Genetic determinants for susceptibility, progression and prognosis of prostate cancer

Prostate cancer is the most commonly diagnosed form of non-skin cancer among men in developed countries. Although a large proportion of patients eventually die from the disease, many indolent tumors are found via prostate specific antigen (PSA) testing. However, todays diagnostic tools are unable to distinguish small localized tumors that will have a benign development from early stage aggressive disease. Thus, over-diagnosis and over-treatment are two major concerns in prostate cancer management. Genetics have been shown to play an important role for prostate cancer initiation with an estimated heritability of 58% and over 100 identified single nucleotide polymorphisms (SNPs) associated with prostate cancer risk. However, much less is known about the involvement of genes in the progression and prognosis of the disease. The overall objective of this thesis is to enhance the understanding of genetic determinants for initiation, progression and prognosis of prostate cancer. The purpose of Study I was to develop a prediction model for prostate cancer susceptibility, based on the current knowledge of genetic risk variants. Furthermore, we aimed to study the potential role of established prostate cancer risk variants in disease progression among men with a localized disease (Study III). In Study II, the heritability of prostate cancer-specific survival among diagnosed men was estimated and a genome-wide search for genetic determinants of the same outcome was performed in Study IV. We found that a polygenic risk score model with 65 established prostate cancer risk SNPs and 68 novel variants optimally separates prostate cancer cases from healthy controls, with a prediction accuracy measured using the area under the curve (AUC) of 0.68. Furthermore, we observed that these 133 SNPs could be used for risk stratification; compared with an intermediate genetic risk score category (40%-60%), men with a low genetic risk score (lowest 5% percentile) had 84% decreased relative risk of prostate cancer and men with 5% highest risk scores had a four-fold increased relative risk. Using a novel conditional likelihood approach for time-to-event data in brother pairs and father-son pairs, the heritability of prostate cancer survival was estimated to be 10%. We could also observe that common family environment had no effect (estimated to 0%) on prostate cancer survival. However, data simulations suggest that this may be underestimated. Furthermore, we could not find any association between SNPs and prostate cancer prognosis. None of 23 established prostate cancer risk SNPs investigated were found to be associated with disease progression in a cohort of men with localized disease. Moreover, in a genome-wide association study (GWAS) we did not find any association with prostate cancer survival at a genome-wide significant level. In conclusion, with the current knowledge of prostate cancer genetics it is possible to identify men with high and low prostate cancer susceptibility risk. However, the predictive performance of established SNPs is not yet sufficient to be used alone in a screening program of prostate cancer. Furthermore, the findings in this thesis regarding prostate cancer progression and survival suggest that development of prostate cancer and progression to lethal disease may be two separate biological mechanisms that involve different genes. In order to identify genetic risk variants associated with prostate cancer progression, future studies should be designed to find common variants with very low penetrance or rare variants with moderate to large effect

Prediction of individual genetic risk to prostate cancer using a polygenic score

BACKGROUND Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P-=-0.0012) and the net reclassification index with 0.21 (P-=-8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction

Fast and efficient QTL mapper for thousands of molecular phenotypes

In order to discover quantitative trait loci, multi-dimensional genomic datasets combining DNA-seq and ChiP-/RNA-seq require methods that rapidly correlate tens of thousands of molecular phenotypes with millions of genetic variants while appropriately controlling for multiple testing

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction

Germline variation at 8q24 and prostate cancer risk in men of European ancestry

Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10−15), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62–4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling. © 2018 The Author(s).Peer reviewe

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale

Differences in neighborhood accessibility to health-related resources: A nationwide comparison between deprived and affluent neighborhoods in Sweden

This nationwide Swedish study used geocoded data from all businesses in Sweden to examine the distribution of 12 main categories of goods, services, and resources in 6986 neighborhoods, categorized as low, moderate, and high neighborhood deprivation. The main findings were that high- and moderate-deprivation neighborhoods had a significantly higher prevalence of all types of goods, services, and resources than low-deprivation neighborhoods. These findings do not support previous research that hypothesizes that poorer health among people in deprived neighborhoods is explained by a lack of health-promoting resources, although a higher presence of health-damaging resources may play a role. (C) 2010 Elsevier Ltd. All rights reserved