Search

Background: Fusobacterium nucleatum (F. nucleatum) activates oncogenic signaling pathways and induces inflammation to promote colorectal carcinogenesis. Methods: We characterized F. nucleatum and its subspecies in colorectal tumors and examined associations with tumor characteristics and colorectal cancer–specific survival. We conducted deep sequencing of nusA, nusG, and bacterial 16s rRNA genes in tumors from 1,994 patients with colorectal cancer and assessed associations between F. nucleatum presence and clinical characteristics, colorectal cancer–specific mortality, and somatic mutations. Results: F. nucleatum, which was present in 10.3% of tumors, was detected in a higher proportion of right-sided and advanced-stage tumors, particularly subspecies animalis. Presence of F. nucleatum was associated with higher colorectal cancer–specific mortality (HR, 1.97; P = 0.0004). This association was restricted to nonhypermutated, microsatellite-stable tumors (HR, 2.13; P = 0.0002) and those who received chemotherapy [HR, 1.92; confidence interval (CI), 1.07–3.45; P = 0.029). Only F. nucleatum subspecies animalis, the main subspecies detected (65.8%), was associated with colorectal cancer–specific mortality (HR, 2.16; P = 0.0016), subspecies vincentii and nucleatum were not (HR, 1.07; P = 0.86). Additional adjustment for tumor stage suggests that the effect of F. nucleatum on mortality is partly driven by a stage shift. Presence of F. nucleatum was associated with microsatellite instable tumors, tumors with POLE exonuclease domain mutations, and ERBB3 mutations, and suggestively associated with TP53 mutations. Conclusions: F. nucleatum, and particularly subspecies animalis, was associated with a higher colorectal cancer–specific mortality and specific somatic mutated genes. Impact: Our findings identify the F. nucleatum subspecies animalis as negatively impacting colorectal cancer mortality, which may occur through a stage shift and its effect on chemoresistance.

ObjectiveAn understanding of the etiologic heterogeneity of colorectal cancer (CRC) is critical for improving precision prevention, including individualized screening recommendations and the discovery of novel drug targets and repurposable drug candidates for chemoprevention. Known differences in molecular characteristics and environmental risk factors among tumors arising in different locations of the colorectum suggest partly distinct mechanisms of carcinogenesis. The extent to which the contribution of inherited genetic risk factors for sporadic CRC differs by anatomical subsite of the primary tumor has not been examined.DesignTo identify new anatomical subsite-specific risk loci, we performed genome-wide association study (GWAS) meta-analyses including data of 48,214 CRC cases and 64,159 controls of European ancestry. We characterized effect heterogeneity at CRC risk loci using multinomial modeling.ResultsWe identified 13 loci that reached genome-wide significance (P−8) and that were not reported by previous GWAS for overall CRC risk. Multiple lines of evidence support candidate genes at several of these loci. We detected substantial heterogeneity between anatomical subsites. Just over half (61) of 109 known and new risk variants showed no evidence for heterogeneity. In contrast, 22 variants showed association with distal CRC (including rectal cancer), but no evidence for association or an attenuated association with proximal CRC. For two loci, there was strong evidence for effects confined to proximal colon cancer.ConclusionGenetic architectures of proximal and distal CRC are partly distinct. Studies of risk factors and mechanisms of carcinogenesis, and precision prevention strategies should take into consideration the anatomical subsite of the tumor.Significance of this studyWhat is already known about this subject?Heterogeneity among colorectal cancer (CRC) tumors originating at different locations of the colorectum has been revealed in somatic genomes, epigenomes, and transcriptomes, and in some established environmental risk factors for CRC.Genome-wide association studies (GWAS) have identified over 100 genetic variants for overall CRC risk; however, a comprehensive analysis of the extent to which genetic risk factors differ by the anatomical sublocation of the primary tumor is lacking.What are the new findings?In this large consortium-based study, we analyzed clinical and genome-wide genotype data of 112,373 CRC cases and controls of European ancestry to comprehensively examine whether CRC case subgroups defined by anatomical sublocation have distinct germline genetic etiologies.We discovered 13 new loci at genome-wide significance (P−8) that were specific to certain anatomical sublocations and that were not reported by previous GWAS for overall CRC risk; multiple lines of evidence support strong candidate target genes at several of these loci, including PTGER3, LCT, MLH1, CDX1, KLF14, PYGL, BCL11B, and BMP7.Systematic heterogeneity analysis of genetic risk variants for CRC identified thus far, revealed that the genetic architectures of proximal and distal CRC are partly distinct.Taken together, our results further support the idea that tumors arising in different anatomical sublocations of the colorectum may have distinct etiologies.How might it impact on clinical practice in the foreseeable future?Our results provide an informative resource for understanding the differential role that genes and pathways may play in the mechanisms of proximal and distal CRC carcinogenesis.The new insights into the etiologies of proximal and distal CRC may inform the development of new precision prevention strategies, including individualized screening recommendations and the discovery of novel drug targets and repurposable drug candidates for chemoprevention.Our findings suggest that future studies of etiological risk factors for CRC and molecular mechanisms of carcinogenesis should take into consideration the anatomical sublocation of the colorectal tumor.

Background Previous genome-wide association studies (GWAS) have identified 42 loci (P < 5 × 10−8) associated with risk of colorectal cancer (CRC). Expanded consortium efforts facilitating the discovery of additional susceptibility loci may capture unexplained familial risk. Methods We conducted a GWAS in European descent CRC cases and control subjects using a discovery–replication design, followed by examination of novel findings in a multiethnic sample (cumulative n = 163 315). In the discovery stage (36 948 case subjects/30 864 control subjects), we identified genetic variants with a minor allele frequency of 1% or greater associated with risk of CRC using logistic regression followed by a fixed-effects inverse variance weighted meta-analysis. All novel independent variants reaching genome-wide statistical significance (two-sided P < 5 × 10−8) were tested for replication in separate European ancestry samples (12 952 case subjects/48 383 control subjects). Next, we examined the generalizability of discovered variants in East Asians, African Americans, and Hispanics (12 085 case subjects/22 083 control subjects). Finally, we examined the contributions of novel risk variants to familial relative risk and examined the prediction capabilities of a polygenic risk score. All statistical tests were two-sided. Results The discovery GWAS identified 11 variants associated with CRC at P < 5 × 10−8, of which nine (at 4q22.2/5p15.33/5p13.1/6p21.31/6p12.1/10q11.23/12q24.21/16q24.1/20q13.13) independently replicated at a P value of less than .05. Multiethnic follow-up supported the generalizability of discovery findings. These results demonstrated a 14.7% increase in familial relative risk explained by common risk alleles from 10.3% (95% confidence interval [CI] = 7.9% to 13.7%; known variants) to 11.9% (95% CI = 9.2% to 15.5%; known and novel variants). A polygenic risk score identified 4.3% of the population at an odds ratio for developing CRC of at least 2.0. Conclusions This study provides insight into the architecture of common genetic variation contributing to CRC etiology and improves risk prediction for individualized screening.

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P, Reporting Summary. Further information on experimental design is available in the Life Sciences Reporting Summary linked to this article.

