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      Genetic differences among ethnic groups

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      , ,
      BMC Genomics
      BioMed Central

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          Abstract

          Background

          Many differences between different ethnic groups have been observed, such as skin color, eye color, height, susceptibility to some diseases, and response to certain drugs. However, the genetic bases of such differences have been under-investigated. Since the HapMap project, large-scale genotype data from Caucasian, African and Asian population samples have been available. The project found that these populations were located in different areas of the PCA (Principal Component Analysis) plot. However, as an unsupervised method, PCA does not measure the differences in each single nucleotide polymorphism (SNP) among populations.

          Results

          We applied an advanced mutual information-based feature selection method to detect associations between SNP status and ethnic groups using the latest HapMap Phase 3 release version 3, which included more sub-populations. A total of 299 SNPs were identified, and they can accurately predicted the ethnicity of all HapMap populations. The 10-fold cross validation accuracy of the SMO (sequential minimal optimization) model on training dataset was 0.901, and the accuracy on independent test dataset was 0.895.

          Conclusions

          In-depth functional analysis of these SNPs and their nearby genes revealed the genetic bases of skin and eye color differences among populations.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-015-2328-0) contains supplementary material, which is available to authorized users.

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          Most cited references76

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          Data mining in bioinformatics using Weka.

          The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. http://www.cs.waikato.ac.nz/ml/weka.
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            An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people.

            Rare genetic variants contribute to complex disease risk; however, the abundance of rare variants in human populations remains unknown. We explored this spectrum of variation by sequencing 202 genes encoding drug targets in 14,002 individuals. We find rare variants are abundant (1 every 17 bases) and geographically localized, so that even with large sample sizes, rare variant catalogs will be largely incomplete. We used the observed patterns of variation to estimate population growth parameters, the proportion of variants in a given frequency class that are putatively deleterious, and mutation rates for each gene. We conclude that because of rapid population growth and weak purifying selection, human populations harbor an abundance of rare variants, many of which are deleterious and have relevance to understanding disease risk.
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              Variants of the melanocyte-stimulating hormone receptor gene are associated with red hair and fair skin in humans.

              Melanin pigmentation protects the skin from the damaging effects of ultraviolet radiation (UVR). There are two types of melanin, the red phaeomelanin and the black eumelanin, both of which are present in human skin. Eumelanin is photoprotective whereas phaeomelanin, because of its potential to generate free radicals in response to UVR, may contribute to UV-induced skin damage. Individuals with red hair have a predominance of phaeomelain in hair and skin and/or a reduced ability to produce eumelanin, which may explain why they fail to tan and are at risk from UVR. In mammals the relative proportions of phaeomelanin and eumelanin are regulated by melanocyte stimulating hormone (MSH), which acts via its receptor (MC1R), on melanocytes, to increase the synthesis of eumelanin and the product of the agouti locus which antagonises this action. In mice, mutations at either the MC1R gene or agouti affect the pattern of melanogenesis resulting in changes in coat colour. We now report the presence of MC1R gene sequence variants in humans. These were found in over 80% of individuals with red hair and/or fair skin that tans poorly but in fewer than 20% of individuals with brown or black hair and in less than 4% of those who showed a good tanning response. Our findings suggest that in humans, as in other mammals, the MC1R is a control point in the regulation of pigmentation phenotype and, more importantly, that variations in this protein are associated with a poor tanning response.
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                Author and article information

                Contributors
                tohuangtao@126.com
                shuyang1986@gmail.com
                cai_yud@126.com
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                21 December 2015
                21 December 2015
                2015
                : 16
                : 1093
                Affiliations
                [ ]College of Life Science, Shanghai University, Shanghai, 200444 P. R. China
                [ ]Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031 P. R. China
                [ ]Sate Key Laboratory of Biotherapy, Sichuan University, Sichuan, 610041 P. R. China
                Author information
                http://orcid.org/0000-0003-1975-9693
                Article
                2328
                10.1186/s12864-015-2328-0
                4687076
                26690364
                b5256ece-4c37-49c2-8c04-991475fc66fe
                © Huang et al. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 22 May 2015
                : 15 December 2015
                Funding
                Funded by: National Basic Research Program of China
                Award ID: 2011CB510102, 2011CB510101
                Award Recipient :
                Funded by: National Natural Science Foundation of China
                Award ID: 31371335
                Award Recipient :
                Funded by: Innovation Program of Shanghai Municipal Education Commission
                Award ID: 12ZZ087
                Award Recipient :
                Funded by: The First-class Discipline of Universities in Shanghai
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2015

                Genetics
                Genetics

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