Publications by authors named "Lang Wu"

114 Publications

Associations Between Genetically Predicted Plasma N-Glycans and Prostate Cancer Risk: Analysis of Over 140,000 European Descendants.

Pharmgenomics Pers Med 2021 22;14:1211-1220. Epub 2021 Sep 22.

Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA.

Background: Previous studies suggest a potential link between glycosylation and prostate cancer. To better characterize the relationship between the two, we performed a study to comprehensively evaluate the associations between genetically predicted blood plasma N-glycan levels and prostate cancer risk.

Methods: Using genetic variants associated with N-glycan levels as instruments, we evaluated the associations between levels of 138 plasma N-glycans and prostate cancer risk. We analyzed data of 79,194 cases and 61,112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL.

Results: We identified three N-glycans with genetically predicted levels in plasma to be associated with prostate cancer risk after Bonferroni correction. The estimated odds ratios (95% confidence intervals) were 1.29 (1.20-1.40), 0.80 (0.74-0.88), and 0.79 (0.72-0.87) for PGP18, PGP33, and PGP109, respectively, per every one standard deviation increase in genetically predicted levels of N-glycan. However, the instruments for these N-glycans only involved one to two variants. The proportions of variations that can be explained by the instruments range from 1.58% to 2.95% for these three N-glycans.

Conclusion: We observed associations between genetically predicted levels of three N-glycans PGP18, PGP33, and PGP109 and prostate cancer risk. Given the correlated nature of the N-glycans and that many N-glycans share genetic loci, pleiotropy is a major concern. Future work is warranted to better characterize the relationship between N-glycans and prostate cancer.
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http://dx.doi.org/10.2147/PGPM.S319308DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8473033PMC
September 2021

A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk.

Int J Cancer 2021 Sep 14. Epub 2021 Sep 14.

Division of Cancer Epidemiology, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, Hawaii, USA.

A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome-wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype-Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint-Tissue Imputation. We applied these prediction models to the genetic data of 79 194 prostate cancer cases and 61 112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology.
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http://dx.doi.org/10.1002/ijc.33808DOI Listing
September 2021

Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi-phased study of prostate cancer.

Cancer Commun (Lond) 2021 Sep 14. Epub 2021 Sep 14.

Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA.

Background: DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer (PCa). However, it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores (PRSs). Here, we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation, and other genomic information using an integrative method.

Methods: Using data from the PRACTICAL consortium, we derived multiple sets of genetic scores, including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding, LDpred, LDpred-funt, AnnoPred, and EBPRS, as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding strategy. In the tuning step, using the UK Biobank data (1458 prevalent cases and 1467 controls), we selected PRSs with the best performance. Using an independent set of data from the UK Biobank, we developed an integrative PRS combining information from individual scores. Furthermore, in the testing step, we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and controls.

Results: Our constructed PRS had improved performance (C statistics: 76.1%) over PRSs constructed by individual benchmark methods (from 69.6% to 74.7%). Furthermore, our new PRS had much higher risk assessment power than family history. The overall net reclassification improvement was 69.0% by adding PRS to the baseline model compared with 12.5% by adding family history.

Conclusions: We developed and validated a new PRS which may improve the utility in predicting the risk of developing PCa. Our innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.
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http://dx.doi.org/10.1002/cac2.12205DOI Listing
September 2021

Integrating Genome and Methylome Data to Identify Candidate DNA Methylation Biomarkers for Pancreatic Cancer Risk.

Cancer Epidemiol Biomarkers Prev 2021 Sep 8. Epub 2021 Sep 8.

Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, Hawaii.

Background: The role of methylation in pancreatic cancer risk remains unclear. We integrated genome and methylome data to identify CpG sites (CpG) with the genetically predicted methylation to be associated with pancreatic cancer risk. We also studied gene expression to understand the identified associations.

Methods: Using genetic data and white blood cell methylation data from 1,595 subjects of European descent, we built genetic models to predict DNA methylation levels. After internal and external validation, we applied prediction models with satisfactory performance to the genetic data of 8,280 pancreatic cancer cases and 6,728 controls of European ancestry to investigate the associations of predicted methylation with pancreatic cancer risk. For associated CpGs, we compared their measured levels in pancreatic tumor versus benign tissue.

Results: We identified 45 CpGs at nine loci showing an association with pancreatic cancer risk, including 15 CpGs showing an association independent from identified risk variants. We observed significant correlations between predicted methylation of 16 of the 45 CpGs and predicted expression of eight adjacent genes, of which six genes showed associations with pancreatic cancer risk. Of the 45 CpGs, we were able to compare measured methylation of 16 in pancreatic tumor versus benign pancreatic tissue. Of them, six showed differentiated methylation.

Conclusions: We identified methylation biomarker candidates associated with pancreatic cancer using genetic instruments and added additional insights into the role of methylation in regulating gene expression in pancreatic cancer development.

Impact: A comprehensive study using genetic instruments identifies 45 CpG sites at nine genomic loci for pancreatic cancer risk.
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http://dx.doi.org/10.1158/1055-9965.EPI-21-0400DOI Listing
September 2021

A transcriptome-wide association study of Alzheimer's disease using prediction models of relevant tissues identifies novel candidate susceptibility genes.

Genome Med 2021 Sep 1;13(1):141. Epub 2021 Sep 1.

Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA.

Background: Genome-wide association studies (GWAS) have identified over 56 susceptibility loci associated with Alzheimer's disease (AD), but the genes responsible for these associations remain largely unknown.

Methods: We performed a large transcriptome-wide association study (TWAS) leveraging modified UTMOST (Unified Test for MOlecular SignaTures) prediction models of ten brain tissues that are potentially related to AD to discover novel AD genetic loci and putative target genes in 71,880 (proxy) cases and 383,378 (proxy) controls of European ancestry.

Results: We identified 53 genes with predicted expression associations with AD risk at Bonferroni correction threshold (P value < 3.38 × 10). Based on fine-mapping analyses, 21 genes at nine loci showed strong support for being causal.

Conclusions: Our study provides new insights into the etiology and underlying genetic architecture of AD.
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http://dx.doi.org/10.1186/s13073-021-00959-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8408990PMC
September 2021

A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer's disease risk.

Hum Mol Genet 2021 Aug 13. Epub 2021 Aug 13.

Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA.

Alzheimer's disease (ad) adversely affects the health, quality of life and independence of patients. There is a critical need to identify novel blood gene biomarkers for ad risk assessment. We performed a transcriptome-wide association study to identify biomarker candidates for ad risk. We leveraged two sets of gene expression prediction models of blood developed using different reference panels and modelling strategies. By applying the prediction models to a meta-GWAS including 71 880 (proxy) cases and 383 378 (proxy) controls, we identified significant associations of genetically determined expression of 108 genes in blood with ad risk. Of these, 15 genes were differentially expressed between ad patients and controls with concordant directions in measured expression data. With evidence from the analyses based on both genetic instruments and directly measured expression levels, this study identifies 15 genes with strong support as biomarkers in blood for ad risk, which may enhance ad risk assessment and mechanism-focused studies.
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http://dx.doi.org/10.1093/hmg/ddab229DOI Listing
August 2021

A patient-oriented analysis of pain side effect: A step to improve the patient's experience during rTMS?

Brain Stimul 2021 Sep-Oct;14(5):1147-1153. Epub 2021 Aug 5.

Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, Canada, V6T 2A1. Electronic address:

Background: Repetitive transcranial magnetic stimulation (rTMS) is an efficacious and well-tolerated intervention for treatment-resistant depression (TRD). A novel rTMS protocol, intermittent theta burst stimulation (iTBS) has been recently implemented in clinical practice, and it is essential to characterize the factors associated to pain and the trajectory of pain of iTBS compared to standard rTMS protocols.

Objective: We aimed to characterize the side effect profile and the pain trajectories of High-Frequency Left (HFL) and iTBS in TRD patients in the THREE-D trial. We also investigated factors associated to pain and the relationship between pain and clinical outcomes.

Methods: 414 patients were randomized to either HFL or iTBS. Severity of pain was measured after every treatment. General Estimating Equation was used to investigate factors associated with pain. Latent class linear mixed model was used to investigate latent classes of pain trajectories over the course of rTMS.

Results: Higher level of pain was associated with older age, higher stimulation intensity, higher anxiety, female, and non-response. The severity of pain significantly declined over the course of treatments with a steeper decrease early on in the course of the treatment in both protocols, and four latent pain trajectories were identified. The less favorable pain trajectories were associated with non-response and higher stimulation intensity.

Conclusions: HFL and iTBS were associated with similar factors and pain trajectories, although iTBS was more uncomfortable. Response to rTMS was associated with less pain and more favorable pain trajectories furthering the evince base of overlapping neurobiological underpinnings of mood and pain. We translated these results into patient-oriented information to aid in the decision-making process when considering rTMS.
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http://dx.doi.org/10.1016/j.brs.2021.07.015DOI Listing
August 2021

InTACT: An adaptive and powerful framework for joint-tissue transcriptome-wide association studies.

Genet Epidemiol 2021 Jul 13. Epub 2021 Jul 13.

Department of Statistics, Florida State University, Tallahassee, Florida, USA.

Transcriptome-wide association studies (TWAS) that integrate transcriptomic reference data and genome-wide association studies (GWAS) have successfully enhanced the discovery of candidate genes for many complex traits. However, existing methods may suffer from substantial power loss because they fail to effectively consider that expression of many genes tends to be consistent across tissues. Here we propose a computationally efficient testing method, referred to as Integrative Test for Associations via Cauchy Transformation (InTACT), that effectively combines information across multiple tissues and thus improves the power of identifying associated genes. Through simulation studies, we show that InTACT maintains high power while properly controls for Type 1 error rates. We applied InTACT to the largest GWAS of Alzheimer's disease (AD) to date and identified 227 genome-wide significant genes, of which 130 were not identified by benchmark methods, TWAS and MultiXcan. Importantly, InTACT identified five novel loci for AD. We implemented InTACT in publicly available software, "InTACT."
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http://dx.doi.org/10.1002/gepi.22425DOI Listing
July 2021

An integrative multiomics analysis identifies putative causal genes for COVID-19 severity.

Genet Med 2021 Jun 28. Epub 2021 Jun 28.

Department of Statistics, Florida State University, Tallahassee, FL, USA.

