Likelihood-Based Inference in Isolation-by-Distance Models Using the Spatial Distribution of Low-Frequency Alleles

Novembre, John; Slatkin, Montgomery

doi:10.1111/j.1558-5646.2009.00775.x

Cited by 22 publications

(20 citation statements)

References 32 publications

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“…Our results (File S3) indicated that, in cases when the boundaries were close to the samples such that the distance to the nearest samples was on the same order of magnitude as s, the estimates for the dispersal rate s and density D become biased downward; an effect also observed for the inference method of Novembre and Slatkin (2009) which is based on the sharing of rare alleles. Similarly, we observed that the estimates for D and s become biased downward for habitats of width % 10s: Therefore, we recommend to always check whether most of the samples are collected far from the habitat edges ð .…”

Section: Sampling Guidelinesmentioning

confidence: 74%

“…For example, fitting increasing pairwise genetic diversity with geographic distance is widely used (Rousset 1997(Rousset , 2000Vekemans and Hardy 2004), and approximate-Bayesiancomputation methods have been applied (Joseph et al 2016). Similarly, the extent of geographic clustering of rare frequency alleles can be used as a signal for inference (Novembre and Slatkin 2009). While the signal of locally decreased pairwise genetic diversity mostly stems from recent times (Leblois et al 2004), such patterns can be severely confounded by deeper, often unknown, ancestral patterns (Meirmans 2012).…”

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confidence: 99%

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Inferring Recent Demography from Isolation by Distance of Long Shared Sequence Blocks

2017

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Recently it has become feasible to detect long blocks of nearly identical sequence shared between pairs of genomes. These identity-by-descent (IBD) blocks are direct traces of recent coalescence events and, as such, contain ample signal to infer recent demography. Here, we examine sharing of such blocks in two-dimensional populations with local migration. Using a diffusion approximation to trace genetic ancestry, we derive analytical formulas for patterns of isolation by distance of IBD blocks, which can also incorporate recent population density changes. We introduce an inference scheme that uses a composite-likelihood approach to fit these formulas. We then extensively evaluate our theory and inference method on a range of scenarios using simulated data. We first validate the diffusion approximation by showing that the theoretical results closely match the simulated block-sharing patterns. We then demonstrate that our inference scheme can accurately and robustly infer dispersal rate and effective density, as well as bounds on recent dynamics of population density. To demonstrate an application, we use our estimation scheme to explore the fit of a diffusion model to Eastern European samples in the Population Reference Sample data set. We show that ancestry diffusing with a rate of s % 50 À À100 km= ffiffiffiffiffiffiffiffi gen p during the last centuries, combined with accelerating population growth, can explain the observed exponential decay of block sharing with increasing pairwise sample distance.KEYWORDS demographic inference; identity by descent; isolation by distance; dispersal rate; effective population size T HERE has been a long-standing interest in estimating demography, as migration and population density are key parameters for studying evolution and ecology. Demographic models are essential for disentangling the effects of neutral evolution from selection, and are crucial to understanding local adaptation. Moreover, the inference of demographic parameters is important for conservation and breeding management. Given the intensive nature of obtaining such parameters by direct observations, which are moreover necessarily limited to short timescales, the increasing availability of genetic markers has spurred efforts to develop inference methods based on genetic data.This work focuses on estimating dispersal rate and population density in two-dimensional habitats by analyzing the geographic distribution of so-called identity-by-descent (IBD) blocks, which are commonly defined as co-inherited segments delimited by recombination events (see Figure 1). It has now become feasible to detect long regions of exceptional pairwise similarities from dense SNP or whole genome sequences (Gusev et al. 2009;Browning and Browning 2011). For regions longer than a few cM, the bulk mostly consists of a single IBD block unbroken by recombination, at least when inbreeding is rare (Chiang et al. 2016). This yields novel opportunities for inferring recent demography, as one can study the direct traces of coancestry.M...

