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

Julian R Homburger; Cynthia L Neben; Gilad Mishne; Alicia Y Zhou; Sekar Kathiresan; Amit V Khera

doi:10.1186/s13073-019-0682-2

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

Genome Med. 2019 Nov 26;11(1):74. doi: 10.1186/s13073-019-0682-2.

Authors

Julian R Homburger¹, Cynthia L Neben¹, Gilad Mishne¹, Alicia Y Zhou¹, Sekar Kathiresan², Amit V Khera^{3

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Affiliations

¹ Color Genomics, 831 Mitten Road, Suite 100, Burlingame, CA, 94010, USA.
² Verve Therapeutics, Cambridge, MA, USA.
³ Center for Genomic Medicine and Cardiology Division, Department of Medicine, Massachusetts General Hospital, Simches Research Building | CPZN 6.256, Boston, MA, 02114, USA. avkhera@mgh.harvard.edu.
⁴ Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA. avkhera@mgh.harvard.edu.
⁵ Harvard Medical School, Boston, MA, 02115, USA. avkhera@mgh.harvard.edu.

Abstract

Background: Inherited 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.

Methods: First, 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.

Results: We 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.

Conclusions: lcWGS 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.

Keywords: Atrial fibrillation; Breast cancer; Coronary artery disease; Genome-wide polygenic score; Low coverage whole genome sequencing.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Genetic Predisposition to Disease
Genetic Variation*
Genetics, Population
Genome, Human*
Genome-Wide Association Study*
Genomics* / methods
Genotype
Humans
Reproducibility of Results
Whole Genome Sequencing