Reproductive isolation of hybrid populations driven by genetic incompatibilities

Reproductive isolation of hybrid populations driven by genetic incompatibilities

Molly Schumer, Rongfeng Cui, Gil G Rosenthal, Peter Andolfatto

Despite its role in homogenizing populations, hybridization has also been proposed as a means to generate new species. The conceptual basis for this idea is that hybridization can result in novel phenotypes through recombination between the parental genomes, allowing a hybrid population to occupy ecological niches unavailable to parental species. A key feature of these models is that these novel phenotypes ecologically isolate hybrid populations from parental populations, precipitating speciation. Here we present an alternative model of the evolution of reproductive isolation in hybrid populations that occurs as a simple consequence of selection against incompatibilities. Unlike previous models, our model does not require small population sizes, the availability of new niches for hybrids or ecological or sexual selection on hybrid traits. We show that reproductive isolation between hybrids and parents evolves frequently and rapidly under this model, even in the presence of ongoing migration with parental species and strong selection against hybrids. Our model predicts that multiple distinct hybrid species can emerge from replicate hybrid populations formed from the same parental species, potentially generating patterns of species diversity and relatedness that mimic adaptive radiations.

YFitter: Maximum likelihood assignment of Y chromosome haplogroups from low-coverage sequence data

YFitter: Maximum likelihood assignment of Y chromosome haplogroups from low-coverage sequence data

Luke Jostins, Yali Xu, Shane McCarthy, Qasim Ayub, Richard Durbin, Jeff Barrett, Chris Tyler-Smith
(Submitted on 30 Jul 2014)

Low-coverage short-read resequencing experiments have the potential to expand our understanding of Y chromosome haplogroups. However, the uncertainty associated with these experiments mean that haplogroups must be assigned probabilistically to avoid false inferences. We propose an efficient dynamic programming algorithm that can assign haplogroups by maximum likelihood, and represent the uncertainty in assignment. We apply this to both genotype and low-coverage sequencing data, and show that it can assign haplogroups accurately and with high resolution. The method is implemented as the program YFitter, which can be downloaded from this http URL

Inferring the Clonal Structure of Viral Populations from Time Series Sequencing

Inferring the Clonal Structure of Viral Populations from Time Series Sequencing

Donatien Fotso-Chedom, Pablo R. Murcia, Chris D. Greenman
(Submitted on 30 Jul 2014)

RNA virus populations will undergo processes of mutation and selection resulting in a mixed population of viral particles. High throughput sequencing of a viral population subsequently contains a mixed signal of the underlying clones. We would like to identify the underlying evolutionary structures. We utilize two sources of information to attempt this; within segment linkage information, and mutation prevalence. We demonstrate that clone haplotypes, their prevalence, and maximum parsimony reticulate evolutionary structures can be identified, although the solutions may not be unique, even for complete sets of information. This is applied to a chain of influenza infection, where we infer evolutionary structures, including reassortment, and demonstrate some of the difficulties of interpretation that arise from deep sequencing due to artifacts such as template switching during PCR amplification.

The Genetic Legacy of the Expansion of Turkic-Speaking Nomads Across Eurasia

The Genetic Legacy of the Expansion of Turkic-Speaking Nomads Across Eurasia

Bayazit Yunusbayev, Mait Metspalu, Ene Metspalu, Albert Valeev, Sergei Litvinov, Ruslan Valiev, Vita Akhmetova, Elena Balanovska, Oleg Balanovsky, Shahlo Turdikulova, Dilbar Dalimova, Pagbajabyn Nymadawa, Ardeshir Bahmanimehr, Hovhannes Sahakyan, Kristiina Tambets, Sardana Fedorova, Nikolay Barashkov, Irina Khidiatova, Evelin Mihailov, Rita Khusainova, Larisa Damba, Miroslava Derenko, Boris Malyarchuk, Ludmila Osipova, Mikhail Voevoda, Levon Yepiskoposyan, Toomas Kivisild, Elza Khusnutdinova, Richard Villems

The Turkic peoples represent a diverse collection of ethnic groups defined by the Turkic languages. These groups have dispersed across a vast area, including Siberia, Northwest China, Central Asia, East Europe, the Caucasus, Anatolia, the Middle East, and Afghanistan. The origin and early dispersal history of the Turkic peoples is disputed, with candidates for their ancient homeland ranging from the Transcaspian steppe to Manchuria in Northeast Asia. Previous genetic studies have not identified a clear-cut unifying genetic signal for the Turkic peoples, which lends support for language replacement rather than demic diffusion as the model for the Turkic language?s expansion. We addressed the genetic origin of 373 individuals from 22 Turkic-speaking populations, representing their current geographic range, by analyzing genome-wide high-density genotype data. Most of the Turkic peoples studied, except those in Central Asia, genetically resembled their geographic neighbors, in agreement with the elite dominance model of language expansion. However, western Turkic peoples sampled across West Eurasia shared an excess of long chromosomal tracts that are identical by descent (IBD) with populations from present-day South Siberia and Mongolia (SSM), an area where historians center a series of early Turkic and non-Turkic steppe polities. The observed excess of long chromosomal tracts IBD (> 1cM) between populations from SSM and Turkic peoples across West Eurasia was statistically significant. Finally, we used the ALDER method and inferred admixture dates (~9th?17th centuries) that overlap with the Turkic migrations of the 5th?16th centuries. Thus, our results indicate historical admixture among Turkic peoples, and the recent shared ancestry with modern populations in SSM supports one of the hypothesized homelands for their nomadic Turkic and related Mongolic ancestors.

