Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data

Genome-wide association studies (GWASs) identify single nucleotide polymorphisms (SNPs) that are enriched in individuals suffering from a given disease. Most disease-associated SNPs fall into non-coding regions, so that it is not straightforward to infer phenotype or function; moreover, many SNPs are in tight genetic linkage, so that a SNP identified as associated with a particular disease may not itself be causal, but rather signify the presence of a linked SNP that is functionally relevant to disease pathogenesis. Here, we present an analysis method that takes advantage of the recent rapid accumulation of epigenomics data to address these problems for some SNPs. Using asthma as a prototypic example; we show that non-coding disease-associated SNPs are enriched in genomic regions that function as regulators of transcription, such as enhancers and promoters. Identifying enhancers based on the presence of the histone modification marks such as H3K4me1 in different cell types, we show that the location of enhancers is highly cell-type specific. We use these findings to predict which SNPs are likely to be directly contributing to disease based on their presence in regulatory regions, and in which cell types their effect is expected to be detectable. Moreover, we can also predict which cell types contribute to a disease based on overlap of the disease-associated SNPs with the locations of enhancers present in a given cell type. Finally, we suggest that it will be possible to re-analyze GWAS studies with much higher power by limiting the SNPs considered to those in coding or regulatory regions of cell types relevant to a given disease

Early Diagnosis, Treatment and Follow-Up of Cystic Echinococcosis in Remote Rural Areas in Patagonia: Impact of Ultrasound Training of Non-Specialists

Cystic echinococcosis (CE) is an important and widespread disease that affects sheep, cattle, and humans living in areas where sheep and cattle are raised. CE is highly endemic in rural sections of Rio Negro, Argentina, where our group is based. However, it requires continuous monitoring of both populations with human disease best assessed by means of ultrasound (US) screening. This is challenging in remote rural areas due to the shortage of imaging specialists. To overcome this hurdle, we set up a two-day training program of Focused Assessment with Sonography for Echinococcosis (FASE) on CE for family medicine practitioners with no previous experience in US. After the course, they were equipped with portable US scanners and dispatched to remote rural areas in Rio Negro where they screened patients, located and staged the cysts and decided on the treatment with the help of surgeons and radiologists in local tertiary care centers

The Greenland and Antarctic ice sheets under 1.5◦C global warming

Even if anthropogenic warming were constrained to less than 2°C above pre-industrial, the Greenland and Antarctic ice sheets will continue to lose mass this century, with rates similar to those observed over the last decade. However, nonlinear responses cannot be excluded, which may lead to larger rates of mass loss. Furthermore, large uncertainties in future projections still remain, pertaining to knowledge gaps in atmospheric (Greenland) and oceanic (Antarctica) forcing. On millennial timescales, both ice sheets have tipping points at or slightly above the 1.5-2.0°C threshold; for Greenland, this may lead to irreversible mass loss due to the surface mass balance elevation feedback, while for Antarctica, this could result in a collapse of major drainage basins due to ice-shelf weakening

Identification of 19 new risk loci and potential regulatory mechanisms influencing susceptibility to testicular germ cell tumor

Genome-wide association studies (GWAS) have transformed understanding of susceptibility to testicular germ cell tumors (TGCTs), but much of the heritability remains unexplained. Here we report a new GWAS, a meta-analysis with previous GWAS and a replication series, totaling 7,319 TGCT cases and 23,082 controls. We identify 19 new TGCT risk loci, roughly doubling the number of known TGCT risk loci to 44. By performing in situ Hi-C in TGCT cells, we provide evidence for a network of physical interactions among all 44 TGCT risk SNPs and candidate causal genes. Our findings implicate widespread disruption of developmental transcriptional regulators as a basis of TGCT susceptibility, consistent with failed primordial germ cell differentiation as an initiating step in oncogenesis. Defective microtubule assembly and dysregulation of KIT-MAPK signaling also feature as recurrently disrupted pathways. Our findings support a polygenic model of risk and provide insight into the biological basis of TGCT.We acknowledge National Health Service funding to the National Institute for Health Research Biomedical Research Centre. Genotyping of the OncoArray was funded by the US National Institutes of Health (NIH) (U19 CA 148537 for Elucidating Loci Involved in Prostate cancer Susceptibility (ELLIPSE) project and X01HG007492 to the Center for Inherited Disease Research (CIDR) under contract number HHSN268201200008I). Additional analytical support was provided by NIH NCI U01 CA188392. The PRACTICAL consortium was supported by Cancer Research UK Grants C5047/A7357, C1287/A10118, C1287/A16563, C5047/A3354, C5047/A10692 and C16913/A6135; the European Commission’s Seventh Framework Programme grant agreement 223175 (HEALTH-F2-2009-223175) (D.F.E., R.E. and Z.K.-J.); and the NIH Cancer Post-Cancer GWAS initiative grant 1 U19 CA 148537-01 (the GAME-ON initiative). We thank the following for funding support: the Institute of Cancer Research and the Everyman Campaign, the Prostate Cancer Research Foundation, Prostate Research Campaign UK (now Prostate Action), the Orchid Cancer Appeal, the National Cancer Research Network UK and the National Cancer Research Institute (NCRI) UK. We are grateful for NIHR funding to the Biomedical Research Centre at the Institute of Cancer Research and the Royal Marsden NHS Foundation Trust. We acknowledge funding from the Swedish Cancer Society (CAN2011/484 and CAN2012/823), the Norwegian Cancer Society (grants 418975-71081-PR-2006-0387 and PK01-2007- 0375) and the Nordic Cancer Union (grant S-12/07). This study was supported by the Movember Foundation and the Institute of Cancer Research. K.L. is supported by a PhD fellowship from Cancer Research UK. R.S.H. and P.B. are supported by Cancer Research UK (C1298/A8362 Bobby Moore Fund for Cancer Research UK)

A quantitative estimation of the global translational activity in logarithmically growing yeast cells.

BACKGROUND: Translation of messenger mRNAs makes significant contributions to the control of gene expression in all eukaryotes. Because translational control often involves fractional changes in translational activity, good quantitative descriptions of translational activity will be required to achieve a comprehensive understanding of this aspect of biology. Data on translational activity are difficult to generate experimentally under physiological conditions, however, translational activity as a parameter is in principle accessible through published genome-wide datasets. RESULTS: An examination of the accuracy of genome-wide expression datasets generated for Saccharomyces cerevisiae shows that the available datasets suffer from large random errors within studies as well as systematic shifts in reported values between studies, which make predictions of translational activity at the level of individual genes relatively inaccurate. In contrast, predictions of cell-wide translational activity are possible from such datasets with higher accuracy, and current datasets predict a production rate of about 13,000 proteins per haploid cell per second under fast growth conditions. This prediction is shown to be consistent with independently derived kinetic information on nucleotide exchange reactions that occur during translation, and on the ribosomal content of yeast cells. CONCLUSIONS: This study highlights some of the limitations in published genome-wide expression datasets, but also demonstrates a novel use for such datasets in examining global properties of cells. The global translational activity of yeast cells predicted in this study is a useful benchmark against which biochemical data on individual translation factor activities can be interpreted