Purpose: It is critical to identify putative causal targets for SARS coronavirus 2, which may guide drug repurposing options to reduce the public health burden of COVID-19.

Methods: We applied complementary methods and multiphased design to pinpoint the most likely causal genes for COVID-19 severity. First, we applied cross-methylome omnibus (CMO) test and leveraged data from the COVID-19 Host Genetics Initiative (HGI) comparing 9,986 hospitalized COVID-19 patients and 1,877,672 population controls. Second, we evaluated associations using the complementary S-PrediXcan method and leveraging blood and lung tissue gene expression prediction models. Third, we assessed associations of the identified genes with another COVID-19 phenotype, comparing very severe respiratory confirmed COVID versus population controls. Finally, we applied a fine-mapping method, fine-mapping of gene sets (FOGS), to prioritize putative causal genes.

Results: Through analyses of the COVID-19 HGI using complementary CMO and S-PrediXcan methods along with fine-mapping, XCR1, CCR2, SACM1L, OAS3, NSF, WNT3, NAPSA, and IFNAR2 are identified as putative causal genes for COVID-19 severity.

Conclusion: We identified eight genes at five genomic loci as putative causal genes for COVID-19 severity.
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http://dx.doi.org/10.1038/s41436-021-01243-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237048PMC
June 2021

PPTFH: Robust Local Descriptor Based on Point-Pair Transformation Features for 3D Surface Matching.

Sensors (Basel) 2021 May 7;21(9). Epub 2021 May 7.

State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

Three-dimensional feature description for a local surface is a core technology in 3D computer vision. Existing descriptors perform poorly in terms of distinctiveness and robustness owing to noise, mesh decimation, clutter, and occlusion in real scenes. In this paper, we propose a 3D local surface descriptor using point-pair transformation feature histograms (PPTFHs) to address these challenges. The generation process of the PPTFH descriptor consists of three steps. First, a simple but efficient strategy is introduced to partition the point-pair sets on the local surface into four subsets. Then, three feature histograms corresponding to each point-pair subset are generated by the point-pair transformation features, which are computed using the proposed Darboux frame. Finally, all the feature histograms of the four subsets are concatenated into a vector to generate the overall PPTFH descriptor. The performance of the PPTFH descriptor is evaluated on several popular benchmark datasets, and the results demonstrate that the PPTFH descriptor achieves superior performance in terms of descriptiveness and robustness compared with state-of-the-art algorithms. The benefits of the PPTFH descriptor for 3D surface matching are demonstrated by the results obtained from five benchmark datasets.
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http://dx.doi.org/10.3390/s21093229DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124800PMC
May 2021

Associations between Genetically Predicted Circulating Protein Concentrations and Endometrial Cancer Risk.

Cancers (Basel) 2021 Apr 26;13(9). Epub 2021 Apr 26.

Population Sciences in the Pacific Program, Cancer Epidemiology Division, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI 96813, USA.

Endometrial cancer (EC) is the leading female reproductive tract malignancy in developed countries. Currently, genome-wide association studies (GWAS) have identified 17 risk loci for EC. To identify novel EC-associated proteins, we used previously reported protein quantitative trait loci for 1434 plasma proteins as instruments to evaluate associations between genetically predicted circulating protein concentrations and EC risk. We studied 12,906 cases and 108,979 controls of European descent included in the Endometrial Cancer Association Consortium, the Epidemiology of Endometrial Cancer Consortium, and the UK Biobank. We observed associations between genetically predicted concentrations of nine proteins and EC risk at a false discovery rate of <0.05 (-values range from 1.14 × 10 to 3.04 × 10). Except for vascular cell adhesion protein 1, all other identified proteins were independent from known EC risk variants identified in EC GWAS. The respective odds ratios (95% confidence intervals) per one standard deviation increase in genetically predicted circulating protein concentrations were 1.21 (1.13, 1.30) for DNA repair protein RAD51 homolog 4, 1.27 (1.14, 1.42) for desmoglein-2, 1.14 (1.07, 1.22) for MHC class I polypeptide-related sequence B, 1.05 (1.02, 1.08) for histo-blood group ABO system transferase, 0.77 (0.68, 0.89) for intestinal-type alkaline phosphatase, 0.82 (0.74, 0.91) for carbohydrate sulfotransferase 15, 1.07 (1.03, 1.11) for D-glucuronyl C5-epimerase, and 1.07 (1.03, 1.10) for CD209 antigen. In conclusion, we identified nine potential EC-associated proteins. If validated by additional studies, our findings may contribute to understanding the pathogenesis of endometrial tumor development and identifying women at high risk of EC along with other EC risk factors and biomarkers.
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http://dx.doi.org/10.3390/cancers13092088DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8123478PMC
April 2021

Red meat consumption, cooking mutagens, NAT1/2 genotypes and pancreatic cancer risk in two ethnically diverse prospective cohorts.

Int J Cancer 2021 Apr 12. Epub 2021 Apr 12.

Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.

