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Abstract Genome-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.

Summary: Background: Consumption of fibre, fruits and vegetables have been linked with lower colorectal cancer (CRC) risk. A genome-wide gene-environment (G × E) analysis was performed to test whether genetic variants modify these associations. Methods: A pooled sample of 45 studies including up to 69,734 participants (cases: 29,896; controls: 39,838) of European ancestry were included. To identify G × E interactions, we used the traditional 1--degree-of-freedom (DF) G × E test and to improve power a 2-step procedure and a 3DF joint test that investigates the association between a genetic variant and dietary exposure, CRC risk and G × E interaction simultaneously. Findings: The 3-DF joint test revealed two significant loci with p-value

T-cell receptor (TCR) plays critical roles in recognizing antigen peptides and mediating adaptive immune response against disease. High-throughput technologies have enabled the sequencing of TCR repertoire at the single nucleotide level, allowing researchers to characterize TCR sequences with high resolutions. The TCR sequences provide important information about patients’ adaptive immune system, and have the potential to improve clinical outcome prediction. However, it is challenging to incorporate the TCR repertoire data for prediction, because the data is unstructured, highly complex, and TCR sequences vary widely in their compositions and abundances across different individuals. We introduce TCRpred, an analytic tool for incorporating TCR repertoire for clinical outcome prediction. The TCRpred is able to utilize features that can be extracted from the TCR amino acid sequences, as well as features that are hidden in the TCR amino acid sequences and are hard to extract. Simulation studies show that the proposed approach has a good performance in predicting clinical outcome and tends to be more powerful than potential alternative approaches. We apply the TCRpred to real cancer datasets and demonstrate its practical utility in clinical outcome prediction.

Summary: Background: Obesity has been positively associated with most molecular subtypes of colorectal cancer (CRC); however, the magnitude and the causality of these associations is uncertain. Methods: We used Mendelian randomization (MR) to examine potential causal relationships between body size traits (body mass index [BMI], waist circumference, and body fat percentage) with risks of Jass classification types and individual subtypes of CRC (microsatellite instability [MSI] status, CpG island methylator phenotype [CIMP] status, BRAF and KRAS mutations). Summary data on tumour markers were obtained from two genetic consortia (CCFR, GECCO). Findings: A 1-standard deviation (SD:5.1 kg/m2) increment in BMI levels was found to increase risks of Jass type 1MSI-high,CIMP-high,BRAF-mutated,KRAS-wildtype (odds ratio [OR]: 2.14, 95% confidence interval [CI]: 1.46, 3.13; p-value = 9 × 10−5) and Jass type 2non-MSI-high,CIMP-high,BRAF-mutated,KRAS-wildtype CRC (OR: 2.20, 95% CI: 1.26, 3.86; p-value = 0.005). The magnitude of these associations was stronger compared with Jass type 4non-MSI-high,CIMP-low/negative,BRAF-wildtype,KRAS-wildtype CRC (p-differences: 0.03 and 0.04, respectively). A 1-SD (SD:13.4 cm) increment in waist circumference increased risk of Jass type 3non-MSI-high,CIMP-low/negative,BRAF-wildtype,KRAS-mutated (OR 1.73, 95% CI: 1.34, 2.25; p-value = 9 × 10−5) that was stronger compared with Jass type 4 CRC (p-difference: 0.03). A higher body fat percentage (SD:8.5%) increased risk of Jass type 1 CRC (OR: 2.59, 95% CI: 1.49, 4.48; p-value = 0.001), which was greater than Jass type 4 CRC (p-difference: 0.03). Interpretation: Body size was more strongly linked to the serrated (Jass types 1 and 2) and alternate (Jass type 3) pathways of colorectal carcinogenesis in comparison to the traditional pathway (Jass type 4). Funding: Cancer Research UK, National Institute for Health Research, Medical Research Council, National Institutes of Health, National Cancer Institute, American Institute for Cancer Research, Brigham and Women's Hospital, Prevent Cancer Foundation, Victorian Cancer Agency, Swedish Research Council, Swedish Cancer Society, Region Västerbotten, Knut and Alice Wallenberg Foundation, Lion’s Cancer Research Foundation, Insamlingsstiftelsen, Umeå University. Full funding details are provided in acknowledgements.

