Demographic inference using genetic data from a single individual: separating population size variation from population structure
Olivier Mazet, Willy Rodríguez, Lounès Chikhi
The rapid development of sequencing technologies represents new opportunities for population genetics research. It is expected that genomic data will increase our ability to reconstruct the history of populations. While this increase in genetic information will likely help biologists and anthropologists to reconstruct the demographic history of populations, it also represents new challenges. Recent work has shown that structured populations generate signals of population size change. As a consequence it is often difficult to determine whether demographic events such as expansions or contractions (bottlenecks) inferred from genetic data are real or due to the fact that populations are structured in nature. Given that few inferential methods allow us to account for that structure, and that genomic data will necessarily increase the precision of parameter estimates, it is important to develop new approaches. In the present study we analyse two demographic models. The first is a model of instantaneous population size change whereas the second is the classical symmetric island model. We (i) re-derive the distribution of coalescence times under the two models for a sample of size two, (ii) use a maximum likelihood approach to estimate the parameters of these models (iii) validate this estimation procedure under a wide array of parameter combinations, (iv) implement and validate a model choice procedure by using a Kolmogorov-Smirnov test. Altogether we show that it is possible to estimate parameters under several models and perform efficient model choice using genetic data from a single diploid individual.
A Hidden Markov Model for Investigating Recent Positive Selection through Haplotype Structure
Hua Chen, Jody Hey, Montgomery Slatkin
Recent positive selection can increase the frequency of an advantageous mutant rapidly enough that a relatively long ancestral haplotype will be remained intact around it. We present a hidden Markov model (HMM) to identify such haplotype structures. With HMM identified haplotype structures, a population genetic model for the extent of ancestral haplotypes is then adopted for parameter inference of the selection intensity and the allele age. Simulations show that this method can detect selection under a wide range of conditions and has higher power than the existing frequency spectrum-based method. In addition, it provides good estimate of the selection coefficients and allele ages for strong selection. The method analyzes large data sets in a reasonable amount of running time. This method is applied to HapMap III data for a genome scan, and identifies a list of candidate regions putatively under recent positive selection. It is also applied to several genes known to be under recent positive selection, including the LCT, KITLG and TYRP1 genes in Northern Europeans, and OCA2 in East Asians, to estimate their allele ages and selection coefficients.
Estimating the Relative Rate of Recombination to Mutation in Bacteria from Single-Locus Variants using Composite Likelihood Methods
Paul Fearnhead, Shoukai Yu, Patrick Biggs, Barbara Holland, Nigel French
(Submitted on 5 Nov 2014)
A number of studies have suggested using comparisons between DNA sequences of closely related bacterial isolates to estimate the relative rate of recombination to mutation for that bacterial species. We consider such an approach which uses single locus variants: pairs of isolates whose DNA differ at a single gene locus. One way of deriving point estimates for the relative rate of recombination to mutation from such data is to use composite likelihood methods. We extend recent work in this area so as to be able to construct confidence intervals for our estimates, without needing to resort to computationally-intensive bootstrap procedures, and to develop a test for whether the relative rate varies across loci. Both our test and method for constructing confidence intervals are obtained by modelling the dependence structure in the data, and then applying asymptotic theory regarding the distribution of estimators obtained using a composite likelihood. We applied these methods to multi-locus sequence typing (MLST) data from eight bacteria, finding strong evidence for considerable rate variation in three of these: Bacillus cereus, Enterococcus faecium and Klebsiella pneumoniae.
Conflations of short IBD blocks can bias inferred length of IBD
Charleston W.K. Chiang, Peter Ralph, John Novembre
Comments: 12 figures, 1 table
Subjects: Populations and Evolution (q-bio.PE)
Identity-by-descent (IBD) is a fundamental concept in genetics with many applications. Often, segments between two haplotypes are said to be IBD if they are inherited from a recent shared common ancestor without intervening recombination. Long IBD blocks (> 1cM) can be efficiently detected by a number of computer programs using high-density SNP array data from a population sample. However, all programs detect IBD based on contiguous segments of identity-by-state, and can therefore be due to the conflation of smaller, nearby IBD blocks. We quantified this effect using coalescent simulations, finding that nearly 40% of inferred blocks 1-2cM long are false conflations of two or more longer blocks, under demographic scenarios typical for modern humans. This biases the inferred IBD block length distribution, and so can affect downstream inferences. We observed this conflation effect universally across different IBD detection programs and human demographic histories, and found inference of segments longer than 2cM to be much more reliable (less than 5% conflation rate). We then present and analyze a novel estimator of the de novo mutation rate using IBD blocks, and demonstrate that the biased length distribution of the IBD segments due to conflation can strongly affect this estimator if the conflation is not modeled. Thus, the conflation effect should be carefully considered, especially as methods to detect shorter IBD blocks using sequencing data are being developed.
Ancestry Composition: A Novel, Efficient Pipeline for Ancestry Deconvolution
Eric Y Durand, Chuong B Do, Joanna L Mountain, J. Michael Macpherson
Ancestry deconvolution, the task of identifying the ancestral origin of chromosomal segments in admixed individuals, has important implications, from mapping disease genes to identifying candidate loci under natural selection. To date, however, most existing methods for ancestry deconvolution are typically limited to two or three ancestral populations, and cannot resolve contributions from populations related at a sub-continental scale. We describe Ancestry Composition, a modular three-stage pipeline that efficiently and accurately identifies the ancestral origin of chromosomal segments in admixed individuals. It assumes the genotype data have been phased. In the first stage, a support vector machine classifier assigns tentative ancestry labels to short local phased genomic regions. In the second stage, an autoregressive pair hidden Markov model simultaneously corrects phasing errors and produces reconciled local ancestry estimates and confidence scores based on the tentative ancestry labels. In the third stage, confidence estimates are recalibrated using isotonic regression. We compiled a reference panel of almost 10,000 individuals of homogeneous ancestry, derived from a combination of several publicly available datasets and over 8,000 individuals reporting four grandparents with the same country-of-origin from the member database of the personal genetics company, 23andMe, Inc., and excluding outliers identified through principal components analysis (PCA). In cross-validation experiments, Ancestry Composition achieves high precision and recall for labeling chromosomal segments across over 25 different populations worldwide.
