Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors

<p>Abstract</p> <p>Background</p> <p>Assays of multiple tumor samples frequently reveal recurrent genomic aberrations, including point mutations and copy-number alterations, that affect individual genes. Analyses that extend beyond single genes are often restricted to examining pathways, interactions and functional modules that are already known.</p> <p>Methods</p> <p>We present a method that identifies functional modules without any information other than patterns of recurrent and mutually exclusive aberrations (RME patterns) that arise due to positive selection for key cancer phenotypes. Our algorithm efficiently constructs and searches networks of potential interactions and identifies significant modules (RME modules) by using the algorithmic significance test.</p> <p>Results</p> <p>We apply the method to the TCGA collection of 145 glioblastoma samples, resulting in extension of known pathways and discovery of new functional modules. The method predicts a role for <it>EP300 </it>that was previously unknown in glioblastoma. We demonstrate the clinical relevance of these results by validating that expression of <it>EP300 </it>is prognostic, predicting survival independent of age at diagnosis and tumor grade.</p> <p>Conclusions</p> <p>We have developed a sensitive, simple, and fast method for automatically detecting functional modules in tumors based solely on patterns of recurrent genomic aberration. Due to its ability to analyze very large amounts of diverse data, we expect it to be increasingly useful when applied to the many tumor panels scheduled to be assayed in the near future.</p

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity (vol 50, pg 26, 2017)

An amendment to this paper has been published and can be accessed via a link at the top of the paper

Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity (vol 50, pg 26, 2018)

A.P.R. was supported by R01DK089256. A.W.H. is supported by an NHMRC Practitioner Fellowship (APP1103329). A.K.M. received funding from NIH/NIDDK K01DK107836. A.T.H. is a Wellcome Trust Senior Investigator (WT098395) and an NIH Research Senior Investigator. A.P.M. is a Wellcome Trust Senior Fellow in Basic Biomedical Science (WT098017). A.R.W. is supported by the European Research Council (SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC). A.U.J. is supported by the American Heart Association (13POST16500011) and the NIH (R01DK089256, R01DK101855, K99HL130580). B.K. and E.K.S. were supported by the Doris Duke Medical Foundation, the NIH (R01DK106621), the University of Michigan Internal Medicine Department, Division of Gastroenterology, the University of Michigan Biological Sciences Scholars Program and the Central Society for Clinical Research. C.J.W. is supported by the NIH (HL094535, HL109946). D.J.L. is supported by R01HG008983 and R21DA040177. D.R.W. is supported by the Danish Diabetes Academy, which is funded by the Novo Nordisk Foundation. V. Salomaa has been supported by the Finnish Foundation for Cardiovascular Research. F.W.A. is supported by Dekker scholarship–Junior Staff Member 2014T001 Netherlands Heart Foundation and the UCL Hospitals NIHR Biomedical Research Centre. F.D. is supported by the UK MRC (MC_UU_12013/1-9). G.C.-P. received scholarship support from the University of Queensland and QIMR Berghofer. G.L. is funded by the Montreal Heart Institute Foundation and the Canada Research Chair program. H.Y. and T.M.F. are supported by the European Research Council (323195; SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC). I.M.H. is supported by BMBF (01ER1206) and BMBF (01ER1507m), the NIH and the Max Planck Society. J. Haessler was supported by NHLBI R21HL121422. J.N.H. is supported by NIH R01DK075787. K.E.N. was supported by the NIH (R01DK089256, R01HD057194, U01HG007416, R01DK101855) and the American Heart Association (13GRNT16490017). M.A.R. is supported by the Nuffield Department of Clinical Medicine Award, Clarendon Scholarship. M.I.M. is a Wellcome Trust Senior Investigator (WT098381) and an NIH Research Senior Investigator. M.D. is supported by the NCI (R25CA94880, P30CA008748). P.R.N. is supported by the European Research Council (AdG; 293574), the Research Council of Norway, the University of Bergen, the KG Jebsen Foundation and the Helse Vest, Norwegian Diabetes Association. P.T.E. is supported by the NIH (1R01HL092577, R01HL128914, K24HL105780), by an Established Investigator Award from the American Heart Association (13EIA14220013) and by the Foundation Leducq (14CVD01). P.L.A. was supported by NHLBI R21HL121422 and R01DK089256. P.L.H. is supported by the NIH (NS33335, HL57818). R.S.F. is supported by the NIH (T32GM096911). R.J.F.L. is supported by the NIH (R01DK110113, U01HG007417, R01DK101855, R01DK107786). S.A.L. is supported by the NIH (K23HL114724) and a Doris Duke Charitable Foundation Clinical Scientist Development Award. T.D.S. holds an ERC Advanced Principal Investigator award. T.A.M. is supported by an NHMRC Fellowship (APP1042255). T.H.P. received Lundbeck Foundation and Benzon Foundation support. V.T. is supported by a postdoctoral fellowship from the Canadian Institutes of Health Research (CIHR). Z.K. is supported by the Leenaards Foundation, the Swiss National Science Foundation (31003A-143914) and SystemsX.ch (51RTP0_151019). Part of this work was conducted using the UK Biobank resource (project numbers 1251 and 9072)

Publisher Correction:Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity

In the published version of this paper, the name of author Emanuele Di Angelantonio was misspelled. This error has now been corrected in the HTML and PDF versions of the article

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology