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      Sardinians Genetic Background Explained by Runs of Homozygosity and Genomic Regions under Positive Selection

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          Abstract

          The peculiar position of Sardinia in the Mediterranean sea has rendered its population an interesting biogeographical isolate. The aim of this study was to investigate the genetic population structure, as well as to estimate Runs of Homozygosity and regions under positive selection, using about 1.2 million single nucleotide polymorphisms genotyped in 1077 Sardinian individuals. Using four different methods - fixation index, inflation factor, principal component analysis and ancestry estimation - we were able to highlight, as expected for a genetic isolate, the high internal homogeneity of the island. Sardinians showed a higher percentage of genome covered by RoHs>0.5 Mb (F RoH%0.5) when compared to peninsular Italians, with the only exception of the area surrounding Alghero. We furthermore identified 9 genomic regions showing signs of positive selection and, we re-captured many previously inferred signals. Other regions harbor novel candidate genes for positive selection, like TMEM252, or regions containing long non coding RNA. With the present study we confirmed the high genetic homogeneity of Sardinia that may be explained by the shared ancestry combined with the action of evolutionary forces.

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          Most cited references35

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          A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.

          We present a statistical model for patterns of genetic variation in samples of unrelated individuals from natural populations. This model is based on the idea that, over short regions, haplotypes in a population tend to cluster into groups of similar haplotypes. To capture the fact that, because of recombination, this clustering tends to be local in nature, our model allows cluster memberships to change continuously along the chromosome according to a hidden Markov model. This approach is flexible, allowing for both "block-like" patterns of linkage disequilibrium (LD) and gradual decline in LD with distance. The resulting model is also fast and, as a result, is practicable for large data sets (e.g., thousands of individuals typed at hundreds of thousands of markers). We illustrate the utility of the model by applying it to dense single-nucleotide-polymorphism genotype data for the tasks of imputing missing genotypes and estimating haplotypic phase. For imputing missing genotypes, methods based on this model are as accurate or more accurate than existing methods. For haplotype estimation, the point estimates are slightly less accurate than those from the best existing methods (e.g., for unrelated Centre d'Etude du Polymorphisme Humain individuals from the HapMap project, switch error was 0.055 for our method vs. 0.051 for PHASE) but require a small fraction of the computational cost. In addition, we demonstrate that the model accurately reflects uncertainty in its estimates, in that probabilities computed using the model are approximately well calibrated. The methods described in this article are implemented in a software package, fastPHASE, which is available from the Stephens Lab Web site.
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            Estimating and interpreting F ST: The impact of rare variants

            In a pair of seminal papers, Sewall Wright and Gustave Malécot introduced F ST as a measure of structure in natural populations. In the decades that followed, a number of papers provided differing definitions, estimation methods, and interpretations beyond Wright's. While this diversity in methods has enabled many studies in genetics, it has also introduced confusion regarding how to estimate F ST from available data. Considering this confusion, wide variation in published estimates of F ST for pairs of HapMap populations is a cause for concern. These estimates changed—in some cases more than twofold—when comparing estimates from genotyping arrays to those from sequence data. Indeed, changes in F ST from sequencing data might be expected due to population genetic factors affecting rare variants. While rare variants do influence the result, we show that this is largely through differences in estimation methods. Correcting for this yields estimates of F ST that are much more concordant between sequence and genotype data. These differences relate to three specific issues: (1) estimating F ST for a single SNP, (2) combining estimates of F ST across multiple SNPs, and (3) selecting the set of SNPs used in the computation. Changes in each of these aspects of estimation may result in F ST estimates that are highly divergent from one another. Here, we clarify these issues and propose solutions.
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              rehh: an R package to detect footprints of selection in genome-wide SNP data from haplotype structure.

              With the development of next-generation sequencing and genotyping approaches, large single nucleotide polymorphism haplotype datasets are becoming available in a growing number of both model and non-model species. Identifying genomic regions with unexpectedly high local haplotype homozygosity relatively to neutral expectation represents a powerful strategy to ascertain candidate genes responding to natural or artificial selection. To facilitate genome-wide scans of selection based on the analysis of long-range haplotypes, we developed the R package rehh. It provides a versatile tool to detect the footprints of recent or ongoing selection with several graphical functions that help visual interpretation of the results. Stable version is available from CRAN: http://cran.r-project.org/. Development version is available from the R-forge repository: http://r-forge.r-project.org/projects/rehh. Both versions can be installed directly from R. Function documentation and example data files are provided within the package and a tutorial is available as Supplementary Material. rehh is distributed under the GNU General Public Licence (GPL ≥ 2).
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2014
                20 March 2014
                : 9
                : 3
                : e91237
                Affiliations
                [1 ]Department of Medical Sciences, University of Turin, Turin, Italy
                [2 ]HuGeF Human Genetics Foundation, Turin, Italy
                [3 ]Hypertension and Related Diseases Center, AOU, University of Sassari, Sassari, Italy
                [4 ]Department of Health Sciences, University of Milan, Milan, Italy
                [5 ]Filarete Foundation, Genomic and Bioinformatics Unit, Viale Ortles 22/4, Milan, Italy
                Estonian Biocentre and Tartu University, Estonia
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: CDG GM AP. Performed the experiments: CDG SG BP. Analyzed the data: CDG G. Fiorito FR GM. Contributed reagents/materials/analysis tools: MFO DC FF CB CT GA RZ G. Fresu NG AP GM. Wrote the paper: CDG GM AP.

                Article
                PONE-D-13-36867
                10.1371/journal.pone.0091237
                3961211
                24651212
                5b662275-9d8e-4029-bb0c-269b1c7c8b9d
                Copyright @ 2014

                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
                : 9 September 2013
                : 10 February 2014
                Page count
                Pages: 8
                Funding
                The Human Genetic Foundation (HuGeF) and the Compagnia di San Paolo of Turin (to M.G.) were the main supporter of this project. This study used data from the following grants: HYPERGENES project (funded by the Seventh Framework Programme FP7 - HEALTH-F4-2007-201550) and InterOmics an Italian Flagship project (funded b 1 y Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR) and Consiglio Nazionale delle Ricerche (CNR) Project PB.05 (to C.D.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Evolutionary Biology
                Evolutionary Processes
                Natural Selection
                Population Genetics
                Genetic Polymorphism
                Genetics
                Genomics
                Human Genetics
                Population Biology
                Earth Sciences
                Geography
                Human Geography
                Social Sciences

                Uncategorized
                Uncategorized

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