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  1. Article ; Online: Development and validation of a simple prostate cancer risk prediction model based on age, family history, and polygenic risk.

    Dite, Gillian S / Spaeth, Erika / Murphy, Nicholas M / Allman, Richard

    The Prostate

    2023  Volume 83, Issue 10, Page(s) 962–969

    Abstract: Background: Accurate prostate cancer risk assessment will enable identification of men who are at increased risk of the disease. Using the UK Biobank population-based cohort, we developed and validated a simple model comprising age, family history, and ... ...

    Abstract Background: Accurate prostate cancer risk assessment will enable identification of men who are at increased risk of the disease. Using the UK Biobank population-based cohort, we developed and validated a simple model comprising age, family history, and a polygenic risk score (PRS) to predict 5-year risk of prostate cancer.
    Methods: Eligible participants were unaffected Caucasian men aged 40-69 years at their baseline assessment who had genotyping data available and had completed 6 or more weeks of follow-up. Family history was the number of affected first-degree relatives: 0, 1, or 2+. We used 264 single-nucleotide polymorphisms (SNPs) of a previously developed 269-SNP PRS and population standardized the PRS to have a mean of 1. Age was categorized into 10-year groups: 40-49, 50-59, and 60-69. In a 70% training data set, we used Cox regression with age as the time axis to model family history, PRS, and age group. The model estimates were used with prostate cancer incidences to derive 5-year risks of prostate cancer. Using 5 years of follow-up in a 30% testing data set, the model was tested in terms of its association per quintile of risk, discrimination, and calibration.
    Results: Of the 198 334 eligible participants, 8996 (4.5%) were diagnosed with incident prostate cancer during follow-up and had a mean age of 67.9 (SD = 5.8) years at diagnosis. The best-fitting model included the PRS, family history, 10-year age group, interactions between age and PRS, and age and family history. In the 30% testing data set with follow-up limited to 5 years, the hazard ratio per SD of 5-year risk was 3.058 (95% confidence interval [CI], 2.720-3.438) and the Harrell's C-index was 0.811 (95% CI, 0.800-0.821). Overall, there were 1088 observed and 1159.1 expected prostate cancers, a standardized incidence ratio of 0.939 (95% CI, 0.885-0.996).
    Conclusions: Men at increased risk of prostate cancer could benefit from informed discussions around the risks and benefits of available options for screening for prostate cancer. Although the model was developed in Caucasian men, it can be used with ethnicity-specific polygenic risk and incidence rates for other populations.
    MeSH term(s) Male ; Humans ; Aged ; Child ; Risk Assessment ; Risk Factors ; Prostatic Neoplasms/epidemiology ; Prostatic Neoplasms/genetics ; Proportional Hazards Models ; Incidence ; Polymorphism, Single Nucleotide ; Genetic Predisposition to Disease
    Language English
    Publishing date 2023-04-16
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 604707-5
    ISSN 1097-0045 ; 0270-4137
    ISSN (online) 1097-0045
    ISSN 0270-4137
    DOI 10.1002/pros.24537
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Validation of a clinical and genetic model for predicting severe COVID-19.

    Dite, Gillian S / Murphy, Nicholas M / Spaeth, Erika / Allman, Richard

    Epidemiology and infection

    2022  , Page(s) 1–15

    Language English
    Publishing date 2022-04-25
    Publishing country England
    Document type Journal Article
    ZDB-ID 632982-2
    ISSN 1469-4409 ; 0950-2688
    ISSN (online) 1469-4409
    ISSN 0950-2688
    DOI 10.1017/S0950268822000541
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  3. Article ; Online: An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case-control study.

    Dite, Gillian S / Murphy, Nicholas M / Allman, Richard

    PloS one

    2021  Volume 16, Issue 2, Page(s) e0247205

    Abstract: Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate ... ...

    Abstract Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). A model incorporating the SNP score and clinical risk factors (AUC = 0.786; 95% confidence interval = 0.763 to 0.808) had 111% better discrimination of disease severity than a model with just age and gender (AUC = 0.635; 95% confidence interval = 0.607 to 0.662). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors-not age and gender-that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone (or only clinical factors) to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.
    MeSH term(s) Age Factors ; Aged ; COVID-19/epidemiology ; COVID-19/genetics ; COVID-19/pathology ; Comorbidity ; Female ; Humans ; Male ; Middle Aged ; Polymorphism, Single Nucleotide ; Race Factors ; Severity of Illness Index ; Sex Factors
    Language English
    Publishing date 2021-02-16
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1932-6203
    ISSN (online) 1932-6203
    DOI 10.1371/journal.pone.0247205
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  4. Article ; Online: A combined clinical and genetic model for predicting risk of ovarian cancer.