Abstract Polygenic 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

Abstract Background Genetic testing can identify cancer risk early, enabling prevention and early detection. We describe use of risk management interventions following genetic testing in the Cancer Health Assessment Reaching Many (CHARM) study. CHARM assessed risk and provided genetic testing to low income, low literacy, and other underserved populations that historically face barriers to accessing cancer genetic services. Methods CHARM was implemented in Kaiser Permanente Northwest (KPNW) and Denver Health (DH) between 2018 and 2020. We identified post‐testing screening (mammography, breast MRI, colonoscopy) and surgical (mastectomy, oophorectomy) procedures using electronic health records. We examined utilization in participants who did and did not receive actionable risk management recommendations from study genetic counselors following national guidelines. Results CHARM participants were followed for an average of 15.4 months (range: 0.4–27.8 months) after results disclosure. Less than 2% (11/680) received actionable risk management recommendations (i.e., could be completed in the initial years following testing) based on their test result. Among those who received actionable recommendations, risk management utilization was moderate (54.5%, 6/11 completed any procedure) and varied by procedure (mammogram: 0/3; MRI: 2/4; colonoscopy: 4/5; mastectomy: 1/5; oophorectomy: 0/3). Cancer screening and surgery procedures were rare in participants without actionable recommendations. Conclusion Though the number of participants who received actionable risk management recommendations was small, our results suggest that implementing CHARM's risk assessment and testing model increased access to evidence‐based genetic services and provided opportunities for patients to engage in recommended preventive care, without encouraging risk management overuse.

Abstract Background The anti‐tumor immune response plays a key role in colorectal cancer (CRC) progression and survival. The T cell‐inflamed gene expression profile (GEP) is a biomarker predicting response to checkpoint inhibitor immunotherapy across immunogenic cancer types, but the prognostic value in CRC is unknown. We evaluated associations with disease‐specific survival, somatic mutations, and examined its differentially expressed genes and pathways among 84 sporadic CRC patients from the Seattle Colon Cancer Family Registry. Methods Gene expression profiling was performed using Nanostring's nCounter PanCancer IO 360 panel. Somatic mutations were identified by a targeted DNA sequencing panel. Results The T cell‐inflamed GEP was positively associated with tumor mutation burden and microsatellite instability high (MSI‐H). Higher T cell‐inflamed GEP had favorable CRC‐specific survival (hazard ratio [HR] per standard deviation unit = 0.50, p = 0.004) regardless of hypermutation or MSI status. Analysis of recurrently mutated genes having at least 10 mutation carriers, suggested that the T cell‐inflamed GEP is positively associated with RYR1, and negatively associated with APC. However, these associations were attenuated after adjusting for hypermutation or MSI status. We also found that expression of genes RPL23, EPCAM, AREG and ITGA6, and the Wnt signaling pathway was negatively associated with the T cell‐inflamed GEP, which might indicate immune‐inhibitory mechanisms. Conclusions Our results show that the T cell‐inflamed GEP is a prognostic biomarker in non‐hypermutated microsatellite‐stable CRC. This also suggests that patient stratification for immunotherapy within this CRC subgroup should be explored further. Moreover, reported immune‐inhibitory gene expression signals may suggest targets for therapeutic combination with immunotherapy.

Abstract Observational studies have shown higher folate consumption to be associated with lower risk of colorectal cancer (CRC). Understanding whether and how genetic risk factors interact with folate could further elucidate the underlying mechanism. Aggregating functionally relevant genetic variants in set-based variant testing has higher power to detect gene–environment (G × E) interactions and may provide information on the underlying biological pathway. We investigated interactions between folate consumption and predicted gene expression on colorectal cancer risk across the genome. We used variant weights from the PrediXcan models of colon tissue-specific gene expression as a priori variant information for a set-based G × E approach. We harmonized total folate intake (mcg/day) based on dietary intake and supplemental use across cohort and case–control studies and calculated sex and study specific quantiles. Analyses were performed using a mixed effects score tests for interactions between folate and genetically predicted expression of 4839 genes with available genetically predicted expression. We pooled results across 23 studies for a total of 13,498 cases with colorectal tumors and 13,918 controls of European ancestry. We used a false discovery rate of 0.2 to identify genes with suggestive evidence of an interaction. We found suggestive evidence of interaction with folate intake on CRC risk for genes including glutathione S-Transferase Alpha 1 (GSTA1; p = 4.3E−4), Tonsuko Like, DNA Repair Protein (TONSL; p = 4.3E−4), and Aspartylglucosaminidase (AGA: p = 4.5E−4). We identified three genes involved in preventing or repairing DNA damage that may interact with folate consumption to alter CRC risk. Glutathione is an antioxidant, preventing cellular damage and is a downstream metabolite of homocysteine and metabolized by GSTA1. TONSL is part of a complex that functions in the recovery of double strand breaks and AGA plays a role in lysosomal breakdown of glycoprotein.

