Very low-depth whole-genome sequencing in complex trait association studies

Abstract

Motivation Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking. Results We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design. Availability and implementation The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter, the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype. Supplementary information Supplementary data are available at Bioinformatics online.

Clinical use of current polygenic risk scores may exacerbate health disparities

“…The lack of supporting resources for diverse ancestries creates financial challenges for association studies with limited resources, e.g. raising questions about whether to genotype samples on GWAS arrays that may favor European allele frequencies versus sequence samples, and how dense of an array to choose or how deeply to sequence 71,72 .…”

The transferability of lipid loci across African, Asian and European cohorts

“…We employ several strategies which account for these effects and do not require knowledge of the specific causal variants to quantify the extent to which genetic variants affecting lipid biomarkers are shared between individuals from Europe/North America, Asia, and Africa. We assess the transferability of individual signals and compare association patterns across the genome using data from the African Partnership for Chronic Disease Research – Uganda (APCDR-Uganda, N = 6407) 8 , China Kadoorie Biobank (CKB, N = 21,295) 9 , the Hellenic Isolated Cohorts (HELIC-MANOLIS, N = 1641 and HELIC-Pomak, N = 1945) 10,11 , and the UK Household Longitudinal Study (UKHLS, N = 9961) 12 . We also use summary statistics from Biobank Japan (BBJ, N = 162,255) 13 and the Global Lipid Genetics Consortium (European ancestry, GLGC2013 N = 188,577, GLGC2017 N = 237,050) 14,15 .…”

Genome-Wide Association Study Identifies Genomic Loci Associated With Neurotransmitter Concentration in Cattle

“…Although our sample size was smaller for GWAS, the variants with high frequency and large effect have been identified, and further GWAS with a larger sample size will result in the identification of additional variants with low frequency and small effect in future. In addition, our genome coverage was very low (∼5.60×), but a previous study has shown that very low-depth whole-genome sequencing is an efficient alternative to complex trait association studies (Schwartzentruber et al, 2018).…”