    Dite, Gillian S / Spaeth, Erika / Murphy, Nicholas M / Allman, Richard

    European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP)

    2022  Volume 32, Issue 1, Page(s) 57–64

    Abstract: Objective: Women with a family history of ovarian cancer or a pathogenic or likely pathogenic gene variant are at high risk of the disease, but very few women have these risk factors. We assessed whether a combined polygenic and clinical risk score ... ...

    Abstract Objective: Women with a family history of ovarian cancer or a pathogenic or likely pathogenic gene variant are at high risk of the disease, but very few women have these risk factors. We assessed whether a combined polygenic and clinical risk score could predict risk of ovarian cancer in population-based women who would otherwise be considered as being at average risk.
    Methods: We used the UK Biobank to conduct a prospective cohort study assessing the performance of 10-year ovarian cancer risks based on a polygenic risk score, a clinical risk score and a combined risk score. We used Cox regression to assess association, Harrell's C-index to assess discrimination and Poisson regression to assess calibration.
    Results: The combined risk model performed best and problems with calibration were overcome by recalibrating the model, which then had a hazard ratio per quintile of risk of 1.338 [95% confidence interval (CI), 1.152-1.553], a Harrell's C-index of 0.663 (95% CI, 0.629-0.698) and overall calibration of 1.000 (95% CI, 0.874-1.145). In the refined model with estimates based on the entire dataset, women in the top quintile of 10-year risk were at 1.387 (95% CI, 1.086-1.688) times increased risk, while women in the top quintile of full-lifetime risk were at 1.527 (95% CI, 1.187-1.866) times increased risk compared with the population.
    Conclusion: Identification of women who are at high risk of ovarian cancer can allow healthcare providers and patients to engage in joint decision-making discussions around the risks and benefits of screening options or risk-reducing surgery.
    MeSH term(s) Humans ; Female ; Models, Genetic ; Prospective Studies ; Ovarian Neoplasms/diagnosis ; Ovarian Neoplasms/epidemiology ; Ovarian Neoplasms/genetics ; Health Personnel
    Chemical Substances AT-511
    Language English
    Publishing date 2022-10-27
    Publishing country England
    Document type Journal Article
    ZDB-ID 1137033-6
    ISSN 1473-5709 ; 0959-8278
    ISSN (online) 1473-5709
    ISSN 0959-8278
    DOI 10.1097/CEJ.0000000000000771
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Development and validation of a clinical and genetic model for predicting risk of severe COVID-19.

    Dite, Gillian S / Murphy, Nicholas M / Allman, Richard

    Epidemiology and infection

    2021  Volume 149, Page(s) e162

    Abstract: Clinical and genetic risk factors for severe coronavirus disease 2019 (COVID-19) are often considered independently and without knowledge of the magnitudes of their effects on risk. Using severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) ... ...

    Abstract Clinical and genetic risk factors for severe coronavirus disease 2019 (COVID-19) are often considered independently and without knowledge of the magnitudes of their effects on risk. Using severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) positive participants from the UK Biobank, we developed and validated a clinical and genetic model to predict risk of severe COVID-19. We used multivariable logistic regression on a 70% training dataset and used the remaining 30% for validation. We also validated a previously published prototype model. In the validation dataset, our new model was associated with severe COVID-19 (odds ratio per quintile of risk = 1.77, 95% confidence interval (CI) 1.64-1.90) and had acceptable discrimination (area under the receiver operating characteristic curve = 0.732, 95% CI 0.708-0.756). We assessed calibration using logistic regression of the log odds of the risk score, and the new model showed no evidence of over- or under-estimation of risk (α = -0.08; 95% CI -0.21-0.05) and no evidence or over-or under-dispersion of risk (β = 0.90, 95% CI 0.80-1.00). Accurate prediction of individual risk is possible and will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.
    MeSH term(s) Aged ; Aged, 80 and over ; COVID-19/epidemiology ; COVID-19/genetics ; COVID-19/physiopathology ; Comorbidity ; Female ; Humans ; Male ; Models, Genetic ; Models, Statistical ; Polymorphism, Single Nucleotide/genetics ; ROC Curve ; Reproducibility of Results ; Risk Assessment/methods ; SARS-CoV-2 ; Severity of Illness Index
    Language English
    Publishing date 2021-07-02
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 632982-2
    ISSN 1469-4409 ; 0950-2688
    ISSN (online) 1469-4409
    ISSN 0950-2688
    DOI 10.1017/S095026882100145X
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  6. Article ; Online: Melanoma risk prediction based on a polygenic risk score and clinical risk factors.

