A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner

Toni Boltz; Benjamin B. Chu; Calwing Liao; Julia Sealock; Robert Ye; Lerato Majara; Jack Fu; Susan K. Service; Lingyu Zhan; Sarah E. Medland; Sinéad B. Chapman; Simone Rubinacci; Matthew DeFelice; Jonna Grimsby; Tamrat Abebe; Melkam Alemayehu; Fred K. Ashaba; Elizabeth G. Atkinson; Tim B. Bigdeli; Amanda B Bradway; Harrison Brand; Lori B. Chibnik; Abebaw Fekadu; Michael Gatzen; Bizu Gelaye; Stella Gichuru; M. Gildea; Toni C Hill; Hailiang Huang; Kalyn M Hubbard; Wilfred Emonyi; Roxanne James; Moses Joloba; Christopher Kachulis; Phillip R Kalmbach; Rogers Kamulegeya; Gabriel Kigen; Soyeon Kim; Nastassja Koen; Edith Kwobah; Joseph Kyebuzibwa; Seungmo Lee; Niall J. Lennon; Penelope A. Lind; Esteban A. Lopera-Maya; Johnstone Makale; Serghei Mangul; Justin McMahon; Pierre Mowlem; Henry Musinguzi; Rehema M. Mwema; Noeline Nakasujja; Carter P. Newman; Lethukuthula L. Nkambule; Conor R O'Neil; Ana Maria Olivares; Catherine M. Olsen; Linnet Ongeri; Sophie Parsa; Adele Pretorius; Raj Ramesar; Faye L. Reagan; Chiara Sabatti; Jacquelyn A Schneider; Welelta Shiferaw; Anne Stevenson; Erik Stricker; Rocky E. Stroud; Junling Tang; David C. Whiteman; Mary T. Yohannes; Mingrui Yu; Kai Yuan; Dickens Akena; Lukoye Atwoli; Symon M. Kariuki; Karestan C. Koenen; Charles R. Newton; Dan Joseph Stein; Solomon Teferra; Zukiswa Zingela; Carlos N. Pato; Michele T. Pato; Carlos López‐Jaramillo; Nelson B. Freimer; Roel A. Ophoff; Loes M. Olde Loohuis; Michael E. Talkowski; Benjamin M. Neale; Daniel P. Howrigan; Alicia R. Martin

doi:10.1101/2024.09.06.611689

Dan Joseph Stein

Institution not specified

Abstract

We deployed the Blended Genome Exome (BGE), a DNA library blending approach that generates low pass whole genome (1-4× mean depth) and deep whole exome (30-40× 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 R 2 concordance ≥95% among SNPs with MAF≥1%, and never fell below ≥90% R 2 for 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.

How to cite this publication

Toni Boltz, Benjamin B. Chu, Calwing Liao, Julia Sealock, Robert Ye, Lerato Majara, Jack Fu, Susan K. Service, Lingyu Zhan, Sarah E. Medland, Sinéad B. Chapman, Simone Rubinacci, Matthew DeFelice, Jonna Grimsby, Tamrat Abebe, Melkam Alemayehu, Fred K. Ashaba, Elizabeth G. Atkinson, Tim B. Bigdeli, Amanda B Bradway, Harrison Brand, Lori B. Chibnik, Abebaw Fekadu, Michael Gatzen, Bizu Gelaye, Stella Gichuru, M. Gildea, Toni C Hill, Hailiang Huang, Kalyn M Hubbard, Wilfred Emonyi, Roxanne James, Moses Joloba, Christopher Kachulis, Phillip R Kalmbach, Rogers Kamulegeya, Gabriel Kigen, Soyeon Kim, Nastassja Koen, Edith Kwobah, Joseph Kyebuzibwa, Seungmo Lee, Niall J. Lennon, Penelope A. Lind, Esteban A. Lopera-Maya, Johnstone Makale, Serghei Mangul, Justin McMahon, Pierre Mowlem, Henry Musinguzi, Rehema M. Mwema, Noeline Nakasujja, Carter P. Newman, Lethukuthula L. Nkambule, Conor R O'Neil, Ana Maria Olivares, Catherine M. Olsen, Linnet Ongeri, Sophie Parsa, Adele Pretorius, Raj Ramesar, Faye L. Reagan, Chiara Sabatti, Jacquelyn A Schneider, Welelta Shiferaw, Anne Stevenson, Erik Stricker, Rocky E. Stroud, Junling Tang, David C. Whiteman, Mary T. Yohannes, Mingrui Yu, Kai Yuan, Dickens Akena, Lukoye Atwoli, Symon M. Kariuki, Karestan C. Koenen, Charles R. Newton, Dan Joseph Stein, Solomon Teferra, Zukiswa Zingela, Carlos N. Pato, Michele T. Pato, Carlos López‐Jaramillo, Nelson B. Freimer, Roel A. Ophoff, Loes M. Olde Loohuis, Michael E. Talkowski, Benjamin M. Neale, Daniel P. Howrigan, Alicia R. Martin (2024). A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner. , DOI: https://doi.org/10.1101/2024.09.06.611689.

A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner

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A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner

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Dan Joseph Stein

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