the biological impact of inversions, it is important to establish their effect on methods that exploit genetic variation data to...

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Recombination rates in admixed individuals identified by ancestry-based inference

Cited by 116 publications

References 53 publications

Population Genetics in the Genomic Era

Population Genetics in the Genomic Era

A Spatial Haplotype Copying Model with Applications to Genotype Imputation

The Effect of Genomic Inversions on Estimation of Population Genetic Parameters from SNP Data

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