| Predicted Trait | |
| Reported Trait | Abdominal aortic aneurysm |
| Mapped Trait(s) | Abdominal Aortic Aneurysm (EFO_0004214) |
| Score Construction | |
| PGS Name | PRS29_AAA |
| Development Method | |
| Name | Pruning and Thresholding (P+T) |
| Parameters | R^2 < 0.0001, p < 1e-6; LD panel = 20000 UKB Europeans |
| Variants | |
| Original Genome Build | GRCh37 |
| Number of Variants | 29 |
| Effect Weight Type | beta |
| PGS Source | |
| PGS Catalog Publication (PGP) ID | PGP000159 |
| Citation (link to publication) | Klarin D et al. Circulation (2020) |
| Study Identifiers | Sample Numbers | Sample Ancestry | Cohort(s) |
|---|---|---|---|
Europe PMC: 32981348 |
[ ,
92.68 % Male samples |
European | MVP |
| Study Identifiers | Sample Numbers | Sample Ancestry | Cohort(s) | Phenotype Definitions & Methods | Age of Study Participants | Participant Follow-up Time | Additional Ancestry Description | Additional Sample/Cohort Information |
|---|---|---|---|---|---|---|---|---|
| — | [ ,
61.51 % Male samples |
European | MAYO-VDB | Abdominal aortic aneurysm (AAA) cases were defined as having an infrarenal abdominal aortic diameter ≥3 cm or a history of open or endovascular AAA repair. Controls were not known to have AAA and had no ICD-9 diagnosis codes for AAA. | — | — | — | — |
|
PGS Performance Metric ID (PPM) |
PGS Sample Set ID (PSS) |
Performance Source | Trait |
PGS Effect Sizes (per SD change) |
Classification Metrics | Other Metrics | Covariates Included in the Model |
PGS Performance: Other Relevant Information |
|---|---|---|---|---|---|---|---|---|
| PPM001912 | PSS000958| European Ancestry| 46,564 individuals |
PGP000159 | Klarin D et al. Circulation (2020) |
Reported Trait: Prevalent abdominal aortic aneurysm | OR: 1.37 [1.3, 1.44] | — | — | Age, sex, PCs (1-5) | — |
| PPM001913 | PSS000956| African Ancestry| 47,098 individuals |
PGP000159 | Klarin D et al. Circulation (2020) |
Reported Trait: Prevalent abdominal aortic aneurysm | OR: 1.15 [1.07, 1.24] | — | — | Age, sex, PCs (1-5) | — |
| PPM001915 | PSS000959| European Ancestry| 10,231 individuals |
PGP000159 | Klarin D et al. Circulation (2020) |
Reported Trait: Prevalent abdominal aortic aneurysm | OR: 1.31 [1.18, 1.46] | — | — | Age, sex, PCs (1-5) | — |
| PPM001917 | PSS000956| African Ancestry| 47,098 individuals |
PGP000159 | Klarin D et al. Circulation (2020) |
Reported Trait: Prevalent abdominal aortic aneurysm | OR: 1.13 [1.04, 1.22] | — | — | Age, sex, PCs (1-5), smoking, hypertension, low-density lipoprotein cholesterol with statin adjustment, high-density lipoprotein cholesterol, triglycerides, coronary artery disease as a marker of atherosclerosis burden. | — |
| PPM001918 | PSS000957| European Ancestry| 9,525 individuals |
PGP000159 | Klarin D et al. Circulation (2020) |
Reported Trait: Prevalent abdominal aortic aneurysm | OR: 1.58 [1.25, 1.98] | — | — | Age, sex, PCs (1-5), smoking, hypertension, low-density lipoprotein cholesterol with statin adjustment, high-density lipoprotein cholesterol, triglycerides, coronary artery disease as a marker of atherosclerosis burden. | — |
| PPM001914 | PSS000957| European Ancestry| 9,525 individuals |
PGP000159 | Klarin D et al. Circulation (2020) |
Reported Trait: Prevalent abdominal aortic aneurysm | OR: 2.46 [1.46, 4.14] | — | — | Age, sex, PCs (1-5) | — |
| PPM001916 | PSS000958| European Ancestry| 46,564 individuals |
PGP000159 | Klarin D et al. Circulation (2020) |
Reported Trait: Prevalent abdominal aortic aneurysm | OR: 1.34 [1.27, 1.41] | — | — | Age, sex, PCs (1-5), smoking, hypertension, low-density lipoprotein cholesterol with statin adjustment, high-density lipoprotein cholesterol, triglycerides, coronary artery disease as a marker of atherosclerosis burden. | — |
|
PGS Sample Set ID (PSS) |
Phenotype Definitions and Methods | Participant Follow-up Time | Sample Numbers | Age of Study Participants | Sample Ancestry | Additional Ancestry Description | Cohort(s) | Additional Sample/Cohort Information |
|---|---|---|---|---|---|---|---|---|
| PSS000958 | Abdominal aortic aneurysm cases were defined as the presence of 2 instances of any of the following International Classification of Diseases (ICD)–9 or ICD-10 codes in a participant’s EHR: 441.3, 441.4, I71.3, or I71.4. Controls were defined as possessing no occurrences of the aforementioned ICD codes, as well as no occurrences of the ICD-9 codes 440 through 448 or ICD-10 codes I71 through I75, I77 through I79, or K55. | — | [ ,
92.1 % Male samples |
Mean = 63.8 years Sd = 13.7 years |
European | — | MVP | Sample is independent to the MVP sample used to identify SNPs and determine their weights |
| PSS000959 | Abdominal aortic aneurysm cases were defined as the presence of 2 instances of any of the following International Classification of Diseases (ICD)–9 or ICD-10 codes in a participant’s EHR: 441.3, 441.4, I71.3, or I71.4. | — | [ ,
65.4 % Male samples |
Mean = 71.0 years Sd = 13.7 years |
European | — | PMB | — |
| PSS000956 | Abdominal aortic aneurysm cases were defined as the presence of 2 instances of any of the following International Classification of Diseases (ICD)–9 or ICD-10 codes in a participant’s EHR: 441.3, 441.4, I71.3, or I71.4. Controls were defined as possessing no occurrences of the aforementioned ICD codes, as well as no occurrences of the ICD-9 codes 440 through 448 or ICD-10 codes I71 through I75, I77 through I79, or K55. | — | [ ,
86.0 % Male samples |
Mean = 56.3 years Sd = 11.8 years |
African unspecified | — | MVP | — |
| PSS000957 | Abdominal aortic aneurysm cases were defined as the presence of 2 instances of any of the following International Classification of Diseases (ICD)–9 or ICD-10 codes in a participant’s EHR: 441.3, 441.4, I71.3, or I71.4. | — | [ ,
48.1 % Male samples |
Mean = 58.3 years Sd = 19.5 years |
European | — | BioMe | — |