GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study
Abstract In recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost gen...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-85203-8 |
| _version_ | 1818981952138510336 |
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| author | Mahdi Akbarzadeh Saeid Rasekhi Dehkordi Mahmoud Amiri Roudbar Mehdi Sargolzaei Kamran Guity Bahareh Sedaghati-khayat Parisa Riahi Fereidoun Azizi Maryam S. Daneshpour |
| author_facet | Mahdi Akbarzadeh Saeid Rasekhi Dehkordi Mahmoud Amiri Roudbar Mehdi Sargolzaei Kamran Guity Bahareh Sedaghati-khayat Parisa Riahi Fereidoun Azizi Maryam S. Daneshpour |
| author_sort | Mahdi Akbarzadeh |
| collection | DOAJ |
| description | Abstract In recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs’ subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy. |
| first_indexed | 2024-12-20T17:39:29Z |
| format | Article |
| id | doaj.art-3beb39ab856f44bea8e96c98fd97889a |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-20T17:39:29Z |
| publishDate | 2021-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-3beb39ab856f44bea8e96c98fd97889a2022-12-21T19:31:08ZengNature PortfolioScientific Reports2045-23222021-03-011111910.1038/s41598-021-85203-8GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic StudyMahdi Akbarzadeh0Saeid Rasekhi Dehkordi1Mahmoud Amiri Roudbar2Mehdi Sargolzaei3Kamran Guity4Bahareh Sedaghati-khayat5Parisa Riahi6Fereidoun Azizi7Maryam S. Daneshpour8Cellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesCellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesDepartment of Animal Science, Safiabad-Dezful Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education & Extension Organization (AREEO)Department of Pathobiology, Ontario Veterinary College, University of GuelphCellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesCellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesCellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesEndocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesCellular and Molecular Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical SciencesAbstract In recent decades, ongoing GWAS findings discovered novel therapeutic modifications such as whole-genome risk prediction in particular. Here, we proposed a method based on integrating the traditional genomic best linear unbiased prediction (gBLUP) approach with GWAS information to boost genetic prediction accuracy and gene-based heritability estimation. This study was conducted in the framework of the Tehran Cardio-metabolic Genetic study (TCGS) containing 14,827 individuals and 649,932 SNP markers. Five SNP subsets were selected based on GWAS results: top 1%, 5%, 10%, 50% significant SNPs, and reported associated SNPs in previous studies. Furthermore, we randomly selected subsets as large as every five subsets. Prediction accuracy has been investigated on lipid profile traits with a tenfold and 10-repeat cross-validation algorithm by the gBLUP method. Our results revealed that genetic prediction based on selected subsets of SNPs obtained from the dataset outperformed the subsets from previously reported SNPs. Selected SNPs’ subsets acquired a more precise prediction than whole SNPs and much higher than randomly selected SNPs. Also, common SNPs with the most captured prediction accuracy in the selected sets caught the highest gene-based heritability. However, it is better to be mindful of the fact that a small number of SNPs obtained from GWAS results could capture a highly notable proportion of variance and prediction accuracy.https://doi.org/10.1038/s41598-021-85203-8 |
| spellingShingle | Mahdi Akbarzadeh Saeid Rasekhi Dehkordi Mahmoud Amiri Roudbar Mehdi Sargolzaei Kamran Guity Bahareh Sedaghati-khayat Parisa Riahi Fereidoun Azizi Maryam S. Daneshpour GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study Scientific Reports |
| title | GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study |
| title_full | GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study |
| title_fullStr | GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study |
| title_full_unstemmed | GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study |
| title_short | GWAS findings improved genomic prediction accuracy of lipid profile traits: Tehran Cardiometabolic Genetic Study |
| title_sort | gwas findings improved genomic prediction accuracy of lipid profile traits tehran cardiometabolic genetic study |
| url | https://doi.org/10.1038/s41598-021-85203-8 |
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