There is limited evidence on the association between red meat consumption and pancreatic cancer among ethnic minorities. We assessed this relationship in two large prospective cohorts: the Multiethnic Cohort Study (MEC) and the Southern Community Cohort Study (SCCS). Demographic, dietary and other risk factor data were collected at cohort entry. Red meat intake was assessed using cohort-specific validated food frequency questionnaires. Incident pancreatic cancer cases were identified via linkages to state cancer registries. Cox regression was used to calculate relative risks (RRs) and 95% confidence intervals (CIs) for the association of red meat intake with pancreatic cancer risk in each cohort. We performed additional analyses to evaluate cooking methods, mutagens and effect modification by NAT1/2 genotypes. From a total of 184 542 (MEC) and 66 793 (SCCS) at-risk participants, we identified 1618 (MEC) and 266 (SCCS) incident pancreatic cancer cases. Red meat consumption was associated with pancreatic cancer risk in the MEC (RR 1.18, 95% CI 1.02-1.37) and with borderline statistical significance in the SCCS (RR 1.31, 95% CI 0.93-1.86). This association was significant in African Americans (RR 1.49, 95% CI 1.06-2.11) and Latinos (RR 1.44, 95% CI 1.02-2.04) in the MEC, and among African Americans (RR 1.55, 95% CI 1.03-2.33) in the SCCS. NAT2 genotypes appeared to modify the relationship between red meat and pancreatic cancer in the MEC (p = 0.03). Our findings suggest that the associations for red meat may be strongest in African Americans and Latinos. The mechanisms underlying the increased risk for these populations should be further investigated.
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http://dx.doi.org/10.1002/ijc.33598DOI Listing
April 2021

Multiparameter one-sided tests for nonlinear mixed effects models with censored responses.

Authors:
Guohai Zhou Lang Wu

Stat Med 2021 06 5;40(13):3138-3152. Epub 2021 Apr 5.

Department of Statistics, University of British Columbia, Vancouver, British Columbia, Canada.

Nonlinear mixed-effects (NLME) models are commonly used in longitudinal studies such as pharmacokinetics and HIV viral dynamics studies. NLME models are often derived based on underlying data-generating mechanisms, therefore the parameters in these models often have natural physical interpretations that may suggest reasonable constraints on certain parameters. For example, the HIV viral decay rates for populations receiving anti-HIV treatments may be reasonably expected to be nonnegative. Hypothesis testing for these parameters should incorporate practically reasonable constraints to increase statistical power. Motivated from HIV viral dynamic models, in this article we propose multiparameter one-sided or constrained tests for NLME models with censored responses, for example, viral dynamic models with viral loads subject to lower detection limits. We propose approximate likelihood-based tests that are computationally efficient. We evaluate the tests via simulations and show that the proposed tests are more powerful than the corresponding two-sided or unrestricted tests. We apply the proposed tests to two AIDS datasets with new findings.
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http://dx.doi.org/10.1002/sim.8966DOI Listing
June 2021

Systematic identification of risk factors and drug repurposing options for Alzheimer's disease.

Alzheimers Dement (N Y) 2021 3;7(1):e12148. Epub 2021 Mar 3.

Tulane Center for Biomedical Informatics and Genomics Deming Department of Medicine Tulane University School of Medicine New Orleans Louisiana USA.

Introduction: Several Mendelian randomization studies have been conducted that identified multiple risk factors for Alzheimer's disease (AD). However, they typically focus on a few pre-selected risk factors.

Methods: A two-sample Mendelian randomization (MR) study was used to systematically examine the potential causal associations of 1037 risk factors/medical conditions and 31 drugs with the risk of late-onset AD. To correct for multiple comparisons, the false discovery rate was set at  0.05.

Results: There was strong evidence of a causal association between glioma risk, reduced trunk fat-free mass, lower education levels, lower intelligence and a higher risk of AD. For 31 investigated treatments (such as antihypertensive drugs), we found limited evidence for their associations.

Discussion: MR found robust evidence of causal associations between glioma, trunk fat-free, and AD. Our study also confirms that higher educational attainment and higher intelligence are associated with a reduced risk of AD.
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http://dx.doi.org/10.1002/trc2.12148DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7927163PMC
March 2021

A gene-level methylome-wide association analysis identifies novel Alzheimer's disease genes.

Bioinformatics 2021 Feb 1. Epub 2021 Feb 1.

Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine.

Motivation: Transcriptome-wide association studies (TWAS) have successfully facilitated the discovery of novel genetic risk loci for many complex traits, including late-onset Alzheimer's disease (AD). However, most existing TWAS methods rely only on gene expression and ignore epigenetic modification (i.e., DNA methylation) and functional regulatory information (i.e., enhancer-promoter interactions), both of which contribute significantly to the genetic basis of AD.

Results: We develop a novel gene-level association testing method that integrates genetically regulated DNA methylation and enhancer-target gene pairs with genome-wide association study (GWAS) summary results. Through simulations, we show that our approach, referred to as the CMO (cross methylome omnibus) test, yielded well controlled type I error rates and achieved much higher statistical power than competing methods under a wide range of scenarios. Furthermore, compared with TWAS, CMO identified an average of 124% more associations when analyzing several brain imaging-related GWAS results. By analyzing to date the largest AD GWAS of 71,880 cases and 383,378 controls, CMO identified six novel loci for AD, which have been ignored by competing methods.

