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

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.