Author Archives: cooplab

Markov mutation models on Yule trees: pairwise species comparisons

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Markov mutation models on Yule trees: pairwise species comparisons
Willem H. Mulder, Forrest W. Crawford
Subjects: Populations and Evolution (q-bio.PE)

Efforts to reconstruct phylogenetic trees and understand evolutionary processes depend fundamentally on stochastic models of speciation and mutation. The simplest continuous-time model for speciation in phylogenetic trees is the Yule process, in which new species are “born” from existing lineages at a constant rate. Recent work has illuminated some of the structural properties of Yule trees, but it remains mostly unknown how these properties affect sequence and trait patterns observed at the tips of the phylogenetic tree. Understanding the interplay between speciation and mutation under simple models of evolution is essential for deriving valid phylogenetic inference methods and gives insight into the optimal design of phylogenetic studies. In this work, we derive the probability distribution of interspecies covariance under Brownian motion and Ornstein-Uhlenbeck processes on a Yule tree. We compute the probability distribution of the number of mutations shared between two randomly chosen taxa in a Yule tree under several mutation models. These results suggest summary measures of phylogenetic information content, illuminate the correlation between site patterns in sequences or traits of related organisms, and provide heuristics for experimental design and reconstruction of phylogenetic trees.

Gaussian process test for high-throughput sequencing time series: application to experimental evolution

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Gaussian process test for high-throughput sequencing time series: application to experimental evolution
Hande Topa, Ágnes Jónás, Robert Kofler, Carolin Kosiol, Antti Honkela
Comments: 26 pages, 13 figures
Subjects: Populations and Evolution (q-bio.PE); Genomics (q-bio.GN); Quantitative Methods (q-bio.QM); Applications (stat.AP)

Motivation: Recent advances in high-throughput sequencing (HTS) have made it possible to monitor genomes in great detail. New experiments not only use HTS to measure genomic features at one time point but to monitor them changing over time with the aim of identifying significant changes in their abundance. In population genetics, for example, allele frequencies are monitored over time to detect significant frequency changes that indicate selection pressures. Previous attempts at analysing data from HTS experiments have been limited as they could not simultaneously include data at intermediate time points, replicate experiments and sources of uncertainty specific to HTS such as sequencing depth.
Results: We present the beta-binomial Gaussian process (BBGP) model for ranking features with significant non-random variation in abundance over time. The features are assumed to represent proportions, such as proportion of an alternative allele in a population. We use the beta-binomial model to capture the uncertainty arising from finite sequencing depth and combine with a Gaussian process model over the time series. In simulations that mimic the features of experimental evolution data, the proposed method clearly outperforms classical testing in average precision of finding selected alleles. We also present results on real data from Drosophila experimental evolution experiment in temperature adaptation.
Availability: R software implementing the test is available at https://github.com/handetopa/BBGP.

A chromatin structure based model accurately predicts DNA replication timing in human cells

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A chromatin structure based model accurately predicts DNA replication timing in human cells
Yevgeniy Gindin, Manuel S. Valenzuela, Mirit I. Aladjem, Paul S. Meltzer, Sven Bilke
Subjects: Subcellular Processes (q-bio.SC); Genomics (q-bio.GN)

The metazoan genome is replicated in precise cell lineage specific temporal order. However, the mechanism controlling this orchestrated process is poorly understood as no molecular mechanisms have been identified that actively regulate the firing sequence of genome replication. Here we develop a mechanistic model of genome replication capable of predicting, with accuracy rivaling experimental repeats, observed empirical replication timing program in humans. In our model, replication is initiated in an uncoordinated (time-stochastic) manner at well-defined sites. The model contains, in addition to the choice of the genomic landmark that localizes initiation, only a single adjustable parameter of direct biological relevance: the number of replication forks. We find that DNase hypersensitive sites are optimal and independent determinants of DNA replication initiation. We demonstrate that the DNA replication timing program in human cells is a robust emergent phenomenon that, by its very nature, does not require a regulatory mechanism determining a proper replication initiation firing sequence.