Availability: Software: https://github.com/ChongWuLab/CMO.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab045DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8337007PMC
February 2021

Selective and rapid removal of Mo(VI) from water using functionalized FeO-based Mo(VI) ion-imprinted polymer.

Water Sci Technol 2021 Jan;83(2):435-448

College of Biotechnology and Pharmaceutical Engineering, Nanjing Tech University, 30 # Puzhu South Road, Nanjing 211816, China E-mail:

FeO nanoparticles-based magnetic Mo(VI) surface ion-imprinted polymer (Mo(VI)-MIIP) was elaborated employing 4-vinyl pyridine as a functional monomer. The adsorbent preparation was confirmed by Fourier-transform infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray spectrometry, X-ray diffraction, vibrating sample magnetometer, thermogravimetric analysis, and surface area analysis. Batch adsorption experiments showed that the maximum adsorption capacity of Mo(VI)-MIIP was 296.40 mg g at pH 3, while that of the magnetic non-imprinted polymer (MNIP) was only 147.10 mg g. The adsorption isotherm model was well fitted by the Langmuir isotherm model. The adsorption experiments revealed that Mo(VI)-MIIP reached adsorption equilibrium within 30 min, and the kinetics data fitting showed that the pseudo-second-order kinetics model suitably described the adsorption process. Mo(VI)-MIIP exhibited an excellent adsorption selectivity to Mo(VI) in binary mixtures of Mo(VI)/Cr(VI), Mo(VI)/Cu(II), Mo(VI)/HPO4, Mo(VI)/Zn(II), and Mo(VI)/I, with relative selectivity coefficients toward MNIP of 13.71, 30.27, 20.01, 23.53, and 15.89, respectively. After six consecutive adsorption-desorption cycles, the adsorption capacity of Mo(VI)-MIIP decreased by 9.5% (from 228.4 mg g to 206.7 mg g at initial Mo(VI) concentration of 250 mg L), demonstrating its reusability.
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http://dx.doi.org/10.2166/wst.2020.594DOI Listing
January 2021

Associations Between Genetically Predicted Protein Levels and COVID-19 Severity.

J Infect Dis 2021 01;223(1):19-22

Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, Hawaii, USA.

It is critical to identify potential causal targets for SARS-CoV-2, which may guide drug repurposing options. We assessed the associations between genetically predicted protein levels and COVID-19 severity. Leveraging data from the COVID-19 Host Genetics Initiative comparing 6492 hospitalized COVID-19 patients and 1 012 809 controls, we identified 18 proteins with genetically predicted levels to be associated with COVID-19 severity at a false discovery rate of <0.05, including 12 that showed an association even after Bonferroni correction. Of the 18 proteins, 6 showed positive associations and 12 showed inverse associations. In conclusion, we identified 18 candidate proteins for COVID-19 severity.
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http://dx.doi.org/10.1093/infdis/jiaa660DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7797748PMC
January 2021

Comprehensive Analysis of RNA-Seq Gene Expression Profiling of Brain Transcriptomes Reveals Novel Genes, Regulators, and Pathways in Autism Spectrum Disorder.

Brain Sci 2020 Oct 17;10(10). Epub 2020 Oct 17.

Department of Biomedical and Biotechnological Sciences, University of Catania, 95124 Catania CT, Italy.

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with deficits in social communication ability and repetitive behavior. The pathophysiological events involved in the brain of this complex disease are still unclear.

Methods: In this study, we aimed to profile the gene expression signatures of brain cortex of ASD patients, by using two publicly available RNA-seq studies, in order to discover new ASD-related genes.

Results: We detected 1567 differentially expressed genes (DEGs) by meta-analysis, where 1194 were upregulated and 373 were downregulated genes. Several ASD-related genes previously reported were also identified. Our meta-analysis identified 235 new DEGs that were not detected using the individual RNA-seq studies used. Some of those genes, including seven DEGs ( and ), have been confirmed in previous reports to be associated with ASD. Gene Ontology (GO) and pathways analysis showed several molecular pathways enriched by the DEGs, namely, osteoclast differentiation, TNF signaling pathway, complement and coagulation cascade. Topological analysis of protein-protein interaction of the ASD brain cortex revealed proteomics hub gene signatures: and . We also identified the transcriptional factors (TFs) regulating DEGs, namely, .

Conclusion: Novel core genes and molecular signatures involved with ASD were identified by our meta-analysis.
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http://dx.doi.org/10.3390/brainsci10100747DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7603078PMC
October 2020

Mendelian Randomization Analysis of n-6 Polyunsaturated Fatty Acid Levels and Pancreatic Cancer Risk.

Cancer Epidemiol Biomarkers Prev 2020 12 23;29(12):2735-2739. Epub 2020 Sep 23.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.

Background: Whether circulating polyunsaturated fatty acid (PUFA) levels are associated with pancreatic cancer risk is uncertain. Mendelian randomization (MR) represents a study design using genetic instruments to better characterize the relationship between exposure and outcome.

Methods: We utilized data from genome-wide association studies within the Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case-Control Consortium, involving approximately 9,269 cases and 12,530 controls of European descent, to evaluate associations between pancreatic cancer risk and genetically predicted plasma n-6 PUFA levels. Conventional MR analyses were performed using individual-level and summary-level data.