Abstract Colorectal cancer (CRC) is a heterogeneous disease with evidence of distinct tumor types that develop through different somatically altered pathways. To better understand the impact of the host genome on somatically mutated genes and pathways, we assessed associations of germline variations with somatic events via two complementary approaches. We first analyzed the association between individual germline genetic variants and the presence of non-silent somatic mutations in genes in 1375 CRC cases with genome-wide SNPs data and a tumor sequencing panel targeting 205 genes. In the second analysis, we tested if germline variants located within previously identified regions of somatic allelic imbalance were associated with overall CRC risk using summary statistics from a recent large scale GWAS (n≃125 k CRC cases and controls). The first analysis revealed that a variant (rs78963230) located within a CNA region associated with TLR3 was also associated with a non-silent mutation within gene FBXW7. In the secondary analysis, the variant rs2302274 located in CDX1/PDGFRB frequently gained/lost in colorectal tumors was associated with overall CRC risk (OR = 0.96, p = 7.50e-7). In summary, we demonstrate that an integrative analysis of somatic and germline variation can lead to new insights about CRC.

Germline biallelic pathogenic MUTYH variants predispose patients to colorectal cancer (CRC); however, approaches to identify MUTYH variant carriers are lacking. Here, the authors evaluated mutational signatures that could distinguish MUTYH carriers in large CRC cohorts, and found MUTYH-associated somatic mutations.

Abstract Background T cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed. Methods We introduce an analysis tool, TCR-L, for evaluating the association between the TCR repertoire and disease outcomes. Our approach is developed under a mixed effect modeling, where the fixed effect represents features that can be explicitly extracted from TCR sequences while the random effect represents features that are hidden in TCR sequences and are difficult to be extracted. Statistical tests are developed to examine the two types of effects independently, and then the p values are combined. Results Simulation studies demonstrate that (1) the proposed approach can control the type I error well; and (2) the power of the proposed approach is greater than approaches that consider fixed effect only or random effect only. The analysis of real data from a skin cutaneous melanoma study identifies an association between the TCR repertoire and the short/long-term survival of patients. Conclusion The TCR-L can accommodate features that can be extracted as well as features that are hidden in TCR sequences. TCR-L provides a powerful approach for identifying association between TCR repertoire and disease outcomes.

Abstract Identification of new genetic markers may improve the prediction of colorectal cancer prognosis. Our objective was to examine genome-wide associations of germline genetic variants with disease-specific survival in an analysis of 16,964 cases of colorectal cancer. We analyzed genotype and colorectal cancer-specific survival data from a consortium of 15 studies. Approximately 7.5 million SNPs were examined under the log-additive model using Cox proportional hazards models, adjusting for clinical factors and principal components. Additionally, we ran secondary analyses stratifying by tumor site and disease stage. We used a genome-wide p-value threshold of 5 × 10–8 to assess statistical significance. No variants were statistically significantly associated with disease-specific survival in the full case analysis or in the stage-stratified analyses. Three SNPs were statistically significantly associated with disease-specific survival for cases with tumors located in the distal colon (rs698022, HR = 1.48, CI 1.30–1.69, p = 8.47 × 10–9) and the proximal colon (rs189655236, HR = 2.14, 95% CI 1.65–2.77, p = 9.19 × 10–9 and rs144717887, HR = 2.01, 95% CI 1.57–2.58, p = 3.14 × 10–8), whereas no associations were detected for rectal tumors. Findings from this large genome-wide association study highlight the potential for anatomical-site-stratified genome-wide studies to identify germline genetic risk variants associated with colorectal cancer-specific survival. Larger sample sizes and further replication efforts are needed to more fully interpret these findings.

Abstract Background Higher adiposity increases the risk of colorectal cancer (CRC), but whether this relationship varies by anatomical sub-site or by sex is unclear. Further, the metabolic alterations mediating the effects of adiposity on CRC are not fully understood. Methods We examined sex- and site-specific associations of adiposity with CRC risk and whether adiposity-associated metabolites explain the associations of adiposity with CRC. Genetic variants from genome-wide association studies of body mass index (BMI) and waist-to-hip ratio (WHR, unadjusted for BMI; N = 806,810), and 123 metabolites from targeted nuclear magnetic resonance metabolomics (N = 24,925), were used as instruments. Sex-combined and sex-specific Mendelian randomization (MR) was conducted for BMI and WHR with CRC risk (58,221 cases and 67,694 controls in the Genetics and Epidemiology of Colorectal Cancer Consortium, Colorectal Cancer Transdisciplinary Study, and Colon Cancer Family Registry). Sex-combined MR was conducted for BMI and WHR with metabolites, for metabolites with CRC, and for BMI and WHR with CRC adjusted for metabolite classes in multivariable models. Results In sex-specific MR analyses, higher BMI (per 4.2 kg/m2) was associated with 1.23 (95% confidence interval (CI) = 1.08, 1.38) times higher CRC odds among men (inverse-variance-weighted (IVW) model); among women, higher BMI (per 5.2 kg/m2) was associated with 1.09 (95% CI = 0.97, 1.22) times higher CRC odds. WHR (per 0.07 higher) was more strongly associated with CRC risk among women (IVW OR = 1.25, 95% CI = 1.08, 1.43) than men (IVW OR = 1.05, 95% CI = 0.81, 1.36). BMI or WHR was associated with 104/123 metabolites at false discovery rate-corrected P ≤ 0.05; several metabolites were associated with CRC, but not in directions that were consistent with the mediation of positive adiposity-CRC relations. In multivariable MR analyses, associations of BMI and WHR with CRC were not attenuated following adjustment for representative metabolite classes, e.g., the univariable IVW OR for BMI with CRC was 1.12 (95% CI = 1.00, 1.26), and this became 1.11 (95% CI = 0.99, 1.26) when adjusting for cholesterol in low-density lipoprotein particles. Conclusions Our results suggest that higher BMI more greatly raises CRC risk among men, whereas higher WHR more greatly raises CRC risk among women. Adiposity was associated with numerous metabolic alterations, but none of these explained associations between adiposity and CRC. More detailed metabolomic measures are likely needed to clarify the mechanistic pathways.