Contains fulltext : 178026.pdf (Publisher’s version ) (Closed access) In acute myeloid leukaemia, long-term survival is poor as most patients relapse despite achieving remission. Historically, the failure of therapy has been thought to be due to mutations that produce drug resistance, possibly arising as a consequence of the mutagenic properties of chemotherapy drugs. However, other lines of evidence have pointed to the pre-existence of drug-resistant cells. For example, deep sequencing of paired diagnosis and relapse acute myeloid leukaemia samples has provided direct evidence that relapse in some cases is generated from minor genetic subclones present at diagnosis that survive chemotherapy, suggesting that resistant cells are generated by evolutionary processes before treatment and are selected by therapy. Nevertheless, the mechanisms of therapy failure and capacity for leukaemic regeneration remain obscure, as sequence analysis alone does not provide insight into the cell types that are fated to drive relapse. Although leukaemia stem cells have been linked to relapse owing to their dormancy and self-renewal properties, and leukaemia stem cell gene expression signatures are highly predictive of therapy failure, experimental studies have been primarily correlative and a role for leukaemia stem cells in acute myeloid leukaemia relapse has not been directly proved. Here, through combined genetic and functional analysis of purified subpopulations and xenografts from paired diagnosis/relapse samples, we identify therapy-resistant cells already present at diagnosis and two major patterns of relapse. In some cases, relapse originated from rare leukaemia stem cells with a haematopoietic stem/progenitor cell phenotype, while in other instances relapse developed from larger subclones of immunophenotypically committed leukaemia cells that retained strong stemness transcriptional signatures. The identification of distinct patterns of relapse should lead to improved methods for disease management and monitoring in acute myeloid leukaemia. Moreover, the shared functional and transcriptional stemness properties that underlie both cellular origins of relapse emphasize the importance of developing new therapeutic approaches that target stemness to prevent relapse.

Colorectal cancer (CRC) is a biologically heterogeneous disease. To characterize its mutational profile, we conduct targeted sequencing of 205 genes for 2,105 CRC cases with survival data. Our data shows several findings in addition to enhancing the existing knowledge of CRC. We identify PRKCI, SPZ1, MUTYH, MAP2K4, FETUB, and TGFBR2 as additional genes significantly mutated in CRC. We find that among hypermutated tumors, an increased mutation burden is associated with improved CRC-specific survival (HR = 0.42, 95% CI: 0.21–0.82). Mutations in TP53 are associated with poorer CRC-specific survival, which is most pronounced in cases carrying TP53 mutations with predicted 0% transcriptional activity (HR = 1.53, 95% CI: 1.21–1.94). Furthermore, we observe differences in mutational frequency of several genes and pathways by tumor location, stage, and sex. Overall, this large study provides deep insights into somatic mutations in CRC, and their potential relationships with survival and tumor features., Large scale sequencing study is of paramount importance to unravel the heterogeneity of colorectal cancer. Here, the authors sequenced 205 cancer genes in more than 2000 tumours and identified additional mutated driver genes, determined that mutational burden and specific mutations in TP53 are associated with survival odds.

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Targeted sequencing of 103 leukemia-associated genes in leukemia cells from 841 treatment-naive patients with chronic lymphocytic leukemia (CLL) identified 89 (11%) patients as having CLL cells with mutations in genes encoding proteins that putatively are involved in hedgehog (Hh) signaling. Consistent with this finding, there was a significant association between the presence of these mutations and the expression of GLI1 (χ2 test, P < .0001), reflecting activation of the Hh pathway. However, we discovered that 38% of cases without identified mutations also were GLI1+. Patients with GLI1+ CLL cells had a shorter median treatment-free survival than patients with CLL cells lacking expression of GLI1 independent of IGHV mutation status. We found that GANT61, a small molecule that can inhibit GLI1, was highly cytotoxic for GLI1+ CLL cells relative to that of CLL cells without GLI1. Collectively, this study shows that a large proportion of patients have CLL cells with activated Hh signaling, which is associated with early disease progression and enhanced sensitivity to inhibition of GLI1.

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 FDR0.2 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=2.2x10(-4), replication P=0.01), and PYGL (discovery P=2.3x10(-4), replication P=6.7x10(-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 (PA Correction to this article was published on 28 June 2019. Every author has erroneously been assigned to the affiliation "62". The affiliation 62 belongs to the author Graham Casey.HUMAN GENETICS, Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer. Hum Genet 138, 789–791 (2019) DOI: 10.1007/s00439-019-02030-8

Germline polymorphic variants in cancer predisposition genes such as TP53 have been shown to impact the risk of premenopausal cancer. Accordingly, the aim of this study was to assess the spectrum of polymorphisms in TP53 and its negative regulatory gene, MDM2 (SNP309:T>G) in patients with premenopausal breast cancer. Our findings in a cohort of 40 female patients demonstrate no significant correlation between the studied polymorphisms and risk of premenopausal breast cancer. Although one polymorphism is found in high frequency in this cohort (rs1800372:A>G, 9.0%), it was not associated with the risk of developing cancer before the age of 35 years in an extended cohort of 1,420 breast cancer cases. Functional studies of the rs1800372:A>G polymorphic allele reveal that it does not affect p53 transactivation function. Further study of variants or mutations in other cancer susceptibility genes is warranted to refine our understanding of the germline contribution to premenopausal breast cancer susceptibility.

Purpose Although the link between mutant TP53 and human cancer is unequivocal, a significant knowledge gap exists in clinically actionable molecular targets in Li-Fraumeni syndrome (LFS), a highly penetrant cancer predisposition syndrome associated with germline mutations in TP53. This study surveyed the epigenome to identify functionally and clinically relevant novel genes implicated in LFS. Patients and Methods We performed genome-wide methylation analyses of peripheral blood leukocyte DNA in germline TP53 mutation carriers (n = 72) and individuals with TP53 wild type in whom histologically comparable malignancies developed (n = 111). Targeted bisulfite pyrosequencing was performed on a validation cohort of 30 TP53 mutation carriers and 46 patients with TP53 wild type, and candidate sites were evaluated in primary tumors from patients with LFS across multiple histologic tumor types. Results In 183 patients, distinct DNA methylation signatures were associated with deleterious TP53 mutations in peripheral blood leukocytes. TP53-associated DNA methylation marks occurred in genomic regions that harbored p53 binding sites and in genes encoding p53 pathway proteins. Moreover, loss-of-function TP53 mutations were significantly associated with differential methylation at the locus encoding microRNA miR-34A, a key component of the p53 regulatory network (adjusted P < .001), and validated in an independent patient cohort (n = 76, P < .001). Targeted bisulfite pyrosequencing demonstrated that miR-34A was inactivated by hypermethylation across many histologic types of primary tumors from patients with LFS. Moreover, miR-34A tumor hypermethylation was associated with decreased overall survival in a cohort of 29 patients with choroid plexus carcinomas, a characteristic LFS tumor (P < .05). Conclusion Epigenetic dysregulation of miR-34A may comprise an important path in TP53-associated cancer predisposition and represents a therapeutically actionable target with potential clinical relevance.

