The role of genetic interactions in yeast quantitative traits

The role of genetic interactions in yeast quantitative traits
Joshua S Bloom, Iulia Kotenko, Meru Sadhu, Sebastian Treusch, Frank W Albert, Leonid Kruglyak
doi: http://dx.doi.org/10.1101/019513

Genetic mapping studies of quantitative traits typically focus on detecting loci that contribute additively to trait variation. Genetic interactions are often proposed as a contributing factor to trait variation, but the relative contribution of interactions to trait variation is a subject of debate. Here, we use a very large cross between two yeast strains to accurately estimate the fraction of phenotypic variance due to pairwise QTL-QTL interactions for 20 quantitative traits. We find that this fraction is 9% on average, substantially less than the contribution of additive QTL (43%). Statistically significant QTL-QTL pairs typically have small individual effect sizes, but collectively explain 40% of the pairwise interaction variance. We show that pairwise interaction variance is largely explained by pairs of loci at least one of which has a significant additive effect. These results refine our understanding of the genetic architecture of quantitative traits and help guide future mapping studies.

Ecological and evolutionary adaptations shape the gut microbiome of BaAka African rainforest hunter-gatherers

Ecological and evolutionary adaptations shape the gut microbiome of BaAka African rainforest hunter-gatherers
Andres Gomez , Klara Petrzelkova , Carl J Yeoman , Micahel B Burns , Katherine R Amato , Klara Vlckova , David Modry , Angelique Todd , Carolyn A Jost Robbinson , Melissa Remis , Manolito Torralba , Karen E Nelson , Franck Carbonero , H Rex Gaskins , Brenda A Wilson , Rebecca M Stumpf , Bryan A White , Steven R Leigh , Ran Blekhman
doi: http://dx.doi.org/10.1101/019232

The gut microbiome provides access to otherwise unavailable metabolic and immune functions, likely affecting mammalian fitness and evolution. To investigate how this microbial ecosystem impacts evolutionary adaptation of humans to particular habitats, we explore the gut microbiome and metabolome of the BaAka rainforest hunter-gatherers from Central Africa. The data demonstrate that the BaAka harbor a colonic ecosystem dominated by Prevotellaceae and other taxa likely related to an increased capacity to metabolize plant structural polysaccharides, phenolics, and lipids. A comparative analysis shows that the BaAka gut microbiome shares similar patterns with that of the Hadza, another hunter-gatherer population from Tanzania. Nevertheless, the BaAka harbor significantly higher bacterial diversity and pathogen load compared to the Hadza, as well as other Western populations. We show that the traits unique to the BaAka microbiome and metabolome likely reflect adaptations to hunter-gatherer lifestyles and particular subsistence patterns. We hypothesize that the observed increase in microbial diversity and potential pathogenicity in the BaAka microbiome has been facilitated by evolutionary adaptations in immunity genes, resulting in a more tolerant immune system.

Bayesian Nonparametric Inference of Population Size Changes from Sequential Genealogies

Bayesian Nonparametric Inference of Population Size Changes from Sequential Genealogies
Julia A Palacios , John Wakeley, Sohini Ramachandran
doi: http://dx.doi.org/10.1101/019216

Sophisticated inferential tools coupled with the coalescent model have recently emerged for estimating past population sizes from genomic data. Accurate methods are available for data from a single locus or from independent loci. Recent methods that model recombination require small sample sizes, make constraining assumptions about population size changes, and do not report measures of uncertainty for estimates. Here, we develop a Gaussian process-based Bayesian nonparametric method coupled with a sequentially Markov coalescent model which allows accurate inference of population sizes over time from a set of genealogies. In contrast to current methods, our approach considers a broad class of recombination events, including those that do not change local genealogies. We show that our method outperforms recent likelihood-based methods that rely on discretization of the parameter space. We illustrate the application of our method to multiple demographic histories, including population bottlenecks and exponential growth. In simulation, our Bayesian approach produces point estimates four times more accurate than maximum likelihood estimation (based on the sum of absolute differences between the truth and the estimated values). Further, our method’s credible intervals for population size as a function of time cover 90 percent of true values across multiple demographic scenarios, enabling formal hypothesis testing about population size differences over time. Using genealogies estimated with ARGweaver, we apply our method to European and Yoruban samples from the 1000 Genomes Project and confirm key known aspects of population size history over the past 150,000 years.

