Haplostrips: revealing population structure through haplotype visualization

Summary Population genetic analyses often identify polymorphic variants in regions of the genome that indicate the effect of non‐neutral evolutionary processes. However, in order to obtain deeper insights into the evolutionary processes at play, we often resort to summary statistics, sacrificing the information encoded in the complexity of the original data. Here, we present haplostrips, a tool to visualize polymorphisms of a given region of the genome in the form of independently clustered and sorted haplotypes. Haplostrips is a command‐line tool written in Python and R, that uses variant call format files as input and generates a heatmap view. Haplostrips is available at: https://bitbucket.org/dmarnetto/haplostrips. It can be applied in several fields and in all living systems for which a phased haplotype is available to visualize complex effects of, among others: introgression, domestication, selection, demographic events. Haplostrips can reveal hidden patterns of genetic variation without losing the basic information encoded in variant sequences

Am I Too Fat? Bulimia as an Epidemic

26 pages, 1 article*Am I Too Fat? Bulimia as an Epidemic* (Gonzalez, Beverly; Huerta-Sanchez, Emilia; Ortiz-Nieves, Angela; Vazquez-Alvarez, Terannie; Kribs-Zaleta, Christopher) 26 page

On the relevance of thrombomodulin variants in atypical hemolytic uremic syndrome

5 p.-1 fig.-1 tab.This project was funded by the Instituto de Salud Carlos III: REDinREN (RD016/009/009) and Instituto de Investigacion Puerta de Hierro-Segovia Arana (IDIPHISA) to AH and by grants from the Spanish Ministerio de Economía y Competitividad–FEDER (European Regional Development Fund) (PID2019-104912RB-I00) and the Autonomous Region of Madrid (S2017/BMD-3673 and S2022/BMD-7278) to SRdC. TC was supported by grant from National Health Institute Carlos III (RETIC ISCIII RD21/0005. RICORS),Peer reviewe

Natural Selection Affects Multiple Aspects of Genetic Variation at Putatively Neutral Sites across the Human Genome

A major question in evolutionary biology is how natural selection has shaped patterns of genetic variation across the human genome. Previous work has documented a reduction in genetic diversity in regions of the genome with low recombination rates. However, it is unclear whether other summaries of genetic variation, like allele frequencies, are also correlated with recombination rate and whether these correlations can be explained solely by negative selection against deleterious mutations or whether positive selection acting on favorable alleles is also required. Here we attempt to address these questions by analyzing three different genome-wide resequencing datasets from European individuals. We document several significant correlations between different genomic features. In particular, we find that average minor allele frequency and diversity are reduced in regions of low recombination and that human diversity, human-chimp divergence, and average minor allele frequency are reduced near genes. Population genetic simulations show that either positive natural selection acting on favorable mutations or negative natural selection acting against deleterious mutations can explain these correlations. However, models with strong positive selection on nonsynonymous mutations and little negative selection predict a stronger negative correlation between neutral diversity and nonsynonymous divergence than observed in the actual data, supporting the importance of negative, rather than positive, selection throughout the genome. Further, we show that the widespread presence of weakly deleterious alleles, rather than a small number of strongly positively selected mutations, is responsible for the correlation between neutral genetic diversity and recombination rate. This work suggests that natural selection has affected multiple aspects of linked neutral variation throughout the human genome and that positive selection is not required to explain these observations

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Leveraging Multiple Populations across Time Helps Define Accurate Models of Human Evolution: A Reanalysis of the Lactase Persistence Adaptation

Access to a geographically diverse set of modern human samples from the present time and from ancient remains, combined with archaic hominin samples, provides an unprecedented level of resolution to study both human history and adaptation. The amount and quality of ancient human data continue to improve and enable tracking the trajectory of genetic variation over time. These data have the potential to help us redefijine or generate new hypotheses of how human evolution occurred and to revise previous conjectures. In this article, we argue that leveraging all these data will help us better detail adaptive histories in humans. As a case in point, we focus on one of the most celebrated examples of human adaptation: the evolution of lactase persistence. We briefly review this dietary adaptation and argue that, effectively, the evolutionary history of lactase persistence is still not fully resolved. We propose that, by leveraging data from multiple populations across time and space, we will find evidence of a more nuanced history than just a simple selective sweep. We support our hypotheses with simulation results and make some cautionary notes regarding the use of haplotype-based summary statistics to estimate evolutionary parameters

Distinguishing between Selective Sweeps from Standing Variation and from a <em>De Novo</em> Mutation

<div><p>An outstanding question in human genetics has been the degree to which adaptation occurs from standing genetic variation or from <em>de novo</em> mutations. Here, we combine several common statistics used to detect selection in an Approximate Bayesian Computation (ABC) framework, with the goal of discriminating between models of selection and providing estimates of the age of selected alleles and the selection coefficients acting on them. We use simulations to assess the power and accuracy of our method and apply it to seven of the strongest sweeps currently known in humans. We identify two genes, ASPM and PSCA, that are most likely affected by selection on standing variation; and we find three genes, ADH1B, LCT, and EDAR, in which the adaptive alleles seem to have swept from a new mutation. We also confirm evidence of selection for one further gene, TRPV6. In one gene, G6PD, neither neutral models nor models of selective sweeps fit the data, presumably because this locus has been subject to balancing selection.</p> </div