Pancreatic cancer, a highly aggressive tumour type with uniformly poor prognosis, exemplifies the classically held view of stepwise cancer development. The current model of tumorigenesis, based on analyses of precursor lesions, termed pancreatic intraepithelial neoplasm (PanINs) lesions, makes two predictions: first, that pancreatic cancer develops through a particular sequence of genetic alterations (KRAS, followed by CDKN2A, then TP53 and SMAD4); and second, that the evolutionary trajectory of pancreatic cancer progression is gradual because each alteration is acquired independently. A shortcoming of this model is that clonally expanded precursor lesions do not always belong to the tumour lineage, indicating that the evolutionary trajectory of the tumour lineage and precursor lesions can be divergent. This prevailing model of tumorigenesis has contributed to the clinical notion that pancreatic cancer evolves slowly and presents at a late stage. However, the propensity for this disease to rapidly metastasize and the inability to improve patient outcomes, despite efforts aimed at early detection, suggest that pancreatic cancer progression is not gradual. Here, using newly developed informatics tools, we tracked changes in DNA copy number and their associated rearrangements in tumour-enriched genomes and found that pancreatic cancer tumorigenesis is neither gradual nor follows the accepted mutation order. Two-thirds of tumours harbour complex rearrangement patterns associated with mitotic errors, consistent with punctuated equilibrium as the principal evolutionary trajectory. In a subset of cases, the consequence of such errors is the simultaneous, rather than sequential, knockout of canonical preneoplastic genetic drivers that are likely to set-off invasive cancer growth. These findings challenge the current progression model of pancreatic cancer and provide insights into the mutational processes that give rise to these aggressive tumours. Funding sources for this study include grants to the Pancreatic Cancer Sequencing Initiative program from the Ontario Institute for Cancer Research (OICR), through support from the Ontario Ministry of Research and Innovation, the Canada Foundation for Innovation; research award to F.N. from the OICR and the Canadian Institutes for Health Research (CIHR); Canadian Friends of the Hebrew University, the SMGS Family Foundation, NCI grant P50 CA102701 (Mayo Clinic SPORE in Pancreatic Cancer) and NCI grant R01 CA97075 (Pancreatic Cancer Genetic Epidemiology Consortium). F.N. is supported by a fellowship award from CIHR and is a recipient of a scholar’s research award from the Ontario Institute of Cancer Research (OICR), through support from the Ontario Ministry of Research and Innovation. G.Z. is a Clinician–Scientist of the Fonds de la Recherche en Sante du Quebec. P.J.C. is a Wellcome Trust Senior Clinical Fellow. T.J.H., L.D.S., J.D.M. and S.G. are recipients of Senior or Clinician–Scientist Awards from the Ontario Institute for Cancer Research

Every author has erroneously been assigned to the affiliation “62”. The affiliation 62 belongs to the author Graham Casey.

In mammals, large caliber axons are ensheathed by myelin, a glial specialization supporting axon integrity and conferring accelerated and energy-efficient action potential conduction. Myelin basic protein (MBP) is required for normal myelin elaboration with maximal mbp transcription in oligodendrocytes requiring the upstream M3 enhancer. To further characterize the mechanism regulating mbp transcription, we defined M3 structure/function relationships by evaluating its evolutionary conservation, DNA footprints and the developmental programing conferred in mice by M3 derivatives. Multiple M3 regulatory element combinations were found to drive expression in oligodendrocytes and Schwann cells with a minimal 129 bp sequence conferring expression in oligodendrocytes throughout myelin elaboration, maintenance and repair. Unexpectedly, M3 derivatives conferred markedly different spatial and temporal expression programs thus illuminating striking transcriptional heterogeneity within post-mitotic oligodendrocytes. Finally, one M3 derivative engaged only during primary myelination, not during adult remyelination, demonstrating that transcriptional regulation in the two states is not equivalent.

Over 50 loci associated with colorectal cancer (CRC) have been uncovered by genome-wide association studies (GWAS). Identifying additional loci has the potential to help elucidate aspects of the underlying biological processes leading to better understanding of the pathogenesis of the disease. We re-evaluated a GWAS by excluding controls that have family history of CRC or personal history of colorectal polyps, as we hypothesized that their inclusion reduces power to detect associations. This is supported empirically and through simulations. Two-phase GWAS analysis was performed in a total of 16,517 cases and 14,487 controls. We identified rs17094983, a SNP associated with risk of CRC [p = 2.5 × 10(-10); odds ratio estimated by re-including all controls (OR) = 0.87, 95% confidence interval (CI) 0.83-0.91; minor allele frequency (MAF) = 13%]. Results were replicated in samples of African descent (1894 cases and 4703 controls; p = 0.01; OR = 0.86, 95% CI 0.77-0.97; MAF = 16 %). Gene expression data in 195 colon adenocarcinomas and 59 normal colon tissues from two different studies revealed that this locus has genotypes that are associated with RTN1 (Reticulon 1) expression (p = 0.001), a protein-coding gene involved in survival and proliferation of cancer cells which is highly expressed in normal colon tissues but has significantly reduced expression in tumor cells (p = 1.3 × 10(-8)).

SummaryLeukemia stem cells (LSCs) play a pivotal role in the resistance of chronic myeloid leukemia (CML) to tyrosine kinase inhibitors (TKIs) and its progression to blast crisis (BC), in part, through the alternative splicing of self-renewal and survival genes. To elucidate splice-isoform regulators of human BC LSC maintenance, we performed whole-transcriptome RNA sequencing, splice-isoform-specific quantitative RT-PCR (qRT-PCR), nanoproteomics, stromal coculture, and BC LSC xenotransplantation analyses. Cumulatively, these studies show that the alternative splicing of multiple prosurvival BCL2 family genes promotes malignant transformation of myeloid progenitors into BC LSCS that are quiescent in the marrow niche and that contribute to therapeutic resistance. Notably, sabutoclax, a pan-BCL2 inhibitor, renders marrow-niche-resident BC LSCs sensitive to TKIs at doses that spare normal progenitors. These findings underscore the importance of alternative BCL2 family splice-isoform expression in BC LSC maintenance and suggest that the combinatorial inhibition of prosurvival BCL2 family proteins and BCR-ABL may eliminate dormant LSCs and obviate resistance.

Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10(-15)), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10(-6)). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk. peerReviewed

Although genome-wide association studies (GWAS) have separately identified many genetic susceptibility loci for ulcerative colitis (UC), Crohn's disease (CD) and colorectal cancer (CRC), there has been no large-scale examination for pleiotropy, or shared genetic susceptibility, for these conditions. We used logistic regression modeling to examine the associations of 181 UC and CD susceptibility variants previously identified by GWAS with risk of CRC using data from the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry. We also examined associations of significant variants with clinical and molecular characteristics in a subset of the studies. Among 11794 CRC cases and 14190 controls, rs11676348, the susceptibility single nucleotide polymorphism (SNP) for UC, was significantly associated with reduced risk of CRC (P = 7E-05). The multivariate-adjusted odds ratio of CRC with each copy of the T allele was 0.93 (95% CI 0.89-0.96). The association of the SNP with risk of CRC differed according to mucinous histological features (P heterogeneity = 0.008). In addition, the (T) allele was associated with lower risk of tumors with Crohn's-like reaction but not tumors without such immune infiltrate (P heterogeneity = 0.02) and microsatellite instability-high (MSI-high) but not microsatellite stable or MSI-low tumors (P heterogeneity = 0.03). The minor allele (T) in SNP rs11676348, located downstream from CXCR2 that has been implicated in CRC progression, is associated with a lower risk of CRC, particularly tumors with a mucinous component, Crohn's-like reaction and MSI-high. Our findings offer the promise of risk stratification of inflammatory bowel disease patients for complications such as CRC.

