Development and validation of a simple prostate cancer risk prediction model based on age, family history, and polygenic risk.

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.

Melanoma risk prediction based on a polygenic risk score and clinical risk factors.

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.

A combined clinical and genetic model for predicting risk of ovarian cancer.

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.

Polygenic risk scores for cardiovascular diseases and type 2 diabetes.

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.

Development and validation of a clinical and genetic model for predicting risk of severe COVID-19.

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 (ß = 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.

An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case-control study.

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.

Genome sequencing of human in vitro fertilisation embryos for pathogenic variation screening.

Whole-genome sequencing of preimplantation human embryos to detect and screen for genetic diseases is a technically challenging extension to preconception screening. Combining preconception genetic screening with preimplantation testing of human embryos facilitates the detection of de novo mutations and self-validates transmitted variant detection in both the reproductive couple and the embryo's samples. Here we describe a trio testing workflow that involves whole-genome sequencing of amplified DNA from biopsied embryo trophectoderm cells and genomic DNA from both parents. Variant prediction software and annotation databases were used to assess variants of unknown significance and previously not described de novo variants in five single-gene preimplantation genetic testing couples and eleven of their embryos. Pathogenic variation, tandem repeat, copy number and structural variations were examined against variant calls for compound heterozygosity and predicted disease status was ascertained. Multiple trio testing showed complete concordance with known variants ascertained by single-nucleotide polymorphism array and uncovered de novo and transmitted pathogenic variants. This pilot study describes a method of whole-genome sequencing and analysis for embryo selection in high-risk couples to prevent early life fatal genetic conditions that adversely affect the quality of life of the individual and families.

Polymorphism rs1385129 Within Glut1 Gene SLC2A1 Is Linked to Poor CD4+ T Cell Recovery in Antiretroviral-Treated HIV+ Individuals.

Untreated HIV infection is associated with progressive CD4+ T cell depletion, which is generally recovered with combination antiretroviral therapy (cART). However, a significant proportion of cART-treated individuals have poor CD4+ T cell reconstitution. We investigated associations between HIV disease progression and CD4+ T cell glucose transporter-1 (Glut1) expression. We also investigated the association between these variables and specific single nucleotide polymorphisms (SNPs) within the Glut1 regulatory gene AKT (rs1130214, rs2494732, rs1130233, and rs3730358) and in the Glut1-expressing gene SLC2A1 (rs1385129 and rs841853) and antisense RNA 1 region SLC2A1-AS1 (rs710218). High CD4+Glut1+ T cell percentage is associated with rapid CD4+ T cell decline in HIV-positive treatment-naïve individuals and poor T cell recovery in HIV-positive individuals on cART. Evidence suggests that poor CD4+ T cell recovery in treated HIV-positive individuals is linked to the homozygous genotype (GG) associated with SLC2A1 SNP rs1385129 when compared to those with a recessive allele (GA/AA) (odds ratio = 4.67; P = 0.04). Furthermore, poor response to therapy is less likely among Australian participants when compared against American participants (odds ratio: 0.12; P = 0.01) despite there being no difference in prevalence of a specific genotype for any of the SNPs analyzed between nationalities. Finally, CD4+Glut1+ T cell percentage is elevated among those with a homozygous dominant genotype for SNPs rs1385129 (GG) and rs710218 (AA) when compared to those with a recessive allele (GA/AA and AT/TT respectively) (P < 0.04). The heterozygous genotype associated with AKT SNP 1130214 (GT) had a higher CD4+Glut1+ T cell percentage when compared to the dominant homozygous genotype (GG) (P = 0.0068). The frequency of circulating CD4+Glut1+ T cells and the rs1385129 SLC2A1 SNP may predict the rate of HIV disease progression and CD4+ T cell recovery in untreated and treated infection, respectively.

High Molecular Weight DNA Enrichment with Peptide Nucleic Acid Probes.