Abstract Background Bilirubin, a byproduct of hemoglobin breakdown and purported anti-oxidant, is thought to be cancer preventive. We conducted complementary serological and Mendelian randomization (MR) analyses to investigate whether alterations in circulating levels of bilirubin are associated with risk of colorectal cancer (CRC). We decided a priori to perform analyses separately in men and women based on suggestive evidence that associations may differ by sex. Methods In a case-control study nested in the European Prospective Investigation into Cancer and Nutrition (EPIC), pre-diagnostic unconjugated bilirubin (UCB, the main component of total bilirubin) concentrations were measured by high-performance liquid chromatography in plasma samples of 1386 CRC cases and their individually matched controls. Additionally, 115 single-nucleotide polymorphisms (SNPs) robustly associated (P

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.

In cancer many gene variants may contribute to disease etiology, but the impact of a given gene variant may have varied effect size. Here, the authors analyse summary statistics of genome-wide association studies from fourteen cancers, and show the utility of polygenic risk scores may vary depending on cancer type.

Abstract Background Body mass index (BMI) and diabetes are established risk factors for colorectal cancer (CRC), likely through perturbations in metabolic traits (e.g. insulin resistance and glucose homeostasis). Identification of interactions between variation in genes and these metabolic risk factors may identify novel biologic insights into CRC etiology. Methods To improve statistical power and interpretation for gene‐environment interaction (G × E) testing, we tested genetic variants that regulate expression of a gene together for interaction with BMI (kg/m2) and diabetes on CRC risk among 26 017 cases and 20 692 controls. Each variant was weighted based on PrediXcan analysis of gene expression data from colon tissue generated in the Genotype‐Tissue Expression Project for all genes with heritability ≥1%. We used a mixed‐effects model to jointly measure the G × E interaction in a gene by partitioning the interactions into the predicted gene expression levels (fixed effects), and residual G × E effects (random effects). G × BMI analyses were stratified by sex as BMI‐CRC associations differ by sex. We used false discovery rates to account for multiple comparisons and reported all results with FDR

Physical activity has been linked to lower risks of colorectal and breast cancer. Here, the authors present a Mendelian randomisation analysis supporting a potentially causal relationship between higher physical activity levels and lower risks of breast cancer and colorectal cancer.

Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package space-log. We show that the space-log is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs.Space-log is open source and available at GitHub, https://github.com/wuqian77/SpaceLog

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

Similarities in cancers can be studied to interrogate their etiology. Here, the authors use genome-wide association study summary statistics from six cancer types based on 296,215 cases and 301,319 controls of European ancestry, showing that solid tumours arising from different tissues share a degree of common germline genetic basis.

Building upon prior research on intergenerational income mobility, we assess class effects versus racial effects on the probability of becoming a poor adult, broken down by gender. We define the class effect (for each race-and-gender group) as the difference between the probability that a person who was born into the lowest income quintile becomes poor and the probability that a person who was born into the highest income quintile becomes poor. For each minority-by-gender group, using Whites as the baseline, the racial effect is defined as the average racial differential in the probability of becoming a poor adult, irrespective of class origins. The results indicate that, for all minority-by-gender groups, the class effect is larger than the racial effect. Our findings underscore the continuing significance of the comparatively large effects of class origins, which have not been adequately acknowledged in recent research.

