African American men have the highest prostate cancer morbidity and mortality rates than any other racial or ethnic group in the US. Although the overall incidence of and mortality from prostate ...cancer has been declining in White men since 1991, the decline in African American men lags behind White men. Of particular concern is the growing literature on the disproportionate burden of prostate cancer among other Black men of West African ancestry in the Caribbean Islands, United Kingdom and West Africa. This higher incidence of prostate cancer observed in populations of African descent may be attributed to the fact that these populations share ancestral genetic factors. To better understand the burden of prostate cancer among men of West African Ancestry, we conducted a review of the literature on prostate cancer incidence, prevalence, and mortality in the countries connected by the Transatlantic Slave Trade.
Several published studies indicate high prostate cancer burden in Nigeria and Ghana. There was no published literature for the countries Benin, Gambia and Senegal that met our review criteria. Prostate cancer morbidity and/or mortality data from the Caribbean Islands and the United Kingdom also provided comparable or worse prostate cancer burden to that of US Blacks.
The growing literature on the disproportionate burden of prostate cancer among other Black men of West African ancestry follows the path of the Transatlantic Slave Trade. To better understand and address the global prostate cancer disparities seen in Black men of West African ancestry, future studies should explore the genetic and environmental risk factors for prostate cancer among this group.
A Bayesian mixed model approach using integrated nested Laplace approximations (INLA) allows us to construct flexible models that can account for pedigree structure. Using these models, we estimate ...genome-wide patterns of DNA methylation heritability (
), which are currently not well understood, as well as
of blood lipid measurements.
We included individuals from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study with Infinium 450 K cytosine-phosphate-guanine (CpG) methylation and blood lipid data pre- and posttreatment with fenofibrate in families with up to three-generation pedigrees. For genome-wide patterns, we constructed 1 model per CpG with methylation as the response variable, with a random effect to model kinship, and age and gender as fixed effects.
In total, 425,791 CpG sites pre-, but only 199,027 CpG sites posttreatment were found to have nonzero heritability. Across these CpG sites, the distributions of
estimates are similar in pre- and posttreatment (
median = 0.31, interquartile range IQR = 0.16;
median = 0.34, IQR = 0.20). Blood lipid
estimates were similar pre- and posttreatment with overlapping 95% credibility intervals. Heritability was nonzero for treatment effect, that is, the difference between pre- and posttreatment blood lipids. Estimates for triglycerides
are 0.48 (pre), 0.42 (post), and 0.21 (difference); likewise for high-density lipoprotein cholesterol
the estimates are 0.61, 0.68, and 0.10.
We show that with INLA, a fully Bayesian approach to estimate DNA methylation
is possible on a genome-wide scale. This provides uncertainty assessment of the estimates, and allows us to perform model selection via deviance information criterion (DIC) to identify CpGs with strong evidence for nonzero heritability.
Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few ...best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.
In a typical genome-enabled prediction problem there are many more predictor variables than response variables. This prohibits the application of multiple linear regression, because the unique ...ordinary least squares estimators of the regression coefficients are not defined. To overcome this problem, penalized regression methods have been proposed, aiming at shrinking the coefficients toward zero.
We explore prediction of phenotype from single nucleotide polymorphism (SNP) data in the GAW20 data set using a penalized regression approach (LASSO least absolute shrinkage and selection operator regression). We use 10-fold cross-validation to assess predictive performance and 10-fold nested cross-validation to specify a penalty parameter.
By analyzing approximately 600,000 SNPs we find that, when the sample size comprises a few hundred individuals, SNP effects are heavily penalized, resulting in a poor predictive performance. Increasing the sample size to a few thousand individuals results in a much smaller penalization of the true effects, thus greatly improving the prediction.
LASSO regression results in a heavy shrinkage of the regression coefficients, and also requires large sample sizes (several thousand individuals) to achieve good prediction.
Understanding caries etiology and distribution is central to understanding potential opportunities for and likely impact of new biotechnologies and biomaterials to reduce the caries burden worldwide. ...This review asserts the appropriateness of characterizing caries as a "pandemic" and considers static and temporal trend reports of worldwide caries distribution. Oral health disparities within and between countries are related to sugar consumption, fluoride usage, dental care, and social determinants of health. Findings of international and U.S. studies are considered in promoting World Health Organization's and others' recommendations for science-based preventive and disease management interventions at the individual, clinical, public health, and public policy levels.