To understand the genetic drivers of immune recognition and evasion in colorectal cancer, we analyzed 1,211 colorectal cancer primary tumor samples, including 179 classified as microsatellite instability–high (MSI-high). This set includes The Cancer Genome Atlas colorectal cancer cohort of 592 samples, completed and analyzed here. MSI-high, a hypermutated, immunogenic subtype of colorectal cancer, had a high rate of significantly mutated genes in important immune-modulating pathways and in the antigen presentation machinery, including biallelic losses of B2M and HLA genes due to copy-number alterations and copy-neutral loss of heterozygosity. WNT/β-catenin signaling genes were significantly mutated in all colorectal cancer subtypes, and activated WNT/β-catenin signaling was correlated with the absence of T-cell infiltration. This large-scale genomic analysis of colorectal cancer demonstrates that MSI-high cases frequently undergo an immunoediting process that provides them with genetic events allowing immune escape despite high mutational load and frequent lymphocytic infiltration and, furthermore, that colorectal cancer tumors have genetic and methylation events associated with activated WNT signaling and T-cell exclusion. Significance: This multi-omic analysis of 1,211 colorectal cancer primary tumors reveals that it should be possible to better monitor resistance in the 15% of cases that respond to immune blockade therapy and also to use WNT signaling inhibitors to reverse immune exclusion in the 85% of cases that currently do not. Cancer Discov; 8(6); 730–49. ©2018 AACR. This article is highlighted in the In This Issue feature, p. 663

Item does not contain fulltext In acute myeloid leukaemia (AML), the cell of origin, nature and biological consequences of initiating lesions, and order of subsequent mutations remain poorly understood, as AML is typically diagnosed without observation of a pre-leukaemic phase. Here, highly purified haematopoietic stem cells (HSCs), progenitor and mature cell fractions from the blood of AML patients were found to contain recurrent DNMT3A mutations (DNMT3A(mut)) at high allele frequency, but without coincident NPM1 mutations (NPM1c) present in AML blasts. DNMT3A(mut)-bearing HSCs showed a multilineage repopulation advantage over non-mutated HSCs in xenografts, establishing their identity as pre-leukaemic HSCs. Pre-leukaemic HSCs were found in remission samples, indicating that they survive chemotherapy. Therefore DNMT3A(mut) arises early in AML evolution, probably in HSCs, leading to a clonally expanded pool of pre-leukaemic HSCs from which AML evolves. Our findings provide a paradigm for the detection and treatment of pre-leukaemic clones before the acquisition of additional genetic lesions engenders greater therapeutic resistance.

A locus on human chromosome 11q23 tagged by marker rs3802842 was associated with colorectal cancer (CRC) in a genome-wide association study; this finding has been replicated in case-control studies worldwide. In order to identify biologic factors at this locus that are related to the etiopathology of CRC, we used microarray-based target selection methods, coupled to next-generation sequencing, to study 103 kb at the 11q23 locus. We genotyped 369 putative variants from 1,030 patients with CRC (cases) and 1,061 individuals without CRC (controls) from the Ontario Familial Colorectal Cancer Registry. Two previously uncharacterized genes, COLCA1 and COLCA2, were found to be co-regulated genes that are transcribed from opposite strands. Expression levels of COLCA1 and COLCA2 transcripts correlate with rs3802842 genotypes. In colon tissues, COLCA1 co-localizes with crystalloid granules of eosinophils and granular organelles of mast cells, neutrophils, macrophages, dendritic cells and differentiated myeloid-derived cell lines. COLCA2 is present in the cytoplasm of normal epithelial, immune and other cell lineages, as well as tumor cells. Tissue microarray analysis demonstrates the association of rs3802842 with lymphocyte density in the lamina propria (p = 0.014) and levels of COLCA1 in the lamina propria (p = 0.00016) and COLCA2 (tumor cells, p = 0.0041 and lamina propria, p = 6 × 10(-5)). In conclusion, genetic, expression and immunohistochemical data implicate COLCA1 and COLCA2 in the pathogenesis of colon cancer. Histologic analyses indicate the involvement of immune pathways.

Background: Experimental evidence has demonstrated an antineoplastic role for vitamin D in the colon, and higher circulating 25-hydroxyvitamin D [25(OH)D] levels are consistently associated with a lower risk of colorectal cancer. Genome-wide association studies have identified loci associated with levels of circulating 25(OH)D. The identified single-nucleotide polymorphisms (SNPs) from four gene regions collectively explain approximately 5% of the variance in circulating 25(OH)D. Methods: We investigated whether five polymorphisms in GC, CYP2R1, CYP24A1, and DHCR7/NADSYN1, genes previously shown to be associated with circulating 25(OH)D levels, were associated with colorectal cancer risk in 10,061 cases and 12,768 controls drawn from 13 studies included in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and Colon Cancer Family Registry (CCFR). We conducted a meta-analysis of crude and multivariate-adjusted logistic regression models to calculate odds ratios and associated confidence intervals for SNPs individually, SNPs simultaneously, and for a vitamin D additive genetic risk score (GRS). Results: We did not observe a statistically significant association between the 25(OH)D-associated SNPs and colorectal cancer marginally, conditionally, or as a GRS, or for colon or rectal cancer separately. Conclusions: Our findings do not support an association between SNPs associated with circulating 25(OH)D and risk of colorectal cancer. Additional work is warranted to investigate the complex relationship between 25(OH)D and colorectal cancer risk. Impact: There was no association observed between genetic markers of circulating 25(OH)D and colorectal cancer. These genetic markers account for a small proportion of the variance in 25(OH)D. Cancer Epidemiol Biomarkers Prev; 22(11); 2037–46. ©2013 AACR.

Objective Genome-wide association studies have identified a large number of single nucleotide polymorphisms (SNPs) associated with a wide array of cancer sites. Several of these variants demonstrate associations with multiple cancers, suggesting pleiotropic effects and shared biological mechanisms across some cancers. We hypothesised that SNPs previously associated with other cancers may additionally be associated with colorectal cancer. In a large-scale study, we examined 171 SNPs previously associated with 18 different cancers for their associations with colorectal cancer. Design We examined 13 338 colorectal cancer cases and 40 967 controls from three consortia: Population Architecture using Genomics and Epidemiology (PAGE), Genetic Epidemiology of Colorectal Cancer (GECCO), and the Colon Cancer Family Registry (CCFR). Study-specific logistic regression results, adjusted for age, sex, principal components of genetic ancestry, and/or study specific factors (as relevant) were combined using fixed-effect meta-analyses to evaluate the association between each SNP and colorectal cancer risk. A Bonferroni-corrected p value of 2.92×10 −4 was used to determine statistical significance of the associations. Results Two correlated SNPs—rs10090154 and rs4242382—in Region 1 of chromosome 8q24, a prostate cancer susceptibility region, demonstrated statistically significant associations with colorectal cancer risk. The most significant association was observed with rs4242382 (meta-analysis OR=1.12; 95% CI 1.07 to 1.18; p=1.74×10 −5 ), which also demonstrated similar associations across racial/ethnic populations and anatomical sub-sites. Conclusions This is the first study to clearly demonstrate Region 1 of chromosome 8q24 as a susceptibility locus for colorectal cancer; thus, adding colorectal cancer to the list of cancer sites linked to this particular multicancer risk region at 8q24.

Most disease associated genetic risk factors are non-coding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer downstream effects of disease variants but the large majority remains unexplained.. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. However, a large-scale, combined analysis of methylome and transcriptome data to infer downstream effects of disease variants is lacking. Here, we show that disease variants have wide-spread effects on DNA methylation in trans that likely reflect the downstream effects on binding sites of cis-regulated transcription factors. Using data on 3,841 Dutch samples, we detected 272,037 independent cis-meQTLs (FDR < 0.05) and identified 1,907 trait-associated SNPs that affect methylation levels of 10,141 different CpG sites in trans (FDR < 0.05), an eight-fold increase in the number of downstream effects that was known from trans-eQTL studies. Trans-meQTL CpG sites are enriched for active regulatory regions, being correlated with gene expression and overlap with Hi-C determined interchromosomal contacts. We detected many trans-meQTL SNPs that affect expression levels of nearby transcription factors (including NFKB1, CTCF and NKX2-3), while the corresponding trans-meQTL CpG sites frequently coincide with its respective binding site. Trans-meQTL mapping therefore provides a strategy for identifying and better understanding downstream functional effects of many disease-associated variants.

