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Version 1.0.8
Read our full manuscript here (preprint)
Using exome seqencing and data from over 200,000 individuals from the UK Biobank, Clintegrate is an intergrative risk prediction framework designed to address issues around variant interpretation as it pertains to current practices in personalized risk assessment.
From our manuscript:
Mapping germline variants to personalized clinical risk is a major goal in precision medicine.[1] While clinical diagnostic testing has advanced dramatically, the interpretation of monogenic variants remains challenging, and is generally done at the variant level.[2] To date, such genetic testing has largely been conducted in the presence of a phenotypic indication, where the prior probability of detecting a causal variant is high.[3] However, subsequent population screening efforts have identified substantial incomplete penetrance or reduced expressivity in these previously identified monogenic disease variants[4–6] and assessed how the risk attributable to these variants can be modified by clinical and polygenic risk factors.[7–9]
We present a framework that integrates patient-level information (Polygenic Risk Scores, Family History, Sex, etc.), monogenic variant-level features (CADD, Allele Frequency, GERP, CpG Context, phyloP, etc.) and protein regional information to create a comprehensive measure of risk. With growing availability of population-level sequencing efforts and diagnostic data, we aim to continually expand this model to include additioanl phenotypes and risk factors (genetic and non-genetic).
Install:
pip install -i https://test.pypi.org/simple/ clintegrateThe following disease models are available with varaint integration in genes associated with each condition:
| Condition | Genes |
|---|---|
| Breast Cancer | BRCA1, BRCA2 |
| Colorectal Cancer | MLH1, MSH2, MSH6, PMS2 |
| Coronary Artery Disease | APOB, LDLR, PCSK9 |
from clintegrate.predictors.risk import IntegrativePredictiveModel
# Making predictions of coronary artery disease risk using APOB variants
ipm = IntegrativePredictiveModel()
ipm.initialize("APOB")Disease models have different required fields for accurate risk assessments, which we're actively updating. It's always a good idea to look at the example data:
ipm.load_example_data()| id | sex | PRS | Family History | variant |
|---|---|---|---|---|
| person1 | M | -0.54 | 0 | |
| person2 | F | 2.51 | 1 | 2-21001432-G-A |
| person3 | F | 0 | 1 | 2-21001769-G-T |
| person4 | F | 1.3 | 0 |
Variants are always optional for making risk assessments, however the field and the remaining fields are required.
To predict partial hazard values
example_data = ipm.load_example_data()
ipm.generate_risk_predictions(example_data)| id | sex | PRS | Family History | variant | Partial Hazard Prediction |
|---|---|---|---|---|---|
| person1 | M | -0.54 | 0 | 1.37421 | |
| person2 | F | 2.51 | 1 | 2-21001432-G-A | 2.06113 |
| person3 | F | 0 | 1 | 2-21001769-G-T | 0.230054 |
| person4 | F | 1.3 | 0 | 0.645135 |
The information presented here should not be a substitute for professional medical treatment, diagnosis, or advice. We encourage you to investigate information from this manuscript or the accompanying software tools as it pertains to your health with medical supervision and expert opinion.
[1] Green, E. D. et al. Strategic vision for improving human health at The Forefront of Genomics. Nature 586, 683–692 (2020).
[2] Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–423 (2015).
[3] Rehm, H. L. et al. ClinGen — The Clinical Genome Resource. N. Engl. J. Med. 372, 2235–2242 (2015).
[4] Goodrich, J. K. et al. Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes. Nat. Commun. 12, 3505 (2021).
[5] Cassa, C. A., Tong, M. Y. & Jordan, D. M. Large numbers of genetic variants considered to be pathogenic are common in asymptomatic individuals. Hum Mutat (2013) doi:10.1002/humu.22375.
[6] Forrest, I. S. et al. Ancestrally and Temporally Diverse Analysis of Penetrance of Clinical Variants in 72,434 Individuals. http://medrxiv.org/lookup/doi/10.1101/2021.03.11.21253430 (2021) doi:10.1101/2021.03.11.21253430.
[7] Fahed, A. C. et al. Polygenic background modifies penetrance of monogenic variants conferring risk for coronary artery disease, breast cancer, or colorectal cancer. http://medrxiv.org/lookup/doi/10.1101/19013086 (2019) doi:10.1101/19013086.
[8] Friebel, T. M., Domchek, S. M. & Rebbeck, T. R. Modifiers of Cancer Risk in BRCA1 and BRCA2 Mutation Carriers: A Systematic Review and Meta-Analysis. JNCI J. Natl. Cancer Inst. 106, (2014).
[9] Saadatagah, S. et al. Genetic basis of hypercholesterolemia in adults. Npj Genomic Med. 6, 1–7 (2021).