Genome-wide association studies (GWAS) have successfully identified tens of thousands of genetic variants associated with various phenotypes, but together they explain only a fraction of heritability, suggesting many variants have yet to be discovered. Recently it has been recognized that incorporating functional information of genetic variants can improve power for identifying novel loci. For example, S-PrediXcan and TWAS tested the association of predicted gene expression with phenotypes based on GWAS summary statistics by leveraging the information on genetic regulation of gene expression and found many novel loci. However, as genetic variants may have effects on more than one gene and through different mechanisms, these methods likely only capture part of the total effects of these variants. In this paper, we propose a summary statistics-based mixed effects score test (sMiST) that tests for the total effect of both the effect of the mediator by imputing genetically predicted gene expression, like S-PrediXcan and TWAS, and the direct effects of individual variants. It allows for multiple functional annotations and multiple genetically predicted mediators. It can also perform conditional association analysis while adjusting for other genetic variants (e.g., known loci for the phenotype). Extensive simulation and real data analyses demonstrate that sMiST yields p-values that agree well with those obtained from individual level data but with substantively improved computational speed. Importantly, a broad application of sMiST to GWAS is possible, as only summary statistics of genetic variant associations are required. We apply sMiST to a large-scale GWAS of colorectal cancer using summary statistics from ∼120, 000 study participants and gene expression data from the Genotype-Tissue Expression (GTEx) project. We identify several novel and secondary independent genetic loci.

The R package frailtySurv for simulating and fitting semi-parametric shared frailty models is introduced. Package frailtySurv implements semi-parametric consistent estimators for a variety of frailty distributions, including gamma, log-normal, inverse Gaussian and power variance function, and provides consistent estimators of the standard errors of the parameters' estimators. The parameters' estimators are asymptotically normally distributed, and therefore statistical inference based on the results of this package, such as hypothesis testing and confidence intervals, can be performed using the normal distribution. Extensive simulations demonstrate the flexibility and correct implementation of the estimator. Two case studies performed with publicly available datasets demonstrate applicability of the package. In the Diabetic Retinopathy Study, the onset of blindness is clustered by patient, and in a large hard drive failure dataset, failure times are thought to be clustered by the hard drive manufacturer and model.

Abstract Background Previous population-based studies have described first primary breast cancer tumor characteristics and their association with contralateral breast cancer (CBC) risk. However, information on influential covariates such as treatment, family history of breast cancer, and BRCA1/2 mutation carrier status was not available. In a large, population-based, case-control study, we evaluated whether tumor characteristics of the first primary breast cancer are associated with risk of developing second primary asynchronous CBC, overall and in subgroups of interest, including among BRCA1/2 mutation non-carriers, women who are not treated with tamoxifen, and women without a breast cancer family history. Methods The Women’s Environmental Cancer and Radiation Epidemiology Study is a population-based case-control study of 1521 CBC cases and 2212 individually-matched controls with unilateral breast cancer. Detailed information about breast cancer risk factors, treatment for and characteristics of first tumors, including estrogen receptor (ER) and progesterone receptor (PR) status, was obtained by telephone interview and medical record abstraction. Multivariable risk ratios (RRs) and 95% confidence intervals (CIs) were estimated in conditional logistic regression models, adjusting for demographics, treatment, and personal medical and family history. A subset of women was screened for BRCA1/2 mutations. Results Lobular histology of the first tumor was associated with a 30% increase in CBC risk (95% CI 1.0–1.6). Compared to women with ER+/PR+ first tumors, those with ER-/PR- tumors had increased risk of CBC (RR = 1.4, 95% CI 1.1–1.7). Notably, women with ER-/PR- first tumors were more likely to develop CBC with the ER-/PR- phenotype (RR = 5.4, 95% CI 3.0–9.5), and risk remained elevated in multiple subgroups: BRCA1/2 mutation non-carriers, women younger than 45 years of age, women without a breast cancer family history, and women who were not treated with tamoxifen. Conclusions Having a hormone receptor negative first primary breast cancer is associated with increased risk of CBC. Women with ER-/PR- primary tumors were more likely to develop ER-/PR- CBC, even after excluding BRCA1/2 mutation carriers. Hormone receptor status, which is routinely evaluated in breast tumors, may be used clinically to determine treatment protocols and identify patients who may benefit from increased surveillance for CBC.

The popular Genome-wide Complex Trait Analysis (GCTA) software uses the random-effects models for estimating the narrow-sense heritability based on GWAS data of unrelated individuals without knowing and identifying the causal loci. Many methods have since extended this approach to various situations. However, since the proportion of causal loci among the variants is typically very small and GCTA uses all variants to calculate the similarities among individuals, the estimation of heritability may be unstable, resulting in a large variance of the estimates. Moreover, if the causal SNPs are not genotyped, GCTA sometimes greatly underestimates the true heritability. We present a novel narrow-sense heritability estimator, named HERRA, using well-developed ultra-high dimensional machine-learning methods, applicable to continuous or dichotomous outcomes, as other existing methods. Additionally, HERRA is applicable to time-to-event or age-at-onset outcome, which, to our knowledge, no existing method can handle. Compared to GCTA and LDAK for continuous and binary outcomes, HERRA often has a smaller variance, and when causal SNPs are not genotyped, HERRA has a much smaller empirical bias. We applied GCTA, LDAK and HERRA to a large colorectal cancer dataset using dichotomous outcome (4,312 cases, 4,356 controls, genotyped using Illumina 300K), the respective heritability estimates of GCTA, LDAK and HERRA are 0.068 (SE = 0.017), 0.072 (SE = 0.021) and 0.110 (SE = 5.19 x 10-3). HERRA yields over 50% increase in heritability estimate compared to GCTA or LDAK.

