Journal Articles
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Item Comprehensive analysis of molecular characteristics between primary and breast-derived metastatic ovarian cancer(AME Publishing Company, 2025-03-30) Long J; Liu B; Li J; Ji X; Zhu N; Zhuang X; Wang H; Li L; Chen Y; Li X; Zhao SBackground: The molecular basis for the disparities between primary ovarian cancer (POC) and ovarian cancer secondary to breast cancer (OCSTBC) remains poorly understood. This study aimed to explore the different characteristics between them through genomic analysis. Methods: We performed differentially expressed genes (DEGs) analysis between POC (n=96) and OCSTBC (n=44) groups with transcriptome data and revealed the enriched biological pathways with Kyoto Encyclopedia of Genes and Genomes (KEGG) and Hallmark gene sets between these two groups. Then, the Microenvironment Cell Populations (MCP)-counter and Cell-type Identification by Estimating Relative Subsets of RNA Transcript (CIBERSORT) algorithms were applied to evaluate the immune infiltration in tumor microenvironment (TME) between them. Finally, we performed the association analysis within single nucleotide polymorphism (SNP) data and obtained some meaningful SNPs and candidate genes for further transcriptomic analysis. Results: We identified a total of 13 cancer-related genes including GATA3, FOXA1, CCND1, and TTK between POC (n=96) and OCSTBC (n=44) groups with DEGs analysis. Integrated analysis revealed more significant immune-enriched pathways in the POC than in the OCSTBC group. Most immune cells had higher infiltration abundance in POC, except M2 macrophages, which was higher in OCSTBC. In SNP analysis, four SNP regions (8q12.1, 11q21, 11q24.3, and 17q25.3) were found to be significantly correlated with phenotypes (POC/OCSTBC), and importantly, some new susceptibility genes such as ETS1, CWC15, and XKR4 were revealed to potentially be associated with distinction between POC and OCSTBC. Conclusions: Our study provides a systematic molecular characteristic between POC and OCSTBC and suggests a pressing need to develop some specific therapeutic strategies in certain types of ovarian cancer.Item Hierarchical graph learning with convolutional network for brain disease prediction(Springer Nature, 2024-10-23) Liu T; Liu F; Wan Y; Hu R; Zhu Y; Li LIn computer-aided diagnostic systems, the functional connectome approach has become a common method for detecting neurological disorders. However, the existing methods either ignore the uniqueness of different subjects across the functional connectivities or neglect the commonality of the same disease for the functional connectivity of each subject, resulting in a lack of capacity of capturing a comprehensive functional model. To solve the issues, we develop a hierarchical graph learning with convolutional network that not only considers the unique information of each subject, but also takes the common information across subjects into account. Specifically, the proposed method consists of two structures, one is the individual graph model which selects the representative brain regions by combining each subject feature and its related brain region-based graph. The other is the population graph model to directly conduct classification performance by updating the information of each subject which considers both the subject itself and the nearest neighbours. Experimental results indicate that the proposed method on four real datasets outperforms the state-of-the-art approaches.Item Integrative analysis identifies two molecular