While our knowledge of the dental caries process and its prevention has greatly advanced over the past fifty years, it is fair to state that the management of this disease at the level of the ...individual patient remains largely empirical. Recommendations for fluoride use by patients at different levels of caries risk are mainly based on the adage that more is better. There is a general understanding that the fluoride compound, concentration, frequency of use, duration of exposure, and method of delivery can influence fluoride efficacy. Two important factors are (1) the initial interaction of relatively high concentrations of fluoride with the tooth surface and plaque during application and (2) the retention of fluoride in oral fluids after application. Fluoride dentifrices remain the most widely used method of delivering topical fluoride. The efficacy of this approach in preventing dental caries is beyond dispute. However, the vast majority of currently marketed dentifrice products have not been clinically tested and have met only the minimal requirements of the FDA monograph using mainly laboratory testing and animal caries testing. Daily use of fluoride dental rinses as an adjunct to fluoride dentifrice has been shown to be clinically effective as has biweekly use of higher concentration fluoride rinses. The use of remineralizing agents (other than fluoride), directed at reversing or arresting non-cavitated lesions, remains a promising yet largely unproven strategy. High fluoride concentration compounds, e.g., AgF, Ag(NH3)2F, to arrest more advanced carious lesions with and without prior removal of carious tissue are being used in several countries as part of the Atraumatic Restorative Treatment (ART) approach. Most of the recent innovations in oral care products have been directed toward making cosmetic marketing claims. There continues to be a need for innovation and collaboration with other scientific disciplines to fully understand and prevent dental caries.
Genomic prediction is now widely recognized as an efficient, cost-effective and theoretically well-founded method for estimating breeding values using molecular markers spread over the whole genome. ...The prediction problem entails estimating the effects of all genes or chromosomal segments simultaneously and aggregating them to yield the predicted total genomic breeding value. Many potential methods for genomic prediction exist but have widely different relative computational costs, complexity and ease of implementation, with significant repercussions for predictive accuracy. We empirically evaluate the predictive performance of several contending regularization methods, designed to accommodate grouping of markers, using three synthetic traits of known accuracy.
Each of the competitor methods was used to estimate predictive accuracy for each of the three quantitative traits. The traits and an associated genome comprising five chromosomes with 10000 biallelic Single Nucleotide Polymorphic (SNP)-marker loci were simulated for the QTL-MAS 2012 workshop. The models were trained on 3000 phenotyped and genotyped individuals and used to predict genomic breeding values for 1020 unphenotyped individuals. Accuracy was expressed as the Pearson correlation between the simulated true and the estimated breeding values.
All the methods produced accurate estimates of genomic breeding values. Grouping of markers did not clearly improve accuracy contrary to expectation. Selecting the penalty parameter with replicated 10-fold cross validation often gave better accuracy than using information theoretic criteria.
All the regularization methods considered produced satisfactory predictive accuracies for most practical purposes and thus deserve serious consideration in genomic prediction research and practice. Grouping markers did not enhance predictive accuracy for the synthetic data set considered. But other more sophisticated grouping schemes could potentially enhance accuracy. Using cross validation to select the penalty parameters for the methods often yielded more accurate estimates of predictive accuracy than using information theoretic criteria.
DNA methylation, an epigenetic modification, can be affected by environmental factors and thus regulate gene expression levels that can lead to alterations of certain phenotypes. Network analysis has ...been used successfully to discover gene sets that are expressed differently across multiple disease states and suggest possible pathways of disease progression. We applied this framework to compare DNA methylation levels before and after lipid-lowering medication and to identify modules that differ topologically between the two time points, revealing the association between lipid medication and these triglyceride-related methylation sites.
We performed quality control using beta-mixture quantile normalization on 463,995 cytosine-phosphate-guanine (CpG) sites and deleted problematic sites, resulting in 423,004 probes. We identified 14,850 probes that were nominally associated with triglycerides prior to treatment and performed weighted gene correlation network analysis (WGCNA) to construct pre- and posttreatment methylation networks of these probes. We then applied both WGCNA module preservation and generalized Hamming distance (GHD) to identify modules with topological differences between the pre- and posttreatment. For modules with structural changes between 2 time points, we performed pathway-enrichment analysis to gain further insight into the biological function of the genes from these modules.
Six triglyceride-associated modules were identified using pretreatment methylation probes. The same 3 modules were not preserved in posttreatment data using both the module-preservation and the GHD methods. Top-enriched pathways for the 3 differentially methylated modules are sphingolipid signaling pathway, proteoglycans in cancer, and metabolic pathways (
values < 0.005). One module in particular included an enrichment of lipid-related pathways among the top results.
The same 3 modules, which were differentially methylated between pre- and posttreatment, were identified using both WGCNA module-preservation and GHD methods. Pathway analysis revealed that triglyceride-associated modules contain groups of genes that are involved in lipid signaling and metabolism. These 3 modules may provide insight into the effect of fenofibrate on changes in triglyceride levels and these methylation sites.
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