Abstract
BackgroundLow-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Predicting the risk of complex diseases such as Parkinson’s disease (PD) using polygenic risk score (PRS) based on the genetic variations has shown decent prediction accuracy. Although ultra-LPS has been shown to be effective in PRS calculation, array data has been favored to the majority of PRS analysis, especially for PD.ResultsUsing eight high-coverage WGS, we assessed imputation approaches for downsampled LPS data ranging from 0.5 × to 7.0 × . We demonstrated that uncertain genotype calls of LPS diminished imputation accuracy, and an imputation approach using genotype likelihoods was plausible for LPS. Additionally, comparing imputation accuracies between LPS and simulated array illustrated that LPS had higher accuracies particularly at rare frequencies. To evaluate ultra-low coverage data in PRS calculation for PD, we prepared low-coverage WGS and genotype array of 87 PD cases and 101 controls. Genotype imputation of array and downsampled LPS were conducted using a population-specific reference panel, and we calculated risk scores based on the PD-associated SNPs from an East Asian meta-GWAS. The PRS models discriminated cases and controls as previously reported when both LPS and genotype array were used. Also strong correlations in PRS models for PD between LPS and genotype array were discovered.ConclusionsOverall, this study highlights the potentials of LPS under 1.0 × followed by genotype imputation in PRS calculation and suggests LPS as attractive alternatives to genotype array in the area of precision medicine for PD.
Highlights
Low-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness
From 5.0 × LPS, covering rates were increased to the extent of high-depth wholegenome sequencing (WGS)
Consistent with the previous results [16, 24], we discovered that imputed dosages from LPS were relatively more accurate than those from global screening array (GSA) at each allele frequency (AF), at rare AFs
Summary
Low-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Kim et al Hum Genomics (2021) 15:58 using array [5] These cases suggested that LPS followed by genotype imputation is a decent alternative to genotyping arrays [6]. Correlations between genetic factors and this disease still remain unclear due to limited understanding of biological functions of causative variants [11] and complex characteristics of PD including heterogeneity and association with multiple genes and pathways [12]. Most risk-associated variants for PD were identified from the patients of European ancestry, and little is known for other populations including East Asian populations [11]
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