QuASAR: Quantitative Allele Specific Analysis of Reads

QuASAR: Quantitative Allele Specific Analysis of Reads

Chris Harvey, Gregory A Moyebrailean, Omar Davis, Xiaoquan Wen, Francesca Luca, Roger Pique-Regi

Expression quantitative trait loci (eQTL) studies have discovered thousands of genetic variants that regulate gene expression and have been crucial to enable a better understanding of the functional role of non-coding sequences. However, eQTL studies are generally quite expensive, requiring a large sample size and genome-wide genotyping. On the other hand, allele specific expression (ASE) is becoming a very popular approach to detect the effect of a genetic variant on gene expression, even with a single individual. This is typically achieved by counting the number of RNA-seq reads for each allele at heterozygous sites and rejecting the null hypothesis of 1:1 ratio. When genotype information is not readily available it could be inferred from the RNA-seq reads directly, but there are no methods available that can incorporate the uncertainty on the genotype call with the ASE inference step. Here, we present QuASAR, Quantitative Allele Specific Analysis of Reads, a novel statistical learning method for jointly detecting heterozygote genotypes and inferring ASE. The proposed ASE inference step takes into consideration the uncertainty in the genotype calls while including parameters that model base-call errors in sequencing and allelic over-dispersion. We validated our method with experimental data for which high quality genotypes are available. Results on an additional dataset with multiple replicates at different sequencing depths demonstrate that QuASAR is a powerful tool for ASE analysis when genotypes are not available.

A Statistical Test for Clades in Phylogenies

A Statistical Test for Clades in Phylogenies

Thurston H. Y. Dang, Elchanan Mossel
(Submitted on 29 Jul 2014)

We investigated testing the likelihood of a phylogenetic tree by comparison to its subtree pruning and regrafting (SPR) neighbors, with or without re-optimizing branch lengths. This is inspired by aspects of Bayesian significance tests, and the use of SPRs for heuristically finding maximum likelihood trees. Through a number of simulations with the Jukes-Cantor model on various topologies, it is observed that the SPR tests are informative, and reasonably fast compared to searching for the maximum likelihood tree. This suggests that the SPR tests would be a useful addition to the suite of existing statistical tests, for identifying potential inaccuracies of inferred topologies.

Are all genetic variants in DNase I sensitivity regions functional?

Are all genetic variants in DNase I sensitivity regions functional?

Gregory A Moyerbrailean, Chris T Harvey, Cynthia A Kalita, Xiaoquan Wen, Francesca Luca, Roger Pique-Regi

A detailed mechanistic understanding of the direct functional consequences of DNA variation on gene regulatory mechanism is critical for a complete understanding of complex trait genetics and evolution. Here, we present a novel approach that integrates sequence information and DNase I footprinting data to predict the impact of a sequence change on transcription factor binding. Applying this approach to 653 DNase-seq samples, we identified 3,831,862 regulatory variants predicted to affect active regulatory elements for a panel of 1,372 transcription factor motifs. Using QuASAR, we validated the non-coding variants predicted to be functional by examining allele-specific binding (ASB). Combining the predictive model and the ASB signal, we identified 3,217 binding variants within footprints that are significantly imbalanced (20% FDR). Even though most variants in DNase I hypersensitive regions may not be functional, we estimate that 56% of our annotated functional variants show actual evidence of ASB. To assess the effect these variants may have on complex phenotypes, we examined their association with complex traits using GWAS and observed that ASB-SNPs are enriched 1.22-fold for complex traits variants. Furthermore, we show that integrating footprint annotations into GWAS meta-study results improves identification of likely causal SNPs and provides a putative mechanism by which the phenotype is affected.

Bayesian mixture analysis for metagenomic community profiling.

Bayesian mixture analysis for metagenomic community profiling.