BACKGROUND:The evaluation of less frequent genetic variants and their effect on complex disease pose new challenges for genomic research. To investigate whether epigenetic data can be used to inform aggregate rare-variant association methods (RVAM), we assessed whether variants more significantly associated with colorectal cancer (CRC) were preferentially located in non-coding regulatory regions, and whether enrichment was specific to colorectal tissues. METHODS:Active regulatory elements (ARE) were mapped using data from 127 tissues and cell-types from NIH Roadmap Epigenomics and Encyclopedia of DNA Elements (ENCODE) projects. We investigated whether CRC association p-values were more significant for common variants inside versus outside AREs, or 2) inside colorectal (CR) AREs versus AREs of other tissues and cell-types. We employed an integrative epigenomic RVAM for variants with allele frequency

Recent heritability analyses have indicated that genome-wide association studies (GWAS) have the potential to improve genetic risk prediction for complex diseases based on polygenic risk score (PRS), a simple modelling technique that can be implemented using summary-level data from the discovery samples. We herein propose modifications to improve the performance of PRS. We introduce threshold-dependent winner's-curse adjustments for marginal association coefficients that are used to weight the single-nucleotide polymorphisms (SNPs) in PRS. Further, as a way to incorporate external functional/annotation knowledge that could identify subsets of SNPs highly enriched for associations, we propose variable thresholds for SNPs selection. We applied our methods to GWAS summary-level data of 14 complex diseases. Across all diseases, a simple winner's curse correction uniformly led to enhancement of performance of the models, whereas incorporation of functional SNPs was beneficial only for selected diseases. Compared to the standard PRS algorithm, the proposed methods in combination led to notable gain in efficiency (25-50% increase in the prediction R2) for 5 of 14 diseases. As an example, for GWAS of type 2 diabetes, winner's curse correction improved prediction R2 from 2.29% based on the standard PRS to 3.10% (P = 0.0017) and incorporating functional annotation data further improved R2 to 3.53% (P = 2×10-5). Our simulation studies illustrate why differential treatment of certain categories of functional SNPs, even when shown to be highly enriched for GWAS-heritability, does not lead to proportionate improvement in genetic risk-prediction because of non-uniform linkage disequilibrium structure.

Selenium is an essential trace element and circulating selenium concentrations have been associated with a wide range of diseases. Candidate gene studies suggest that circulating selenium concentrations may be impacted by genetic variation; however, no study has comprehensively investigated this hypothesis. Therefore, we conducted a two-stage genome-wide association study to identify genetic variants associated with serum selenium concentrations in 1203 European descents from two cohorts: the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening and the Women’s Health Initiative (WHI). We tested association between 2,474,333 single nucleotide polymorphisms (SNPs) and serum selenium concentrations using linear regression models. In the first stage (PLCO) 41 SNPs clustered in 15 regions had p < 1 × 10−5. None of these 41 SNPs reached the significant threshold (p = 0.05/15 regions = 0.003) in the second stage (WHI). Three SNPs had p < 0.05 in the second stage (rs1395479 and rs1506807 in 4q34.3/AGA-NEIL3; and rs891684 in 17q24.3/SLC39A11) and had p between 2.62 × 10−7 and 4.04 × 10−7 in the combined analysis (PLCO + WHI). Additional studies are needed to replicate these findings. Identification of genetic variation that impacts selenium concentrations may contribute to a better understanding of which genes regulate circulating selenium concentrations.

Genome-wide association studies (GWAS) have identified many genetic susceptibility loci for colorectal cancer (CRC). However, variants in these loci explain only a small proportion of familial aggregation, and there are likely additional variants that are associated with CRC susceptibility. Genome-wide studies of gene-environment interactions may identify variants that are not detected in GWAS of marginal gene effects. To study this, we conducted a genome-wide analysis for interaction between genetic variants and alcohol consumption and cigarette smoking using data from the Colon Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO). Interactions were tested using logistic regression. We identified interaction between CRC risk and alcohol consumption and variants in the 9q22.32/HIATL1 (Pinteraction = 1.76×10-8; permuted p-value 3.51x10-8) region. Compared to non-/occasional drinking light to moderate alcohol consumption was associated with a lower risk of colorectal cancer among individuals with rs9409565 CT genotype (OR, 0.82 [95% CI, 0.74-0.91]; P = 2.1×10-4) and TT genotypes (OR,0.62 [95% CI, 0.51-0.75]; P = 1.3×10-6) but not associated among those with the CC genotype (p = 0.059). No genome-wide statistically significant interactions were observed for smoking. If replicated our suggestive finding of a genome-wide significant interaction between genetic variants and alcohol consumption might contribute to understanding colorectal cancer etiology and identifying subpopulations with differential susceptibility to the effect of alcohol on CRC risk.

