Inferring admixture proportions from molecular data: extension to any number of parental populations

The relative contribution of two parental populations to a hybrid group (the admixture proportions) can be estimated using not only the frequencies of different alleles, but also the degree of molecular divergence between them. In this paper, we extend this possibility to the case of any number of parental populations. The newly derived multiparental estimator is tested by Monte Carlo simulations and by generating artificial hybrid groups by pooling mtDNA samples from human populations. The general properties (including the variance) of the two-parental estimator seem to be retained by the multiparental estimator. When mixed human populations are considered and hypervariable single-locus data are analyzed (mtDNA control region), errors in the estimated contributions appear reasonably low only when highly differentiated parental populations are involved. Finally, the method applied to the hybrid Canary Island population points to a much lower female contribution from Spain than has previously been estimated.     

Maximum-likelihood estimation of admixture proportions from genetic data

For an admixed population, an important question is how much genetic contribution comes from each parental population. Several methods have been developed to estimate such admixture proportions, using data on genetic markers sampled from parental and admixed populations. In this study, I propose a likelihood method to estimate jointly the admixture proportions, the genetic drift that occurred to the admixed population and each parental population during the period between the hybridization and sampling events, and the genetic drift in each ancestral population within the interval between their split and hybridization. The results from extensive simulations using various combinations of relevant parameter values show that in general much more accurate and precise estimates of admixture proportions are obtained from the likelihood method than from previous methods. The likelihood method also yields reasonable estimates of genetic drift that occurred to each population, which translate into relative effective sizes (N(e)) or absolute average N(e)'s if the times when the relevant events (such as population split, admixture, and sampling) occurred are known. The proposed likelihood method also has features such as relatively low computational requirement compared with previous ones, flexibility for admixture models, and marker types. In particular, it allows for missing data from a contributing parental population. The method is applied to a human data set and a wolflike canids data set, and the results obtained are discussed in comparison with those from other estimators and from previous studies.     

Inferring purging from pedigree data

The harmful effects of inbreeding can be reduced if deleterious recessive alleles were removed (purged) by selection against homozygotes in earlier generations. If only a few generations are involved, purging is due almost entirely to recessive alleles that reduce fitness to near zero. In this case the amount of purging and allele frequency change can be inferred approximately from pedigree data alone and are independent of the allele frequency. We examined pedigrees of 59,778 U.S. Jersey cows. Most of the pedigrees were for six generations, but a few went back slightly farther. Assuming recessive homozygotes have fitness 0, the reduction of total genetic load due to purging is estimated at 17%, but most of this is not expressed, being concealed by dominant alleles. Considering those alleles that are currently expressed due to inbreeding, the estimated amount of purging is such as to reduce the expressed load (inbreeding depression) in the current generation by 12.6%. That the reduction is not greater is due mainly to (1) generally low inbreeding levels because breeders in the past have tended to avoid consanguineous matings, and (2) there is essentially no information more than six generations back. The methods used here should be applicable to other populations for which there is pedigree information.     

Estimating admixture proportions with microsatellites: comparison of methods based on simulated data

Several methods have been developed to estimate the parental contributions in the genetic pool of an admixed population. Some pair-comparisons have been performed on real data but, to date, no systematic comparison of a large number of methods has been attempted. In this study, we performed a simulated data-based comparison of six of the most cited methods in the literature of the last 20 years. Five of these methods use allele frequencies and differ in the statistical treatment of the data. The last one also considers the degree of molecular divergence by estimating the coalescence times. Comparisons are based on the frequency at which the method can be applied, the bias and the mean square error of the estimation, and the frequency at which the true value is within the confidence interval. Eventually, each method was applied to a real data set of variously introgressed honeybee populations. In optimal conditions (highly differentiated parental populations, recent hybridization event), all methods perform equally well. When conditions are not optimal, the methods perform differently, but no method is always better or worse than all others. Some guidelines are given for the choice of the method.     

