Unlocking personalised, preventative healthcare
We are transforming healthcare from the one-size-fits-all model to a personalised, preventive approach.
By developing the next generation of integrated predictive genetic testing and assessment tools, we are empowering medical practitioners and their patients to proactively manage health.
Genetics for life
The mission is simple: to provide personalised, preventative healthcare solutions that may help individuals and their medical practitioners screen and reduce the risk of life-changing serious diseases.
About polygenic risk
Our experts have been at the forefront of integrated genomic risk assessment and has practiced high standards in creating, optimising and risk reporting for over 10 years. As the first, and the most scientifically sound commercial integrated risk assessment provider, we are proud of our work. We are even more proud to announce that all of our rigorous and robust R&D methods with which we used to develop our integrated risk assessment models fit seamlessly within the recent polygenic risk score Reporting Standards (PRS-RS; Wand, Nature 2021) defined by the ClinGen Complex Disease working group. We are thrilled that this working group is taking steps to enable polygenic risk adoption into mainstream clinical care by emphasising and publishing a best practices framework. Our current practices are being highlighted as best practice in the industry.
Polygenic risk scores (PRS)
are derived from a subset of common genetic markers known as single nucleotide polymorphisms (SNP) that are disease-associated. Each SNP has a possible allele combination, or nucleotide (A,T,C,G) at that specific location.
Discovered in genome-wide association studies (GWAS), each allele has a weighted effect size associated with the disease in question.
Everybody has these SNPs, but we all have different combinations of alleles. Polygenic risk of disease can be quantified when we look at the combination of many disease-associated SNP at one time.
Integrated risk model
is a risk model that combines polygenic risk with other risk factors such as demographics, anthropometrics, and clinical measurements. Traditional models look at the latter components. geneType™ is unique in its integration of polygenic risk which enables a much better stratification of disease risk within the general population.
Example of sporadic breast cancer risk stratification in the general population.
How does geneType measure up?
All geneType risk assessments are specifically developed, calibrated and validated for the general population. Direct comparisons against gold-standard clinical models are carried out in cohorts and/or case-control datasets using a variety of statistical methods common in the field of epidemiology.
What does this mean to your practice?
Clinical practice today
Clinical practice with geneType
Traditional risk models identify a very small proportion of very high-risk patients. In fact, the majority of patients fall into general population level risk category. But patients aren’t all the same, are they?
Using an integrated risk prediction model, you can stratify your general population. Identify more at-risk adults who can benefit from already-existing risk-reducing recommendations.
We are proud to publish
RISE UP for Breast Cancer Abstract: Real-world example of breast cancer risk assessment as gateway to multi-disease risk stratification
SABCS Abstract: Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard
Predicting 10-Year Risk of Pancreatic Cancer Using a Combined Genetic and Clinical Model.
Dite GS, Spaeth E, Wong CK, Murphy NM, Allman R. Gastro Hep Adv. 2023 Jun 12;2(7):979-989.
Melanoma risk prediction based on a polygenic risk score and clinical risk factors
Wong CK, Dite GS, Spaeth E, Murphy NM, Allman R. [published online ahead of print, 2023 Apr 24]. Melanoma Res. 2023;10.1097/CMR.0000000000000896.
Development and validation of a simple prostate cancer risk prediction model based on age, family history, and polygenic risk
Dite GS, Spaeth E, Murphy, N, Allman R. The Prostate. 2023 https://doi.org/10.1002/pros.24537
Validation of an abridged breast cancer risk prediction model for the general population.
Spaeth E, Dite GS, Hopper JL, Allman R. Cancer Prevention Research. 2023 DOI: 10.1158/1940-6207.CAPR-22-0460
Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk
Allman R, Mu Y, Dite G, Spaeth E, Hopper J, Rosner B (2023)
Breast Cancer Research and Treatment DOI: 10.1007/s10549-022-06834-7
ASCOGI Abstract: Improvement of a clinical colorectal cancer risk prediction model integrating polygenic risk.
Erika Spaeth Tuff, Aviv Gafni, Gillian S. Dite, Richard Allman
DOI: 10.1200/JCO.2023.41.4_suppl.81 Journal of Clinical Oncology 41, no. 4_suppl (February 01, 2023) 81-81.
Polygenic risk scores for cardiovascular diseases and type 2 diabetes
Wong CK, Makalic E, Dite GS, Whiting L, Murphy NM, et al. (2022)
PLOS ONE 17(12): e0278764.
A combined clinical and genetic model for predicting risk of ovarian cancer
Dite GS, Spaeth E, Murphy NM, Allman R.
Eur J Cancer Prev. 2023;32(1):57-64.
SABCS Abstract: Validation of abridged breast cancer risk assessment model for the general population
Validation of a clinical and genetic model for predicting severe COVID-19
Dite GS, Murphy NM, Spaeth E, Allman R, Lifelines Corona Research Initiative
Epidemiol Infect. 2022 Apr 25;150:1-15.