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Section: Sampling Guidelinesmentioning

confidence: 74%

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confidence: 99%

Inferring Recent Demography from Isolation by Distance of Long Shared Sequence Blocks

2017

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“…This strategy computes successive conditional likelihoods by a post-order tree traversal in a procedure akin to Felsenstein’s peeling algorithm (Felsenstein, 1981). Its effectiveness has been explored in similar contexts in univariate (Novembre and Slatkin, 2009; Blum et al , 2004) and multivariate Brownian motion (Freckleton, 2012) and to estimate the Gaussian component of Lévy processes (Landis, Schraiber and Liang, 2013). A related post-order traversal approach improves computation in the context of phylogenetic regressions for some Gaussian and non-Gaussian models (Ho and Ané, 2014).…”

Section: Discussionmentioning

confidence: 99%

Assessing phenotypic correlation through the multivariate phylogenetic latent liability model

Cybis¹,

Sinsheimer²,

Bedford³

et al. 2015

Ann. Appl. Stat.

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Understanding which phenotypic traits are consistently correlated throughout evolution is a highly pertinent problem in modern evolutionary biology. Here, we propose a multivariate phylogenetic latent liability model for assessing the correlation between multiple types of data, while simultaneously controlling for their unknown shared evolutionary history informed through molecular sequences. The latent formulation enables us to consider in a single model combinations of continuous traits, discrete binary traits, and discrete traits with multiple ordered and unordered states. Previous approaches have entertained a single data type generally along a fixed history, precluding estimation of correlation between traits and ignoring uncertainty in the history. We implement our model in a Bayesian phylogenetic framework, and discuss inference techniques for hypothesis testing. Finally, we showcase the method through applications to columbine flower morphology, antibiotic resistance in Salmonella, and epitope evolution in influenza.

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“…Since there are strong relationships between F ST and PCA, it is conceivable that the same restrictions apply to the F ST -based anisotropic methods presented here. For instance, we cannot determine whether the European anisotropic pattern is explained by a nonequilibrium range expansion model (François et al 2010) or by an equilibrium isolation-by-distance model, which assumes long-term unequal migration rates in the two orthogonal spatial dimensions (Wilkinson-Herbots and Ettridge 2004; Novembre and Slatkin 2009). A second limitation of the anisotropic methods presented here concerns their sensitivity to the sampling scheme.…”

Section: Discussionmentioning

confidence: 99%

Anisotropic Isolation by Distance: The Main Orientations of Human Genetic Differentiation

Jay¹,

Sjödin²,

Jakobsson³

et al. 2012

Molecular Biology and Evolution

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Genetic differentiation among human populations is greatly influenced by geography due to the accumulation of local allele frequency differences. However, little is known about the possibly different increment of genetic differentiation along the different geographical axes (north–south, east–west, etc.). Here, we provide new methods to examine the asymmetrical patterns of genetic differentiation. We analyzed genome-wide polymorphism data from populations in Africa (n = 29), Asia (n = 26), America (n = 9), and Europe (n = 38), and we found that the major orientations of genetic differentiation are north–south in Europe and Africa, and east–west in Asia, but no preferential orientation was found in the Americas. Additionally, we showed that the localization of the individual geographic origins based on single nucleotide polymorphism data was not equally precise along all orientations. Confirming our findings, we obtained that, in each continent, the orientation along which the precision is maximal corresponds to the orientation of maximum differentiation. Our results have implications for interpreting human genetic variation in terms of isolation by distance and spatial range expansion processes. In Europe, for instance, the precise northnorthwest–southsoutheast axis of main European differentiation cannot be explained by a simple Neolithic demic diffusion model without admixture with the local populations because in that case the orientation of greatest differentiation should be perpendicular to the direction of expansion. In addition to humans, anisotropic analyses can guide the description of genetic differentiation for other organisms and provide information on expansions of invasive species or the processes of plant dispersal.

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Likelihood-Based Inference in Isolation-by-Distance Models Using the Spatial Distribution of Low-Frequency Alleles

Cited by 22 publications

References 32 publications

Inferring Recent Demography from Isolation by Distance of Long Shared Sequence Blocks

Inferring Recent Demography from Isolation by Distance of Long Shared Sequence Blocks

Assessing phenotypic correlation through the multivariate phylogenetic latent liability model

Anisotropic Isolation by Distance: The Main Orientations of Human Genetic Differentiation

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