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      Signatures of Environmental Genetic Adaptation Pinpoint Pathogens as the Main Selective Pressure through Human Evolution

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          Abstract

          Previous genome-wide scans of positive natural selection in humans have identified a number of non-neutrally evolving genes that play important roles in skin pigmentation, metabolism, or immune function. Recent studies have also shown that a genome-wide pattern of local adaptation can be detected by identifying correlations between patterns of allele frequencies and environmental variables. Despite these observations, the degree to which natural selection is primarily driven by adaptation to local environments, and the role of pathogens or other ecological factors as selective agents, is still under debate. To address this issue, we correlated the spatial allele frequency distribution of a large sample of SNPs from 55 distinct human populations to a set of environmental factors that describe local geographical features such as climate, diet regimes, and pathogen loads. In concordance with previous studies, we detected a significant enrichment of genic SNPs, and particularly non-synonymous SNPs associated with local adaptation. Furthermore, we show that the diversity of the local pathogenic environment is the predominant driver of local adaptation, and that climate, at least as measured here, only plays a relatively minor role. While background demography by far makes the strongest contribution in explaining the genetic variance among populations, we detected about 100 genes which show an unexpectedly strong correlation between allele frequencies and pathogenic environment, after correcting for demography. Conversely, for diet regimes and climatic conditions, no genes show a similar correlation between the environmental factor and allele frequencies. This result is validated using low-coverage sequencing data for multiple populations. Among the loci targeted by pathogen-driven selection, we found an enrichment of genes associated to autoimmune diseases, such as celiac disease, type 1 diabetes, and multiples sclerosis, which lends credence to the hypothesis that some susceptibility alleles for autoimmune diseases may be maintained in human population due to past selective processes.

          Author Summary

          Adaptation to local environments is one of the most important factors shaping human genetic variation among different geographically distributed populations. Here we develop a statistical framework aimed at identifying signals of genetic adaptation. We correlate the spatial distribution of allele frequencies of a large sample of SNPs, genotyped in more than 50 populations distributed worldwide, to a set of environmental factors, describing local geographical features such as climate conditions, diet regimes, and pathogens load. Our results show an excess of putative functional variants for high levels of population differentiation, measured by the degree to which genetic variation correlates with a set of environmental variables. We demonstrate that selection on pathogens is the primary driver of local adaptation and affects the distribution of genetic variation at a large number of genes. Among the selected genes, we also identify an excess of genes associated with autoimmune diseases, such as celiac disease, type 1 diabetes, and multiples sclerosis.

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          Most cited references59

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          Genomic scans for selective sweeps using SNP data.

          Detecting selective sweeps from genomic SNP data is complicated by the intricate ascertainment schemes used to discover SNPs, and by the confounding influence of the underlying complex demographics and varying mutation and recombination rates. Current methods for detecting selective sweeps have little or no robustness to the demographic assumptions and varying recombination rates, and provide no method for correcting for ascertainment biases. Here, we present several new tests aimed at detecting selective sweeps from genomic SNP data. Using extensive simulations, we show that a new parametric test, based on composite likelihood, has a high power to detect selective sweeps and is surprisingly robust to assumptions regarding recombination rates and demography (i.e., has low Type I error). Our new test also provides estimates of the location of the selective sweep(s) and the magnitude of the selection coefficient. To illustrate the method, we apply our approach to data from the Seattle SNP project and to Chromosome 2 data from the HapMap project. In Chromosome 2, the most extreme signal is found in the lactase gene, which previously has been shown to be undergoing positive selection. Evidence for selective sweeps is also found in many other regions, including genes known to be associated with disease risk such as DPP10 and COL4A3.
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            A high-resolution recombination map of the human genome.

            Determination of recombination rates across the human genome has been constrained by the limited resolution and accuracy of existing genetic maps and the draft genome sequence. We have genotyped 5,136 microsatellite markers for 146 families, with a total of 1,257 meiotic events, to build a high-resolution genetic map meant to: (i) improve the genetic order of polymorphic markers; (ii) improve the precision of estimates of genetic distances; (iii) correct portions of the sequence assembly and SNP map of the human genome; and (iv) build a map of recombination rates. Recombination rates are significantly correlated with both cytogenetic structures (staining intensity of G bands) and sequence (GC content, CpG motifs and poly(A)/poly(T) stretches). Maternal and paternal chromosomes show many differences in locations of recombination maxima. We detected systematic differences in recombination rates between mothers and between gametes from the same mother, suggesting that there is some underlying component determined by both genetic and environmental factors that affects maternal recombination rates.
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              The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation.

              There has long been interest in understanding the genetic basis of human adaptation. To what extent are phenotypic differences among human populations driven by natural selection? With the recent arrival of large genome-wide data sets on human variation, there is now unprecedented opportunity for progress on this type of question. Several lines of evidence argue for an important role of positive selection in shaping human variation and differences among populations. These include studies of comparative morphology and physiology, as well as population genetic studies of candidate loci and genome-wide data. However, the data also suggest that it is unusual for strong selection to drive new mutations rapidly to fixation in particular populations (the 'hard sweep' model). We argue, instead, for alternatives to the hard sweep model: in particular, polygenic adaptation could allow rapid adaptation while not producing classical signatures of selective sweeps. We close by discussing some of the likely opportunities for progress in the field. Copyright 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                November 2011
                November 2011
                3 November 2011
                : 7
                : 11
                : e1002355
                Affiliations
                [1 ]Scientific Institute IRCCS E. Medea, Bioinformatic Lab, Bosisio Parini, Italy
                [2 ]Bioengineering Department, Politecnico di Milano, Milan, Italy
                [3 ]Departments of Integrative Biology and Statistics, University of California Berkeley, Berkeley, California, United States of America
                University of Washington, United States of America
                Author notes

                Conceived and designed the experiments: MF MS LP RN. Performed the experiments: MF AF-A. Analyzed the data: MF MS UP AF-A LP RN. Wrote the paper: MF MS AF-A LP RN.

                Article
                PGENETICS-D-11-00857
                10.1371/journal.pgen.1002355
                3207877
                22072984
                1e06b2fc-90b5-4507-a95d-6e7951135d55
                Fumagalli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 27 April 2011
                : 8 September 2011
                Page count
                Pages: 14
                Categories
                Research Article
                Biology
                Computational Biology
                Population Genetics
                Natural Selection
                Evolutionary Biology
                Evolutionary Processes
                Adaptation
                Population Genetics
                Microbiology
                Host-Pathogen Interaction
                Medicine
                Clinical Immunology
                Autoimmune Diseases

                Genetics
                Genetics

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