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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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.
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:
Development and Standardization of an Improved Type 1 Diabetes Genetic
Risk Score for Use in Newborn Screening and Incident Diagnosis
Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis
Multistage genome-wide association meta-analyses identified two new loci for
bone mineral density
Genome-wide polygenic scores for common diseases identify individuals with
risk equivalent to monogenic mutations
Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease
Read more →Polygenic Contribution in Individuals With Early-Onset Coronary Artery Disease
Read more →Risk prediction by genetic risk scores for coronary heart disease is independent
of self-reported family history
Exploring the genetics of irritable bowel syndrome. A study in the general
population and replication in multinational case-control cohorts
Genome-wide association study identifies two novel genomic regions in
irritable bowel syndrome
Genome-wide association analysis identifies novel blood pressure loci and
offers biological insights into cardiovascular risk
Personalized risk prediction for type 2 diabetes: the potential of genetic risk
scores
The genetic architecture of type 2 diabetes
A bivariate genome-wide approach to metabolic syndrome: STAMPEED
consortium
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