and clinical subsets in Luminal B breast cancer(Elsevier Inc, 2023-09-15) Wang H; Liu B; Long J; Yu J; Ji X; Li J; Zhu N; Zhuang X; Li L; Chen Y; Liu Z; Wang S; Zhao SComprehensive multiplatform analysis of Luminal B breast cancer (LBBC) specimens identifies two molecularly distinct, clinically relevant subtypes: Cluster A associated with cell cycle and metabolic signaling and Cluster B with predominant epithelial mesenchymal transition (EMT) and immune response pathways. Whole-exome sequencing identified significantly mutated genes including TP53, PIK3CA, ERBB2, and GATA3 with recurrent somatic mutations. Alterations in DNA methylation or transcriptomic regulation in genes (FN1, ESR1, CCND1, and YAP1) result in tumor microenvironment reprogramming. Integrated analysis revealed enriched biological pathways and unexplored druggable targets (cancer-testis antigens, metabolic enzymes, kinases, and transcription regulators). A systematic comparison between mRNA and protein displayed emerging expression patterns of key therapeutic targets (CD274, YAP1, AKT1, and CDH1). A potential ceRNA network was developed with a significantly different prognosis between the two subtypes. This integrated analysis reveals a complex molecular landscape of LBBC and provides the utility of targets and signaling pathways for precision medicine.Item Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations.(Springer Nature, 2023-10-02) Thomas M; Su Y-R; Rosenthal EA; Sakoda LC; Schmit SL; Timofeeva MN; Chen Z; Fernandez-Rozadilla C; Law PJ; Murphy N; Carreras-Torres R; Diez-Obrero V; van Duijnhoven FJB; Jiang S; Shin A; Wolk A; Phipps AI; Burnett-Hartman A; Gsur A; Chan AT; Zauber AG; Wu AH; Lindblom A; Um CY; Tangen CM; Gignoux C; Newton C; Haiman CA; Qu C; Bishop DT; Buchanan DD; Crosslin DR; Conti DV; Kim D-H; Hauser E; White E; Siegel E; Schumacher FR; Rennert G; Giles GG; Hampel H; Brenner H; Oze I; Oh JH; Lee JK; Schneider JL; Chang-Claude J; Kim J; Huyghe JR; Zheng J; Hampe J; Greenson J; Hopper JL; Palmer JR; Visvanathan K; Matsuo K; Matsuda K; Jung KJ; Li L; Le Marchand L; Vodickova L; Bujanda L; Gunter MJ; Matejcic M; Jenkins MA; Slattery ML; D'Amato M; Wang M; Hoffmeister M; Woods MO; Kim M; Song M; Iwasaki M; Du M; Udaltsova N; Sawada N; Vodicka P; Campbell PT; Newcomb PA; Cai Q; Pearlman R; Pai RK; Schoen RE; Steinfelder RS; Haile RW; Vandenputtelaar R; Prentice RL; Küry S; Castellví-Bel S; Tsugane S; Berndt SI; Lee SC; Brezina S; Weinstein SJ; Chanock SJ; Jee SH; Kweon S-S; Vadaparampil S; Harrison TA; Yamaji T; Keku TO; Vymetalkova V; Arndt V; Jia W-H; Shu X-O; Lin Y; Ahn Y-O; Stadler ZK; Van Guelpen B; Ulrich CM; Platz EA; Potter JD; Li CI; Meester R; Moreno V; Figueiredo JC; Casey G; Lansdorp Vogelaar I; Dunlop MG; Gruber SB; Hayes RB; Pharoah PDP; Houlston RS; Jarvik GP; Tomlinson IP; Zheng W; Corley DA; Peters U; Hsu LPolygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.Item Probing the diabetes and colorectal cancer relationship using gene - environment interaction analyses.(Springer Nature, 2023-06-26) Dimou N; Kim AE; Flanagan O; Murphy N; Diez-Obrero V; Shcherbina A; Aglago EK; Bouras E; Campbell PT; Casey G; Gallinger S; Gruber SB; Jenkins MA; Lin Y; Moreno V; Ruiz-Narvaez E; Stern MC; Tian Y; Tsilidis KK; Arndt V; Barry EL; Baurley JW; Berndt SI; Bézieau S; Bien SA; Bishop DT; Brenner H; Budiarto A; Carreras-Torres R; Cenggoro TW; Chan AT; Chang-Claude J; Chanock SJ; Chen X; Conti DV; Dampier CH; Devall M; Drew DA; Figueiredo JC; Giles GG; Gsur A; Harrison TA; Hidaka A; Hoffmeister M; Huyghe JR; Jordahl