    Wong, Chi Kuen / Dite, Gillian S / Spaeth, Erika / Murphy, Nicholas M / Allman, Richard

    Melanoma research

    2023  Volume 33, Issue 4, Page(s) 293–299

    Abstract: Melanoma is one of the most commonly diagnosed cancers in the Western world: third in Australia, fifth in the USA and sixth in the European Union. Predicting an individual's personal risk of developing melanoma may aid them in undertaking effective risk ... ...

    Abstract Melanoma is one of the most commonly diagnosed cancers in the Western world: third in Australia, fifth in the USA and sixth in the European Union. Predicting an individual's personal risk of developing melanoma may aid them in undertaking effective risk reduction measures. The objective of this study was to use the UK Biobank to predict the 10-year risk of melanoma using a newly developed polygenic risk score (PRS) and an existing clinical risk model. We developed the PRS using a matched case-control training dataset ( N  = 16 434) in which age and sex were controlled by design. The combined risk score was developed using a cohort development dataset ( N  = 54 799) and its performance was tested using a cohort testing dataset ( N  = 54 798). Our PRS comprises 68 single-nucleotide polymorphisms and had an area under the receiver operating characteristic curve of 0.639 [95% confidence interval (CI) = 0.618-0.661]. In the cohort testing data, the hazard ratio per SD of the combined risk score was 1.332 (95% CI = 1.263-1.406). Harrell's C-index was 0.685 (95% CI = 0.654-0.715). Overall, the standardized incidence ratio was 1.193 (95% CI = 1.067-1.335). By combining a PRS and a clinical risk score, we have developed a risk prediction model that performs well in terms of discrimination and calibration. At an individual level, information on the 10-year risk of melanoma can motivate people to take risk-reduction action. At the population level, risk stratification can allow more effective population-level screening strategies to be implemented.
    MeSH term(s) Humans ; Melanoma/genetics ; Melanoma/epidemiology ; Risk Assessment ; Skin Neoplasms/genetics ; Skin Neoplasms/epidemiology ; Risk Factors ; Incidence ; Genetic Predisposition to Disease ; Genome-Wide Association Study
    Language English
    Publishing date 2023-04-24
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1095779-0
    ISSN 1473-5636 ; 0960-8931
    ISSN (online) 1473-5636
    ISSN 0960-8931
    DOI 10.1097/CMR.0000000000000896
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  7. Article ; Online: Genetic associations with severe COVID-19

    Murphy, Nicholas M / Dite, Gillian S / Allman, Richard

    medRxiv

    Abstract: Identification of host genetic factors that predispose individuals to severe COVID-19 is important, not only for understanding the disease and guiding the development of treatments, but also for risk prediction when combined to form a polygenic risk ... ...

    Abstract Identification of host genetic factors that predispose individuals to severe COVID-19 is important, not only for understanding the disease and guiding the development of treatments, but also for risk prediction when combined to form a polygenic risk score (PRS). Using population controls, Pairo-Castineira et al. identified 12 SNPs (a panel of 8 SNPs and a panel of 6 SNPs, with two SNPs in both panels) associated with severe COVID-19. Using controls with asymptomatic or mild COVID-19, we were able to replicate the association with severe COVID-19 for only three of their SNPs and found marginal evidence for an association for one other. When combined as an 8-SNP PRS and a 6-SNP PRS, we found no evidence of association with severe COVID-19. The difference in our results and the results of Pairo-Castineira et al. might be the choice of controls: population controls vs controls with asymptomatic or mild COVID-19.
    Keywords covid19
    Language English
    Publishing date 2021-03-31
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.03.29.21254509
    Database COVID19

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  8. Article ; Online: Development and validation of a clinical and genetic model for predicting risk of severe COVID-19

    Dite, Gillian S / Murphy, Nicholas M / Allman, Richard

    medRxiv

    Abstract: Age, sex, and comorbidities are known risk factors for severe COVID-19 but are frequently considered independently and without accurate knowledge of the magnitude of their effects on risk. Single-nucleotide polymorphisms (SNPs) associated with risk of ... ...