Here, we describe how peptide nucleic acid (PNA) probes can be used to enrich genomic DNA fractions to facilitate downstream analysis, such as the haplotype phasing of the isolated genomic pieces. This method enriches for polymorphic regions of fragmented chromosomes by physically separating the desired sequence and flanking regions. The PNA probes used for enrichment are novel synthetic nucleic acids with highly specific targeting and hybridization properties. Using a enrichment technique, we capture high molecular weight genomic DNA using nothing more than a simple modification to standard genomic DNA extraction from blood.

Haplotyping the human leukocyte antigen system from single chromosomes.

We describe a method for determining the parental HLA haplotypes of a single individual without recourse to conventional segregation genetics. Blood samples were cultured to identify and sort chromosome 6 by bivariate flow cytometry. Single chromosome 6 amplification products were confirmed with a single nucleotide polymorphism (SNP) array and verified by deep sequencing to enable assignment of both alleles at the HLA loci, defining the two haplotypes. This study exemplifies a rapid and efficient method of haplotyping that can be applied to any chromosome pair, or indeed all chromosome pairs, using a single sorting operation. The method represents a cost-effective approach to complete phasing of SNPs, which will facilitate a deeper understanding of the links between SNPs, gene regulation and protein function.

Human leukocyte antigen haplotype phasing by allele-specific enrichment with peptide nucleic acid probes.

Targeted capture of large fragments of genomic DNA that enrich for human leukocyte antigen (HLA) system haplotypes has utility in haematopoietic stem cell transplantation. Current methods of HLA matching are based on inference or familial studies of inheritance; and each approach has its own inherent limitations. We have designed and tested a probe-target-extraction method for capturing specific HLA haplotypes by hybridization of peptide nucleic acid (PNA) probes to alleles of the HLA-DRB1 gene. Short target fragments contained in plasmids were initially used to optimize the method followed by testing samples of genomic DNA from human subjects with preselected HLA haplotypes and obtained approximately 10% enrichment for the specific haplotype. When performed with high-molecular-weight genomic DNA, 99.0% versus 84.0% alignment match was obtained for the specific haplotype probed. The allele-specific target enrichment that we obtained can facilitate the elucidation of haplotypes between the 65 kb separating the HLA-DRB1 and the HLA-DQA1 genes, potentially spanning a total distance of at least 130 kb. Allele-specific target enrichment with PNA probes is a straightforward technique that has the capability to improve the resolution of DNA and whole genome sequencing technologies by allowing haplotyping of enriched DNA and crucially, retaining the DNA methylation profile.

The effect of folinic acid on methylenetetrahydrofolate reductase polymorphisms in methotrexate-treated allogeneic hematopoietic stem cell transplants.

This study examined the contribution single nucleotide polymorphisms (SNPs) of the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene have on clinical outcomes in hematopoietic stem cell transplant patients treated with the antiproliferative drug methotrexate. Two common SNPs, 677C>T and 1298A>C, were genotyped by polymerase chain reaction-restriction fragment-length polymorphism (PCR-RFLP) from samples obtained from patient DNA samples. Eleven clinical outcomes including survival and graft-versus-host disease (GVHD) were assessed against donor and recipient MTHFR genotypes against pretransplantation variables. Folinic acid (FA) as treatment for oral mucositis toxicity was used at investigator discretion in 72 of 140. Donor MTHFR 1298AA genotype was associated with decreased 5-year survival (P = .03) and event-free survival (EFS) (P = .02) in patients withheld FA. Donor MTHFR 677CC genotype was associated with earlier GVHD (P = .003), and more severe acute GVHD (P = 0.02). FA was significantly associated with decreased survival (P = 0.02) in patients given a donor MTHFR 677CT transplant. FA was significantly associated with decreased survival (P = .04), EFS (P = .009) in patients given a donor MTHFR 1298AC transplant MTHFR gene polymorphisms indicate a potentially useful gene for donor selection where more than one donor is available. Use of FA following transplantation should be reconsidered in the context of patient and donor MTHFR genotypes.