K; Kawaguchi E; Keku TO; Larsson SC; Le Marchand L; Lewinger JP; Li L; Mahesworo B; Morrison J; Newcomb PA; Newton CC; Obon-Santacana M; Ose J; Pai RK; Palmer JR; Papadimitriou N; Pardamean B; Peoples AR; Pharoah PDP; Platz EA; Potter JD; Rennert G; Scacheri PC; Schoen RE; Su Y-R; Tangen CM; Thibodeau SN; Thomas DC; Ulrich CM; Um CY; van Duijnhoven FJB; Visvanathan K; Vodicka P; Vodickova L; White E; Wolk A; Woods MO; Qu C; Kundaje A; Hsu L; Gauderman WJ; Gunter MJ; Peters UBACKGROUND: Diabetes is an established risk factor for colorectal cancer. However, the mechanisms underlying this relationship still require investigation and it is not known if the association is modified by genetic variants. To address these questions, we undertook a genome-wide gene-environment interaction analysis. METHODS: We used data from 3 genetic consortia (CCFR, CORECT, GECCO; 31,318 colorectal cancer cases/41,499 controls) and undertook genome-wide gene-environment interaction analyses with colorectal cancer risk, including interaction tests of genetics(G)xdiabetes (1-degree of freedom; d.f.) and joint testing of Gxdiabetes, G-colorectal cancer association (2-d.f. joint test) and G-diabetes correlation (3-d.f. joint test). RESULTS: Based on the joint tests, we found that the association of diabetes with colorectal cancer risk is modified by loci on chromosomes 8q24.11 (rs3802177, SLC30A8 - ORAA: 1.62, 95% CI: 1.34-1.96; ORAG: 1.41, 95% CI: 1.30-1.54; ORGG: 1.22, 95% CI: 1.13-1.31; p-value3-d.f.: 5.46 × 10-11) and 13q14.13 (rs9526201, LRCH1 - ORGG: 2.11, 95% CI: 1.56-2.83; ORGA: 1.52, 95% CI: 1.38-1.68; ORAA: 1.13, 95% CI: 1.06-1.21; p-value2-d.f.: 7.84 × 10-09). DISCUSSION: These results suggest that variation in genes related to insulin signaling (SLC30A8) and immune function (LRCH1) may modify the association of diabetes with colorectal cancer risk and provide novel insights into the biology underlying the diabetes and colorectal cancer relationship.Item Non-Targeted Metabolomic Profiling Identifies Metabolites with Potential Antimicrobial Activity from an Anaerobic Bacterium Closely Related to Terrisporobacter Species.(MDPI (Basel, Switzerland), 2023-02-09) Pahalagedara ASNW; Flint S; Palmer J; Brightwell G; Luo X; Li L; Gupta TB; Eisenreich WThis work focused on the metabolomic profiling of the conditioned medium (FS03CM) produced by an anaerobic bacterium closely related to Terrisporobacter spp. to identify potential antimicrobial metabolites. The metabolome of the conditioned medium was profiled by two-channel Chemical Isotope Labelling (CIL) LC-MS. The detected metabolites were identified or matched by conducting a library search using different confidence levels. Forty-eight significantly changed metabolites were identified with high confidence after the growth of isolate FS03 in cooked meat glucose starch (CMGS) medium. Some of the secondary metabolites identified with known antimicrobial activities were 4-hydroxyphenyllactate, 3-hydroxyphenylacetic acid, acetic acid, isobutyric acid, valeric acid, and tryptamine. Our findings revealed the presence of different secondary metabolites with previously reported antimicrobial activities and suggested the capability of producing antimicrobial metabolites by the anaerobic bacterium FS03.Item Two genome-wide interaction loci modify the association of nonsteroidal anti-inflammatory drugs with colorectal cancer.