Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations

Authors

Alicia R. Martin, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston
Elizabeth G. Atkinson​, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston
Sinéad B. Chapman, Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge
Anne Stevenson, Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge
Rocky E. Stroud​, Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge
Tamrat Abebe, Department of Microbiology, Immunology, and Parasitology, School of Medicine, College of Health Sciences,Addis Ababa University, Addis Ababa, Ethiopia
Dickens Akena, Department of Psychiatry, School of Medicine, College of Health Sciences, Makerere University, Kampala,Uganda
Melkam Alemayehu, Department of Psychiatry, School of Medicine, College of Health Sciences, Addis Ababa University, AddisAbaba, Ethiopia
Fred K. Ashaba, Department of Immunology & Molecular Biology, College of Health Sciences, Makerere University, Kampala,Uganda
Lukoye Atwoli, Aga Khan UniversityFollow

Document Type

Article

Department

Faculty of Health Sciences, East Africa

Abstract

Background: Genetic studies of biomedical phenotypes in underrepresented populations identify disproportionate numbers of novel associations. However, current genomics infrastructure--including most genotyping arrays and sequenced reference panels--best serves populations of European descent. A critical step for facilitating genetic studies in underrepresented populations is to ensure that genetic technologies accurately capture variation in all populations. Here, we quantify the accuracy of low-coverage sequencing in diverse African populations.

Results: We sequenced the whole genomes of 91 individuals to high-coverage (>20X) from the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study, in which participants were recruited from Ethiopia, Kenya, South Africa, and Uganda. We empirically tested two data generation strategies, GWAS arrays versus low-coverage sequencing, by calculating the concordance of imputed variants from these technologies with those from deep whole genome sequencing data. We show that low-coverage sequencing at a depth of ≥4X captures variants of all frequencies more accurately than all commonly used GWAS arrays investigated and at a comparable cost. Lower depths of sequencing (0.5-1X) performed comparable to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation, with 4X sequencing detecting 45% of singletons and 95% of common variants identified in high-coverage African whole genomes.

Conclusion: These results indicate that low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, including those that capture variation most common in Europeans and Africans. Low-coverage sequencing effectively identifies novel variation (particularly in underrepresented populations), and presents opportunities to enhance variant discovery at a similar cost to traditional approaches.

Comments

This work was published before the author joined Aga Khan University.

Publication (Name of Journal)

bioRxiv

Recommended Citation

Martin, A. R., Atkinson​, E. G., Chapman, S. B., Stevenson, A., Stroud​, R. E., Abebe, T., Akena, D., Alemayehu, M., Ashaba, F. K., Atwoli, L. (2020). Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations. bioRxiv, 1-16.
Available at: https://ecommons.aku.edu/eastafrica_fhs_mc_intern_med/156