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).

Over the last two decades, the incidence of esophageal adenocarcinoma (EA) has increased dramatically in the US and Western Europe. It has been shown that EAs evolve from premalignant Barrett's esophagus (BE) tissue by a process of clonal expansion and evolution. However, the molecular phenotype of the premalignant metaplasia, and its relationship to those of the normal upper gastrointestinal (GI) mucosae, including gastric, duodenal, and squamous epithelium of the esophagus, has not been systematically characterized. Therefore, we used oligonucleotide-based microarrays to characterize gene expression profiles in each of these tissues. The similarity of BE to each of the normal tissues was compared using a series of computational approaches. Our analyses included esophageal squamous epithelium, which is present at the same anatomic site and exposed to similar conditions as Barrett's epithelium, duodenum that shares morphologic similarity to Barrett's epithelium, and adjacent gastric epithelium. There was a clear distinction among the expression profiles of gastric, duodenal, and squamous epithelium whereas the BE profiles showed considerable overlap with normal tissues. Furthermore, we identified clusters of genes that are specific to each of the tissues, to the Barrett's metaplastic epithelia, and a cluster of genes that was distinct between squamous and nonsquamous epithelia.

Dietary factors, including meat, fruits, vegetables and fiber, are associated with colorectal cancer; however, there is limited information as to whether these dietary factors interact with genetic variants to modify risk of colorectal cancer. We tested interactions between these dietary factors and approximately 2.7 million genetic variants for colorectal cancer risk among 9,287 cases and 9,117 controls from ten studies. We used logistic regression to investigate multiplicative gene-diet interactions, as well as our recently developed Cocktail method that involves a screening step based on marginal associations and gene-diet correlations and a testing step for multiplicative interactions, while correcting for multiple testing using weighted hypothesis testing. Per quartile increment in the intake of red and processed meat were associated with statistically significant increased risks of colorectal cancer and vegetable, fruit and fiber intake with lower risks. From the case-control analysis, we detected a significant interaction between rs4143094 (10p14/near GATA3) and processed meat consumption (OR = 1.17; p = 8.7E-09), which was consistently observed across studies (p heterogeneity = 0.78). The risk of colorectal cancer associated with processed meat was increased among individuals with the rs4143094-TG and -TT genotypes (OR = 1.20 and OR = 1.39, respectively) and null among those with the GG genotype (OR = 1.03). Our results identify a novel gene-diet interaction with processed meat for colorectal cancer, highlighting that diet may modify the effect of genetic variants on disease risk, which may have important implications for prevention.

Genome-wide association studies (GWAS) have successfully identified a number of single-nucleotide polymorphisms (SNPs) associated with colorectal cancer (CRC) risk. However, these susceptibility loci known today explain only a small fraction of the genetic risk. Gene-gene interaction (GxG) is considered to be one source of the missing heritability. To address this, we performed a genome-wide search for pair-wise GxG associated with CRC risk using 8,380 cases and 10,558 controls in the discovery phase and 2,527 cases and 2,658 controls in the replication phase. We developed a simple, but powerful method for testing interaction, which we term the Average Risk Due to Interaction (ARDI). With this method, we conducted a genome-wide search to identify SNPs showing evidence for GxG with previously identified CRC susceptibility loci from 14 independent regions. We also conducted a genome-wide search for GxG using the marginal association screening and examining interaction among SNPs that pass the screening threshold (p

Chromosome ends are known hotspots of meiotic recombination and double-strand breaks. We monitored mitotic sister chromatid exchange (SCE) in telomeres and subtelomeres and found that 17% of all SCE occurs in the terminal 0.1% of the chromosome. Telomeres and subtelomeres are significantly enriched for SCEs, exhibiting rates of SCE per basepair that are at least 1,600 and 160 times greater, respectively, than elsewhere in the genome.

