Weibull regression with Bayesian variable selection to identify prognostic tumour markers of breast cancer survival.

As data-rich medical datasets are becoming routinely collected, there is a growing demand for regression methodology that facilitates variable selection over a large number of predictors. Bayesian variable selection algorithms offer an attractive solution, whereby a sparsity inducing prior allows inclusion of sets of predictors simultaneously, leading to adjusted effect estimates and inference of which covariates are most important. We present a new implementation of Bayesian variable selection, based on a Reversible Jump MCMC algorithm, for survival analysis under the Weibull regression model. A realistic simulation study is presented comparing against an alternative LASSO-based variable selection strategy in datasets of up to 20,000 covariates. Across half the scenarios, our new method achieved identical sensitivity and specificity to the LASSO strategy, and a marginal improvement otherwise. Runtimes were comparable for both approaches, taking approximately a day for 20,000 covariates. Subsequently, we present a real data application in which 119 protein-based markers are explored for association with breast cancer survival in a case cohort of 2287 patients with oestrogen receptor-positive disease. Evidence was found for three independent prognostic tumour markers of survival, one of which is novel. Our new approach demonstrated the best specificity.PJN and SR were funded by the Medical Research Council. PJN also acknowledges partial support from the NIHR Cambridge Biomedical Research Centre.This is the accepted manuscript. The final version is available from SAGE at http://dx.doi.org/10.1177/096228021454874

Fine-Scale mapping of the 11q13 breast cancer susceptibility locus

The Cancer Genetic Markers of Susceptibility genome-wide association study (GWAS) originally identified a single nucleotide polymorphism (SNP) rs11249433 at 1p11.2 associated with breast cancer risk. To fine-map this locus, we genotyped 92 SNPs in a 900kb region (120,505,799–121,481,132) flanking rs11249433 in 45,276 breast cancer cases and 48,998 controls of European, Asian and African ancestry from 50 studies in the Breast Cancer Association Consortium. Genotyping was done using iCOGS, a custom-built array. Due to the complicated nature of the region on chr1p11.2: 120,300,000–120,505,798, that lies near the centromere and contains seven duplicated genomic segments, we restricted analyses to 429 SNPs excluding the duplicated regions (42 genotyped and 387 imputed). Perallelic associations with breast cancer risk were estimated using logistic regression models adjusting for study and ancestry-specific principal components. The strongest association observed was with the original identified index SNP rs11249433 (minor allele frequency (MAF) 0.402; per-allele odds ratio (OR) = 1.10, 95% confidence interval (CI) 1.08–1.13, $\small \textit{P}$ = 1.49 x 10$^{-21}$). The association for rs11249433 was limited to ER-positive breast cancers (test for heterogeneity $\small \textit{P}$ $\leq$ 8.41 x 10$^{-5}$). Additional analyses by other tumor characteristics showed stronger associations with moderately/well differentiated tumors and tumors of lobular histology. Although no significant eQTL associations were observed, in silico analyses showed that rs11249433 was located in a region that is likely a weak enhancer/promoter. Fine-mapping analysis of the 1p11.2 breast cancer susceptibility locus confirms this region to be limited to risk to cancers that are ER-positive.Cancer Research UK (Grant IDs: C1287/A10118, C1287/A12014, C490/A10124, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), European Community's Seventh Framework Programme (Grant ID: HEALTH-F2-2009-223175), National Institutes of Health (Grant IDs: CA128978, CA116167, CA176785, CA116201, CA63464, CA54281, CA098758, CA132839, R01CA100374, R01CA64277, R01CA148667, R37CA70867, R01CA092447), National Institute for Health Research. See also Table K via http://dx.doi.org/10.1371/journal.pone.0160316.s003This is the final version of the article. It first appeared from the Public Library of Science via http://dx.doi.org/10.1371/journal.pone.016031

Knowledge and attitudes of parents and professionals to neonatal BCG vaccination in light of recent UK policy changes: A questionnaire study

<p>Abstract</p> <p>Background</p> <p>Universal BCG vaccination in the UK ended in 2005. The new vaccination policy instead offers a number of different forms of selective vaccination to newborns based on risk of acquiring TB. We set out to assess the attitudes and knowledge of both parents and professionals to the new policy for neonatal BCG vaccination.</p> <p>Methods</p> <p>A short questionnaire was designed, made up of demographic and attitude questions, as well as very basic knowledge questions. The researchers handed out the questionnaire to all parents and professionals in the antenatal and postnatal areas, as well as the paediatric and neonatal units during a 6-week period. The site was the Royal Oldham hospital, a district general hospital with 3250 deliveries per year and multi-ethnic in its population mix.</p> <p>Results</p> <p>A total of 253 completed questionnaires were collected. The ethnic origin of responders was 50.6% White British, 18.2% Bangladeshi, 8.7% Indian, 4% White/Asian, the remaining 18.5% of other origins. 71.5% of responders said they had heard of BCG vaccine. When asked if they knew the new policy for its use, 33.2% answered yes. 24.5% gave the most accurate response when asked who now receives BCG.</p> <p>Conclusion</p> <p>We have found that amongst parents and professionals alike there is a lack of knowledge of the new policy. This has lead to confusion and as knowledge amongst the professionals who identify neonates for vaccination is low, uptake may be sub-optimal. We suggest that units investigate the issue and ensure that the new policy is understood and implemented correctly.</p

Public health genomics and personalized prevention: lessons from the COGS project.