Inferring biogeographic history from molecular phylogenies

The present study illustrates a method for analysing the biogeography of a group that is based on the group's phylogeny but does not invoke founder dispersal or centre of origin. The case studies presented include groups from many different parts of the world, but most are from the south‐west Pacific. The idea that basal groups are ancestral is not valid as a generalization. Neither the basal group, nor the oldest fossil represents the centre of origin, the time of origin or the ancestral ecology. Basal groups comprise less diverse sister groups and their distributions occur around centres of differentiation in already widespread ancestors, and not centres of origin for the whole group. Thus, the sequence of nodes in a phylogeny may indicate the spatial sequence of differentiation in a widespread ancestor rather than a series of founder dispersal events. Allocation of clades to a priori geographic areas, such as the continents, in the initial stages of biogeographic analysis has often involved incorrect assumptions of sympatry. This has led to the idea that the ‘areas of sympatry’ were centres of origin. Areas other than those defined by the taxa themselves need not be used in analysis. The fossil‐calibrated molecular clock, with dates transmogrified from minimum to maximum dates, has been used to test for vicariance. Recent work in population genetics, however, indicates that allopatry is caused by vicariance rather than founder dispersal, and so vicariance can instead be used to test the clock. Deriving evolutionary chronology by calibrating spatial vicariance in molecular clades with associated tectonic events is more reasonable than relying on the fossil record to give maximum (absolute) dates. © 2009 The Linnean Society of London, Biological Journal of the Linnean Society, 2009, 98 , 757–774.     

Inferring phylogenies from RAD sequence data

Reduced-representation genome sequencing represents a new source of data for systematics, and its potential utility in interspecific phylogeny reconstruction has not yet been explored. One approach that seems especially promising is the use of inexpensive short-read technologies (e.g., Illumina, SOLiD) to sequence restriction-site associated DNA (RAD)--the regions of the genome that flank the recognition sites of restriction enzymes. In this study, we simulated the collection of RAD sequences from sequenced genomes of different taxa (Drosophila, mammals, and yeasts) and developed a proof-of-concept workflow to test whether informative data could be extracted and used to accurately reconstruct "known" phylogenies of species within each group. The workflow consists of three basic steps: first, sequences are clustered by similarity to estimate orthology; second, clusters are filtered by taxonomic coverage; and third, they are aligned and concatenated for "total evidence" phylogenetic analysis. We evaluated the performance of clustering and filtering parameters by comparing the resulting topologies with well-supported reference trees and we were able to identify conditions under which the reference tree was inferred with high support. For Drosophila, whole genome alignments allowed us to directly evaluate which parameters most consistently recovered orthologous sequences. For the parameter ranges explored, we recovered the best results at the low ends of sequence similarity and taxonomic representation of loci; these generated the largest supermatrices with the highest proportion of missing data. Applications of the method to mammals and yeasts were less successful, which we suggest may be due partly to their much deeper evolutionary divergence times compared to Drosophila (crown ages of approximately 100 and 300 versus 60 Mya, respectively). RAD sequences thus appear to hold promise for reconstructing phylogenetic relationships in younger clades in which sufficient numbers of orthologous restriction sites are retained across species.     

Estimating ethnic admixture from pedigree data

This paper introduces a likelihood method of estimating ethnic admixture that uses individuals, pedigrees, or a combination of individuals and pedigrees. For each founder of a pedigree, admixture proportions are calculated by conditioning on the pedigree-wide genotypes at all ancestry-informative markers. These estimates are then propagated down the pedigree to the nonfounders by a simple averaging process. The large-sample standard errors of the founders' proportions can be similarly transformed into standard errors for the admixture proportions of the descendants. These standard errors are smaller than the corresponding standard errors when each individual is treated independently. Both hard and soft information on a founder's ancestry can be accommodated in this scheme, which has been implemented in the genetic software package Mendel. The utility of the method is demonstrated on simulated data and a real data example involving Mexican families of mixed Amerindian and Spanish ancestry.     

