Defining type 2 diabetes polygenic risk scores through colocalization and network-based clustering of metabolic trait genetic associations
Abstract Background Type 2 diabetes (T2D) is a heterogeneous and polygenic disease. Previous studies have leveraged the highly polygenic and pleiotropic nature of T2D variants to partition the heterogeneity of T2D, in order to stratify patient risk and gain mechanistic insight. We expanded on these...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | Genome Medicine |
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| Online Access: | https://doi.org/10.1186/s13073-023-01255-7 |
| _version_ | 1797355583566774272 |
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| author | Samuel Ghatan Jeroen van Rooij Mandy van Hoek Cindy G. Boer Janine F. Felix Maryam Kavousi Vincent W. Jaddoe Eric J. G. Sijbrands Carolina Medina-Gomez Fernando Rivadeneira Ling Oei |
| author_facet | Samuel Ghatan Jeroen van Rooij Mandy van Hoek Cindy G. Boer Janine F. Felix Maryam Kavousi Vincent W. Jaddoe Eric J. G. Sijbrands Carolina Medina-Gomez Fernando Rivadeneira Ling Oei |
| author_sort | Samuel Ghatan |
| collection | DOAJ |
| description | Abstract Background Type 2 diabetes (T2D) is a heterogeneous and polygenic disease. Previous studies have leveraged the highly polygenic and pleiotropic nature of T2D variants to partition the heterogeneity of T2D, in order to stratify patient risk and gain mechanistic insight. We expanded on these approaches by performing colocalization across GWAS traits while assessing the causality and directionality of genetic associations. Methods We applied colocalization between T2D and 20 related metabolic traits, across 243 loci, to obtain inferences of shared casual variants. Network-based unsupervised hierarchical clustering was performed on variant-trait associations. Partitioned polygenic risk scores (PRSs) were generated for each cluster using T2D summary statistics and validated in 21,742 individuals with T2D from 3 cohorts. Inferences of directionality and causality were obtained by applying Mendelian randomization Steiger’s Z-test and further validated in a pediatric cohort without diabetes (aged 9–12 years old, n = 3866). Results We identified 146 T2D loci that colocalized with at least one metabolic trait locus. T2D variants within these loci were grouped into 5 clusters. The clusters corresponded to the following pathways: obesity, lipodystrophic insulin resistance, liver and lipid metabolism, hepatic glucose metabolism, and beta-cell dysfunction. We observed heterogeneity in associations between PRSs and metabolic measures across clusters. For instance, the lipodystrophic insulin resistance (Beta − 0.08 SD, 95% CI [− 0.10–0.07], p = 6.50 × 10−32) and beta-cell dysfunction (Beta − 0.10 SD, 95% CI [− 0.12, − 0.08], p = 1.46 × 10−47) PRSs were associated to lower BMI. Mendelian randomization Steiger analysis indicated that increased T2D risk in these pathways was causally associated to lower BMI. However, the obesity PRS was conversely associated with increased BMI (Beta 0.08 SD, 95% CI 0.06–0.10, p = 8.0 × 10−33). Analyses within a pediatric cohort supported this finding. Additionally, the lipodystrophic insulin resistance PRS was associated with a higher odds of chronic kidney disease (OR 1.29, 95% CI 1.02–1.62, p = 0.03). Conclusions We successfully partitioned T2D genetic variants into phenotypic pathways using a colocalization first approach. Partitioned PRSs were associated to unique metabolic and clinical outcomes indicating successful partitioning of disease heterogeneity. Our work expands on previous approaches by providing stronger inferences of shared causal variants, causality, and directionality of GWAS variant-trait associations. |
| first_indexed | 2024-03-08T14:14:13Z |
| format | Article |
| id | doaj.art-78b2f341190c4e8991af36ff313c38bc |
| institution | Directory Open Access Journal |
| issn | 1756-994X |
| language | English |
| last_indexed | 2024-03-08T14:14:13Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Medicine |
| spelling | doaj.art-78b2f341190c4e8991af36ff313c38bc2024-01-14T12:31:24ZengBMCGenome Medicine1756-994X2024-01-0116111510.1186/s13073-023-01255-7Defining type 2 diabetes polygenic risk scores through colocalization and network-based clustering of metabolic trait genetic associationsSamuel Ghatan0Jeroen van Rooij1Mandy van Hoek2Cindy G. Boer3Janine F. Felix4Maryam Kavousi5Vincent W. Jaddoe6Eric J. G. Sijbrands7Carolina Medina-Gomez8Fernando Rivadeneira9Ling Oei10Department of Internal Medicine, Erasmus MC University Medical Center RotterdamDepartment of Internal Medicine, Erasmus MC University Medical Center RotterdamDepartment of Internal Medicine, Erasmus MC University Medical Center RotterdamDepartment of Internal Medicine, Erasmus MC University Medical Center RotterdamThe