Dataset Information

HostSeq: a Canadian whole genome sequencing and clinical data resource.

ABSTRACT: HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community.

SUBMITTER: Yoo S

PROVIDER: S-EPMC10152008 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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HostSeq: a Canadian whole genome sequencing and clinical data resource.

Yoo S S Garg E E Elliott L T LT Hung R J RJ Halevy A R AR Brooks J D JD Bull S B SB Gagnon F F Greenwood Cmt C Lawless J F JF Paterson A D AD Sun L L Zawati M H MH Lerner-Ellis J J Abraham Rjs R Birol I I Bourque G G Garant J-M JM Gosselin C C Li J J Whitney J J Thiruvahindrapuram B B Herbrick J-A JA Lorenti M M Reuter M S MS Adeoye O O OO Liu S S Allen U U Bernier F P FP Biggs C M CM Cheung A M AM Cowan J J Herridge M M Maslove D M DM Modi B P BP Mooser V V Morris S K SK Ostrowski M M Parekh R S RS Pfeffer G G Suchowersky O O Taher J J Upton J J Warren R L RL Yeung Rsm R Aziz N N Turvey S E SE Knoppers B M BM Lathrop M M Jones Sjm S Scherer S W SW Strug L J LJ

BMC genomic data 20230502 1

HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration a ...[more]

PMID: 37131148

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Project description:Background In large-scale high-throughput sequencing projects and biobank construction, sample tagging is essential to prevent sample mix-ups. Despite the availability of fingerprint panels for DNA data, little research has been conducted on sample tagging of whole genome bisulfite sequencing (WGBS) data. This study aims to construct a pipeline and identify applicable fingerprint panels to address this problem. Results Using autosome-wide A/T polymorphic single nucleotide variants (SNVs) obtained from whole genome sequencing (WGS) and WGBS of individuals from the Third China National Stroke Registry, we designed a fingerprint panel and constructed an optimized pipeline for tagging WGBS data. This pipeline used Bis-SNP to call genotypes from the WGBS data, and optimized genotype comparison by eliminating wildtype homozygous and missing genotypes, and retaining variants with identical genomic coordinates and reference/alternative alleles. WGS-based and WGBS-based genotypes called from identical or different samples were extensively compared using hap.py. In the first batch of 94 samples, the genotype consistency rates were between 71.01%-84.23% and 51.43%-60.50% for the matched and mismatched WGS and WGBS data using the autosome-wide A/T polymorphic SNV panel. This capability to tag WGBS data was validated among the second batch of 240 samples, with genotype consistency rates ranging from 70.61%-84.65% to 49.58%-61.42% for the matched and mismatched data, respectively. We also determined that the number of genetic variants required to correctly tag WGBS data was on the order of thousands through testing six fingerprint panels with different orders for the number of variants. Additionally, we affirmed this result with two self-designed panels of 1351 and 1278 SNVs, respectively. Furthermore, this study confirmed that using the number of genetic variants with identical coordinates and ref/alt alleles, or identical genotypes could not correctly tag WGBS data. Conclusion This study proposed an optimized pipeline, applicable fingerprint panels, and a lower boundary for the number of fingerprint genetic variants needed for correct sample tagging of WGBS data, which are valuable for tagging WGBS data and integrating multi-omics data for biobanks. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-023-09413-2.

Dataset Information

HostSeq: a Canadian whole genome sequencing and clinical data resource.

Publications

HostSeq: a Canadian whole genome sequencing and clinical data resource.

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