We have achieved a significant breakthrough in the preparation and development of two‐dimensional nanocomposites with P‐N heterojunction interfaces as efficient cathode catalysts for electrochemical hydrogen evolution reaction (HER) and iodide oxidation reaction (IOR). P‐type acid‐doped polyaniline (PANI) and N‐type exfoliated molybdenum disulfide (MoS2) nanosheets can form structurally stable composites due to formation of P‐N heterojunction structures at their interfaces. These P‐N heterojunctions facilitate charge transfer from PANI to MoS2 structures and thus significantly enhance the catalytic efficiency of MoS2 in the HER and IOR. Herein, by combining efficient sodium‐functionalized chitosan‐assisted MoS2 exfoliation, electropolymerization of PANI on nickel foam (NF) substrate, and electrochemical activation, controllable and scalable Na‐Chitosan/MoS2/PANI/NF electrodes are successfully constructed as non‐noble metal‐based electrochemical catalysts. Compared to a commercial platinum/carbon (Pt/C) catalyst, the Na‐Chitosan/MoS2/PANI/NF electrode exhibits significantly lower resistance and overpotential, a similar Tafel slope, and excellent catalytic stability at high current densities, demonstrating excellent catalytic performance in the HER under acidic conditions. More importantly, results obtained from proton exchange membrane fuel cell devices confirm the Na‐Chitosan/MoS2/PANI/NF electrode exhibits a low turn‐on voltage, high current density, and stable operation at 2 V. Thus, this system holds potential as a replacement for Pt/C with feasibility for applications in energy‐related fields. [ABSTRACT FROM AUTHOR]

The leopard cat (Prionailurus bengalensis) is an endangered wildlife that is protected under Taiwan's regulations. The body of a road-killed leopard cat was found to contain sequences of feline calicivirus (FCV), designated W109-1443. Analysis of the complete genomic sequence revealed that it shared approximately 81% similarity with a Chinese strain of FCV found in a domestic cat. Phylogenetic analysis of the VP1 gene indicated that the W109-1443 isolate belonged to genogroup II. Recombination analysis revealed that the W109-1443 isolate may have resulted from recombination between two FCV strains. Given the potential impact of FCV on the health and survival of wild felids, further investigation is necessary to assess its pathogenicity in the leopard cat population. [ABSTRACT FROM AUTHOR]

Gene expression data have been used to infer gene-gene networks (GGN) where an edge between two genes implies the conditional dependence of these two genes given all the other genes. Such gene-gene networks are of-ten referred to as gene regulatory networks since it may reveal expression regulation. Most of existing methods for identifying GGN employ penalized regression with L1 (lasso), L2 (ridge), or elastic net penalty, which spans the range of L1 to L2 penalty. However, for high dimensional gene expression data, a penalty that spans the range of L0 and L1 penalty, such as the log penalty, is often needed for variable selection consistency. Thus, we develop a novel method that em-ploys log penalty within the framework of an earlier network identification method space (Sparse PArtial Correlation Estimation), and implement it into a R package space-log. We show that the space-log is computationally efficient (source code implemented in C), and has good performance comparing with other methods, particularly for networks with hubs. Space-log is open source and available at GitHub, https://github.com/wuqian77/SpaceLog

Introduction: Angiotensin-converting enzyme 2 (ACE2) played an important role in the renin-angiotensin-aldosterone system (RAAS) and it was proved to be renoprotective in renal disease. Urinary angiotensin-converting enzyme 2 (uACE2) has been shown to reflect renal injury in human and experimental studies, but its role in feline kidney disease remains unknown. Aims: Our objectives involve comparing uACE2 concentrations and activities in cats across CKD stages with healthy controls, investigating the relationship between uACE2 concentrations, activities, and clinicopathological data in feline CKD patients, and assessing the predictive abilities of both for CKD progression. Methods: A retrospective, case-control study. The concentration and activity of uACE2 were measured by commercial ELISA and fluorometric assay kits, respectively. The concentration was adjusted to give uACE2 concentration-tocreatinine ratios (UACCRs). Results: In total, 67 cats consisting of 24 control and 43 chronic kidney disease (CKD), including 24 early-stage CKD and 19 late-stage CKD, were enrolled in this study. UACCR values were significantly higher in both early-stage (2.100 [1.142-4.242] x 10-6) and late-stage feline CKD (4.343 [2.992-5.0.71] x 10-6) compared to healthy controls (0.894 [0.610-1.076] x 10-6; p < 0.001), and there was also significant difference between-early stage group and late-stage group (p = 0.026). Urinary ACE2 activity (UAA) was significantly lower in CKD cats (1.338 [0.644-2.755] x pmol/min/ml) compared to the healthy cats (7.989 [3.711-15.903] x pmol/min/ml; p < 0.001). UACCR demonstrated an independent, positive correlation with BUN (p < 0.001), and UAA exhibited an independent, negative correlation with plasma creatinine (p < 0.001). Both UACCR and UAA did not yield significant results in predicting CKD progression based on the ROC curve analysis. Conclusion and clinical importance: uACE2 concentration and activity exhibit varying changes as renal function declines, particularly in advanced CKD cats. [ABSTRACT FROM AUTHOR]