15 research outputs found
Pesticide Fate and Transport throughout Unsaturated Zones in Five Agricultural Settings, USA
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
Large-scale transcriptome-wide association study identifies new prostate cancer risk regions
Although genome-wide association studies (GWAS) for prostate cancer (PrCa) have identified more than 100 risk regions, most of the risk genes at these regions remain largely unknown. Here we integrate the largest PrCa GWAS (N = 142,392) with gene expression measured in 45 tissues (N = 4458), including normal and tumor prostate, to perform a multi-tissue transcriptome-wide association study (TWAS) for PrCa. We identify 217 genes at 84 independent 1 Mb regions associated with PrCa risk, 9 of which are region
An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk
It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for Cp
Genome-wide association study of classical Hodgkin lymphoma identifies key regulators of disease susceptibility
Several susceptibility loci for classical Hodgkin lymphoma (cHL) have been reported, however much of the heritable risk is unknown. Here, we perform a meta-analysis of two existing genome-wide association studies (GWAS), a new GWAS, and replication totalling 5,314 cases and 16,749 controls. We identify risk loci for all cHL at 6q22.33 (rs9482849, P=1.52 × 10-8) and for nodular sclerosis HL (NSHL) at 3q28 (rs4459895, P=9.43 × 10-17), 6q23.3 (rs6928977, P=4.62 × 10-55 11), 10p14 (rs3781093, P=9.49 × 10-13), 13q34 (rs112998813, P=4.58 × 10-8) and 16p13.13 (rs34972832, P=2.12 × 10-8). Additionally, independent loci within the HLA region are observed for NSHL (rs9269081, HLA-DPB1*03:01, Val86 in HLA-DRB1) and mixed cellularity HL (rs1633096, rs13196329, Val86 in HLA-DRB1). The new and established risk loci localise to areas of active
chromatin and show an over-representation of transcription factor binding for determinants of B-cell development and immune response.In the United Kingdom, Bloodwise (LLR; 10021) provided principal funding for the study. Support from Cancer Research UK (C1298/A8362 supported by the Bobby Moore Fund) and the Lymphoma Research Trust is also acknowledged. A.S. is supported by a clinical fellowship from Cancer Research UK. For the UK-GWAS, sample and data acquisition were supported by Breast Cancer Now, the European Union and the Lymphoma Research Trust. The UK-GWAS made use of control genotyping data generated by the WTCCC. For further information, please visit the publishr's website
Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma
Genome-wide association studies (GWAS) have transformed our understanding of susceptibility to multiple myeloma (MM), but much of the heritability remains unexplained. We report a new GWAS, a meta-analysis with previous GWAS and a replication series, totalling 9974 MM cases and 247,556 controls of European ancestry. Collectively, these data provide evidence for six new MM risk loci, bringing the total number to 23. Integration of information from gene expression, epigenetic profiling and in situ Hi-C data for the 23 risk loci implicate disruption of developmental transcriptional regulators as a basis of MM susceptibility, compatible with altered B-cell differentiation as a key mechanism. Dysregulation of autophagy/apoptosis and cell cycle signalling feature as recurrently perturbed pathways. Our findings provide further insight
Female chromosome X mosaicism is age-related and preferentially affects the inactivated X chromosome
To investigate large structural clonal mosaicism of chromosome X, we analysed the SNP
microarray intensity data of 38,303 women from cancer genome-wide association studies
(20,878 cases and 17,425 controls) and detected 124 mosaic X events42Mb in 97 (0.25%)
women. Here we show rates for X-chromosome mosaicism are four times higher than mean
autosomal rates; X mosaic events more often include the entire chromosome and participants
with X events more likely harbour autosomal mosaic events. X mosaicism frequency
increases with age (0.11% in 50-year olds; 0.45% in 75-year olds), as reported for Y and
autosomes. Methylation array analyses of 33 women with X mosaicism indicate events
preferentially involve the inactive X chromosome. Our results provide further evidence that
the sex chromosomes undergo mosaic events more frequently than autosomes, which could
have implications for understanding the underlying mechanisms of mosaic events and their
possible contribution to risk for chronic diseases
Detectable clonal mosaicism and its relationship to aging and cancer
In an analysis of 31,717 cancer cases and 26,136 cancer-free controls from 13 genome-wide association studies, we observed large chromosomal abnormalities in a subset of clones in DNA obtained from blood or buccal samples. We observed mosaic abnormalities, either aneuploidy or copy-neutral loss of heterozygosity, of >2 Mb in size in autosomes of 517 individuals (0.89%), with abnormal cell proportions of between 7% and 95%. In cancer-free individuals, frequency increased with age, from 0.23% under 50 years to 1.91% between 75 and 79 years (P = 4.8 × 10(-8)). Mosaic abnormalities were more frequent in individuals with solid tumors (0.97% versus 0.74% in cancer-free individuals; odds ratio (OR) = 1.25; P = 0.016), with stronger association with cases who had DNA collected before diagnosis or treatment (OR = 1.45; P = 0.0005). Detectable mosaicism was also more common in individuals for whom DNA was collected at least 1 year before diagnosis with leukemia compared to cancer-free individuals (OR = 35.4; P = 3.8 × 10(-11)). These findings underscore the time-dependent nature of somatic events in the etiology of cancer and potentially other late-onset diseases
Pesticide Fate and Transport throughout Unsaturated Zones in Five Agricultural Settings, USA
Pesticide transport through the unsaturated zone is a function of chemical and soil characteristics, application, and water recharge rate. The fate and transport of 82 pesticides and degradates were investigated at five different agricultural sites. Atrazine and metolachlor, as well as several of the degradates of atrazine, metolachlor, acetochlor, and alachlor, were frequently detected in soil water during the 2004 growing season, and degradates were generally more abundant than parent compounds. Metolachlor and atrazine were applied at a Nebraska site the same year as sampling, and focused recharge coupled with the short time since application resulted in their movement in the unsaturated zone 9 m below the surface. At other sites where the herbicides were applied 1 to 2 yr before sampling, only degradates were found in soil water. Transformations of herbicides were evident with depth and during the 4-mo sampling time and reflected the faster degradation of metolachlor oxanilic acid and persistence of metolachor ethanesulfonic acid. Th e fraction of metolachlor ethanesulfonic acid relative to metolachlor and metolachlor oxanilic acid increased from 0.3 to \u3e0.9 at a site in Maryland where the unsaturated zone was 5 m deep and from 0.3 to 0.5 at the shallowest depth. The flux of pesticide degradates from the deepest sites to the shallow ground water was greatest (3.0–4.9 μmol m−2 yr−1) where upland recharge or focused flow moved the most water through the unsaturated zone. Flux estimates based on estimated recharge rates and measured concentrations were in agreement with fluxes estimated using an unsaturated-zone computer model (LEACHM)