Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores

Abstract

BackgroundInherited susceptibility to common, complex diseases may be caused by rare, pathogenic variants (“monogenic”) or by the cumulative effect of numerous common variants (“polygenic”). Comprehensive genome interpretation should enable assessment for both monogenic and polygenic components of inherited risk. The traditional approach requires two distinct genetic testing technologies—high coverage sequencing of known genes to detect monogenic variants and a genome-wide genotyping array followed by imputation to calculate genome-wide polygenic scores (GPSs). We assessed the feasibility and accuracy of using low coverage whole genome sequencing (lcWGS) as an alternative to genotyping arrays to calculate GPSs.MethodsFirst, we performed downsampling and imputation of WGS data from ten individuals to assess concordance with known genotypes. Second, we assessed the correlation between GPSs for 3 common diseases—coronary artery disease (CAD), breast cancer (BC), and atrial fibrillation (AF)—calculated using lcWGS and genotyping array in 184 samples. Third, we assessed concordance of lcWGS-based genotype calls and GPS calculation in 120 individuals with known genotypes, selected to reflect diverse ancestral backgrounds. Fourth, we assessed the relationship between GPSs calculated using lcWGS and disease phenotypes in a cohort of 11,502 individuals of European ancestry.ResultsWe found imputation accuracy r2 values of greater than 0.90 for all ten samples—including those of African and Ashkenazi Jewish ancestry—with lcWGS data at 0.5×. GPSs calculated using lcWGS and genotyping array followed by imputation in 184 individuals were highly correlated for each of the 3 common diseases (r2 = 0.93–0.97) with similar score distributions. Using lcWGS data from 120 individuals of diverse ancestral backgrounds, we found similar results with respect to imputation accuracy and GPS correlations. Finally, we calculated GPSs for CAD, BC, and AF using lcWGS in 11,502 individuals of European ancestry, confirming odds ratios per standard deviation increment ranging 1.28 to 1.59, consistent with previous studies.ConclusionslcWGS is an alternative technology to genotyping arrays for common genetic variant assessment and GPS calculation. lcWGS provides comparable imputation accuracy while also overcoming the ascertainment bias inherent to variant selection in genotyping array design.

Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays

“…As genome sequencing costs have decreased over the past decade, sequencing-based alternatives to genotyping arrays have been the subject of growing interest (Wetterstrand, 2019). Specifically, low-coverage shotgun whole genome sequencing followed by imputation has been utilized for a number of problems in statistical and population genetics, from providing the backbone for graphbased pangenomes in sorghum to trait mapping in human pharmacogenetics (Tran et al, 2020;Gilly et al, 2019;Rubinacci et al, 2020;Homburger et al, 2019;Wasik et al, 2019;Jensen et al, 2020;Cai et al, 2015a;Liu et al, 2018). As an intuition for why this approach is useful, a sample sequenced at a target coverage of 0.5× is expected to have at least one read on 33 million of the 85 million sites in the 1000 Genomes Phase 3 release, whereas a genotyping array will probe a number of variants which is one to two orders of magnitude fewer, albeit with higher average accuracy (Consortium et al, 2015).…”

“…However, studies in the literature which investigate the applications of low-pass sequencing often do so by means of simulation or by downsampling (often already-aligned) sequence reads from samples previously sequenced at higher coverages (Gilly et al, 2019;Homburger et al, 2019). While useful, these approaches are unable to capture the real-world idiosyncrasies of data generation in extremely-low coverage sequencing and ignore factors such as the use of different library preparation methods which are optimized for sequencing at given target coverages (Aird et al, 2011;Jones et al, 2015).…”

Relative matching using low coverage sequencing

“…One open question is the right moment for consumer genomics to move from array technologies to low coverage sequencing. This manuscript reported that IBD detection can be achieved with 1×LCS and previous work showed that ethnicity estimation (Pasaniuc et al 2012; Li et al 2020; Homburger et al 2019; Rustagi et al 2017) and PRS scores can be calculated with 1×LCS at the same quality of arrays. As such, besides reports based on ultra-rare variants, these lines of study show that 1×LCS covers all main applications in consumer genomics.…”

Color Data v2: a user-friendly, open-access database with hereditary cancer and hereditary cardiovascular conditions datasets

“…Laboratory procedures and imputation for low coverage whole genome sequencing were performed at Color as previously described ( 12 , 13 ). Data from low coverage whole genome sequencing were used to calculate previously published polygenic scores for breast cancer ( 10 ), coronary artery disease ( 9 ) and atrial fibrillation ( 9 ).…”