Publications by authors named "Enes Makalic"

56 Publications

Characterization of brain-derived extracellular vesicle lipids in Alzheimer's disease.

J Extracell Vesicles 2021 May 11;10(7):e12089. Epub 2021 May 11.

The Florey Institute of Neuroscience and Mental Health The University of Melbourne Parkville Victoria Australia.

Lipid dyshomeostasis is associated with the most common form of dementia, Alzheimer's disease (AD). Substantial progress has been made in identifying positron emission tomography and cerebrospinal fluid biomarkers for AD, but they have limited use as front-line diagnostic tools. Extracellular vesicles (EVs) are released by all cells and contain a subset of their parental cell composition, including lipids. EVs are released from the brain into the periphery, providing a potential source of tissue and disease specific lipid biomarkers. However, the EV lipidome of the central nervous system is currently unknown and the potential of brain-derived EVs (BDEVs) to inform on lipid dyshomeostasis in AD remains unclear. The aim of this study was to reveal the lipid composition of BDEVs in human frontal cortex, and to determine whether BDEVs have an altered lipid profile in AD. Using semi-quantitative mass spectrometry, we describe the BDEV lipidome, covering four lipid categories, 17 lipid classes and 692 lipid molecules. BDEVs were enriched in glycerophosphoserine (PS) lipids, a characteristic of small EVs. Here we further report that BDEVs are enriched in ether-containing PS lipids, a finding that further establishes ether lipids as a feature of EVs. BDEVs in the AD frontal cortex offered improved detection of dysregulated lipids in AD over global lipid profiling of this brain region.  AD BDEVs had significantly altered glycerophospholipid and sphingolipid levels, specifically increased plasmalogen glycerophosphoethanolamine and decreased polyunsaturated fatty acyl containing lipids, and altered amide-linked acyl chain content in sphingomyelin and ceramide lipids relative to CTL. The most prominent alteration was a two-fold decrease in lipid species containing anti-inflammatory/pro-resolving docosahexaenoic acid. The in-depth lipidome analysis provided in this study highlights the advantage of EVs over more complex tissues for improved detection of dysregulated lipids that may serve as potential biomarkers in the periphery.
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http://dx.doi.org/10.1002/jev2.12089DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8111496PMC
May 2021

Assessment of a Polygenic Risk Score for Colorectal Cancer to Predict Risk of Lynch Syndrome Colorectal Cancer.

JNCI Cancer Spectr 2021 Apr 8;5(2):pkab022. Epub 2021 Mar 8.

Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Victoria, Australia.

It was not known whether the polygenic risk scores (PRSs) that predict colorectal cancer could predict colorectal cancer for people with inherited pathogenic variants in DNA mismatch repair genes-people with Lynch syndrome. We tested a PRS comprising 107 established single-nucleotide polymorphisms associated with colorectal cancer in European populations for 826 European-descent carriers of pathogenic variants in DNA mismatch repair genes (293 , 314 , 126 , 71 , and 22 ) from the Colon Cancer Family Registry, of whom 504 had colorectal cancer. There was no evidence of an association between the PRS and colorectal cancer risk, irrespective of which DNA mismatch repair gene was mutated, or sex (all 2-sided >.05). The hazard ratio per standard deviation of the PRS for colorectal cancer was 0.97 (95% confidence interval = 0.88 to 1.06; 2-sided =.51). Whereas PRSs are predictive of colorectal cancer in the general population, they do not predict Lynch syndrome colorectal cancer.
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http://dx.doi.org/10.1093/jncics/pkab022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8062848PMC
April 2021

Epigenetic Drift Association with Cancer Risk and Survival, and Modification by Sex.

Cancers (Basel) 2021 Apr 14;13(8). Epub 2021 Apr 14.

Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia.