Evaluation of model fit of inferred admixture proportions

Model based methods for genetic clustering of individuals, such as those implemented in structure or ADMIXTURE, allow the user to infer individual ancestries and study population structure. The underlying model makes several assumptions about the demographic history that shaped the analysed genetic data. One assumption is that all individuals are a result of K homogeneous ancestral populations that are all well represented in the data, while another assumption is that no drift happened after the admixture event. The histories of many real world populations do not conform to that model, and in that case taking the inferred admixture proportions at face value might be misleading. We propose a method to evaluate the fit of admixture models based on estimating the correlation of the residual difference between the true genotypes and the genotypes predicted by the model. When the model assumptions are not violated, the residuals from a pair of individuals are not correlated. In the case of a bad fitting admixture model, individuals with similar demographic histories have a positive correlation of their residuals. Using simulated and real data, we show how the method is able to detect a bad fit of inferred admixture proportions due to using an insufficient number of clusters K or to demographic histories that deviate significantly from the admixture model assumptions, such as admixture from ghost populations, drift after admixture events and nondiscrete ancestral populations. We have implemented the method as an open source software that can be applied to both unphased genotypes and low depth sequencing data.     

Inferring genetic regulatory logic from expression data

MOTIVATION: High-throughput molecular genetics methods allow the collection of data about the expression of genes at different time points and under different conditions. The challenge is to infer gene regulatory interactions from these data and to get an insight into the mechanisms of genetic regulation. RESULTS: We propose a model for genetic regulatory interactions, which has a biologically motivated Boolean logic semantics, but is of a probabilistic nature, and is hence able to confront noisy biological processes and data. We propose a method for learning the model from data based on the Bayesian approach and utilizing Gibbs sampling. We tested our method with previously published data of the Saccharomyces cerevisiae cell cycle and found relations between genes consistent with biological knowledge.     

Inferring population parameters from single-feature polymorphism data

This article is concerned with a statistical modeling procedure to call single-feature polymorphisms from microarray experiments. We use this new type of polymorphism data to estimate the mutation and recombination parameters in a population. The mutation parameter can be estimated via the number of single-feature polymorphisms called in the sample. For the recombination parameter, a two-feature sampling distribution is derived in a way analogous to that for the two-locus sampling distribution with SNP data. The approximate-likelihood approach using the two-feature sampling distribution is examined and found to work well. A coalescent simulation study is used to investigate the accuracy and robustness of our method. Our approach allows the utilization of single-feature polymorphism data for inference in population genetics.     

Inferring dominance interactions from automatically recorded temporal data

Understanding dominance hierarchies is an important aspect of many studies of animal groups. Collecting the necessary data requires a large amount of time and effort to detect only a small number of interactions. Here, we assess the viability of extracting interactions from a temporal data stream of arrival and departures to a feeder equipped with a radio‐frequency identifier antenna. We show that the interactions extracted from this type of data can create similar dominance hierarchies to those produced by analysing video data of interactions. Additionally, we assessed the amount of temporal data required to obtain a dominance hierarchy and discuss the advantages and disadvantages of utilising temporal data streams to generate dominance hierarchies of animal groups. The code to extract the dominance interactions and test the correlation between different dominance hierarchies is included in the Supporting Information .     

Inferring social network structure from bacterial sequence data

Using DNA sequence data from pathogens to infer transmission networks has traditionally been done in the context of epidemics and outbreaks. Sequence data could analogously be applied to cases of ubiquitous commensal bacteria; however, instead of inferring chains of transmission to track the spread of a pathogen, sequence data for bacteria circulating in an endemic equilibrium could be used to infer information about host contact networks. Here, we show--using simulated data--that multilocus DNA sequence data, based on multilocus sequence typing schemes (MLST), from isolates of commensal bacteria can be used to infer both local and global properties of the contact networks of the populations being sampled. Specifically, for MLST data simulated from small-world networks, the small world parameter controlling the degree of structure in the contact network can robustly be estimated. Moreover, we show that pairwise distances in the network--degrees of separation--correlate with genetic distances between isolates, so that how far apart two individuals in the network are can be inferred from MLST analysis of their commensal bacteria. This result has important consequences, and we show an example from epidemiology: how this result could be used to test for infectious origins of diseases of unknown etiology.