A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner
Author:
Boltz Toni AORCID, Chu Benjamin BORCID, Liao CalwingORCID, Sealock Julia MORCID, Ye RobertORCID, Majara LeratoORCID, Fu Jack MORCID, Service Susan, Zhan Lingyu, Medland Sarah EORCID, Chapman Sinéad BORCID, Rubinacci SimoneORCID, DeFelice MatthewORCID, Grimsby Jonna LORCID, Abebe TamratORCID, Alemayehu Melkam, Ashaba Fred K, Atkinson Elizabeth GORCID, Bigdeli TimORCID, Bradway Amanda B, Brand Harrison, Chibnik Lori BORCID, Fekadu Abebaw, Gatzen MichaelORCID, Gelaye BizuORCID, Gichuru Stella, Gildea Marissa L, Hill Toni C, Huang HailiangORCID, Hubbard Kalyn M, Injera Wilfred E.ORCID, James Roxanne, Joloba MosesORCID, Kachulis ChristopherORCID, Kalmbach Phillip R, Kamulegeya RogersORCID, Kigen GabrielORCID, Kim Soyeon, Koen Nastassja, Kwobah Edith K.ORCID, Kyebuzibwa JosephORCID, Lee Seungmo, Lennon Niall JORCID, Lind Penelope AORCID, Lopera-Maya Esteban AORCID, Makale JohnstoneORCID, Mangul SergheiORCID, McMahon JustinORCID, Mowlem Pierre, Musinguzi Henry, Mwema Rehema M., Nakasujja NoelineORCID, Newman Carter PORCID, Nkambule Lethukuthula L, O’Neil Conor R, Olivares Ana MariaORCID, Olsen Catherine M., Ongeri LinnetORCID, Parsa Sophie J, Pretorius Adele, Ramesar Raj, Reagan Faye L, Sabatti ChiaraORCID, Schneider Jacquelyn A, Shiferaw WeleltaORCID, Stevenson AnneORCID, Stricker ErikORCID, Stroud Rocky E.ORCID, Tang Jessie, Whiteman DavidORCID, Yohannes Mary T, Yu Mingrui, Yuan KaiORCID, , Akena DickensORCID, Atwoli LukoyeORCID, Kariuki Symon M.ORCID, Koenen Karestan C.ORCID, Newton Charles R. J. C.ORCID, Stein Dan J.ORCID, Teferra SolomonORCID, Zingela ZukiswaORCID, Pato Carlos N, Pato Michele TORCID, Lopez-Jaramillo CarlosORCID, Freimer Nelson, Ophoff Roel AORCID, Olde Loohuis Loes MORCID, Talkowski Michael EORCID, Neale Benjamin MORCID, Howrigan Daniel PORCID, Martin Alicia RORCID
Abstract
AbstractWe deployed the Blended Genome Exome (BGE), a DNA library blending approach that generates low pass whole genome (1-4x mean depth) and deep whole exome (30-40x mean depth) data in a single sequencing run. This technology is cost-effective, empowers most genomic discoveries possible with deep whole genome sequencing, and provides an unbiased method to capture the diversity of common SNP variation across the globe. To evaluate this new technology at scale, we applied BGE to sequence >53,000 samples from the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which included participants across African, African American, and Latin American populations. We evaluated the accuracy of BGE imputed genotypes against raw genotype calls from the Illumina Global Screening Array. All PUMAS cohorts had R2concordance ≥95% among SNPs with MAF≥1%, and never fell below ≥90% R2for SNPs with MAF<1%. Furthermore, concordance rates among local ancestries within two recently admixed cohorts were consistent among SNPs with MAF≥1%, with only minor deviations in SNPs with MAF<1%. We also benchmarked the discovery capacity of BGE to access protein-coding copy number variants (CNVs) against deep whole genome data, finding that deletions and duplications spanning at least 3 exons had a positive predicted value of ∼90%. Our results demonstrate BGE scalability and efficacy in capturing SNPs, indels, and CNVs in the human genome at 28% of the cost of deep whole-genome sequencing. BGE is poised to enhance access to genomic testing and empower genomic discoveries, particularly in underrepresented populations.
Publisher
Cold Spring Harbor Laboratory
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