Phylogenetic tree shapes resolve disease transmission patterns

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Phylogenetic tree shapes resolve disease transmission patterns
Jennifer Gardy, Caroline Colijn

Whole genome sequencing is becoming popular as a tool for understanding outbreaks of communicable diseases, with phylogenetic trees being used to identify individual transmission events or to characterize outbreak-level overall transmission dynamics. Existing methods to infer transmission dynamics from sequence data rely on well-characterised infectious periods, epidemiological and clinical meta-data which may not always be available, and typically require computationally intensive analysis focussing on the branch lengths in phylogenetic trees. We sought to determine whether the topological structures of phylogenetic trees contain signatures of the overall transmission patterns underlying an outbreak. Here we use simulated outbreaks to train and then test computational classifiers. We test the method on data from two real-world outbreaks. We find that different transmission patterns result in quantitatively different phylogenetic tree shapes. We describe five topological features that summarize a phylogeny’s structure and find that computational classifiers based on these are capable of predicting an outbreak’s transmission dynamics. The method is robust to variations in the transmission parameters and network types, and recapitulates known epidemiology of previously characterized real-world outbreaks. We conclude that there are simple structural properties of phylogenetic trees which, when combined, can distinguish communicable disease outbreaks with a super-spreader, homogeneous transmission, and chains of transmission. This is possible using genome data alone, and can be done during an outbreak. We discuss the implications for management of outbreaks.

Reassortment between influenza B lineages and the emergence of a co-adapted PB1-PB2-HA gene complex

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Reassortment between influenza B lineages and the emergence of a co-adapted PB1-PB2-HA gene complex
Gytis Dudas, Trevor Bedford, Samantha Lycett, Andrew Rambaut
Comments: 33 pages, 21 figures
Subjects: Populations and Evolution (q-bio.PE)

Influenza B viruses are increasingly being recognized as major contributors to morbidity attributed to seasonal influenza. Currently circulating influenza B isolates are known to belong to two antigenically distinct lineages referred to as B/Victoria and B/Yamagata. Frequent exchange of genomic segments of these two lineages has been noted in the past, but the observed patterns of reassortment have not been formalized in detail. We investigate inter-lineage reassortments by comparing phylogenetic trees across genomic segments. Our analyses indicate that of the 8 segments of influenza B viruses only PB1, PB2 and HA segments maintained separate Victoria and Yamagata lineages and that currently circulating strains possess PB1, PB2 and HA segments derived entirely from one or the other lineage; other segments have repeatedly reassorted between lineages thereby reducing genetic diversity. We argue that this difference between segments is due to selection against reassortant viruses with mixed lineage PB1, PB2 and HA segments. Given sufficient time and continued recruitment to the reassortment-isolated PB1-PB2-HA gene complex, we expect influenza B viruses to eventually undergo sympatric speciation.

The limits of selection under plant domestication

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The limits of selection under plant domestication
Robin G. Allaby, Dorian Q. Fuller, James L. Kitchen
Subjects: Populations and Evolution (q-bio.PE)

Plant domestication involved a process of selection through human agency of a series of traits collectively termed the domestication syndrome. Current debate concerns the pace at which domesticated plants emerged from cultivated wild populations and how many genes were involved. Here we present simulations that test how many genes could have been involved by considering the cost of selection. We demonstrate the selection load that can be endured by populations increases with decreasing selection coefficients and greater numbers of loci down to values of about s = 0.005, causing a driving force that increases the number of loci under selection. As the number of loci under selection increases, an effect of co-selection increases resulting in individual unlinked loci being fixed more rapidly in out-crossing populations, representing a second driving force to increase the number of loci under selection. In inbreeding systems co-selection results in interference and reduced rates of fixation but does not reduce the size of the selection load that can be endured. These driving forces result in an optimum pace of genome evolution in which 50-100 loci are the most that could be under selection in a cultivation regime. Furthermore, the simulations do not preclude the existence of selective sweeps but demonstrate that they come at a cost of the selection load that can be endured and consequently a reduction of the capacity of plants to adapt to new environments, which may contribute to the explanation of why selective sweeps have been so rarely detected in genome studies.