Sofia Morfopoulou, Vincent Plagnol

Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated provides an opportunity to detect species even at very low levels, provided that computational tools can effectively interpret potentially complex metagenomic mixtures. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. This interpretation problem can be formulated statistically as a mixture model, where the species of origin of each read is missing, but the complete knowledge of all species present in the mixture helps with the individual reads assignment. Several analytical tools have been proposed to approximately solve this computational problem. Here, we show that the use of parallel Monte Carlo Markov chains (MCMC) for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. The added accuracy comes at a cost of increased computation time. Our approach is useful for solving complex mixtures involving several related species. We designed our method specifically for the analysis of deep transcriptome sequencing datasets and with a particular focus on viral pathogen detection, but the principles are applicable more generally to all types of metagenomics mixtures. The code is available on github ( and the process is currently being implemented in a user friendly R package (metaMix, to be submitted to CRAN).

Long non-coding RNA discovery in Anopheles gambiae using deep RNA sequencing

Long non-coding RNA discovery in Anopheles gambiae using deep RNA sequencing

Adam M Jenkins, Robert M Waterhouse, Alan S Kopin, Marc A.T. Muskavitch

Long non-coding RNAs (lncRNAs) are mRNA-like transcripts longer than 200 bp that have no protein-coding potential. lncRNAs have recently been implicated in epigenetic regulation, transcriptional and post-transcriptional gene regulation, and regulation of genomic stability in mammals, Caenorhabditis elegans, and Drosophila melanogaster. Using deep RNA sequencing of multiple Anopheles gambiae life stages, we have identified over 600 novel lncRNAs and more than 200 previously unannotated putative protein-coding genes. The lncRNAs exhibit differential expression profiles across life stages and adult genders. Those lncRNAs that are antisense to known protein-coding genes or are contained within intronic regions of protein-coding genes may mediate transcriptional repression or stabilization of associated mRNAs. lncRNAs exhibit faster rates of sequence evolution across anophelines compared to previously known and newly identified protein-coding genes. This initial description of lncRNAs in An. gambiae offers the first genome-wide insights into long non-coding RNAs in this vector mosquito and defines a novel set of potential targets for the development of vector-based interventions that may curb the human malaria burden in disease-endemic countries.

Author post: Facilitated diffusion buffers noise in gene expression

This guest post is by Radu Zabet on his preprint (with Armin Schoech) Facilitated diffusion buffers noise in gene expression, arXived here.

How does the binding dynamics of transcription factors affect the noise in gene expression?

Transcription factors (TFs) are proteins that bind to DNA and control gene activity. Gene regulation can be modelled as a chemical reaction, which is fundamentally a stochastic process. Given the importance of an accurate control of the gene regulatory program in the cell, significant efforts have been made in understanding the noise properties of gene expression.

Why can noise in gene expression be modelled assuming an ON/OFF gene model?

With few exceptions, previous studies investigated the noise in gene expression assuming that the regulatory process is a two-state Markov model (genes switch stochastically between ON and OFF states). However, it is known that, mechanistically, transcription factors find their genomic target sites through facilitated diffusion, a combination of 3D diffusion in the cytoplasm/nucleoplasm and 1D random walk along the DNA, and this is likely to influence the noise properties of the gene regulation process. Previous experimental studies (e.g. see successfully modelled the noise measured experimentally by assuming an ON/OFF gene model (two-state Markov model) in bacterial and animal cells. In this manuscript, we built a three-state Markov model that accurately models the facilitated diffusion and we showed that for biologically relevant parameters, at least in bacteria (we assumed lac repressor system, noise in gene expression can be modelled assuming the ON/OFF gene model, but only if the binding/unbinding rates are adjusted accordingly. This explains why in many cases the experimental noise in gene regulation can be modelled assuming an ON/OFF gene model. Note that there are several exceptions where the noise in gene expression does not seem to be accounted by the ON/OFF gene model (e.g. or

What is the effect of facilitated diffusion on the noise in gene expression?

Next, assuming the ON/OFF gene model we investigated the evolutionary advantage that a TF, which performs facilitated diffusion, has on noise in gene expression compared to an equivalent TF that only performs the 3D diffusion (and does not perform 1D random walk on the DNA). Our results show that the noise in gene expression can be reduced significantly when the TF performs facilitated diffusion compared to its equivalent TF that only performs 3D diffusion in the cell. This is important, because while the majority of the studies identify the speedup in the binding site search process as the main evolutionary advantage of why facilitated diffusion exists, we show that, in addition to this speedup in binding kinetics, facilitated diffusion also reduces the noise in gene expression. Interestingly, it seems that the noise level in gene expression is reduced close to the noise level of an unregulated gene (the lowest noise level in gene expression that could be achieved), while the noise of an equivalent TF that performs only 3D diffusion is significantly higher.

Finally, to test our model, we parameterise it with values measured experimentally in the case of lac repressor in E. coli and we estimated the mean mRNA level to be 0.16 and the Fano factor (variance divided by mean) to be 1.3 (as opposed to 2.0 in the case of TF performing only 3D diffusion). These values are similar to the values measured experimentally in the low inducer case of Plac by (mean mRNA level of 0.15 and Fano factor of 1.25) and shows that facilitated diffusion is essential in explaining the experimentally measured noise in mRNA.