The health effects in the US of quarantine policies based on predicted individual risk of severe COVID-19 outcomes.
Sam Lovick, Gillian S. Dite, Richard Allman. medRxiv 2021.03.21.21254065
A streamlined model for use in clinical breast cancer risk assessment maintains predictive power and is further improved with inclusion of a polygenic risk score.
Allman R, Spaeth E, Lai J, Gross SJ, Hopper JL.
PLoS One. 2021 Jan 22;16(1):e0245375. doi: 10.1371/journal.pone.0245375. eCollection 2021.
An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case-control study.
PLoS One. 2021 Feb 16;16(2):e0247205. doi: 10.1371/journal.pone.0247205. eCollection 2021.
Ability of known colorectal cancer susceptibility SNPs to predict colorectal cancer risk: A cohort study within the UK Biobank.
Gafni A, Dite GS, Spaeth Tuff E, Allman R, Hopper JL.
PLoS One. 2021 Sep 15;16(9):e0251469. doi: 10.1371/journal.pone.0251469. eCollection 2021.
Development and validation of a clinical and genetic model for predicting risk of severe COVID-19.
Dite GS, Murphy NM, Allman R.
Epidemiol Infect. 2021 Jul 2;149:e162. doi: 10.1017/S095026882100145X.
Bridging the Data Gap in Breast Cancer Risk Assessment to Enable Widespread Clinical Implementation across the Multiethnic Landscape of the US.
Spaeth E, Starlard-Davenport A, Allman R.
J Cancer Treatment Diagn. 2018;2(4):1-6. doi: 10.29245/2578-2967/2018/4.1137. Epub 2018 Aug 3.
Validation of a genetic risk score for Arkansas women of color.
Starlard-Davenport A, Allman R, Dite GS, Hopper JL, Spaeth Tuff E, Macleod S, Kadlubar S, Preston M, Henry-Tillman R.
PLoS One. 2018 Oct 3;13(10):e0204834. doi: 10.1371/journal.pone.0204834. eCollection 2018.
Breast Cancer Risk Prediction Using Clinical Models and 77 Independent Risk-Associated SNPs for Women Aged Under 50 Years: Australian Breast Cancer Family Registry.
Dite GS, MacInnis RJ, Bickerstaffe A, Dowty JG, Allman R, Apicella C, Milne RL, Tsimiklis H, Phillips KA, Giles GG, Terry MB, Southey MC, Hopper JL.
Cancer Epidemiol Biomarkers Prev. 2016 Feb;25(2):359-65. doi: 10.1158/1055-9965.EPI-15-0838. Epub 2015 Dec 16.
SNPs and breast cancer risk prediction for African American and Hispanic women.
Allman R, Dite GS, Hopper JL, Gordon O, Starlard-Davenport A, Chlebowski R, Kooperberg C.
Breast Cancer Res Treat. 2015 Dec;154(3):583-9. doi: 10.1007/s10549-015-3641-7. Epub 2015 Nov 20.
Economic evaluation of using a genetic test to direct breast cancer chemoprevention in white women with a previous breast biopsy.
Green LE, Dinh TA, Hinds DA, Walser BL, Allman R (2014). Applied Health Economics & Health Policy. 12(2): 203-17
Cost-effectiveness of a genetic test for breast cancer risk.
Folse HJ, Green LE, Kress A, Allman R, Dinh TA.
Cancer Prev Res (Phila). 2013 Dec;6(12):1328-36. doi: 10.1158/1940-6207.CAPR-13-0056.
Our patented technology sets us apart
Patents granted in US
- Patent 11,031,098, Computer systems and methods for genomic analysis
- Patent 10,683,549, Methods for assessing risk of developing breast cancer
- Patent Nos. 9,051,617; 9,068,229 and 9,702,011 covering three of the core genetic markers included in the BREVAGenplus® risk assessment test
- Patent No. 7,127,355 offering broad protection re: methods of genetic analysis (the concept of combining clinical risk assessment with genetic risk factors to improve predictability over clinical risk assessment alone)
- Patent No. 6,969,589 covering the identification of informative SNPs
Patents granted in China
- Patent Nos. 200680051710.0; 201310524782.4; 201310524916.2 and 201310524765.0 “Markers for Breast Cancer”
- Patent No. 201080033130.5 Methods for Breast Cancer Risk Assessment
- Patent Nos. 09101235.4; 12112875.1; 12112368.5 and 12112874.2 “Markers for Breast Cancer”
- Patent No. 12109000.5 Methods for Breast Cancer Risk Assessment
Patent families pending
- Methods for breast cancer risk assessment
- Methods for assessing risk of developing breast cancer
- Improved methods for assessing risk of developing breast cancer
- Markers for breast cancer
- Methods for genetic analysis
- Methods for genomic analysis
- Methods for assessing risk of developing colorectal cancer
- Methods of assessing risk developing a disease
- Methods for assessing risk of developing a severe response to coronavirus infection
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