An individualized Bayesian method for estimating genomic variants of hypertension
Abstract Background Genomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatme...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-11-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-023-09757-9 |
| _version_ | 1797630400486440960 |
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| author | Md Asad Rahman Chunhui Cai Na Bo Dennis M. McNamara Ying Ding Gregory F. Cooper Xinghua Lu Jinling Liu |
| author_facet | Md Asad Rahman Chunhui Cai Na Bo Dennis M. McNamara Ying Ding Gregory F. Cooper Xinghua Lu Jinling Liu |
| author_sort | Md Asad Rahman |
| collection | DOAJ |
| description | Abstract Background Genomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual’s genome that provide a strong explanation of the phenotype observed in this individual. Results We applied the IBI algorithm to the data from the Framingham Heart Study to explore the genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, IBI discovered more individualized and diverse variants that explain hypertension patients better than GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that GWAS missed in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than GWAS, according to the area under the ROC curve. Conclusions The results support IBI as a promising approach for complementing GWAS, especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine. |
| first_indexed | 2024-03-11T11:07:43Z |
| format | Article |
| id | doaj.art-a813ca4909ff442692ff81de67d41f10 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-03-11T11:07:43Z |
| publishDate | 2023-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-a813ca4909ff442692ff81de67d41f102023-11-12T12:07:37ZengBMCBMC Genomics1471-21642023-11-0123S511510.1186/s12864-023-09757-9An individualized Bayesian method for estimating genomic variants of hypertensionMd Asad Rahman0Chunhui Cai1Na Bo2Dennis M. McNamara3Ying Ding4Gregory F. Cooper5Xinghua Lu6Jinling Liu7Department of Engineering Management and Systems Engineering, Missouri University of Science and TechnologyDepartment of Biomedical Informatics, University of PittsburghDepartment of Biostatistics, University of PittsburghDepartment of Medicine, University of PittsburghDepartment of Biostatistics, University of PittsburghDepartment of Biomedical Informatics, University of PittsburghDepartment of Biomedical Informatics, University of PittsburghDepartment of Engineering Management and Systems Engineering, Missouri University of Science and TechnologyAbstract Background Genomic variants of the disease are often discovered nowadays through population-based genome-wide association studies (GWAS). Identifying genomic variations potentially underlying a phenotype, such as hypertension, in an individual is important for designing personalized treatment; however, population-level models, such as GWAS, may not capture all the important, individualized factors well. In addition, GWAS typically requires a large sample size to detect the association of low-frequency genomic variants with sufficient power. Here, we report an individualized Bayesian inference (IBI) algorithm for estimating the genomic variants that influence complex traits, such as hypertension, at the level of an individual (e.g., a patient). By modeling at the level of the individual, IBI seeks to find genomic variants observed in the individual’s genome that provide a strong explanation of the phenotype observed in this individual. Results We applied the IBI algorithm to the data from the Framingham Heart Study to explore the genomic influences of hypertension. Among the top-ranking variants identified by IBI and GWAS, there is a significant number of shared variants (intersection); the unique variants identified only by IBI tend to have relatively lower minor allele frequency than those identified by GWAS. In addition, IBI discovered more individualized and diverse variants that explain hypertension patients better than GWAS. Furthermore, IBI found several well-known low-frequency variants as well as genes related to blood pressure that GWAS missed in the same cohort. Finally, IBI identified top-ranked variants that predicted hypertension better than GWAS, according to the area under the ROC curve. Conclusions The results support IBI as a promising approach for complementing GWAS, especially in detecting low-frequency genomic variants as well as learning personalized genomic variants of clinical traits and disease, such as the complex trait of hypertension, to help advance precision medicine.https://doi.org/10.1186/s12864-023-09757-9Individualized Bayesian inferenceGenome-wide association studiesGenomic variantsSingle nucleotide polymorphismHypertensionBlood pressure |
| spellingShingle | Md Asad Rahman Chunhui Cai Na Bo Dennis M. McNamara Ying Ding Gregory F. Cooper Xinghua Lu Jinling Liu An individualized Bayesian method for estimating genomic variants of hypertension BMC Genomics Individualized Bayesian inference Genome-wide association studies Genomic variants Single nucleotide polymorphism Hypertension Blood pressure |
| title | An individualized Bayesian method for estimating genomic variants of hypertension |
| title_full | An individualized Bayesian method for estimating genomic variants of hypertension |
| title_fullStr | An individualized Bayesian method for estimating genomic variants of hypertension |
| title_full_unstemmed | An individualized Bayesian method for estimating genomic variants of hypertension |
| title_short | An individualized Bayesian method for estimating genomic variants of hypertension |
| title_sort | individualized bayesian method for estimating genomic variants of hypertension |
| topic | Individualized Bayesian inference Genome-wide association studies Genomic variants Single nucleotide polymorphism Hypertension Blood pressure |
| url | https://doi.org/10.1186/s12864-023-09757-9 |
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