DNA methylation, an important type of epigenetic modification in humans, participates in crucial cellular processes, such as embryonic development, X-inactivation, genomic imprinting and chromosome stability. Several platforms have been developed to study genome-wide DNA methylation. Many investigators in the field have chosen the Illumina Infinium HumanMethylation microarray for its ability to reliably assess DNA methylation following sodium bisulfite conversion. Here, we analyzed methylation profiles of 489 adult males and 357 adult females generated by the Infinium HumanMethylation450 microarray. Among the autosomal CpG sites that displayed significant methylation differences between the two sexes, we observed a significant enrichment of cross-reactive probes co-hybridizing to the sex chromosomes with more than 94% sequence identity. This could lead investigators to mistakenly infer the existence of significant autosomal sex-associated methylation. Using sequence identity cutoffs derived from the sex methylation analysis, we concluded that 6% of the array probes can potentially generate spurious signals because of co-hybridization to alternate genomic sequences highly homologous to the intended targets. Additionally, we discovered probes targeting polymorphic CpGs that overlapped SNPs. The methylation levels detected by these probes are simply the reflection of underlying genetic polymorphisms but could be misinterpreted as true signals. The existence of probes that are cross-reactive or of target polymorphic CpGs in the Illumina HumanMethylation microarrays can confound data obtained from such microarrays. Therefore, investigators should exercise caution when significant biological associations are found using these array platforms. A list of all cross-reactive probes and polymorphic CpGs identified by us are annotated in this paper.

The goal of the International HapMap Project is to determine the common patterns of DNA sequence variation in the human genome and to make this information freely available in the public domain. An international consortium is developing a map of these patterns across the genome by determining the genotypes of one million or more sequence variants, their frequencies and the degree of association between them, in DNA samples from populations with ancestry from parts of Africa, Asia and Europe. The HapMap will allow the discovery of sequence variants that affect common disease, will facilitate development of diagnostic tools, and will enhance our ability to choose targets for therapeutic intervention. © 2003 Nature Publishing Group.

Telomere-specific clones are a valuable resource for the characterization of chromosomal rearrangements. We previously reported a first-generation set of human telomere probes consisting of 34 genomic clones, which were a known distance from the end of the chromosome ( approximately 300 kb), and 7 clones corresponding to the most distal markers on the integrated genetic/physical map (1p, 5p, 6p, 9p, 12p, 15q, and 20q). Subsequently, this resource has been optimized and completed: the size of the genomic clones has been expanded to a target size of 100-200 kb, which is optimal for use in genome-scanning methodologies, and additional probes for the remaining seven telomeres have been identified. For each clone we give an associated mapped sequence-tagged site and provide distances from the telomere estimated using a combination of fiberFISH, interphase FISH, sequence analysis, and radiation-hybrid mapping. This updated set of telomeric clones is an invaluable resource for clinical diagnosis and represents an important contribution to genetic and physical mapping efforts aimed at telomeric regions.

Pancreatic cancer is a highly lethal malignancy with few effective therapies. We performed exome sequencing and copy number analysis to define genomic aberrations in a prospectively accrued clinical cohort (n = 142) of early (stage I and II) sporadic pancreatic ductal adenocarcinoma. Detailed analysis of 99 informative tumours identified substantial heterogeneity with 2,016 non-silent mutations and 1,628 copy-number variations. We define 16 significantly mutated genes, reaffirming known mutations (KRAS, TP53, CDKN2A, SMAD4, MLL3, TGFBR2, ARID1A and SF3B1), and uncover novel mutated genes including additional genes involved in chromatin modification (EPC1 and ARID2), DNA damage repair (ATM) and other mechanisms (ZIM2, MAP2K4, NALCN, SLC16A4 and MAGEA6). Integrative analysis with in vitro functional data and animal models provided supportive evidence for potential roles for these genetic aberrations in carcinogenesis. Pathway-based analysis of recurrently mutated genes recapitulated clustering in core signalling pathways in pancreatic ductal adenocarcinoma, and identified new mutated genes in each pathway. We also identified frequent and diverse somatic aberrations in genes described traditionally as embryonic regulators of axon guidance, particularly SLIT/ROBO signalling, which was also evident in murine Sleeping Beauty transposon-mediated somatic mutagenesis models of pancreatic cancer, providing further supportive evidence for the potential involvement of axon guidance genes in pancreatic carcinogenesis.

Myogenesis is a tightly controlled process involving the transcriptional activation and repression of thousands of genes. Although many components of the transcriptional network regulating the later phases of myogenesis have been identified, relatively few studies have described the transcriptional landscape during the first 24 h, when myoblasts commit to differentiate. Through dense temporal profiling of differentiating C2C12 myoblasts, we identify 193 transcriptional regulators (TRs) whose expression is significantly altered within the first 24 h of myogenesis. A high-content shRNA screen of 77 TRs involving 427 stable lines identified 42 genes whose knockdown significantly inhibits differentiation of C2C12 myoblasts. Of the TRs that were differentially expressed within the first 24 h, over half inhibited differentiation when knocked down, including known regulators of myogenesis ( Myod1, Myog, and Myf5), as well as 19 TRs not previously associated with this process. Surprisingly, a similar proportion (55%) of shRNAs targeting TRs whose expression did not change also inhibited C2C12 myogenesis. We further show that a subset of these TRs inhibits myogenesis by downregulating expression of known regulatory and structural proteins. Our findings clearly illustrate that several TRs critical for C2C12 myogenesis are not differentially regulated, suggesting that approaches that focus functional studies on differentially-expressed transcripts will fail to provide a comprehensive view of this complex process.

To determine genetic factors predisposing to neurological complications following West Nile virus infection, we analyzed a cohort of 560 neuroinvasive case patients and 950 control patients for 13 371 mostly nonsynonymous single-nucleotide polymorphisms (SNPs). The top 3 SNPs on the basis of statistical significance were also in genes of biological plausibility: rs2066786 in RFC1 (replication factor C1) (P = 1.88 × 10−5; odds ratio [OR], 0.68 [95% confidence interval {CI}, .56–.81]); rs2298771 in SCN1A (sodium channel, neuronal type I α subunit) (P = 5.87 × 10−5; OR, 1.47 [95% CI, 1.21–1.77]); and rs25651 in ANPEP (ananyl aminopeptidase) (P = 1.44 × 10−4; OR, 0.69 [95% CI, .56–.83]). Additional genotyping of these SNPs in a separate sample of 264 case patients and 296 control patients resulted in a lack of significance in the replication cohort; joint significance was as follows: rs2066786, P = .0022; rs2298771, P = .005; rs25651, P = .042. Using mostly nonsynonymous variants, we therefore did not identify genetic variants associated with neuroinvasive disease.

