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

Toni Boltz, Stanley Center for Psychiatric Research, USA
Benjamin Chu, Stanford University, USA
Calwing Liao, Stanley Center for Psychiatric Research, USA
Julia Sealock, Stanley Center for Psychiatric Research, USA
Robert Ye, Stanley Center for Psychiatric Research, USA
Lerato Majara, Stanley Center for Psychiatric Research, USA
Jack Fu, Program in Medical and Population Genetics, USA
Susan Service, University of California Los Angeles, USA
Lingyu Zhan, University of California Los Angeles, USA
Lukoye Atwoli, Aga Khan University

Abstract

We 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 R² concordance ≥95% among SNPs with MAF≥1%, and never fell below ≥90% R² for SNPs with MAF