(American Association for the Advancement of Science, 2024-05-29) Drew DA; Kim AE; Lin Y; Qu C; Morrison J; Lewinger JP; Kawaguchi E; Wang J; Fu Y; Zemlianskaia N; Díez-Obrero V; Bien SA; Dimou N; Albanes D; Baurley JW; Wu AH; Buchanan DD; Potter JD; Prentice RL; Harlid S; Arndt V; Barry EL; Berndt SI; Bouras E; Brenner H; Budiarto A; Burnett-Hartman A; Campbell PT; Carreras-Torres R; Casey G; Chang-Claude J; Conti DV; Devall MAM; Figueiredo JC; Gruber SB; Gsur A; Gunter MJ; Harrison TA; Hidaka A; Hoffmeister M; Huyghe JR; Jenkins MA; Jordahl KM; Kundaje A; Le Marchand L; Li L; Lynch BM; Murphy N; Nassir R; Newcomb PA; Newton CC; Obón-Santacana M; Ogino S; Ose J; Pai RK; Palmer JR; Papadimitriou N; Pardamean B; Pellatt AJ; Peoples AR; Platz EA; Rennert G; Ruiz-Narvaez E; Sakoda LC; Scacheri PC; Schmit SL; Schoen RE; Stern MC; Su Y-R; Thomas DC; Tian Y; Tsilidis KK; Ulrich CM; Um CY; van Duijnhoven FJB; Van Guelpen B; White E; Hsu L; Moreno V; Peters U; Chan AT; Gauderman WJRegular, long-term aspirin use may act synergistically with genetic variants, particularly those in mechanistically relevant pathways, to confer a protective effect on colorectal cancer (CRC) risk. We leveraged pooled data from 52 clinical trial, cohort, and case-control studies that included 30,806 CRC cases and 41,861 controls of European ancestry to conduct a genome-wide interaction scan between regular aspirin/nonsteroidal anti-inflammatory drug (NSAID) use and imputed genetic variants. After adjusting for multiple comparisons, we identified statistically significant interactions between regular aspirin/NSAID use and variants in 6q24.1 (top hit rs72833769), which has evidence of influencing expression of TBC1D7 (a subunit of the TSC1-TSC2 complex, a key regulator of MTOR activity), and variants in 5p13.1 (top hit rs350047), which is associated with expression of PTGER4 (codes a cell surface receptor directly involved in the mode of action of aspirin). Genetic variants with functional impact may modulate the chemopreventive effect of regular aspirin use, and our study identifies putative previously unidentified targets for additional mechanistic interrogation.Item A Genetic Locus within the FMN1/GREM1 Gene Region Interacts with Body Mass Index in Colorectal Cancer Risk.(American Association for Cancer Research, 2023-08-01) Aglago EK; Kim A; Lin Y; Qu C; Evangelou M; Ren Y; Morrison J; Albanes D; Arndt V; Barry EL; Baurley JW; Berndt SI; Bien SA; Bishop DT; Bouras E; Brenner H; Buchanan DD; Budiarto A; Carreras-Torres R; Casey G; Cenggoro TW; Chan AT; Chang-Claude J; Chen X; Conti DV; Devall M; Diez-Obrero V; Dimou N; Drew D; Figueiredo JC; Gallinger S; Giles GG; Gruber SB; Gsur A; Gunter MJ; Hampel H; Harlid S; Hidaka A; Harrison TA; Hoffmeister M; Huyghe JR; Jenkins MA; Jordahl K; Joshi AD; Kawaguchi ES; Keku TO; Kundaje A; Larsson SC; Marchand LL; Lewinger JP; Li L; Lynch BM; Mahesworo B; Mandic M; Obón-Santacana M; Moreno V; Murphy N; Nan H; Nassir R; Newcomb PA; Ogino S; Ose J; Pai RK; Palmer JR; Papadimitriou N; Pardamean B; Peoples AR; Platz EA; Potter JD; Prentice RL; Rennert G; Ruiz-Narvaez E; Sakoda LC; Scacheri PC; Schmit SL; Schoen RE; Shcherbina A; Slattery ML; Stern MC; Su Y-R; Tangen CM; Thibodeau SN; Thomas DC; Tian Y; Ulrich CM; van Duijnhoven FJ; Van Guelpen B; Visvanathan K; Vodicka P; Wang J; White E; Wolk A; Woods MO; Wu AH; Zemlianskaia N; Hsu L; Gauderman WJ; Peters U; Tsilidis KK; Campbell PTColorectal cancer risk can be impacted by genetic, environmental, and lifestyle factors, including diet and obesity. Gene-environment interactions (G × E) can provide biological insights into the effects of obesity on colorectal cancer risk. Here, we assessed potential genome-wide G × E interactions between