Results: Using genetic instruments, we did not find evidence of associations between genetically predicted plasma n-6 PUFA levels and pancreatic cancer risk [estimates per one SD increase in each PUFA-specific weighted genetic score using summary statistics: linoleic acid odds ratio (OR) = 1.00, 95% confidence interval (CI) = 0.98-1.02; arachidonic acid OR = 1.00, 95% CI = 0.99-1.01; and dihomo-gamma-linolenic acid OR = 0.95, 95% CI = 0.87-1.02]. The OR estimates remained virtually unchanged after adjustment for covariates, using individual-level data or summary statistics, or stratification by age and sex.

Conclusions: Our results suggest that variations of genetically determined plasma n-6 PUFA levels are not associated with pancreatic cancer risk.

Impact: These results suggest that modifying n-6 PUFA levels through food sources or supplementation may not influence risk of pancreatic cancer.
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http://dx.doi.org/10.1158/1055-9965.EPI-20-0651DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710600PMC
December 2020

A Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Pancreatic Cancer Risk.

Cancer Res 2020 10 9;80(20):4346-4354. Epub 2020 Sep 9.

Division of Cancer Epidemiology, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, Hawaii.

Pancreatic cancer is among the most well-characterized cancer types, yet a large proportion of the heritability of pancreatic cancer risk remains unclear. Here, we performed a large transcriptome-wide association study to systematically investigate associations between genetically predicted gene expression in normal pancreas tissue and pancreatic cancer risk. Using data from 305 subjects of mostly European descent in the Genotype-Tissue Expression Project, we built comprehensive genetic models to predict normal pancreas tissue gene expression, modifying the UTMOST (unified test for molecular signatures). These prediction models were applied to the genetic data of 8,275 pancreatic cancer cases and 6,723 controls of European ancestry. Thirteen genes showed an association of genetically predicted expression with pancreatic cancer risk at an FDR ≤ 0.05, including seven previously reported genes (, and ) and six novel genes not yet reported for pancreatic cancer risk [6q27: OR (95% confidence interval (CI), 1.54 (1.25-1.89); 13q12.13: OR (95% CI), 0.78 (0.70-0.88); 14q24.3: OR (95% CI), 1.35 (1.17-1.56); 17q12: OR (95% CI), 6.49 (2.96-14.27); 17q21.1: OR (95% CI), 1.94 (1.45-2.58); and 20p13: OR (95% CI): 1.41 (1.20-1.66)]. The associations for 10 of these genes (, and ) remained statistically significant even after adjusting for risk SNPs identified in previous genome-wide association study. Collectively, this analysis identified novel candidate susceptibility genes for pancreatic cancer that warrant further investigation. SIGNIFICANCE: A transcriptome-wide association analysis identified seven previously reported and six novel candidate susceptibility genes for pancreatic cancer risk.
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http://dx.doi.org/10.1158/0008-5472.CAN-20-1353DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572664PMC
October 2020

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk.

Nat Commun 2020 08 6;11(1):3905. Epub 2020 Aug 6.

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA.

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.
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http://dx.doi.org/10.1038/s41467-020-17673-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413371PMC
August 2020

Fine mapping of the Ca3GT gene controlling anthocyanin biosynthesis in mature unripe fruit of Capsicum annuum L.

Theor Appl Genet 2020 Sep 20;133(9):2729-2742. Epub 2020 Jun 20.

Beijing Key Laboratory of Growth and Developmental Regulation for Protected Vegetable Crops, Department of Vegetable Science, College of Horticulture, China Agricultural University, Beijing, 100193, China.

Key Message: The anthocyanin biosynthesis gene Ca3GT was fine-mapped in a 110.5-kb region through a map-based cloning strategy. Gene expression and promoter analyses confirmed the strong candidate gene Capana10g001978. Pepper (Capsicum annum L.) fruit can be dark green, green, light green, purple, yellow, or ivory at the juvenile stage. Anthocyanins are responsible for fruit color formation in mature unripe pepper fruit, and transient accumulation of anthocyanins is the main problem in breeding pepper plants with mature purple fruit. Only a few genes controlling this trait have been cloned. The present study aimed to map and identify an anthocyanin biosynthesis gene from pepper using an F population derived from a cross between line '17C3808' (purple mature unripe fruit) and line '17C3807' (green mature unripe fruit). The trait was mapped on a 110.5-kb interval between markers SSR18213 and SSR18228 on chromosome 10. There were three open reading frames in this region; Capana10g001978 was predicted in this region as markers CAPS-78-708 and InDel146 co-segregated with it. Capana10g001978 is a structural gene encoding the GTB transcription factor involved in the biosynthesis of anthocyanins. Comparing parental sequences, two base mutations were identified in the exon of Capana10g001978, at positions + 528 bp and + 708 bp, which resulted in changes in the 176th and 236th amino acid residues, from glutamine (CAA) to histidine (CAC), causing a nonsense mutation (from CAG to CAA). Additionally, Capana10g001978 was highly expressed in the pericarp of mature, unripe pepper fruit. There were four single nucleotide polymorphisms, three sequence deletions, and one sequence insertion in the promoter region of purple, mature, and unripe pepper fruit, leading to the formation of a W-box and a GT1-motif. Thus, Capana10g001978 is a strong candidate gene of Ca3GT involved in anthocyanin biosynthesis in mature unripe pepper fruit. These results provide important information regarding the isolation and characterization of Ca3GT, and they are the starting point for studying the regulatory pathway responsible for anthocyanin biosynthesis in pepper.
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http://dx.doi.org/10.1007/s00122-020-03628-7DOI Listing
September 2020

Associations between Genetically Predicted Blood Protein Biomarkers and Pancreatic Cancer Risk.

