inPHAP: Interactive visualization of genotype and phased haplotype data

inPHAP: Interactive visualization of genotype and phased haplotype data
Günter Jäger, Alexander Peltzer, Kay Nieselt
Comments: BioVis 2014 conference
Subjects: Graphics (cs.GR); Genomics (q-bio.GN)

Background: To understand individual genomes it is necessary to look at the variations that lead to changes in phenotype and possibly to disease. However, genotype information alone is often not sufficient and additional knowledge regarding the phase of the variation is needed to make correct interpretations. Interactive visualizations, that allow the user to explore the data in various ways, can be of great assistance in the process of making well informed decisions. But, currently there is a lack for visualizations that are able to deal with phased haplotype data. Results: We present inPHAP, an interactive visualization tool for genotype and phased haplotype data. inPHAP features a variety of interaction possibilities such as zooming, sorting, filtering and aggregation of rows in order to explore patterns hidden in large genetic data sets. As a proof of concept, we apply inPHAP to the phased haplotype data set of Phase 1 of the 1000 Genomes Project. Thereby, inPHAP’s ability to show genetic variations on the population as well as on the individuals level is demonstrated for several disease related loci. Conclusions: As of today, inPHAP is the only visual analytical tool that allows the user to explore unphased and phased haplotype data interactively. Due to its highly scalable design, inPHAP can be applied to large datasets with up to 100 GB of data, enabling users to visualize even large scale input data. inPHAP closes the gap between common visualization tools for unphased genotype data and introduces several new features, such as the visualization of phased data.

Improved genome inference in the MHC using a population reference graph

Improved genome inference in the MHC using a population reference graph
Alexander Dilthey, Charles J Cox, Zamin Iqbal, Matthew R Nelson, Gil McVean

In humans and many other species, while much is known about the extent and structure of genetic variation, such information is typically not used in assembling novel genomes. Rather, a single reference is used against which to map reads, which can lead to poor characterisation of regions of high sequence or structural diversity. Here, we introduce a population reference graph, which combines multiple reference sequences as well as catalogues of SNPs and short indels. The genomes of novel samples are reconstructed as paths through the graph using an efficient hidden Markov Model, allowing for recombination between different haplotypes and variants. By applying the method to the 4.5Mb extended MHC region on chromosome 6, combining eight assembled haplotypes, sequences of known classical HLA alleles and 87,640 SNP variants from the 1000 Genomes Project, we demonstrate, using simulations, SNP genotyping, short-read and long-read data, how the method improves the accuracy of genome inference. Moreover, the analysis reveals regions where the current set of reference sequences is substantially incomplete, particularly within the Class II region, indicating the need for continued development of reference-quality genome sequences.

Bayesian Coalescent Epidemic Inference: Comparison of Stochastic and Deterministic SIR Population Dynamics

Bayesian Coalescent Epidemic Inference: Comparison of Stochastic and Deterministic SIR Population Dynamics

Alex Popinga, Tim Vaughan, Tanja Stadler, Alexei Drummond
Comments: Submitted
Subjects: Populations and Evolution (q-bio.PE)

Estimation of epidemiological and population parameters from molecular sequence data has become central to the understanding of infectious disease dynamics. Various models have been proposed to infer details of the dynamics that describe epidemic progression. These include inference approaches derived from Kingmans coalescent as well as from birth death branching processes. The development of alternative approaches merits investigation of their characteristics and differences. Here we use recently described coalescent theory for epidemic dynamics to develop stochastic and deterministic coalescent SIR tree priors. We implement these in a Bayesian phylogenetic inference framework to permit joint estimation of SIR epidemic parameters and the sample genealogy. We assess the models performance and contrast results obtained with a recently published birth death sampling model for epidemic inference. Comparisons are made by analyzing sets of genealogies simulated under precisely known epidemiological parameters. We also compare results of analyses using published HIV1 sequence data obtained from known UK infection clusters. We show that the coalescent SIR model is effective at estimating epidemiological parameters from data with large fundamental reproductive number R0 and large population size S0. We find that the stochastic variant generally outperforms its deterministic counterpart. However, each of these Bayesian estimators are shown to have undesirable properties in certain circumstances, especially for epidemic outbreaks with R0 close to one or with small susceptible populations.

Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure

Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure

David Mimno, David M Blei, Barbara E Engelhardt
Subjects: Methodology (stat.ME); Genomics (q-bio.GN); Populations and Evolution (q-bio.PE); Applications (stat.AP)

Admixture models are a ubiquitous approach to capture latent population structure in genetic samples. Despite the widespread application of admixture models, little thought has been devoted to the quality of the model fit or the accuracy of the estimates of parameters of interest for a particular study. Here we develop methods for validating admixture models based on posterior predictive checks (PPCs), a Bayesian method for assessing the quality of a statistical model. We develop PPCs for five population-level statistics of interest: within-population genetic variation, background linkage disequilibrium, number of ancestral populations, between-population genetic variation, and the downstream use of admixture parameters to correct for population structure in association studies. Using PPCs, we evaluate the quality of the model estimates for four qualitatively different population genetic data sets: the POPRES European individuals, the HapMap phase 3 individuals, continental Indians, and African American individuals. We found that the same model fitted to different genomic studies resulted in highly study-specific results when evaluated using PPCs, illustrating the utility of PPCs for model-based analyses in large genomic studies.

Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

Anand Bhaskar, Y.X. Rachel Wang, Yun S. Song

With the recent increase in study sample sizes in human genetics, there has been growing interest in inferring historical population demography from genomic variation data. Here, we present an efficient inference method that can scale up to very large samples, with tens or hundreds of thousands of individuals. Specifically, by utilizing analytic results on the expected frequency spectrum under the coalescent and by leveraging the technique of automatic differentiation, which allows us to compute gradients exactly, we develop a very efficient algorithm to infer piecewise-exponential models of the historical effective population size from the distribution of sample allele frequencies. Our method is orders of magnitude faster than previous demographic inference methods based on the frequency spectrum. In addition to inferring demography, our method can also accurately estimate locus-specific mutation rates. We perform extensive validation of our method on simulated data and show that it can accurately infer multiple recent epochs of rapid exponential growth, a signal which is difficult to pick up with small sample sizes. Lastly, we apply our method to analyze data from recent sequencing studies, including a large-sample exome-sequencing dataset of tens of thousands of individuals assayed at a few hundred genic regions.

Multi-locus analysis of genomic time series data from experimental evolution

Multi-locus analysis of genomic time series data from experimental evolution

Jonathan Terhorst, Yun S. Song

Genomic time series data generated by evolve-and-resequence (E&R) experiments offer a powerful window into the mechanisms that drive evolution. However, standard population genetic inference procedures do not account for sampling serially over time, and new methods are needed to make full use of modern experimental evolution data. To address this problem, we develop a Gaussian process approximation to the multi-locus Wright-Fisher process with selection over a time course of tens of generations. The mean and covariance structure of the Gaussian process are obtained by computing the corresponding moments in discrete-time Wright-Fisher models conditioned on the presence of a linked selected site. This enables our method to account for the effects of linkage and selection, both along the genome and across sampled time points, in an approximate but principled manner. Using simulated data, we demonstrate the power of our method to correctly detect, locate and estimate the fitness of a selected allele from among several linked sites. We also study how this power changes for different values of selection strength, initial haplotypic diversity, population size, sampling frequency, experimental duration, number of replicates, and sequencing coverage depth. In addition to providing quantitative estimates of selection parameters from experimental evolution data, our model can be used by practitioners to design E&R experiments with requisite power. Finally, we explore how our likelihood-based approach can be used to infer other model parameters, including effective population size and recombination rate, and discuss extensions to more complex models.

The effective founder effect in a spatially expanding population

The effective founder effect in a spatially expanding population
Benjamin Marco Peter, Montgomery Slatkin

The gradual loss of diversity associated with range expansions is a well known pattern observed in many species, and can be explained with a serial founder model. We show that under a branching process approximation, this loss in diversity is due to the difference in offspring variance between individuals at and away from the expansion front, which allows us to measure the strength of the founder effect, dependant on an effective founder size. We demonstrate that the predictions from the branching process model fit very well with Wright-Fisher forward simulations and backwards simulations under a modified Kingman coalescent, and further show that estimates of the effective founder size are robust to possibly confounding factors such as migration between subpopulations. We apply our method to a data set of Arabidopsis thaliana, where we find that the founder effect is about three times stronger in the Americas than in Europe, which may be attributed to the more recent, faster expansion.

Identifying the Genetic Basis of Functional Protein Evolution Using Reconstructed Ancestors

Identifying the Genetic Basis of Functional Protein Evolution Using Reconstructed Ancestors

Victor Hanson-Smith, Christopher Baker, Alexander Johnson
(Submitted on 11 Jun 2014)

A central challenge in the study of protein evolution is the identification of historic amino acid sequence changes responsible for creating novel functions observed in present-day proteins. To address this problem, we developed a new method to identify and rank amino acid mutations in ancestral protein sequences according to their function-shifting potential. Our approach scans the changes between two reconstructed ancestral sequences in order to find (1) sites with sequence changes that significantly deviate from our model-based probabilistic expectations, (2) sites that demonstrate extreme changes in mutual information, and (3) sites with extreme gains or losses of information content. By taking the overlaps of these statistical signals, the method accurately identifies cryptic evolutionary patterns that are often not obvious when examining only the conservation of modern-day protein sequences. We validated this method with a training set of previously-discovered function-shifting mutations in three essential protein families in animals and fungi, whose evolutionary histories were the prior subject of systematic molecular biological investigation. Our method identified the known function-shifting mutations in the training set with a very low rate of false positive discovery. Further, our approach significantly outperformed other methods that use variability in evolutionary rates to detect functional loci. The accuracy of our approach indicates it could be a useful tool for generating specific testable hypotheses regarding the acquisition of new functions across a wide range of protein families.