Generation R Study Group, Erasmus MC, Erasmus University Medical Center RotterdamDepartment of Epidemiology, Erasmus MC University Medical Center RotterdamThe Generation R Study Group, Erasmus MC, Erasmus University Medical Center RotterdamDepartment of Internal Medicine, Erasmus MC University Medical Center RotterdamDepartment of Internal Medicine, Erasmus MC University Medical Center RotterdamDepartment of Internal Medicine, Erasmus MC University Medical Center RotterdamDepartment of Internal Medicine, Erasmus MC University Medical Center RotterdamAbstract Background Type 2 diabetes (T2D) is a heterogeneous and polygenic disease. Previous studies have leveraged the highly polygenic and pleiotropic nature of T2D variants to partition the heterogeneity of T2D, in order to stratify patient risk and gain mechanistic insight. We expanded on these approaches by performing colocalization across GWAS traits while assessing the causality and directionality of genetic associations. Methods We applied colocalization between T2D and 20 related metabolic traits, across 243 loci, to obtain inferences of shared casual variants. Network-based unsupervised hierarchical clustering was performed on variant-trait associations. Partitioned polygenic risk scores (PRSs) were generated for each cluster using T2D summary statistics and validated in 21,742 individuals with T2D from 3 cohorts. Inferences of directionality and causality were obtained by applying Mendelian randomization Steiger’s Z-test and further validated in a pediatric cohort without diabetes (aged 9–12 years old, n = 3866). Results We identified 146 T2D loci that colocalized with at least one metabolic trait locus. T2D variants within these loci were grouped into 5 clusters. The clusters corresponded to the following pathways: obesity, lipodystrophic insulin resistance, liver and lipid metabolism, hepatic glucose metabolism, and beta-cell dysfunction. We observed heterogeneity in associations between PRSs and metabolic measures across clusters. For instance, the lipodystrophic insulin resistance (Beta − 0.08 SD, 95% CI [− 0.10–0.07], p = 6.50 × 10−32) and beta-cell dysfunction (Beta − 0.10 SD, 95% CI [− 0.12, − 0.08], p = 1.46 × 10−47) PRSs were associated to lower BMI. Mendelian randomization Steiger analysis indicated that increased T2D risk in these pathways was causally associated to lower BMI. However, the obesity PRS was conversely associated with increased BMI (Beta 0.08 SD, 95% CI 0.06–0.10, p = 8.0 × 10−33). Analyses within a pediatric cohort supported this finding. Additionally, the lipodystrophic insulin resistance PRS was associated with a higher odds of chronic kidney disease (OR 1.29, 95% CI 1.02–1.62, p = 0.03). Conclusions We successfully partitioned T2D genetic variants into phenotypic pathways using a colocalization first approach. Partitioned PRSs were associated to unique metabolic and clinical outcomes indicating successful partitioning of disease heterogeneity. Our work expands on previous approaches by providing stronger inferences of shared causal variants, causality, and directionality of GWAS variant-trait associations.https://doi.org/10.1186/s13073-023-01255-7Polygenic risk scoreType 2 diabetesColocalizationClusteringPersonalized medicine |
| spellingShingle | Samuel Ghatan Jeroen van Rooij Mandy van Hoek Cindy G. Boer Janine F. Felix Maryam Kavousi Vincent W. Jaddoe Eric J. G. Sijbrands Carolina Medina-Gomez Fernando Rivadeneira Ling Oei Defining type 2 diabetes polygenic risk scores through colocalization and network-based clustering of metabolic trait genetic associations Genome Medicine Polygenic risk score Type 2 diabetes Colocalization Clustering Personalized medicine |
| title | Defining type 2 diabetes polygenic risk scores through colocalization and network-based clustering of metabolic trait genetic associations |
| title_full | Defining type 2 diabetes polygenic risk scores through colocalization and network-based clustering of metabolic trait genetic associations |
| title_fullStr | Defining type 2 diabetes polygenic risk scores through colocalization and network-based clustering of metabolic trait genetic associations |
| title_full_unstemmed | Defining type 2 diabetes polygenic risk scores through colocalization and network-based clustering of metabolic trait genetic associations |
| title_short | Defining type 2 diabetes polygenic risk scores through colocalization and network-based clustering of metabolic trait genetic associations |
| title_sort | defining type 2 diabetes polygenic risk scores through colocalization and network based clustering of metabolic trait genetic associations |
| topic | Polygenic risk score Type 2 diabetes Colocalization Clustering Personalized medicine |
| url | https://doi.org/10.1186/s13073-023-01255-7 |
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