Background: Genome-wide association studies (GWAS) have led to the identification of a number of common susceptibility loci for colorectal cancer (CRC); however, none of these GWAS have considered gene–environment (G × E) interactions. Therefore, it is unclear whether current hits are modified by environmental exposures or whether there are additional hits whose effects are dependent on environmental exposures. Methods: We conducted a systematic search for G × E interactions using genome wide data from the Colon Cancer Family Registry that included 1,191 cases of microsatellite stable (MSS) or microsatellite instability–low (MSI-L) CRC and 999 controls genotyped using either the Illumina Human1M or Human1M-Duo BeadChip. We tested for interactions between genotypes and 14 environmental factors using 3 methods: a traditional case–control test, a case-only test, and the recently proposed 2-step method by Murcray and colleagues. All potentially significant findings were replicated in the ARCTIC Study. Results: No G × E interactions were identified that reached genome-wide significance by any of the 3 methods. When analyzing previously reported susceptibility loci, 7 significant G × E interactions were found at a 5% significance level. We investigated these 7 interactions in an independent sample and none of the interactions were replicated. Conclusions: Identifying G × E interactions will present challenges in a GWAS setting. Our power calculations illustrate the need for larger sample sizes; however, as CRC is a heterogeneous disease, a tradeoff between increasing sample size and heterogeneity needs to be considered. Impact: The results from this first genome-wide analysis of G × E in CRC identify several challenges, which may be addressed by large consortium efforts. Cancer Epidemiol Biomarkers Prev; 20(5); 758–66. ©2011 AACR.

Systemic lupus erythematosus (SLE) is a multisystem autoimmune disease. Multiple genetic and environmental factors contribute to the pathogenesis of this disease. Recent genome-wide association studies have added substantially to the number of genes associated with SLE. To replicate some of these susceptibility loci, single-nucleotide polymorphisms reported to be associated to SLE were evaluated in a cohort of 245 well-phenotyped Canadian SLE trios. Our results replicate previously reported associations to alleles of interferon regulatory factor 5 (IRF5), major histocompatibility complex (MHC), tumor necrosis factor (ligand) superfamily member 4 (TNFSF4), Kell blood group complex subunit-related family member 6 (XKR6), B-cell scaffold protein with ankyrin repeats 1 (BANK1), protein tyrosine phosphatase non-receptor type 22 (PTPN22), ubiquitin-conjugating enzyme E2L 3 (UBE2L3) and islet cell autoantigen 1 (ICA1). We also identify putative associations to cytotoxic T-lymphocyte-associated protein 4 (CTLA4), a gene associated with several autoimmune disorders, and ERBB3, a locus on 12q13 that was previously reported to be associated with type 1 diabetes. This study confirms the existence of multiple genetic risk factors for SLE, and supports the notion that some risk factors for SLE are shared with other inflammatory disorders.

Background: Immune checkpoint blockade has shown activity in microsatellite-instability high (MSI-H) colorectal cancers (CRCs). However, despite a very high mutational and neoantigen load among virtually all MSI-H tumors, the response rate is around 40-50%. In addition, for the majority of CRCs, which are microsatellite stable (MSS), immune checkpoint inhibitors have so far proven ineffective. Thus, to better understand the genetic drivers of immune evasion in CRC, we integrated next generation sequencing data from over 1200 tumors with transcriptional and immunohistochemical measures of immune infiltration. Methods: We molecularly characterized 1,211 colorectal cancers, including 592 tumors from The Cancer Genome Atlas with whole exome sequencing (WES) and whole transcriptome (RNAseq) data and 619 cancers from two prospective cohort studies with WES and immunohistochemical (IHC) annotations. To identify driver gene alterations and their selection pressures specific to MSI-high tumors, we developed a statistical method to identify significantly mutated microsatellite tracts and we further developed a method to identify copy neutral loss of heterozygosity (CN-LOH) events. We used an established immune-gene transcriptional signature as well as IHC stains against specific subsets of immune-infiltrating cells to identify genetic events associated with immune evasion. Results: We demonstrated that WNT-signaling and immune-related genes are significantly mutated in colorectal cancer. In MSI-H CRCs, we found biallelic antigen-presentation machinery (APM) mutations in the HLA Class I genes, B2M and TAP2, in addition to recurrent mutations in NLRC5 and RFX5, which downregulated HLA Class I expression. In all CRCs, we showed that WNT-signaling activity and APC-biallelic mutations are inversely associated with both transcriptional and IHC measures of T-cell infiltration. Specifically, nuclear CTNNB1 expression was inversely correlated with overall tumor-infiltrating lymphocytes (p = 0.027), CD8+ subset of T-cells (p = 0.0019) and CD45RO+ subset of T-cells (0.0080). Meanwhile, colorectal tumors with biallelic disruptive mutations in APC had a significantly decreased activated T-cell transcriptional signature (p = 4e-12) relative to samples with no disruptive mutations in APC. We further showed that in MSS tumors, AXIN2 (a key WNT-signaling effector) super-enhancer hypomethylation, independent of the APC mutation status, was further associated with decreased T-cell activity. Conclusions: In this largest CRC genomic analysis to date, we identify genetic events that are associated with immune evasion in this disease. Specifically, we find evidence of immuno-editing in MSI-H tumors through disruptive mutations in APM. In the MSS and MSI-H subtype of CRCs, we use transcriptional and immunohistochemical orthogonal analyses to demonstrate exclusion of an effective immune infiltrate in CRC through an active WNT-signaling pathway. Our results shed light on the underlying molecular mechanisms of immune exclusion in CRC and have direct implications for novel combination immunotherapy trials for patients with this disease. This abstract is also being presented as Poster B23. Citation Format: Marios Giannakis, Catherine Grasso, Daniel Wells, Tsuyoshi Hamada, Xinmeng Jasmine Mu, Michael Quist, Jonathan Nowak, Reiko Nishihara, Charles M. Connolly, Sachet Shukla, William M. Grady, David Wheeler, Catherine J. Wu, Jesse Zaretsky, Levi Garraway, Thomas Hudson, Charles Fuchs, Antoni Ribas, Riki Peters, Shuji Ogino. Genetic mechanisms of immune evasion in colorectal cancer [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2017 Oct 1-4; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2018;6(9 Suppl):Abstract nr PR03.

Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence20%) and data for enough risk variants are available (e.g., approximately 140-160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual's CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci associated with CRC before the question of the potential utility of germline genomic profiling can be definitively answered.

The mechanism for the association of type 1 diabetes (T1D) with IL2RA remains to be clarified. Neither of the two distinct, transmission-disequilibrium confirmed loci mapping to this gene can be explained by a coding variant. An effect on the levels of the soluble protein product sIL-2RA has been reported but its cause and relationship to disease risk is not clear. To look for an allelic effect on IL2RA transcription in cis, we examined RNA from 48 heterozygous lymphocyte samples for differential allele expression. Of the 48 samples, 32 showed statistically significant allelic imbalance. No known single nucleotide polymorphism (SNP) had perfect correlation with this transcriptional effect but the one that showed the most significant (p = 1.6 × 10−5) linkage disequilibrium with it was the SNP rs3118470. We had previously shown rs3118470 to confer T1D susceptibility in a Canadian dataset, independently of rs41295061 as the major reported locus (p = 5 × 10−3, after accounting for rs41295061 by conditional regression). Lower IL2RA levels consistently originated from the T1D predisposing allele. We conclude that an as yet unidentified variant or haplotype, best marked by rs3118470, is responsible for this independent effect and increases T1D risk through diminished expression of the IL-2R, likely by interfering with the proper development of regulatory T cells.