STACEY: species delimitation and phylogeny estimation under the multispecies coalescent
Graham R Jones
This article describes a new package called STACEY for BEAST2 which is capable of both species delimitation and species tree estimation using DNA sequences from multiple loci. The focus in this article is on species delimitation. STACEY is based on the multispecies coalescent model, and builds on earlier software (DISSECT), which uses a `birth-death-collapse’ prior to deal with delimitations without the need for reversible-jump Markov chain Monte Carlo moves. Like DISSECT, it requires no a priori assignment of individuals to species or populations, and no guide tree. This paper introduces two innovations. The first is a new model for the populations along the branches of the species tree, and the second is a new MCMC move for exploring the posterior when the multispecies coalescent model is assumed. The main benefit of STACEY over DISSECT is much better convergence. Current practice, using a pipeline approach to species delimitation under the multispecies coalescent, has been shown to have major problems on simulated data. The same simulated data set is used to demonstrate the accuracy and efficiency of STACEY.
Synonymous and Nonsynonymous Distances Help Untangle Convergent Evolution and Recombination
Peter B. Chi, Sujay Chattopadhyay, Philippe Lemey, Evgeni V. Sokurenko, Vladimir N. Minin
(Submitted on 6 Oct 2014)
When estimating a phylogeny from a multiple sequence alignment, researchers often assume the absence of recombination. However, if recombination is present, then tree estimation and all downstream analyses will be impacted, because different segments of the sequence alignment support different phylogenies. Similarly, convergent selective pressures at the molecular level can also lead to phylogenetic tree incongruence across the sequence alignment. Current methods for detection of phylogenetic incongruence are not equipped to distinguish between these two different mechanisms and assume that the incongruence is a result of recombination or other horizontal transfer of genetic information. We propose a new recombination detection method that can make this distinction, based on synonymous codon substitution distances. Although some power is lost by discarding the information contained in the nonsynonymous substitutions, our new method has lower false positive probabilities than the original Dss statistic when the phylogenetic incongruence signal is due to convergent evolution. We conclude with three empirical examples, where we analyze: 1) sequences from a transmission network of the human immunodeficiency virus, 2) tlpB gene sequences from a geographically diverse set of 38 Helicobacter pylori strains, and 3) Hepatitis C virus sequences sampled longitudinally from one patient.
Inference of evolutionary forces acting on human biological pathways
Josephine T Daub, Isabelle Dupanloup, Marc Robinson-Rechavi, Laurent Excoffier
Because natural selection is likely to act on multiple genes underlying a given phenotypic trait, we study here the potential effect of ongoing and past selection on the genetic diversity of human biological pathways. We first show that genes included in gene sets are generally under stronger selective constraints than other genes and that their evolutionary response is correlated. We then introduce a new procedure to detect selection at the pathway level based on a decomposition of the classical McDonald-Kreitman test extended to multiple genes. This new test, called 2DNS, detects outlier gene sets and takes into account past demographic effects as well as evolutionary constraints specific to gene sets. Selective forces acting on gene sets can be easily identified by a mere visual inspection of the position of the gene sets relative to their 2D null distribution. We thus find several outlier gene sets that show signals of positive, balancing, or purifying selection, but also others showing an ancient relaxation of selective constraints. The principle of the 2DNS test can also be applied to other genomic contrasts. For instance, the comparison of patterns of polymorphisms private to African and non-African populations reveals that most pathways show a higher proportion of non-synonymous mutations in non-Africans than in Africans, potentially due to different demographic histories and selective pressures.
WFABC: a Wright-Fisher ABC-based approach for inferring effective population sizes and selection coefficients from time-sampled data
Matthieu Foll, Hyunjin Shim, Jeffrey D. Jensen
With novel developments in sequencing technologies, time-sampled data are becoming more available and accessible. Naturally, there have been efforts in parallel to infer population genetic parameters from these datasets. Here, we compare and analyze four recent approaches based on the Wright-Fisher model for inferring selection coefficients (s) given effective population size (Ne), with simulated temporal datasets. Furthermore, we demonstrate the advantage of a recently proposed ABC-based method that is able to correctly infer genome-wide average Ne from time-serial data, which is then set as a prior for inferring per-site selection coefficients accurately and precisely. We implement this ABC method in a new software and apply it to a classical time-serial dataset of the medionigra genotype in the moth Panaxia dominula. We show that a recessive lethal model is the best explanation for the observed variation in allele frequency by implementing an estimator of the dominance ratio (h).
Thinking too positive? Revisiting current methods of population-genetic selection inference
Claudia Bank, Gregory B Ewing, Anna Ferrer-Admettla, Matthieu Foll, Jeffrey D Jensen
In the age of next-generation sequencing, the availability of increasing amounts and quality of data at decreasing cost ought to allow for a better understanding of how natural selection is shaping the genome than ever before. Yet, alternative forces such as demography and background selection obscure the footprints of positive selection that we would like to identify. Here, we illustrate recent developments in this area, and outline a roadmap for improved selection inference. We argue (1) that the development and obligatory use of advanced simulation tools is necessary for improved identification of selected loci, (2) that genomic information from multiple- time points will enhance the power of inference, and (3) that results from experimental evolution should be utilized to better inform population-genomic studies.