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News

 

 

Development of a new cost-effective genome sequencing method at McGill

February 8, 2024 Claire Le Moigne

WEGS experimental design overview.

The McGill CERC in Genomic Medicine is proud to announce the publication of an article in the NPJ Genomic Medicine, entitled “A cost-effective sequencing method for genetic studies combining high-depth whole exome and low-depth whole genome”. This work is a collaboration between the CERC and the the McGill Genome Centre Sequencing and C3G platforms, lead by Claude Bhérer (first author) and Daniel Taliun (last author), both Assistant Professors in the CERC team. They have developed a new sequencing method called WEGS (Whole Exome Genome Sequencing), cheaper than standard Whole genome sequencing. Please find the abstract below:

 

Whole genome sequencing (WGS) at high-depth (30X) allows the accurate discovery of variants in the coding and non-coding DNA regions and helps elucidate the genetic underpinnings of human health and diseases. Yet, due to the prohibitive cost of high-depth WGS, most large-scale genetic association studies use genotyping arrays or high-depth whole exome sequencing (WES). Here we propose a cost-effective method which we call “Whole Exome Genome Sequencing” (WEGS), that combines low-depth WGS and high-depth WES with up to 8 samples pooled and sequenced simultaneously (multiplexed). We experimentally assess the performance of WEGS with four different depth of coverage and sample multiplexing configurations. We show that the optimal WEGS configurations are 1.7–2.0 times cheaper than standard WES (no-plexing), 1.8–2.1 times cheaper than high-depth WGS, reach similar recall and precision rates in detecting coding variants as WES, and capture more population-specific variants in the rest of the genome that are difficult to recover when using genotype imputation methods. We apply WEGS to 862 patients with peripheral artery disease and show that it directly assesses more known disease-associated variants than a typical genotyping array and thousands of non-imputable variants per disease-associated locus.

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