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

Creating Artificial Human Genomes Using Generative Models

biorxi

Vitamin D binding protein isoforms and apolipoprotein E in cerebrospinal fluid as prognostic biomarkers of multiple sclerosis

Multiple sclerosis (MS) is a multifactorial autoimmune disease of the central nervous system with a heterogeneous and unpredictable course. To date there are no prognostic biomarkers even if they would be extremely useful for early patient intervention with personalized therapies. In this context, the analysis of inter-individual differences in cerebrospinal fluid (CSF) proteome may lead to the discovery of biological markers that are able to distinguish the various clinical forms at diagnosis.To this aim, a two dimensional electrophoresis (2-DE) study was carried out on individual CSF samples from 24 untreated women who underwent lumbar puncture (LP) for suspected MS. The patients were clinically monitored for 5 years and then classified according to the degree of disease aggressiveness and the disease-modifying therapies prescribed during follow up.The hierarchical cluster analysis of 2-DE dataset revealed three protein spots which were identified by means of mass spectrometry as Apolipoprotein E (ApoE) and two isoforms of vitamin D binding protein (DBP). These three protein spots enabled us to subdivide the patients into subgroups correlated with clinical classification (MS aggressive forms identification: 80%). In particular, we observed an opposite trend of values for the two protein spots corresponding to different DBP isoforms suggesting a role of a post-translational modification rather than the total protein content in patient categorization.These findings proved to be very interesting and innovative and may be developed as new candidate prognostic biomarkers of MS aggressiveness, if confirmed

Continental-scale genomic analysis suggests shared post-admixture adaptation in the Americas

We thank the people working at the High Performance Computing Center of the University of Tartu for the help and support provided. We thank Marco Rosario Capodiferro for useful discussions. This work was supported by the European Union through the European Regional Development Fund (Project No. 20142020.4.01.16-0030 to LO, MMe, FM; Project No. 2014-2020.4.01.160271 to RF; Project No. 2014-2020.4.01.16-0125 to RF; Project No. 2014-2020.4.01.16-0024 to DM, LP). This work was supported by the Estonian Research Council grant PUT (PRG243) (to RF, MMe, LP). This work was supported by institutional research funding IUT (IUT24-1) of the EstonianMinistry of Education and Research (to TK). This research was supported by the European Union through Horizon 2020 grant no. 810645 (to MMe). This research was supported by the European Union through the Horizon 2020 research and innovation programme under grant no 810645 and through the European Regional Development Fund project no. MOBEC008 to MMo.American populations are one of the most interesting examples of recently admixed groups, where ancestral components from three major continental human groups (Africans, Eurasians and Native Americans) have admixed within the last 15 generations. Recently, several genetic surveys focusing on thousands of individuals shed light on the geography, chronology and relevance of these events. However, even though gene f low could drive adaptive evolution, it is unclear whether and how natural selection acted on the resulting genetic variation in the Americas. In this study, we analysed the patterns of local ancestry of genomic fragments in genome-wide data for ∼6000 admixed individuals from 10 American countries. In doing so, we identified regions characterized by a divergent ancestry profile (DAP), in which a significant over or under ancestral representation is evident. Our results highlighted a series of genomic regions with DAPs associated with immune system response and relevant medical traits, with the longest DAP region encompassing the human leukocyte antigen locus. Furthermore, we found that DAP regions are enriched in genes linked to cancer-related traits and autoimmune diseases. Then, analysing the biological impact of these regions, we showed that natural selection could have acted preferentially towards variants located in coding and non-coding transcripts and characterized by a high deleteriousness score. Taken together, our analyses suggest that shared patterns of post admixture adaptation occurred at a continental scale in the Americas, affecting more often functional and impactful genomic variants.European Commission 2014-2020.4.01.16-0030 2014-2020.4.01.16-0271 2014-2020.4.01.16-0125 2014-2020.4.01.16-0024 MOBEC008Estonian Research Council grant PUT PRG243institutional research funding IUT of the Estonian Ministry of Education and Research IUT24-1European Union through Horizon 2020 grant 81064

Genome-wide identification and characterization of fixed human-specific regulatory regions.

Changes in gene regulatory networks are believed to have played an important role in the development of human-specific anatomy and behavior. We identified the human genome regions that show the typical chromatin marks of regulatory regions but cannot be aligned to other mammalian genomes. Most of these regions have become fixed in the human genome. Their regulatory targets are enriched in genes involved in neural processes, CNS development, and diseases such as autism, depression, and schizophrenia. Specific transposable elements contributing to the rewiring of the human regulatory network can be identified by the creation of human-specific regulatory regions. Our results confirm the relevance of regulatory evolution in the emergence of human traits and cognitive abilities and the importance of newly acquired genomic elements for such evolution

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