    Abstract Age, sex, and comorbidities are known risk factors for severe COVID-19 but are frequently considered independently and without accurate knowledge of the magnitude of their effects on risk. Single-nucleotide polymorphisms (SNPs) associated with risk of severe COVID-19 have appeared in the literature, but their application in predictive risk testing has not been validated. Reliance on age and sex alone to determine risk of severe COVID-19 will fail to accurately quantify risk. Here, we report the development and validation of a clinical and genetic model to predict risk of severe COVID-19 using confirmed SARS-CoV-2 positive participants from the UK Biobank. Our new model out-performed an age and sex model and had excellent discrimination and was well calibrated in the validation dataset. We also report validation studies of our prototype model and polygenic risk scores based on 8-SNP and 6-SNP panels identified in the literature. Accurate prediction of individual risk will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.
    Keywords covid19
    Language English
    Publishing date 2021-03-12
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.03.09.21253237
    Database COVID19

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  9. Article ; Online: An integrated clinical and genetic model for predicting risk of severe COVID-19

    Dite, Gillian S / Murphy, Nicholas M / Allman, Richard

    medRxiv

    Abstract: Background: Age and gender are often the only considerations in determining risk of severe COVID-19. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk ... ...

    Abstract Background: Age and gender are often the only considerations in determining risk of severe COVID-19. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Methods: Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). Results: A model incorporating the SNP score and clinical risk factors (AUC=0.786) had 111% better discrimination of disease severity than a model with just age and gender (AUC=0.635). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors -- not age and gender -- that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. Conclusions: We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.
    Keywords covid19
    Language English
    Publishing date 2020-09-30
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.09.30.20204453
    Database COVID19

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  10. Article ; Online: Polygenic risk scores for cardiovascular diseases and type 2 diabetes.

    Wong, Chi Kuen / Makalic, Enes / Dite, Gillian S / Whiting, Lawrence / Murphy, Nicholas M / Hopper, John L / Allman, Richard

    PloS one

    2022  Volume 17, Issue 12, Page(s) e0278764

    Abstract: Polygenic risk scores (PRSs) are a promising approach to accurately predict an individual's risk of developing disease. The area under the receiver operating characteristic curve (AUC) of PRSs in their population are often only reported for models that ... ...

    Abstract Polygenic risk scores (PRSs) are a promising approach to accurately predict an individual's risk of developing disease. The area under the receiver operating characteristic curve (AUC) of PRSs in their population are often only reported for models that are adjusted for age and sex, which are known risk factors for the disease of interest and confound the association between the PRS and the disease. This makes comparison of PRS between studies difficult because the genetic effects cannot be disentangled from effects of age and sex (which have a high AUC without the PRS). In this study, we used data from the UK Biobank and applied the stacked clumping and thresholding method and a variation called maximum clumping and thresholding method to develop PRSs to predict coronary artery disease, hypertension, atrial fibrillation, stroke and type 2 diabetes. We created case-control training datasets in which age and sex were controlled by design. We also excluded prevalent cases to prevent biased estimation of disease risks. The maximum clumping and thresholding PRSs required many fewer single-nucleotide polymorphisms to achieve almost the same discriminatory ability as the stacked clumping and thresholding PRSs. Using the testing datasets, the AUCs for the maximum clumping and thresholding PRSs were 0.599 (95% confidence interval [CI]: 0.585, 0.613) for atrial fibrillation, 0.572 (95% CI: 0.560, 0.584) for coronary artery disease, 0.585 (95% CI: 0.564, 0.605) for type 2 diabetes, 0.559 (95% CI: 0.550, 0.569) for hypertension and 0.514 (95% CI: 0.494, 0.535) for stroke. By developing a PRS using a dataset in which age and sex are controlled by design, we have obtained true estimates of the discriminatory ability of the PRSs alone rather than estimates that include the effects of age and sex.
    MeSH term(s) Humans ; Cardiovascular Diseases/genetics ; Diabetes Mellitus, Type 2/genetics ; Coronary Artery Disease/genetics ; Atrial Fibrillation ; Hypertension/genetics ; Risk Factors ; Stroke
    Language English
    Publishing date 2022-12-02
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0278764
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