body mass index (BMI) and common SNPs for colorectal cancer risk using data from 36,415 colorectal cancer cases and 48,451 controls from three international colorectal cancer consortia (CCFR, CORECT, and GECCO). The G × E tests included the conventional logistic regression using multiplicative terms (one degree of freedom, 1DF test), the two-step EDGE method, and the joint 3DF test, each of which is powerful for detecting G × E interactions under specific conditions. BMI was associated with higher colorectal cancer risk. The two-step approach revealed a statistically significant G×BMI interaction located within the Formin 1/Gremlin 1 (FMN1/GREM1) gene region (rs58349661). This SNP was also identified by the 3DF test, with a suggestive statistical significance in the 1DF test. Among participants with the CC genotype of rs58349661, overweight and obesity categories were associated with higher colorectal cancer risk, whereas null associations were observed across BMI categories in those with the TT genotype. Using data from three large international consortia, this study discovered a locus in the FMN1/GREM1 gene region that interacts with BMI on the association with colorectal cancer risk. Further studies should examine the potential mechanisms through which this locus modifies the etiologic link between obesity and colorectal cancer. Significance: This gene-environment interaction analysis revealed a genetic locus in FMN1/GREM1 that interacts with body mass index in colorectal cancer risk, suggesting potential implications for precision prevention strategies.Item Genome-wide interaction analysis of folate for colorectal cancer risk.(Elsevier B.V., 2023-11) Bouras E; Kim AE; Lin Y; Morrison J; Du M; Albanes D; Barry EL; Baurley JW; Berndt SI; Bien SA; Bishop TD; Brenner H; Budiarto A; Burnett-Hartman A; Campbell PT; Carreras-Torres R; Casey G; Cenggoro TW; Chan AT; Chang-Claude J; Conti DV; Cotterchio M; Devall M; Diez-Obrero V; Dimou N; Drew DA; Figueiredo JC; Giles GG; Gruber SB; Gunter MJ; Harrison TA; Hidaka A; Hoffmeister M; Huyghe JR; Joshi AD; Kawaguchi ES; Keku TO; Kundaje A; Le Marchand L; Lewinger JP; Li L; Lynch BM; Mahesworo B; Männistö S; Moreno V; Murphy N; Newcomb PA; Obón-Santacana M; Ose J; Palmer JR; Papadimitriou N; Pardamean B; Pellatt AJ; Peoples AR; Platz EA; Potter JD; Qi L; Qu C; Rennert G; Ruiz-Narvaez E; Sakoda LC; Schmit SL; Shcherbina A; Stern MC; Su Y-R; Tangen CM; Thomas DC; Tian Y; Um CY; van Duijnhoven FJ; Van Guelpen B; Visvanathan K; Wang J; White E; Wolk A; Woods MO; Ulrich CM; Hsu L; Gauderman WJ; Peters U; Tsilidis KKBackground Epidemiological and experimental evidence suggests that higher folate intake is associated with decreased colorectal cancer (CRC) risk; however, the mechanisms underlying this relationship are not fully understood. Genetic variation that may have a direct or indirect impact on folate metabolism can provide insights into folate’s role in CRC. Objectives Our aim was to perform a genome-wide interaction analysis to identify genetic variants that may modify the association of folate on CRC risk. Methods We applied traditional case-control logistic regression, joint 3-degree of freedom, and a 2-step weighted hypothesis approach to test the interactions of common variants (allele frequency >1%) across the genome and dietary folate, folic acid supplement use, and total folate in relation to risk of CRC in 30,550 cases and 42,336 controls from 51 studies from 3 genetic consortia (CCFR, CORECT, GECCO). Results Inverse associations of dietary, total folate, and folic