Using the principles of public health genomics, we examined the opportunities and challenges of implementing personalized prevention programmes for cancer at the population level. Our model-based estimates indicate that polygenic risk stratification can potentially improve the effectiveness and cost-effectiveness of screening programmes. However, compared with 'one-size-fits-all' screening programmes, personalized screening adds further layers of complexity to the organization of screening services and raises ethical, legal and social challenges. Before polygenic inheritance is translated into population screening strategy, evidence from empirical research and engagement with and education of the public and the health professionals are needed

Saliva samples are a viable alternative to blood samples as a source of DNA for high throughput genotyping.

BACKGROUND: The increasing trend for incorporation of biological sample collection within clinical trials requires sample collection procedures which are convenient and acceptable for both patients and clinicians. This study investigated the feasibility of using saliva-extracted DNA in comparison to blood-derived DNA, across two genotyping platforms: Applied Biosystems Taqman™ and Illumina Beadchip™ genome-wide arrays. METHOD: Patients were recruited from the Pharmacogenetics of Breast Cancer Chemotherapy (PGSNPS) study. Paired blood and saliva samples were collected from 79 study participants. The Oragene DNA Self-Collection kit (DNAgenotek®) was used to collect and extract DNA from saliva. DNA from EDTA blood samples (median volume 8 ml) was extracted by Gen-Probe, Livingstone, UK. DNA yields, standard measures of DNA quality, genotype call rates and genotype concordance between paired, duplicated samples were assessed. RESULTS: Total DNA yields were lower from saliva (mean 24 μg, range 0.2-52 μg) than from blood (mean 210 μg, range 58-577 μg) and a 2-fold difference remained after adjusting for the volume of biological material collected. Protein contamination and DNA fragmentation measures were greater in saliva DNA. 78/79 saliva samples yielded sufficient DNA for use on Illumina Beadchip arrays and using Taqman assays. Four samples were randomly selected for genotyping in duplicate on the Illumina Beadchip arrays. All samples were genotyped using Taqman assays. DNA quality, as assessed by genotype call rates and genotype concordance between matched pairs of DNA was high (>97%) for each measure in both blood and saliva-derived DNA. CONCLUSION: We conclude that DNA from saliva and blood samples is comparable when genotyping using either Taqman assays or genome-wide chip arrays. Saliva sampling has the potential to increase participant recruitment within clinical trials, as well as reducing the resources and organisation required for multicentre sample collection.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

A risk prediction algorithm for ovarian cancer incorporating BRCA1, BRCA2, common alleles and other familial effects.

BACKGROUND: Although BRCA1 and BRCA2 mutations account for only ∼27% of the familial aggregation of ovarian cancer (OvC), no OvC risk prediction model currently exists that considers the effects of BRCA1, BRCA2 and other familial factors. Therefore, a currently unresolved problem in clinical genetics is how to counsel women with family history of OvC but no identifiable BRCA1/2 mutations. METHODS: We used data from 1548 patients with OvC and their relatives from a population-based study, with known BRCA1/2 mutation status, to investigate OvC genetic susceptibility models, using segregation analysis methods. RESULTS: The most parsimonious model included the effects of BRCA1/2 mutations, and the residual familial aggregation was accounted for by a polygenic component (SD 1.43, 95% CI 1.10 to 1.86), reflecting the multiplicative effects of a large number of genes with small contributions to the familial risk. We estimated that 1 in 630 individuals carries a BRCA1 mutation and 1 in 195 carries a BRCA2 mutation. We extended this model to incorporate the explicit effects of 17 common alleles that are associated with OvC risk. Based on our models, assuming all of the susceptibility genes could be identified we estimate that the half of the female population at highest genetic risk will account for 92% of all OvCs. CONCLUSIONS: The resulting model can be used to obtain the risk of developing OvC on the basis of BRCA1/2, explicit family history and common alleles. This is the first model that accounts for all OvC familial aggregation and would be useful in the OvC genetic counselling process.This work has been supported by grants from Cancer Research UK (C1005/A12677, C12292/A11174, C490/A10119, C490/A10124) including the PROMISE research programme, the Eve Appeal and the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge.This is the final version of the article. It first appeared from BMJ Publishing via http://dx.doi.org/10.1136/jmedgenet-2015-10307

