PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations
Abstract Despite the tremendous growth of the DNA sequencing data in the last decade, our understanding of the human genome is still in its infancy. To understand the implications of genetic variants in the light of population genetics and molecular evolution, we developed a database, PGG.SNV (https...
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Format: | Article |
Language: | English |
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BMC
2019-10-01
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Series: | Genome Biology |
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Online Access: | http://link.springer.com/article/10.1186/s13059-019-1838-5 |
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doaj-699a94b35796422197ef0be0060b1857 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chao Zhang Yang Gao Zhilin Ning Yan Lu Xiaoxi Zhang Jiaojiao Liu Bo Xie Zhe Xue Xiaoji Wang Kai Yuan Xueling Ge Yuwen Pan Chang Liu Lei Tian Yuchen Wang Dongsheng Lu Boon-Peng Hoh Shuhua Xu |
spellingShingle |
Chao Zhang Yang Gao Zhilin Ning Yan Lu Xiaoxi Zhang Jiaojiao Liu Bo Xie Zhe Xue Xiaoji Wang Kai Yuan Xueling Ge Yuwen Pan Chang Liu Lei Tian Yuchen Wang Dongsheng Lu Boon-Peng Hoh Shuhua Xu PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations Genome Biology Human diversity Population genetics and genomics Single nucleotide variations Indigenous populations Population prevalence Variant annotation |
author_facet |
Chao Zhang Yang Gao Zhilin Ning Yan Lu Xiaoxi Zhang Jiaojiao Liu Bo Xie Zhe Xue Xiaoji Wang Kai Yuan Xueling Ge Yuwen Pan Chang Liu Lei Tian Yuchen Wang Dongsheng Lu Boon-Peng Hoh Shuhua Xu |
author_sort |
Chao Zhang |
title |
PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations |
title_short |
PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations |
title_full |
PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations |
title_fullStr |
PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations |
title_full_unstemmed |
PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations |
title_sort |
pgg.snv: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populations |
publisher |
BMC |
series |
Genome Biology |
issn |
1474-760X |
publishDate |
2019-10-01 |
description |
Abstract Despite the tremendous growth of the DNA sequencing data in the last decade, our understanding of the human genome is still in its infancy. To understand the implications of genetic variants in the light of population genetics and molecular evolution, we developed a database, PGG.SNV (https://www.pggsnv.org), which gives much higher weight to previously under-investigated indigenous populations in Asia. PGG.SNV archives 265 million SNVs across 220,147 present-day genomes and 1018 ancient genomes, including 1009 newly sequenced genomes, representing 977 global populations. Moreover, estimation of population genetic diversity and evolutionary parameters is available in PGG.SNV, a unique feature compared with other databases. |
topic |
Human diversity Population genetics and genomics Single nucleotide variations Indigenous populations Population prevalence Variant annotation |
url |
http://link.springer.com/article/10.1186/s13059-019-1838-5 |
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doaj-699a94b35796422197ef0be0060b18572020-11-25T03:10:19ZengBMCGenome Biology1474-760X2019-10-0120111610.1186/s13059-019-1838-5PGG.SNV: understanding the evolutionary and medical implications of human single nucleotide variations in diverse populationsChao Zhang0Yang Gao1Zhilin Ning2Yan Lu3Xiaoxi Zhang4Jiaojiao Liu5Bo Xie6Zhe Xue7Xiaoji Wang8Kai Yuan9Xueling Ge10Yuwen Pan11Chang Liu12Lei Tian13Yuchen Wang14Dongsheng Lu15Boon-Peng Hoh16Shuhua Xu17Chinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASChinese Academy of Sciences (CAS) Key Laboratory of Computational Biology, Max Planck Independent Research Group on Population Genomics, CAS-MPG Partner Institute for Computational Biology (PICB), Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, CASAbstract Despite the tremendous growth of the DNA sequencing data in the last decade, our understanding of the human genome is still in its infancy. To understand the implications of genetic variants in the light of population genetics and molecular evolution, we developed a database, PGG.SNV (https://www.pggsnv.org), which gives much higher weight to previously under-investigated indigenous populations in Asia. PGG.SNV archives 265 million SNVs across 220,147 present-day genomes and 1018 ancient genomes, including 1009 newly sequenced genomes, representing 977 global populations. Moreover, estimation of population genetic diversity and evolutionary parameters is available in PGG.SNV, a unique feature compared with other databases.http://link.springer.com/article/10.1186/s13059-019-1838-5Human diversityPopulation genetics and genomicsSingle nucleotide variationsIndigenous populationsPopulation prevalenceVariant annotation |