% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Bien:143007,
      author       = {S. A. Bien and Y.-R. Su and D. V. Conti and T. A. Harrison
                      and C. Qu and X. Guo and Y. Lu and D. Albanes and P. L. Auer
                      and B. L. Banbury and S. I. Berndt and S. Bézieau and H.
                      Brenner$^*$ and D. D. Buchanan and B. J. Caan and P. T.
                      Campbell and C. S. Carlson and A. T. Chan and J.
                      Chang-Claude$^*$ and S. Chen and C. M. Connolly and D. F.
                      Easton and E. J. M. Feskens and S. Gallinger and G. G. Giles
                      and M. J. Gunter and J. Hampe and J. R. Huyghe and M.
                      Hoffmeister$^*$ and T. J. Hudson and E. J. Jacobs and M. A.
                      Jenkins and E. Kampman and H. M. Kang and T. Kühn$^*$ and
                      S. Küry and F. Lejbkowicz and L. Le Marchand and R. L.
                      Milne and L. Li and C. I. Li and A. Lindblom and N. M.
                      Lindor and V. Martín and C. E. McNeil and M. Melas and V.
                      Moreno and P. A. Newcomb and K. Offit and P. D. P. Pharaoh
                      and J. D. Potter and C. Qu and E. Riboli and G. Rennert and
                      N. Sala and C. Schafmayer and P. C. Scacheri and S. L.
                      Schmit and G. Severi and M. L. Slattery and J. D. Smith and
                      A. Trichopoulou and R. Tumino and C. M. Ulrich and F. J. B.
                      van Duijnhoven and B. Van Guelpen and S. J. Weinstein and E.
                      White and A. Wolk and M. O. Woods and A. H. Wu and G. R.
                      Abecasis and G. Casey and D. A. Nickerson and S. B. Gruber
                      and L. Hsu and W. Zheng and U. Peters},
      title        = {{G}enetic variant predictors of gene expression provide new
                      insight into risk of colorectal cancer.},
      journal      = {Human genetics},
      volume       = {138},
      number       = {4},
      issn         = {1432-1203},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {DKFZ-2019-00632},
      pages        = {307-326},
      year         = {2019},
      note         = {138(4):307-326},
      abstract     = {Genome-wide association studies have reported 56
                      independently associated colorectal cancer (CRC) risk
                      variants, most of which are non-coding and believed to exert
                      their effects by modulating gene expression. The
                      computational method PrediXcan uses cis-regulatory variant
                      predictors to impute expression and perform gene-level
                      association tests in GWAS without directly measured
                      transcriptomes. In this study, we used reference datasets
                      from colon (n = 169) and whole blood (n = 922)
                      transcriptomes to test CRC association with genetically
                      determined expression levels in a genome-wide analysis of
                      12,186 cases and 14,718 controls. Three novel associations
                      were discovered from colon transverse models at
                      FDR ≤ 0.2 and further evaluated in an independent
                      replication including 32,825 cases and 39,933 controls.
                      After adjusting for multiple comparisons, we found
                      statistically significant associations using colon
                      transcriptome models with TRIM4 (discovery
                      P = 2.2 × 10- 4, replication P = 0.01), and
                      PYGL (discovery P = 2.3 × 10- 4, replication
                      P = 6.7 × 10- 4). Interestingly, both genes
                      encode proteins that influence redox homeostasis and are
                      related to cellular metabolic reprogramming in tumors,
                      implicating a novel CRC pathway linked to cell growth and
                      proliferation. Defining CRC risk regions as one megabase up-
                      and downstream of one of the 56 independent risk variants,
                      we defined 44 non-overlapping CRC-risk regions. Among these
                      risk regions, we identified genes associated with CRC
                      (P < 0.05) in 34/44 CRC-risk regions. Importantly, CRC
                      association was found for two genes in the previously
                      reported 2q25 locus, CXCR1 and CXCR2, which are potential
                      cancer therapeutic targets. These findings provide strong
                      candidate genes to prioritize for subsequent laboratory
                      follow-up of GWAS loci. This study is the first to implement
                      PrediXcan in a large colorectal cancer study and findings
                      highlight the utility of integrating transcriptome data in
                      GWAS for discovery of, and biological insight into, risk
                      loci.},
      cin          = {C070 / C120 / C020 / L101},
      ddc          = {610},
      cid          = {I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331 /
                      I:(DE-He78)C020-20160331 / I:(DE-He78)L101-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:30820706},
      doi          = {10.1007/s00439-019-01989-8},
      url          = {https://inrepo02.dkfz.de/record/143007},
}