Academic Journal
New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits
| Τίτλος: | New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits |
|---|---|
| Συγγραφείς: | Kim, Kipoong, Jun, Tae-Hwan, Ha, Bo-Keun, Wang, Shuang, Sun, Hokeun |
| Έτος έκδοσης: | 2023 |
| Συλλογή: | Columbia University: Academic Commons |
| Θεματικοί όροι: | Genetics--Statistical methods, Phenotype, Human genetics--Variation, Medical genetics, National Human Genome Research Institute (U.S.) |
| Περιγραφή: | Background Identification of pleiotropic variants associated with multiple phenotypic traits has received increasing attention in genetic association studies. Overlapping genetic associations from multiple traits help to detect weak genetic associations missed by single-trait analyses. Many statistical methods were developed to identify pleiotropic variants with most of them being limited to quantitative traits when pleiotropic effects on both quantitative and qualitative traits have been observed. This is a statistically challenging problem because there does not exist an appropriate multivariate distribution to model both quantitative and qualitative data together. Alternatively, meta-analysis methods can be applied, which basically integrate summary statistics of individual variants associated with either a quantitative or a qualitative trait without accounting for correlations among genetic variants. Results We propose a new statistical selection method based on a unified selection score quantifying how a genetic variant, i.e., a pleiotropic variant associates with both quantitative and qualitative traits. In our extensive simulation studies where various types of pleiotropic effects on both quantitative and qualitative traits were considered, we demonstrated that the proposed method outperforms the existing meta-analysis methods in terms of true positive selection. We also applied the proposed method to a peanut dataset with 6 quantitative and 2 qualitative traits, and a cowpea dataset with 2 quantitative and 6 qualitative traits. We were able to detect some potentially pleiotropic variants missed by the existing methods in both analyses. Conclusions The proposed method is able to locate pleiotropic variants associated with both quantitative and qualitative traits. It has been implemented into an R package ‘UNISS’, which can be downloaded from http://github.com/statpng/uniss. |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| DOI: | 10.7916/v12v-2w65 |
| Διαθεσιμότητα: | https://doi.org/10.7916/v12v-2w65 |
| Αριθμός Καταχώρησης: | edsbas.A5DB50F3 |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://doi.org/10.7916/v12v-2w65# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kim%2C+Kipoong%22">Kim, Kipoong</searchLink><br /><searchLink fieldCode="AR" term="%22Jun%2C+Tae-Hwan%22">Jun, Tae-Hwan</searchLink><br /><searchLink fieldCode="AR" term="%22Ha%2C+Bo-Keun%22">Ha, Bo-Keun</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Shuang%22">Wang, Shuang</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Hokeun%22">Sun, Hokeun</searchLink> – Name: DatePubCY Label: Publication Year Group: Date Data: 2023 – Name: Subset Label: Collection Group: HoldingsInfo Data: Columbia University: Academic Commons – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Genetics--Statistical+methods%22">Genetics--Statistical methods</searchLink><br /><searchLink fieldCode="DE" term="%22Phenotype%22">Phenotype</searchLink><br /><searchLink fieldCode="DE" term="%22Human+genetics--Variation%22">Human genetics--Variation</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+genetics%22">Medical genetics</searchLink><br /><searchLink fieldCode="DE" term="%22National+Human+Genome+Research+Institute+%28U%2ES%2E%29%22">National Human Genome Research Institute (U.S.)</searchLink> – Name: Abstract Label: Description Group: Ab Data: Background Identification of pleiotropic variants associated with multiple phenotypic traits has received increasing attention in genetic association studies. Overlapping genetic associations from multiple traits help to detect weak genetic associations missed by single-trait analyses. Many statistical methods were developed to identify pleiotropic variants with most of them being limited to quantitative traits when pleiotropic effects on both quantitative and qualitative traits have been observed. This is a statistically challenging problem because there does not exist an appropriate multivariate distribution to model both quantitative and qualitative data together. Alternatively, meta-analysis methods can be applied, which basically integrate summary statistics of individual variants associated with either a quantitative or a qualitative trait without accounting for correlations among genetic variants. Results We propose a new statistical selection method based on a unified selection score quantifying how a genetic variant, i.e., a pleiotropic variant associates with both quantitative and qualitative traits. In our extensive simulation studies where various types of pleiotropic effects on both quantitative and qualitative traits were considered, we demonstrated that the proposed method outperforms the existing meta-analysis methods in terms of true positive selection. We also applied the proposed method to a peanut dataset with 6 quantitative and 2 qualitative traits, and a cowpea dataset with 2 quantitative and 6 qualitative traits. We were able to detect some potentially pleiotropic variants missed by the existing methods in both analyses. Conclusions The proposed method is able to locate pleiotropic variants associated with both quantitative and qualitative traits. It has been implemented into an R package ‘UNISS’, which can be downloaded from http://github.com/statpng/uniss. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: English – Name: DOI Label: DOI Group: ID Data: 10.7916/v12v-2w65 – Name: URL Label: Availability Group: URL Data: https://doi.org/10.7916/v12v-2w65 – Name: AN Label: Accession Number Group: ID Data: edsbas.A5DB50F3 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7916/v12v-2w65 Languages: – Text: English Subjects: – SubjectFull: Genetics--Statistical methods Type: general – SubjectFull: Phenotype Type: general – SubjectFull: Human genetics--Variation Type: general – SubjectFull: Medical genetics Type: general – SubjectFull: National Human Genome Research Institute (U.S.) Type: general Titles: – TitleFull: New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kim, Kipoong – PersonEntity: Name: NameFull: Jun, Tae-Hwan – PersonEntity: Name: NameFull: Ha, Bo-Keun – PersonEntity: Name: NameFull: Wang, Shuang – PersonEntity: Name: NameFull: Sun, Hokeun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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