Author post: Predicting evolution from the shape of genealogical trees

This guest post by Richard Neher discusses his preprint Predicting evolution from the shape of genealogical trees. Richard A. Neher, Colin A. Russell, Boris I. Shraiman. arXived here. This is cross-posted from the Neher lab website.

In this preprint — a collaboration with Colin Russell and Boris Shraiman — we show that it is possible to predict which individual from a population is most closely related to future populations. To this end, we have developed a method that uses the branching pattern of genealogical trees to estimate which part of the tree contains the “fittest” sequences, where fit means rapidly multiplying. Those that multiply rapidly, are most likely to take over the population. We demonstrate the power of our method by predicting the evolution of seasonal influenza viruses.

How does it work?
Individuals adapt to a changing environment by accumulating beneficial mutations, while avoiding deleterious mutations. We model this process assuming that there are many such mutations which change fitness in small increments. Using this model, we calculate the probability that an individual that lived in the past at time t leaves n descendants in the present. This distributions depends critically on the fitness of the ancestral individual. We then extend this calculation to the probability of observing a certain branch in a genealogical tree reconstructed from a sample of sequences. A branch in a tree connects an individual A that lived at time tA and had fitness xA and with an individual B that lived at a later time tB with fitness xB as illustrated in the figure. B has descendants in the sample, otherwise the branch would not be part of the tree. Furthermore, all sampled descendants of A are also descendants of B, otherwise the connection between A and B would have branched between tA and tB. We call the mathematical object describing fitness evolution between A and B “branch propagator” and propagatordenote it by g(xB,tB|xA,tA). The joint probability distribution of fitness values of all nodes of the tree is given by a product of branch propagators. We then calculate the expected fitness of each node and use it to rank the sampled sequences. The top ranked sequence is our prediction for the sequence of the progenitor of the future population.

Why do we care?
flu_tree Being able to predict evolution could have immediate applications. The best example is the seasonal influenza vaccine, that needs to be updated frequently to keep up with the evolving virus. Vaccine strains are chosen among sampled virus strains, and the more closely this strain matches the future influenza virus population, the better the vaccine is going to be. Hence by predicting a likely progenitor of the future, our method could help to improve influenza vaccines. One of our predictions is shown in the figure, with the top ranked sequence marked by a black arrow. Influenza is not the only possible application. Since the algorithm only requires a reconstructed tree as input, it can be applied to other rapidly evolving pathogens or cancer cell populations. In addition, to being useful, the ability to predict also implies that the model captures an essential aspect of evolutionary dynamics: influenza evolution is to a substantial degree — enough to enable prediction — dependent on the accumulation of small effect mutations.

Comparison to other approaches
Given the importance of good influenza vaccines, there has been a number of previous efforts to anticipate influenza virus evolution, typically based on using patterns of molecular evolution from historical data. Along these lines, Luksza and Lässig have recently presented an explicit fitness model for influenza virus evolution that rewards mutations at positions known to convey antigenic novelty and penalizes likely deleterious mutations (+a few other things). By using molecular influenza specific signatures, this model is complementary to ours that uses only the tree reconstructed from nucleotide sequences. Interestingly, the two models do more or less equally well and combining different methods of prediction should result in more reliable results.

High performance computation of landscape genomic models integrating local indices of spatial association

High performance computation of landscape genomic models integrating local indices of spatial association

Sylvie Stucki, Pablo Orozco-terWengel, Michael W. Bruford, Licia Colli, Charles Masembe, Riccardo Negrini, Pierre Taberlet, Stéphane Joost, the NEXTGEN Consortium
Comments: 1 figure in text, 1 figure in supplementary material
Subjects: Populations and Evolution (q-bio.PE)

Motivation: The increasing availability of high-throughput datasets requires powerful methods to support the detection of signatures of selection in landscape genomics. Results: We present an integrated approach to study signatures of local adaptation, providing rapid processing of whole genome data and enabling assessment of spatial association using molecular markers. Availabilty: Sam{\ss}ada is an open source software written in C++ available at (under the license GNU GPL 3). Compiled versions are provided for Windows, Linux and MacOS X. Contact:, Supplementary material is available online.