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.

Sequencing ultra-long DNA molecules with the Oxford Nanopore MinION

Sequencing ultra-long DNA molecules with the Oxford Nanopore MinION

John M Urban, Jacob Bliss, Charles E Lawrence, Susan A Gerbi
doi: http://dx.doi.org/10.1101/019281

Oxford Nanopore Technologies’ nanopore sequencing device, the MinION, holds the promise of sequencing ultra-long DNA fragments >100kb. An obstacle to realizing this promise is delivering ultra-long DNA molecules to the nanopores. We present our progress in developing cost-effective ways to overcome this obstacle and our resulting MinION data, including multiple reads >100kb.

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.

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 .

Bayesian Inference of Divergence Times and Feeding Evolution in Grey Mullets (Mugilidae)

Bayesian Inference of Divergence Times and Feeding Evolution in Grey Mullets (Mugilidae)
Francesco Santini , Michael R. May , Giorgio Carnevale , Brian R. Moore
doi: http://dx.doi.org/10.1101/019075

Grey mullets (Mugilidae, Ovalentariae) are coastal fishes found in near-shore environments of tropical, subtropical, and temperate regions within marine, brackish, and freshwater habitats throughout the world. This group is noteworthy both for the highly conserved morphology of its members—which complicates species identification and delimitation—and also for the uncommon herbivorous or detritivorous diet of most mullets. In this study, we first attempt to identify the number of mullet species, and then—for the resulting species—estimate a densely sampled time-calibrated phylogeny using three mitochondrial gene regions and three fossil calibrations. Our results identify two major subgroups of mullets that diverged in the Paleocene/Early Eocene, followed by an Eocene/Oligocene radiation across both tropical and subtropical habitats. We use this phylogeny to explore the evolution of feeding preference in mullets, which indicates multiple independent origins of both herbivorous and detritivorous diets within this group. We also explore correlations between feeding preference and other variables, including body size, habitat (marine, brackish, or freshwater), and geographic distribution (tropical, subtropical, or temperate). Our analyses reveal: (1) a positive correlation between trophic index and habitat (with herbivorous and/or detritivorous species predominantly occurring in marine habitats); (2) a negative correlation between trophic index and geographic distribution (with herbivorous species occurring predominantly in subtropical and temperate regions), and; (3) a negative correlation between body size and geographic distribution (with larger species occurring predominantly in subtropical and temperate regions).