Abstract

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

Martin AR, Atkinson EG, Chapman SB, Stevenson A, Stroud RE, Abebe T, Akena D, Alemayehu M, Ashaba FK, Atwoli L, Bowers T, Chibnik LB, Daly MJ, DeSmet T, Dodge S, Fekadu A, Ferriera S, Gelaye B, Gichuru S, Injera WE, James R, Kariuki SM, Kigen G, Koenen KC, Kwobah E, Kyebuzibwa J, Majara L, Musinguzi H, Mwema RM, Neale BM, Newman CP, Newton CRJC, Pickrell JK, Ramesar R, Shiferaw W, Stein DJ, Teferra S, van der Merwe C, Zingela Z, Neuro GAP Psychosis Study Team
Am J Hum Genet. 2021;108

Permenent descriptor

Genetic studies in underrepresented populations identify disproportionate numbers of novel associations. However, most genetic studies use genotyping arrays and sequenced reference panels that best capture variation most common in European ancestry populations. To compare data generation strategies best suited for underrepresented populations, we sequenced the whole genomes of 91 individuals to high coverage as part of the Neuropsychiatric Genetics of African Population-Psychosis (NeuroGAP-Psychosis) study with participants from Ethiopia, Kenya, South Africa, and Uganda. We used a downsampling approach to evaluate the quality of two cost-effective 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 comparably to commonly used low-density GWAS arrays. Low-coverage sequencing is also sensitive to novel variation; 4x sequencing detects 45% of singletons and 95% of common variants identified in high-coverage African whole genomes. Low-coverage sequencing approaches surmount the problems induced by the ascertainment of common genotyping arrays, effectively identify novel variation particularly in underrepresented populations, and present opportunities to enhance variant discovery at a cost similar to traditional approaches.