The contribution of genetic variants to disease depends on the ruler

Our understanding of the genetic basis of disease has evolved from descriptions of overall heritability or familiality to the identification of large numbers of risk loci. One can quantify the impact of such loci on disease using a plethora of measures, which can guide future research decisions. However, different measures can attribute varying degrees of importance to a variant. In this Analysis, we consider and contrast the most commonly used measures-specifically, the heritability of disease liability, approximate heritability, sibling recurrence risk, overall genetic variance using a logarithmic relative risk scale, the area under the receiver-operating curve for risk prediction and the population attributable fraction-and give guidelines for their use that should be explicitly considered when assessing the contribution of genetic variants to disease

Interactions between genes involved in the antioxidant defence system and breast cancer risk

The aim of the study is to examine the association between multilocus genotypes across 10 genes encoding proteins in the antioxidant defence system and breast cancer. The 10 genes are SOD1, SOD2, GPX1, GPX4, GSR, CAT, TXN, TXN2, TXNRD1 and TXNRD2. In all, 2271 cases and 2280 controls were used to examine gene–gene interactions between 52 single nucleotide polymorphisms (SNPs) that are hypothesised to tag all common variants in the 10 genes. The statistical analysis is based on three methods: unconditional logistic regression, multifactor dimensionality reduction and hierarchical cluster analysis. We examined all two- and three-way combinations with unconditional logistic regression and multifactor dimensionality reduction, and used a global approach with all SNPs in the hierarchical cluster analysis. Single-locus studies of an association of genetic variants in the antioxidant defence genes and breast cancer have been contradictory and inconclusive. It is the first time, to our knowledge, the association between multilocus genotypes across genes coding for antioxidant defence enzymes and breast cancer is investigated. We found no evidence of an association with breast cancer with our multilocus approach. The search for two-way interactions gave experiment-wise significance levels of P=0.24 (TXN [t2715c] and TXNRD2 [g23524a]) and P=0.58 (GSR [c39396t] and TXNRD2 [a442g]), for the unconditional logistic regression and multifactor dimensionality reduction, respectively. The experiment-wise significance levels for the three-way interactions were P=0.94 (GPX4 [t2572c], TXN [t2715c] and TXNRD2 [g23524a]) and P=0.29 (GSR [c39396t], TXN [t2715c] and TXNRD2 [a442g]) for the unconditional logistic regression and multifactor dimensionality reduction, respectively. In the hierarchical cluster analysis neither the average across four rounds with replacement of missing values at random (P=0.12) nor a fifth round with more balanced proportion of missing values between cases and controls (P=0.17) was significant

Variation in prescribing for anxiety and depression: a reflection of health inequalities, cultural differences or variations in access to care?

BACKGROUND: There are large variations in mental health prescribing in UK populations. However the underlying reasons for these differences, which may be related to differences in prevalence, cultural expectations or practical difficulties in access to treatment, remain uncertain. METHODS: Linear modelling was used to investigate whether population characteristics or access to primary care account for variations in mental health prescribing across 39 deprived neighbourhoods. RESULTS: The proportion of sampled respondents whose first language was not English and the ratio of general practitioners to population explained 61% of variation. Deprivation and mental health status were not significant predictors of prescribing in these relatively deprived communities. CONCLUSION: These findings suggest that mental health prescribing, within deprived areas, as well as reflecting cultural and social differences in prescribing, may also be a proxy measure of access to care

Common genetic variation associated with increased susceptibility to prostate cancer does not increase risk of radiotherapy toxicity.

BACKGROUND: Numerous germline single-nucleotide polymorphisms increase susceptibility to prostate cancer, some lying near genes involved in cellular radiation response. This study investigated whether prostate cancer patients with a high genetic risk have increased toxicity following radiotherapy. METHODS: The study included 1560 prostate cancer patients from four radiotherapy cohorts: RAPPER (n=533), RADIOGEN (n=597), GenePARE (n=290) and CCI (n=150). Data from genome-wide association studies were imputed with the 1000 Genomes reference panel. Individuals were genetically similar with a European ancestry based on principal component analysis. Genetic risks were quantified using polygenic risk scores. Regression models tested associations between risk scores and 2-year toxicity (overall, urinary frequency, decreased stream, rectal bleeding). Results were combined across studies using standard inverse-variance fixed effects meta-analysis methods. RESULTS: A total of 75 variants were genotyped/imputed successfully. Neither non-weighted nor weighted polygenic risk scores were associated with late radiation toxicity in individual studies (P>0.11) or after meta-analysis (P>0.24). No individual variant was associated with 2-year toxicity. CONCLUSION: Patients with a high polygenic susceptibility for prostate cancer have no increased risk for developing late radiotherapy toxicity. These findings suggest that patients with a genetic predisposition for prostate cancer, inferred by common variants, can be safely treated using current standard radiotherapy regimens