Detection and interpretation of shared genetic influences on 40 human traits
Joseph Pickrell, Tomaz Berisa, Laure Segurel, Joyce Y Tung, David Hinds
doi: http://dx.doi.org/10.1101/019885

We performed a genome-wide scan for genetic variants that influence multiple human phenotypes by comparing large genome-wide association studies (GWAS) of 40 traits or diseases, including anthropometric traits (e.g. nose size and male pattern baldness), immune traits (e.g. susceptibility to childhood ear infections and Crohn’s disease), metabolic phenotypes (e.g. type 2 diabetes and lipid levels), and psychiatric diseases (e.g. schizophrenia and Parkinson’s disease). First, we identified 307 loci (at a false discovery rate of 10%) that influence multiple traits (excluding “trivial” phenotype pairs like type 2 diabetes and fasting glucose). Several loci influence a large number of phenotypes; for example, variants near the blood group gene ABO influence eleven of these traits, including risk of childhood ear infections (rs635634: log-odds ratio = 0.06, P = 1.4 × 10−8) and allergies (log-odds ratio = 0.05, P = 2.5 × 10−8), among others. Similarly, a nonsynonymous variant in the zinc transporter SLC39A8 influences seven of these traits, including risk of schizophrenia (rs13107325: log-odds ratio = 0.15, P = 2 × 10−12) and Parkinson’s disease (log-odds ratio = -0.15, P = 1.6 × 10−7), among others. Second, we used these loci to identify traits that share multiple genetic causes in common. For example, genetic variants that delay age of menarche in women also, on average, delay age of voice drop in men, decrease body mass index (BMI), increase adult height, and decrease risk of male pattern baldness. Finally, we identified four pairs of traits that show evidence of a causal relationship. For example, we show evidence that increased BMI causally increases triglyceride levels, and that increased liability to hypothyroidism causally decreases adult height.

General formulation of Luria-Delbrück distribution of the number of mutants
bahram houchmandzadeh
doi: http://dx.doi.org/10.1101/019869

Abstract The Luria-Delbrück experiment is a cornerstone of evolutionary theory, demonstrating the randomness of mutations before selection. The distribution of the number of mutants in this experiment has been the subject of intense investigation during the last 70 years. Despite this considerable effort, most of the results have been obtained under the assumption of constant growth rate, which is far from the experimental condition. We derive here the properties of this distribution for arbitrary growth function, for both the deterministic and stochastic growth of the mutants. The derivation we propose is surprisingly simple and versatile, allowing many generalizations to be taken easily into account.

Stable eusociality via maternal manipulation when resistance is costless
Mauricio González-Forero
doi: http://dx.doi.org/10.1101/019877

In many eusocial species, workers develop or maintain their non-reproductive condition following maternal influence through aggression, differential feeding, or pheromones. This observation has suggested that eusociality may evolve from maternal manipulation where the mother induces offspring to take worker roles against their inclusive fitness interests. If manipulation is executed via aggression or poor feeding, offspring resistance to manipulation could be costly enough to be disfavored, allowing eusociality via manipulation to be evolutionarily stable. However, if manipulation is executed via pheromones, resistance could be less costly, in principle leading to evolutionarily unstable eusociality. Here I show that maternal manipulation can generate evolutionarily stable eusociality even if resistance has no direct costs provided that maternally neglected offspring use help more efficiently than maternally provisioned offspring (e.g., to regain survival). Manipulation temporarily creates ineffectively resisting helpers that allow the mother to reduce maternal care toward helped offspring. If maternally neglected offspring use help more efficiently, maternal care reduction produces offspring that benefit more from the ineffectively resisting helpers. Thus, maternal care reduction increases the average benefit received by helped offspring, bringing Hamilton’s rule to satisfaction and eliminating selection for resistance. Manipulation can then generate stable eusociality under smaller benefit-cost ratios than when manipulation is absent although resistance is costless. These results predict that eusociality where ignoring maternal influence is rather costless is likely to have originated from maternal manipulation if (1) maternally neglected offspring are highly efficient help users and (2) maternally provisioned offspring can only moderately increase their survival by being helped.

Origins of major archaeal clades do not correspond to gene acquisitions from bacteria
Mathieu Groussin, Bastien Boussau, Gergely Szöllősi, Laura Eme, Manolo Gouy, Céline Brochier-Armanet, Vincent Daubin
doi: http://dx.doi.org/10.1101/019851

In a recent article, Nelson-Sathi et al. [NS] report that the origins of Major Archaeal Lineages [MAL] correspond to massive group-specific gene acquisitions via horizontal gene transfer (HGT) from bacteria (Nelson-Sathi et al., 2015, Nature 517(7532):77-80). If correct, this would have fundamental implications for the process of diversification in microbes. However, a re-examination of these data and results shows that the methodology used by NS systematically inflates the number of genes acquired at the root of each MAL, and incorrectly assumes bacterial origins for these genes. A re-analysis of their data with appropriate phylogenetic models accounting for the dynamics of gene gain and loss between lineages supports the continuous acquisition of genes over long periods in the evolution of Archaea.

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

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
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
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
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
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 .