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.