Resiquimod is a compound belonging to the imidazoquinoline family of compounds known to signal through Toll-like receptor 7. Resiquimod treatment has been demonstrated to inhibit the development of allergen induced asthma in experimental models. The aim of the present study was to elucidate the molecular processes that were altered following resiquimod treatment and allergen challenge in a mouse model of allergic asthma. Employing microarray analysis, we have characterized the “asthmatic” transcriptome of the lungs of A/J and C57BL/6 mice and determined that it includes genes involved in the control of cell cycle progression, the complement and coagulation cascades, and chemokine signaling. Our results demonstrated that resiquimod treatment resulted in the normalization of the expression of genes involved with airway remodeling, and generally, chemokine signaling. Resiquimod treatment also altered the expression of cell adhesion molecules, and molecules involved in natural killer (NK) cell-mediated cytotoxicity. Furthermore, we have demonstrated that systemic resiquimod administration resulted in the recruitment of NK cells to the lungs and livers of the mice, although no causal relationship between NK cell recruitment and treatment efficacy was found. Overall, our findings identified several genes, important in the development of asthma pathology, that were normalized following resiquimod treatment, thus improving our understanding of the molecular consequences of resiquimod treatment in the lung milieu. The recruitment of NK cells to the lungs may also have application in the treatment of virally induced asthma exacerbations.

The Centre for Applied Genomics of the Hospital for Sick Children and the University of Toronto hosted the 10th Human Genome Variation (HGV) Meeting in Toronto, Canada, in October 2008, welcoming about 240 registrants from 34 countries. During the 3 days of plenary workshops, keynote address, and poster sessions, a strong cross-disciplinary trend was evident, integrating expertise from technology and computation, through biology and medicine, to ethics and law. Single nucleotide polymorphisms (SNPs) as well as the larger copy number variants (CNVs) are recognized by ever-improving array and next-generation sequencing technologies, and the data are being incorporated into studies that are increasingly genome-wide as well as global in scope. A greater challenge is to convert data to information, through databases, and to use the information for greater understanding of human variation. In the wake of publications of the first individual genome sequences, an inaugural public forum provided the opportunity to debate whether we are ready for personalized medicine through direct-to-consumer testing. The HGV meetings foster collaboration, and fruits of the interactions from 2008 are anticipated for the 11th annual meeting in September 2009.

Glial cell-derived neurotrophic factor (GDNF) plays an important role in renal development, serving as a trophic factor for outgrowth of the ureteric bud and its continued arborisation. Our previous studies have shown that common variants of the human paired-box 2 (PAX2) gene (a transcriptional activator of GDNF) and rearranged during transfection (RET) gene (encoding the cognate receptor for GDNF) are associated with a subtle reduction in the kidney size of newborns. Since heterozygosity for a mutant GDNF allele causes mild renal hypoplasia and modest hypertension in mice, we considered the possibility that common variants of the GDNF gene might also contribute to renal hypoplasia in humans. We studied the relationship between newborn renal size or umbilical cord cystatin C and 19 common GDNF gene variants [minor allele frequency (MAF) >5%], three single nucleotide polymorphisms (SNPs) related to a putative PAX binding site and one rare SNP (rs36119840 A/G) which changes an amino acid (R93W), based on data from the haplotype map of the human genome (HapMap). However, none of these 23 SNPs was associated with reduced newborn kidney size or function. Among the 163 Caucasians in our cohort, none had the R93W allele.

Background: Serum immunoglobulin E (IgE) level is recognized to be under strong genetic control, but the causal and susceptibility genes remain to be identified. We sought to investigate the association between single nucleotide polymorphisms (SNPs) in the Toll-like receptor (TLR) signaling pathway and total serum IgE level. Methods: A population of 206 patients with severe chronic rhinosinusitis (CRS) was used. Precise phenotyping of patients was accomplished by means of a questionnaire and clinical examination. Blood was drawn for measurement of total serum IgE, as well as DNA extraction. A maximally informative set of SNPs in the TLR1, 2, 3, 4, 6, 9, 10, CD14, MD2, MyD88, IRAK4, and TRAF6 genes were selected and genotyped. Significant findings were replicated in a second independent population of 956 subjects from 227 families with asthma. Results: A total of 97 out of 104 SNPs were successfully genotyped. Three SNPs in IRAK4– rs1461567, rs4251513, and rs4251559 – were associated with total serum IgE levels (P

Huntington disease (HD) is an autosomal-dominant disorder that results from ≥36 CAG repeats in the HD gene (HTT). Approximately 10% of patients inherit a chromosome that underwent CAG expansion from an unaffected parent with

The success of genome-wide association studies (GWAS) to identify risk loci of complex diseases is now well-established. One persistent major hurdle is the cost of those studies, which make them beyond the reach of most research groups. Performing GWAS on pools of DNA samples may be an effective strategy to reduce the costs of these studies. In this study, we performed pooling-based GWAS with more than 550,000 SNPs in two case-control cohorts consisting of patients with Type II diabetes (T2DM) and with chronic rhinosinusitis (CRS). In the T2DM study, the results of the pooling experiment were compared to individual genotypes obtained from a previously published GWAS. TCF7L2 and HHEX SNPs associated with T2DM by the traditional GWAS were among the top ranked SNPs in the pooling experiment. This dataset was also used to refine the best strategy to correctly identify SNPs that will remain significant based on individual genotyping. In the CRS study, the top hits from the pooling-based GWAS located within ten kilobases of known genes were validated by individual genotyping of 1,536 SNPs. Forty-one percent (598 out of the 1,457 SNPs that passed quality control) were associated with CRS at a nominal P value of 0.05, confirming the potential of pooling-based GWAS to identify SNPs that differ in allele frequencies between two groups of subjects. Overall, our results demonstrate that a pooling experiment on high-density genotyping arrays can accurately determine the minor allelic frequency as compared to individual genotyping and produce a list of top ranked SNPs that captures genuine allelic differences between a group of cases and controls. The low cost associated with a pooling-based GWAS clearly justifies its use in screening for genetic determinants of complex diseases.

We used a candidate gene approach to identify a set of SNPs, located in a predicted regulatory region on chromosome 1q44 downstream of NLRP3 (previously known as CIAS1 and NALP3) that are associated with Crohn's disease. The associations were consistently replicated in four sample sets from individuals of European descent. In the combined analysis of all samples (710 father-mother-child trios, 239 cases and 107 controls), these SNPs were strongly associated with risk of Crohn's disease (P(combined) = 3.49 x 10(-9), odds ratio = 1.78, confidence interval = 1.47-2.16 for rs10733113), reaching a level consistent with the stringent significance thresholds imposed by whole-genome association studies. In addition, we observed significant associations between SNPs in the associated regions and NLRP3 expression and IL-1beta production. Mutations in NLRP3 are known to be responsible for three rare autoinflammatory disorders. These results suggest that the NLRP3 region is also implicated in the susceptibility of more common inflammatory diseases such as Crohn's disease.

