AGIDB: a versatile database for genotype imputation and variant decoding across species

文献类型： 外文期刊

第一作者： Zhang, Kaili

作者： Zhang, Kaili;Liang, Jiete;Fu, Yuhua;Chu, Jinyu;Fu, Liangliang;Wang, Yongfei;Li, Wangjiao;Zhou, You;Li, Jinhua;Yin, Xiaoxiao;Wang, Haiyan;Liu, Xiaolei;Yu, Mei;Zhao, Shuhong;Li, Xinyun;Ma, Yunlong;Zhang, Kaili;Liang, Jiete;Fu, Yuhua;Chu, Jinyu;Fu, Liangliang;Wang, Yongfei;Li, Wangjiao;Zhou, You;Li, Jinhua;Yin, Xiaoxiao;Wang, Haiyan;Liu, Xiaolei;Yu, Mei;Zhao, Shuhong;Li, Xinyun;Ma, Yunlong;Fu, Liangliang;Liu, Xiaolei;Yu, Mei;Zhao, Shuhong;Li, Xinyun;Fu, Liangliang;Wang, Haiyan;Mou, Chunyan;Wang, Chonglong;Wang, Heng;Dong, Xinxing;Yan, Dawei;Zhao, Shuhong;Ma, Yunlong

作者机构：

期刊名称：NUCLEIC ACIDS RESEARCH （ 影响因子：14.9； 五年影响因子：16.4 ）

ISSN： 0305-1048

年卷期： 2023 年

页码：

收录情况： SCI

摘要： The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers. Graphical Abstract

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