Conditions for the validity of SNP-based heritability estimation

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Conditions for the validity of SNP-based heritability estimation
James J Lee, Carson C Chow

The heritability of a trait ($h^2$) is the proportion of its population variance caused by genetic differences, and estimates of this parameter are important for interpreting the results of genome-wide association studies (GWAS). In recent years, researchers have adopted a novel method for estimating a lower bound on heritability directly from GWAS data that uses realized genetic similarities between nominally unrelated individuals. The quantity estimated by this method is purported to be the contribution to heritability that could in principle be recovered from association studies employing the given panel of SNPs ($h^2_\textrm{SNP}$). Thus far the validity of this approach has mostly been tested empirically. Here, we provide a mathematical explication and show that the method should remain a robust means of obtaining $h^2_\textrm{SNP}$ under circumstances wider than those under which it has so far been derived.

Genome scans for detecting footprints of local adaptation using a Bayesian factor model

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Genome scans for detecting footprints of local adaptation using a Bayesian factor model
N. Duforet-Frebourg, E. Bazin, M.G.B. Blum
(Submitted on 21 Feb 2014)

A central part of population genomics consists of finding genomic regions implicated in local adaptation. Population genomic analyses are based on genotyping numerous molecular markers and looking for outlier loci in terms of patterns of genetic differentiation. One of the most common approach for selection scan is based on statistics that measure population differentiation such as FST. However they are important caveats with approaches related to FST because they require grouping individuals into populations and they additionally assume a particular model of population structure. Here we implement a more flexible individual-based approach based on Bayesian factor models. Using hierarchical Bayesian modeling, we both infer population structure and identify outlier loci that are candidates for local adaptation. Factor models are strongly related to principal components analysis (PCA) and they model population structure with latent variables called factors. The hierarchical factor model considers that outlier loci are atypically explained by one of the factors. In a model of population divergence, we show that it can achieve a 2-fold or more reduction of false discovery rate compared to the software BayeScan or compared to a FST approach. We show that our software can handle large SNP datasets by analyzing the HGDP SNP dataset. The Bayesian factor model is implemented in the command-line PCAdapt software.

LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies

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LD Score Regression Distinguishes Confounding from Polygenicity in Genome-Wide Association Studies
Brendan Bulik-Sullivan, Po-Ru Loh, Hilary Finucane, Stephan Ripke, Jian Yang, Schizophrenia Working Group Psychiatric Genomics Consortium, Nick Patterson, Mark J Daly, Alkes L Price, Benjamin M Neale

Both polygenicity (i.e. many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield inflated distributions of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from bias and true signal from polygenicity. We have developed an approach that quantifies the contributions of each by examining the relationship between test statistics and linkage disequilibrium (LD). We term this approach LD Score regression. LD Score regression provides an upper bound on the contribution of confounding bias to the observed inflation in test statistics and can be used to estimate a more powerful correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of test statistic inflation in many GWAS of large sample size.

An experimentally determined evolutionary model dramatically improves phylogenetic fit

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An experimentally determined evolutionary model dramatically improves phylogenetic fit
Jesse D Bloom

All modern approaches to molecular phylogenetics require a quantitative model for how genes evolve. Unfortunately, existing evolutionary models do not realistically represent the site-heterogeneous selection that governs actual sequence change. Attempts to remedy this problem have involved augmenting these models with a burgeoning number of free parameters. Here I demonstrate an alternative: experimental determination of a parameter-free evolutionary model via mutagenesis, functional selection, and deep sequencing. Using this strategy, I create an evolutionary model for influenza nucleoprotein that describes the gene phylogeny far better than existing models with dozens or even hundreds of free parameters. High-throughput experimental strategies such as the one employed here provide fundamentally new information that has the potential to transform the sensitivity of phylogenetic analyses.