acid supplement with CRC were found (odds ratio [OR]: 0.93; 95% confidence interval [CI]: 0.90, 0.96; and 0.91; 95% CI: 0.89, 0.94 per quartile higher intake, and 0.82 (95% CI: 0.78, 0.88) for users compared with nonusers, respectively). Interactions (P-interaction < 5×10-8) of folic acid supplement and variants in the 3p25.2 locus (in the region of Synapsin II [SYN2]/tissue inhibitor of metalloproteinase 4 [TIMP4]) were found using traditional interaction analysis, with variant rs150924902 (located upstream to SYN2) showing the strongest interaction. In stratified analyses by rs150924902 genotypes, folate supplementation was associated with decreased CRC risk among those carrying the TT genotype (OR: 0.82; 95% CI: 0.79, 0.86) but increased CRC risk among those carrying the TA genotype (OR: 1.63; 95% CI: 1.29, 2.05), suggesting a qualitative interaction (P-interaction = 1.4×10-8). No interactions were observed for dietary and total folate. Conclusions Variation in 3p25.2 locus may modify the association of folate supplement with CRC risk. Experimental studies and studies incorporating other relevant omics data are warranted to validate this finding.Item Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes.(Springer Nature, 2024-04-26) Chen Z; Guo X; Tao R; Huyghe JR; Law PJ; Fernandez-Rozadilla C; Ping J; Jia G; Long J; Li C; Shen Q; Xie Y; Timofeeva MN; Thomas M; Schmit SL; Díez-Obrero V; Devall M; Moratalla-Navarro F; Fernandez-Tajes J; Palles C; Sherwood K; Briggs SEW; Svinti V; Donnelly K; Farrington SM; Blackmur J; Vaughan-Shaw PG; Shu X-O; Lu Y; Broderick P; Studd J; Harrison TA; Conti DV; Schumacher FR; Melas M; Rennert G; Obón-Santacana M; Martín-Sánchez V; Oh JH; Kim J; Jee SH; Jung KJ; Kweon S-S; Shin M-H; Shin A; Ahn Y-O; Kim D-H; Oze I; Wen W; Matsuo K; Matsuda K; Tanikawa C; Ren Z; Gao Y-T; Jia W-H; Hopper JL; Jenkins MA; Win AK; Pai RK; Figueiredo JC; Haile RW; Gallinger S; Woods MO; Newcomb PA; Duggan D; Cheadle JP; Kaplan R; Kerr R; Kerr D; Kirac I; Böhm J; Mecklin J-P; Jousilahti P; Knekt P; Aaltonen LA; Rissanen H; Pukkala E; Eriksson JG; Cajuso T; Hänninen U; Kondelin J; Palin K; Tanskanen T; Renkonen-Sinisalo L; Männistö S; Albanes D; Weinstein SJ; Ruiz-Narvaez E; Palmer JR; Buchanan DD; Platz EA; Visvanathan K; Ulrich CM; Siegel E; Brezina S; Gsur A; Campbell PT; Chang-Claude J; Hoffmeister M; Brenner H; Slattery ML; Potter JD; Tsilidis KK; Schulze MB; Gunter MJ; Murphy N; Castells A; Castellví-Bel S; Moreira L; Arndt V; Shcherbina A; Bishop DT; Giles GG; Southey MC; Idos GE; McDonnell KJ; Abu-Ful Z; Greenson JK; Shulman K; Lejbkowicz F; Offit K; Su Y-R; Steinfelder R; Keku TO; van Guelpen B; Hudson TJ; Hampel H; Pearlman R; Berndt SI; Hayes RB; Martinez ME; Thomas SS; Pharoah PDP; Larsson SC; Yen Y; Lenz H-J; White E; Li L; Doheny KF; Pugh E; Shelford T; Chan AT; Cruz-Correa M; Lindblom A; Hunter DJ; Joshi AD; Schafmayer C; Scacheri PC; Kundaje A; Schoen RE; Hampe J; Stadler ZK; Vodicka P; Vodickova L; Vymetalkova V; Edlund CK; Gauderman WJ; Shibata D; Toland A; Markowitz S; Kim A; Chanock SJ; van Duijnhoven F; Feskens EJM; Sakoda LC; Gago-Dominguez M; Wolk A; Pardini B; FitzGerald LM; Lee SC; Ogino S; Bien SA; Kooperberg C; Li CI; Lin Y; Prentice R; Qu C; Bézieau S; Yamaji T; Sawada N; Iwasaki M; Le Marchand L; Wu AH; Qu C; McNeil CE; Coetzee G; Hayward C; Deary IJ; Harris SE; Theodoratou E; Reid S; Walker M; Ooi LY; Lau KS; Zhao H; Hsu L; Cai Q; Dunlop MG; Gruber SB; Houlston RS; Moreno V; Casey G; Peters U; Tomlinson I; Zheng WGenome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.