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Evaluation of low-pass genome sequencing in polygenic risk score calculation for Parkinsons disease

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dc.contributor.authorKim, Sungjae-
dc.contributor.authorShin, Jong-Yeon-
dc.contributor.authorKwon, Nak-Jung-
dc.contributor.authorKim, Chang-Uk-
dc.contributor.authorKim, Changhoon-
dc.contributor.authorLee, Chong Sik-
dc.contributor.authorSeo, Jeong-Sun-
dc.date.accessioned2021-09-02T06:14:51Z-
dc.date.available2021-09-02T15:18:36Z-
dc.date.issued2021-08-28-
dc.identifier.citationHuman Genomics. 2021 Aug 28;15(1):58ko_KR
dc.identifier.issn1479-7364-
dc.identifier.urihttps://hdl.handle.net/10371/174862-
dc.description.abstractBackground
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. Predicting the risk of complex diseases such as Parkinsons 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.

Results
Using 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.

Conclusions
Overall, 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.
ko_KR
dc.description.sponsorshipThis work has been supported by Macrogen Inc. (Grant No. MGR20-01).ko_KR
dc.language.isoenko_KR
dc.publisherBMCko_KR
dc.titleEvaluation of low-pass genome sequencing in polygenic risk score calculation for Parkinsons diseaseko_KR
dc.typeArticleko_KR
dc.contributor.AlternativeAuthor김성재-
dc.contributor.AlternativeAuthor신종연-
dc.contributor.AlternativeAuthor권낙정-
dc.contributor.AlternativeAuthor김창욱-
dc.contributor.AlternativeAuthor김창훈-
dc.contributor.AlternativeAuthor이종식-
dc.contributor.AlternativeAuthor서정선-
dc.identifier.doi10.1186/s40246-021-00357-w-
dc.citation.journaltitleHuman Genomicsko_KR
dc.language.rfc3066en-
dc.rights.holderThe Author(s)-
dc.date.updated2021-08-29T03:13:08Z-
dc.citation.number1ko_KR
dc.citation.startpage58ko_KR
dc.citation.volume15ko_KR
Appears in Collections:
College of Medicine/School of Medicine (의과대학/대학원)Dept. of Biomedical Sciences (대학원 의과학과)Journal Papers (저널논문_의과학과)
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