High-quality genome databases derived from large-scale family-based birth cohorts are essential resources for investigating genetic determinants affecting early-life traits and the impact of early-life environments on the health of both parents and offspring. Here, we have established the genome database of Born in Guangzhou Cohort Study (BIGCS), name as GDBIG, which represents the first birth-cohort-based genomic database in China and is designed to facilitate generational genetic research, based on the Phase I results of BIGCS, that contains the low-coverage (~6.63x) whole-genome sequencing (WGS) data and numerous pregnancy phenotypes of 4,053 Chinese participants. The participants were from 30 out of 34 administrative divisions of China, covering Han and 12 minority ethnic groups. Currently, GDBIG provides a comprehensive range of services, including allele frequency inquiries for 56.23 million variants across two generations, a genotype imputation server featuring a high-quality family-based reference panel, and a GWAS meta-analysis interface for various maternal and infant phenotypes. The GDBIG database addresses the dearth of Asian birth-cohort-based genomic resources and provides a valuable platform for conducting genetic analysis online or through application programming interfaces at http://gdbig.bigcs.com.cn/.

The authors have declared no competing interest.

This work was supported by the National Key R&D Program of China (Grant No. 2024YFC2706801); the National Natural Science Foundation of China (Grant No. 32470679, 81673181, 82173525, and 31900487); the Department of Science and Technology of Guangdong Province Research Foundation (Grant No. 32470679, 2022B1515120080, 2022B1212010004, 2020A1515110859, 2020B1111170001, 2019B020227001, and 2019B030301004); Natural Science Foundation of Guangdong Province (Grant No. 2022B1515120080).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Ethics Committee of Guangzhou Women and Children's Medical Center gave ethical approval for this work (no. 2012[015] and 2017102302).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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The raw sequencing data in this work has been deposited at the National Genomics Data Center (NGDC; https://ngdc.cncb.ac.cn), China National Center for Bioinformation/ Beijing Institute of Genomics, Chinese Academy of Sciences, with accession number: HRA002496. Data can be accessed via applications, following the GSA guide (https://ngdc.cncb.ac.cn/gsa-human/document). All of the GWAS summary statistics data in this GDBIG platform has been deposited in the Genome Variation Map (GVM) in NGDC, under accession number GVP000003 (https://ngdc.cncb.ac.cn/gvm/getProjectDetail?project=GVP000003). The user can contact the corresponding author to apply for permission to access this data. In addition, the website of Genome Database of Born in Guangzhou Cohort Study (GDBIG) is freely available at http://gdbig.bigcs.com.cn/. Researchers can be accessed through an interactive API, which can be found at http://gdbig.bigcs.com.cn/api.html. Use tutorials are provided on the same webpage to assist users in navigating the platform effectively. The code for extracting information from GDBIG is available on the Github repository(https://github.com/BIGCS-Lab/GDBIGtools) and BioCode project at NGDC (https://ngdc.cncb.ac.cn/biocode/tool/7586). Variant dataset and the GWAS summary statistics for the 14 traits can also be accessed through the GDBIG server (http://gdbig.bigcs.com.cn/), with approval from the Human Genetic Resources Administration of China (HGRAC) (ID: 2022BAT1051). Researchers who are interesting in collaborating with the BIGCS group are encouraged to contact Xiu Qiu (xiu.qiu@bigcs.org) and data.bigcs@bigcs.org.

https://ngdc.cncb.ac.cn/gvm/getProjectDetail?project=GVP000003