Cancer Epidemiol Biomarkers Prev 2020 07 21;29(7):1501-1508. Epub 2020 May 21.

Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota.

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies, with few known risk factors and biomarkers. Several blood protein biomarkers have been linked to PDAC in previous studies, but these studies have assessed only a limited number of biomarkers, usually in small samples. In this study, we evaluated associations of circulating protein levels and PDAC risk using genetic instruments.

Methods: To identify novel circulating protein biomarkers of PDAC, we studied 8,280 cases and 6,728 controls of European descent from the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case-Control Consortium, using genetic instruments of protein quantitative trait loci.

Results: We observed associations between predicted concentrations of 38 proteins and PDAC risk at an FDR of < 0.05, including 23 of those proteins that showed an association even after Bonferroni correction. These include the protein encoded by , which has been implicated as a potential target gene of PDAC risk variant. Eight of the identified proteins (LMA2L, TM11D, IP-10, ADH1B, STOM, TENC1, DOCK9, and CRBB2) were associated with PDAC risk after adjusting for previously reported PDAC risk variants (OR ranged from 0.79 to 1.52). Pathway enrichment analysis showed that the encoding genes for implicated proteins were significantly enriched in cancer-related pathways, such as STAT3 and IL15 production.

Conclusions: We identified 38 candidates of protein biomarkers for PDAC risk.

Impact: This study identifies novel protein biomarker candidates for PDAC, which if validated by additional studies, may contribute to the etiologic understanding of PDAC development.
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http://dx.doi.org/10.1158/1055-9965.EPI-20-0091DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334065PMC
July 2020

Transcriptome-wide association study of breast cancer risk by estrogen-receptor status.

Genet Epidemiol 2020 07 1;44(5):442-468. Epub 2020 Mar 1.

Department of Radiation Oncology, Hannover Medical School, Hannover, Germany.

Previous transcriptome-wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome-wide association studies (GWAS), but analyses of breast cancer subtype-specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta-analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER-). We further compared associations with ER+ and ER- subtypes, using a case-only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER- breast cancer. We further identified 30 TWAS-significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast-cancer gene in three of six regions harboring multiple TWAS-significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER- breast cancer.
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http://dx.doi.org/10.1002/gepi.22288DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7987299PMC
July 2020

A Transcriptome-Wide Association Study Identifies Novel Candidate Susceptibility Genes for Pancreatic Cancer.

J Natl Cancer Inst 2020 10;112(10):1003-1012

Yale Cancer Center, New Haven, CT, USA.

Background: Although 20 pancreatic cancer susceptibility loci have been identified through genome-wide association studies in individuals of European ancestry, much of its heritability remains unexplained and the genes responsible largely unknown.

Methods: To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74-421 samples).

Results: We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction.

Conclusions: By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.
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http://dx.doi.org/10.1093/jnci/djz246DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7566474PMC
October 2020

Phenotypic, genetic, and molecular function of msc-2, a genic male sterile mutant in pepper (Capsicum annuum L.).

Theor Appl Genet 2020 Mar 20;133(3):843-855. Epub 2019 Dec 20.

Beijing Key Laboratory of Growth and Developmental Regulation for Protected Vegetable Crops, Department of Vegetable Science, College of Horticulture, China Agricultural University, Beijing, 100193, China.

Key Message: Bulked segregant analysis and fine mapping delimited the pepper genic male sterile (msc-2) locus into a 336 kb region on chromosome 5. A strong candidate gene, Capana05g000766, a homolog of AtMS1, was indentified in this region. Genic male sterility (msc-2) is used to produce hybrid seeds in Northern China. However, no co-segregated markers have been reported or candidate genes controlling this trait have been cloned. Here, bulked segregant analysis and genotyping of an F population and a 18Q5431AB line were employed to fine map msc-2, which was delimited to a 336 kb region. In this region, Capana05g000766 was a homolog of AtMS1, which encodes a plant homeodomain finger involved in tapetum development. A "T" deletion in the Capana05g000766 locus leads to a premature stop codon, which may cause a loss-of-function mutation. Real-time PCR analysis revealed that Capana05g000766 was an anther-specific gene and down-regulation of the gene resulted in male sterility. Therefore, Capana05g000766 was identified as the strongest candidate gene for the msc-2 locus. Allelism tests showed that msc-1 and msc-2 were nonallelic, and bimolecular fluorescence complementation analysis indicated that the two genes did not interact directly with each other at the protein level. As msc-1 and msc-2 are homologs of AtDYT1 and AtMS1 in Arabidopsis, they may play similar roles in tapetum development in genic male sterile peppers, and Msc-1 might be up stream of Msc-2 in the regulation of other genes involved in tapetum development.
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http://dx.doi.org/10.1007/s00122-019-03510-1DOI Listing
March 2020

Genome-Wide Correlation of 36 Agronomic Traits in the 287 Pepper () Accessions Obtained from the SLAF-seq-Based GWAS.

