Clinical grade risk prediction

Allelica is the first and only company to offer a Software as a
Service to perform genomic risk prediction based on PRSs.
Our technology uses world class datasets and combines the
best algorithms to generate PRSs with the highest predictive
power. Through the integration of these state-of-the-art PRSs with
clinical risk factors, we provide personalized absolute risk
models with proven clinical utility.

Allelica’s PRSs

Our PRSs identify individuals with a high genetic
liability of life-threatening diseases like breast cancer and
heart disease who are currently unidentified by traditional risk
models. These individuals can be identified through PRS testing,
allowing for early intervention to reduce lifetime risk.

At Allelica, we've developed and benchmarked
the following PRSs against the leaders in the industry,
proving their unmatched predictive power:

  • - Coronary artery disease
  • - Breast cancer
  • - Prostate cancer
  • - Colon cancer
  • - Diabetes Type 2
  • - Alzheimer's
  • - Atrial fibrillation
  • - Early menopause

Allelica PRS Development

Coronary Artery Disease (CAD)

Our team of researchers developed a new polygenic risk score (PRS) for CAD, which takes into account information at 1,970,136 genome-wide variants identified by combining a modified Stacked Clumping and Thresholding approach with a published metaGRS.This state-of-the-art PRS was compared to previously published CAD PRSs and considered together with non-genetic clinical risk factors. 

Breast Cancer (BC)

Our Polygenic Risk Score for BC was developed using two datasets from the UK Biobank and GWAS summary statistics published in Nature Genetics. We used a validation dataset of 68,620 samples, 4,562 of whom had breast cancer, and a testing dataset of 141,795 samples, 10,353 of whom had breast cancer. Our PRS is able to identify a notable fraction of the test population (5%) with a 3 fold or higher increased risk of developing BC compared to the population average.

Prostate Cancer (PC)

In order to improve the predictive performance of previous PC PRSs, our researchers used the Allelica DISCOVER module to test multiple PRS methods in parallel. Using a validation dataset of 54,146 individuals, including 6,463 prostate cancer cases, the new PRS displayed higher predictive performance than other recently published PC PRS. Compared to the most powerful pre-existing PC PRSs, Allelica’s PRS takes into account a significantly higher number of SNPs and shows significant improvement in AUC, identifying a larger fraction of the population at more than three-fold increased risk of disease.

Main studies used to derive the Polygenic Risk Scores implemented in our software


Diabetes Type 1:

Development and Standardization of an Improved Type 1 Diabetes Genetic
Risk Score for Use in Newborn Screening and Incident Diagnosis

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Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis

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Multistage genome-wide association meta-analyses identified two new loci for
bone mineral density

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Cardiovascular Diseases:

Genome-wide polygenic scores for common diseases identify individuals with
risk equivalent to monogenic mutations

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Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease

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Polygenic Contribution in Individuals With Early-Onset Coronary Artery Disease

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Risk prediction by genetic risk scores for coronary heart disease is independent
of self-reported family history

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Irritable Bowel Syndrome:

Exploring the genetics of irritable bowel syndrome. A study in the general
population and replication in multinational case-control cohorts

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Genome-wide association study identifies two novel genomic regions in
irritable bowel syndrome

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Genome-wide association analysis identifies novel blood pressure loci and
offers biological insights into cardiovascular risk

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Colon Cancer:

Novel Common Genetic Surcsceptibility Loci for Colorectal Cancer

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Diabetes type 2:

Personalized risk prediction for type 2 diabetes: the potential of genetic risk

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The genetic architecture of type 2 diabetes

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Metabolic syndrome:

A bivariate genome-wide approach to metabolic syndrome: STAMPEED

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Genome-wide screen for metabolic syndrome susceptibility Loci reveals strong
lipid gene contribution but no evidence for common geneticbasis for
clustering of metabolic syndrome traits

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