Near-optimal RNA-Seq quantification

Near-optimal RNA-Seq quantification
Nicolas Bray, Harold Pimentel, Páll Melsted, Lior Pachter
Subjects: Quantitative Methods (q-bio.QM); Computational Engineering, Finance, and Science (cs.CE); Data Structures and Algorithms (cs.DS); Genomics (q-bio.GN)

We present a novel approach to RNA-Seq quantification that is near optimal in speed and accuracy. Software implementing the approach, called kallisto, can be used to analyze 30 million unaligned RNA-Seq reads in less than 5 minutes on a standard laptop computer while providing results as accurate as those of the best existing tools. This removes a major computational bottleneck in RNA-Seq analysis.

A basic mathematical model for the Lenski experiment and the deceleration of the relative fitness

A basic mathematical model for the Lenski experiment and the deceleration of the relative fitness
Adrián González Casanova, Noemi Kurt, Anton Wakolbinger, Linglong Yuan
Subjects: Probability (math.PR); Populations and Evolution (q-bio.PE)

The Lenski experiment investigates the long-term evolution of bacterial populations. Its design allows the direct comparison of the reproductive fitness of an evolved strain with its founder ancestor. It was observed by Wiser et al. (2013) that the mean fitness over time increases sublinearly, a behaviour which is commonly attributed to effects like clonal interference or epistasis. In this paper we present an individual-based probabilistic model that captures essential features of the design of the Lenski experiment. We assume that each beneficial mutation increases the individual reproduction rate by a fixed amount, which corresponds to the absence of epistasis in the continuous-time (intraday) part of the model, but leads to an epistatic effect in the discrete-time (interday) part of the model. Using an approximation by near-critical Galton-Watson processes, we prove that under some assumptions on the model parameters which exclude clonal interference, the relative fitness process converges, after suitable rescaling, in the large population limit to a power law function.

A vision for ubiquitous sequencing

A vision for ubiquitous sequencing
Yaniv Erlich
doi: http://dx.doi.org/10.1101/019018

Genomics has recently celebrated reaching the \$1000 genome milestone, making affordable DNA sequencing a reality. This goal of the sequencing revolution has been successfully completed. Looking forward, the next goal of the revolution can be ushered in by the advent of sequencing sensors – miniaturized sequencing devices that are manufactured for real time applications and deployed in large quantities at low costs. The first part of this manuscript envisions applications that will benefit from moving the sequencers to the samples in a range of domains. In the second part, the manuscript outlines the critical barriers that need to be addressed in order to reach the goal of ubiquitous sequencing sensors.

Rail-RNA: Scalable analysis of RNA-seq splicing and coverage

Rail-RNA: Scalable analysis of RNA-seq splicing and coverage
Abhinav Nellore , Leonardo Collado-Torres , Andrew E Jaffe , James Morton , Jacob Pritt , José Alquicira-Hernández , Jeffrey T Leek , Ben Langmead
doi: http://dx.doi.org/10.1101/019067

RNA sequencing (RNA-seq) experiments now span hundreds to thousands of samples. A source of frustration for investigators analyzing a given dataset is the inability to rapidly and reproducibly align its samples jointly. Current spliced alignment software is designed to analyze each sample separately. Consequently, no information is gained from analyzing multiple samples together, and it is difficult to reproduce the exact analysis without access to original computing resources. We describe Rail-RNA, a cloud-enabled spliced aligner that analyzes many samples at once. Rail-RNA eliminates redundant work across samples, making it more efficient as samples are added. For many samples, Rail-RNA is more accurate than annotation-assisted aligners. We use Rail-RNA to align 666 RNA-seq samples from the GEUVADIS project on Amazon Web Services in 12 hours for US$0.69 per sample. Rail-RNA produces alignments and base-resolution bigWig coverage files, ready for use with downstream packages for reproducible statistical analysis. We identify 290,416 expressed regions in the GEUVADIS samples, including 21,224 that map to intergenic sequence. We show that these regions show consistent patterns of variation across populations and with respect to known technological confounders. We identify expressed regions in the GEUVADIS samples and show that both annotated and unannotated (novel) expressed regions exhibit consistent patterns of variation across populations and with respect to known confounders. Rail-RNA is open-source software available at http://rail.bio .