Int J Mol Sci 2019 Nov 13;20(22). Epub 2019 Nov 13.

Beijing Key Laboratory of Growth and Developmental Regulation for Protected Vegetable Crops, China Agricultural University, Beijing 100193, China.

There are many agronomic traits of pepper ( L.) with abundant phenotypes that can benefit pepper growth. Using specific-locus amplified fragment sequencing (SLAF-seq), a genome-wide association study (GWAS) of 36 agronomic traits was carried out for 287 representative pepper accessions. To ensure the accuracy and reliability of the GWAS results, we analyzed the genetic diversity, distribution of labels (SLAF tags and single nucleotide polymorphisms (SNPs)) and population differentiation and determined the optimal statistical model. In our study, 1487 SNPs were highly significantly associated with 26 agronomic traits, and 2126 candidate genes were detected in the 100-kb region up- and down-stream near these SNPs. Furthermore, 13 major association peaks were identified for 11 key agronomic traits. Then we examined the correlations among the 36 agronomic traits and analyzed SNP distribution and found 37 SNP polymerization regions (total size: 264.69 Mbp) that could be selected areas in pepper breeding. We found that the stronger the correlation between the two traits, the greater the possibility of them being in more than one polymerization region, suggesting that they may be linked or that one pleiotropic gene controls them. These results provide a theoretical foundation for future multi-trait pyramid breeding of pepper. Finally, we found that the GWAS signals were highly consistent with those from the nuclear restorer-of-fertility () gene for cytoplasmic male sterility (CMS), verifying their reliability. We further identified and as candidate genes by functional annotation and expression analysis, which provided a reference for the study of cytoplasmic male sterility in .
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http://dx.doi.org/10.3390/ijms20225675DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6888518PMC
November 2019

Analysis of Over 140,000 European Descendants Identifies Genetically Predicted Blood Protein Biomarkers Associated with Prostate Cancer Risk.

Cancer Res 2019 09 23;79(18):4592-4598. Epub 2019 Jul 23.

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee.

Several blood protein biomarkers have been associated with prostate cancer risk. However, most studies assessed only a small number of biomarkers and/or included a small sample size. To identify novel protein biomarkers of prostate cancer risk, we studied 79,194 cases and 61,112 controls of European ancestry, included in the PRACTICAL/ELLIPSE consortia, using genetic instruments of protein quantitative trait loci for 1,478 plasma proteins. A total of 31 proteins were associated with prostate cancer risk including proteins encoded by , whose methylation level was shown previously to be associated with prostate cancer risk, and , and , which were previously implicated as potential target genes of prostate cancer risk variants identified in genome-wide association studies. A total of 18 proteins inversely correlated and 13 positively correlated with prostate cancer risk. For 28 of the identified proteins, gene somatic changes of short indels, splice site, nonsense, or missense mutations were detected in patients with prostate cancer in The Cancer Genome Atlas. Pathway enrichment analysis showed that relevant genes were significantly enriched in cancer-related pathways. In conclusion, this study identifies 31 candidates of protein biomarkers for prostate cancer risk and provides new insights into the biology and genetics of prostate tumorigenesis. SIGNIFICANCE: Integration of genomics and proteomics data identifies biomarkers associated with prostate cancer risk.
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http://dx.doi.org/10.1158/0008-5472.CAN-18-3997DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744971PMC
September 2019

Evaluation of associations between genetically predicted circulating protein biomarkers and breast cancer risk.

Int J Cancer 2020 04 16;146(8):2130-2138. Epub 2019 Jul 16.

Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN.

A small number of circulating proteins have been reported to be associated with breast cancer risk, with inconsistent results. Herein, we attempted to identify novel protein biomarkers for breast cancer via the integration of genomics and proteomics data. In the Breast Cancer Association Consortium (BCAC), with 122,977 cases and 105,974 controls of European descendants, we evaluated the associations of the genetically predicted concentrations of >1,400 circulating proteins with breast cancer risk. We used data from a large-scale protein quantitative trait loci (pQTL) analysis as our study instrument. Summary statistics for these pQTL variants related to breast cancer risk were obtained from the BCAC and used to estimate odds ratios (OR) for each protein using the inverse-variance weighted method. We identified 56 proteins significantly associated with breast cancer risk by instrumental analysis (false discovery rate <0.05). Of these, the concentrations of 32 were influenced by variants close to a breast cancer susceptibility locus (ABO, 9q34.2). Many of these proteins, such as insulin receptor, insulin-like growth factor receptor 1 and other membrane receptors (OR: 0.82-1.18, p values: 6.96 × 10 -3.28 × 10 ), are linked to insulin resistance and estrogen receptor signaling pathways. Proteins identified at other loci include those involved in biological processes such as alcohol and lipid metabolism, proteolysis, apoptosis, immune regulation and cell motility and proliferation. Consistent associations were observed for 22 proteins in the UK Biobank data (p < 0.05). The study identifies potential novel biomarkers for breast cancer, but further investigation is needed to replicate our findings.
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http://dx.doi.org/10.1002/ijc.32542DOI Listing
April 2020
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