Squalene synthase is an important component of the cholesterol biosynthetic pathway, and inhibitors of this enzyme have been shown to lower plasma cholesterol levels. Previously, we sequenced the squalene synthase gene, FDFT1 (farnesyl-diphosphate farnesyltransferase), and identified several SNPs, including a nonsynonymous variant, rs11549147:A>G (K45R). To examine the possible association of K45R with plasma lipid traits, we tested 887 individuals from 149 families from the founder population of Saguenay-Lac St. Jean (SLSJ), Quebec. K45R was associated with increased total cholesterol (TC) (P=0.035) and non-high-density lipoprotein cholesterol (non-HDL-C) (P=0.01). These results were replicated in an independent sample of unrelated individuals (P=0.0008 for TC, P=0.004 for non-HDL-C). This SNP also influenced low-density lipoprotein cholesterol (P=0.042) and HDL-C (P=0.025) in the family-based sample, and triglycerides (TG) (P=0.007) in the unrelated subjects. The lysine (K) in codon 45 is conserved across 11 mammals and lies in a potential exonic splicing enhancer (ESE) site. These results suggest that this coding variant in the squalene synthase gene influences plasma cholesterol levels, possibly by affecting the intracellular production of cholesterol. Hum Mutat 29(5), 689–694, 2008. © 2008 Wiley-Liss, Inc.

Systemic lupus erythematosus (SLE) is a complex disease trait of unknown aetiology. Genome-wide linkage studies in human SLE identified several linkage regions, including one at 1q23, which contains multiple susceptibility genes, including the members of the signalling lymphocyte activation molecule (SLAM) locus. In mice there is a syntenic linkage region, Sle1. The SLAM genes are functionally related cell-surface receptors, which regulate signal transduction of cells in the immune system. Family-based association study in UK and Canadian SLE families identified variants in the promoter and coding region of SLAMF7 and LY9 contributing to SLE disease susceptibility. The strongest association was from rs509749, in exon 8 of LY9 (P=0.00209). rs509749 encodes a Val/Met nonsynonymous change in amino acid 602 in the cytoplasmic domain of LY9. In the parents and affected individuals from the Canadian SLE families, the risk allele of rs509049 skews the T-cell population by increasing the number of CD8+ memory T cells, while decreasing the proportion of CD4+ naïve T cells and activated T cells. Since rs509749 lies within the consensus binding site for SAP/SH2D1a, which influences downstream signalling events from LY9, the mechanism for increased CD8+ memory T cells may include differential binding SAP/SH2D1a to the cytoplasmic domain of LY9.

Physiologic allele-specific expression (ASE) in germline tissues occurs during random X-chromosome inactivation and in genomic imprinting, wherein the two alleles of a gene in a heterozygous individual are not expressed equally. Recent studies have confirmed the existence of ASE in apparently non-imprinted autosomal genes; however, the extent of ASE in the human genome is unknown. We explored ASE in lymphoblastoid cell lines of 145 individuals using an oligonucleotide array based assay. ASE of autosomal genes was found to be a very common phenomenon in approximately 20% of heterozygotes at 78% of SNPs at 84% of the genes examined. Comparison of 100 affected individuals from familial pancreatic cancer kindreds and 45 controls revealed three types of changes in the germline: (a) loss of ASE, (b) gain of ASE, and, (c) rare instances of "extreme" (near monoallelic) ASE. The latter changes identified heterozygous deleterious mutations in a subset of these genes. Consequently, an ASE assay efficiently identifies candidate disease genes with altered germline expression properties as compared to controls, and provides insights into mechanisms that confer an inherited disease risk for pancreatic cancer.

Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) associated with colorectal cancer risk. These SNPs may tag correlated variants with biological importance. Fine-mapping around GWAS loci can facilitate detection of functional candidates and additional independent risk variants. We analyzed 11,900 cases and 14,311 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium and the Colon Cancer Family Registry. To fine-map genomic regions containing all known common risk variants, we imputed high-density genetic data from the 1000 Genomes Project. We tested single-variant associations with colorectal tumor risk for all variants spanning genomic regions 250-kb upstream or downstream of 31 GWAS-identified SNPs (index SNPs). We queried the University of California, Santa Cruz Genome Browser to examine evidence for biological function. Index SNPs did not show the strongest association signals with colorectal tumor risk in their respective genomic regions. Bioinformatics analysis of SNPs showing smaller P-values in each region revealed 21 functional candidates in 12 loci (5q31.1, 8q24, 11q13.4, 11q23, 12p13.32, 12q24.21, 14q22.2, 15q13, 18q21, 19q13.1, 20p12.3, and 20q13.33). We did not observe evidence of additional independent association signals in GWAS-identified regions. Our results support the utility of integrating data from comprehensive fine-mapping with expanding publicly available genomic databases to help clarify GWAS associations and identify functional candidates that warrant more onerous laboratory follow-up. Such efforts may aid the eventual discovery of disease-causing variant(s). National Institutes of Health; National Cancer Institute; U.S. Department of Health and Human Services

Using a multistage genetic association approach comprising 7,480 affected individuals and 7,779 controls, we identified markers in chromosomal region 8q24 associated with colorectal cancer. In stage 1, we genotyped 99,632 SNPs in 1,257 affected individuals and 1,336 controls from Ontario. In stages 2-4, we performed serial replication studies using 4,024 affected individuals and 4,042 controls from Seattle, Newfoundland and Scotland. We identified one locus on chromosome 8q24 and another on 9p24 having combined odds ratios (OR) for stages 1-4 of 1.18 (trend; P = 1.41 x 10(-8)) and 1.14 (trend; P = 1.32 x 10(-5)), respectively. Additional analyses in 2,199 affected individuals and 2,401 controls from France and Europe supported the association at the 8q24 locus (OR = 1.16, trend; 95% confidence interval (c.i.): 1.07-1.26; P = 5.05 x 10(-4)). A summary across all seven studies at the 8q24 locus was highly significant (OR = 1.17, c.i.: 1.12-1.23; P = 3.16 x 10(-11)). This locus has also been implicated in prostate cancer.

Congenital nephron number ranges widely in the human population. Suboptimal nephron number may be associated with increased risk for essential hypertension and susceptibility to renal injury, but the factors that set nephron number during kidney development are unknown. In renal-coloboma syndrome, renal hypoplasia and reduced nephron number are due to heterozygous mutations of the PAX2 gene. This study tested for an association between a common haplotype of the PAX2 gene and subtle renal hypoplasia in normal newborns. A PAX2 haplotype was identified to occur in 18.5% of the newborn cohort, which was significantly associated with a 10% reduction in newborn kidney volume adjusted for body surface area. This haplotype was also associated with reduced allele-specific PAX2 mRNA level in a human renal cell carcinoma cell line. Subtle renal hypoplasia in normal newborns may be partially due to a common variant of the PAX2 gene that reduces mRNA expression during kidney development.

It is likely that evolutionary differences among species are driven by sequence changes in regulatory regions. Likewise, polymorphisms in the promoter regions may be responsible for interindividual differences at the level of populations. We present an unbiased survey of genetic variation in 2-kb segments upstream of the transcription start sites of 28 protein-coding genes, characterized in five population groups of different geographic origin. On average, we found 9.1 polymorphisms and 8.8 haplotypes per segment with corresponding nucleotide and haplotype diversities of 0.082% and 58%, respectively. We characterized these segments through different summary statistics, Hardy-Weinberg equilibria fixation index (Fst) estimates, and neutrality tests, as well as by analyzing the distributions of haplotype allelic classes, introduced here to assess the departure from neutrality and examined by coalescent simulations under a simple population model, assuming recombinations or different demography. Our results suggest that genetic diversity in some of these regions could have been shaped by purifying selection and driven by adaptive changes in the other, thus explaining the relatively large variance in the corresponding genetic diversity indices loci. However, some of these effects could be also due to linkage with surrounding sequences, and the neutralists' explanations cannot be ruled out given uncertainty in the underlying demographic histories and the possibility of random effects due to the small size of the studied segments. Hum Mutat 28(5), 441–450, 2007. © 2007 Wiley-Liss, Inc.