To investigate age- and sex-specific DNA methylation alterations related to cancer risk and survival, we used matched case-control studies of colorectal ( = 835), gastric ( = 170), kidney ( = 143), lung ( = 332), prostate ( = 869) and urothelial ( = 428) cancers, and mature B-cell lymphoma ( = 438). Linear mixed-effects models were conducted to identify age-, sex- and age-by-sex-associated methylation markers using a discovery (controls)-replication (cases) strategy. Replication was further examined using summary statistics from Generation Scotland (GS). Associations between replicated markers and risk of and survival from cancer were assessed using conditional logistic regression and Cox models (hazard ratios (HR)), respectively. We found 32,659, 23,141 and 48 CpGs with replicated associations for age, sex and age-by-sex, respectively. The replication rates for these CpGs using GS summary data were 94%, 86% and 91%, respectively. Significant associations for cancer risk and survival were identified at some individual age-related CpGs. Opposite to previous findings using epigenetic clocks, there was a strong negative trend in the association between epigenetic drift and risk of colorectal cancer. Methylation at two CpGs overlapping and genes was associated with survival of overall (HR = 0.91, = 7.7 × 10) and colorectal (HR = 1.52, = 1.8 × 10) cancer, respectively, with significant age-by-sex interaction. Our results may provide markers for cancer early detection and prognosis prediction.
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http://dx.doi.org/10.3390/cancers13081881DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8070898PMC
April 2021

Diet and risk of gastro-oesophageal reflux disease in the Melbourne Collaborative Cohort Study.

Public Health Nutr 2021 Jan 21:1-13. Epub 2021 Jan 21.

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.

Objective: To examine associations between diet and risk of developing gastro-oesophageal reflux disease (GERD).

Design: Prospective cohort with a median follow-up of 15·8 years. Baseline diet was measured using a FFQ. GERD was defined as self-reported current or history of daily heartburn or acid regurgitation beginning at least 2 years after baseline. Sex-specific logistic regressions were performed to estimate OR for GERD associated with diet quality scores and intakes of nutrients, food groups and individual foods and beverages. The effect of substituting saturated fat for monounsaturated or polyunsaturated fat on GERD risk was examined.

Setting: Melbourne, Australia.

Participants: A cohort of 20 926 participants (62 % women) aged 40-59 years at recruitment between 1990 and 1994.

Results: For men, total fat intake was associated with increased risk of GERD (OR 1·05 per 5 g/d; 95 % CI 1·01, 1·09; P = 0·016), whereas total carbohydrate (OR 0·89 per 30 g/d; 95 % CI 0·82, 0·98; P = 0·010) and starch intakes (OR 0·84 per 30 g/d; 95 % CI 0·75, 0·94; P = 0·005) were associated with reduced risk. Nutrients were not associated with risk for women. For both sexes, substituting saturated fat for polyunsaturated or monounsaturated fat did not change risk. For both sexes, fish, chicken, cruciferous vegetables and carbonated beverages were associated with increased risk, whereas total fruit and citrus were associated with reduced risk. No association was observed with diet quality scores.

Conclusions: Diet is a possible risk factor for GERD, but food considered as triggers of GERD symptoms might not necessarily contribute to disease development. Potential differential associations for men and women warrant further investigation.
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http://dx.doi.org/10.1017/S1368980021000197DOI Listing
January 2021

Biological Aging Measures Based on Blood DNA Methylation and Risk of Cancer: A Prospective Study.

JNCI Cancer Spectr 2021 Feb 16;5(1):pkaa109. Epub 2020 Nov 16.

Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.

Background: We previously investigated the association between 5 "first-generation" measures of epigenetic aging and cancer risk in the Melbourne Collaborative Cohort Study. This study assessed cancer risk associations for 3 recently developed methylation-based biomarkers of aging: , , and predicted telomere length.

Methods: We estimated rate ratios (RRs) for the association between these 3 age-adjusted measures and risk of colorectal (N = 813), gastric (N = 165), kidney (N = 139), lung (N = 327), mature B-cell (N = 423), prostate (N = 846), and urothelial (N = 404) cancer using conditional logistic regression models. We also assessed associations by time since blood draw and by cancer subtype, and we investigated potential nonlinearity.

Results: We observed relatively strong associations of age-adjusted with risk of colorectal, kidney, lung, mature B-cell, and urothelial cancers (RR per SD was approximately 1.2-1.3). Similar findings were obtained for age-adjusted , but the association with lung cancer risk was much larger (RR per SD = 1.82, 95% confidence interval [CI] = 1.44 to 2.30), after adjustment for smoking status, pack-years, starting age, time since quitting, and other cancer risk factors. Most associations appeared linear, larger than for the first-generation measures, and were virtually unchanged after adjustment for a large set of sociodemographic, lifestyle, and anthropometric variables. For cancer overall, the comprehensively adjusted rate ratio per SD was 1.13 (95% CI = 1.07 to 1.19) for and 1.12 (95% CI = 1.05 to 1.20) for and appeared larger within 5 years of blood draw (RR = 1.29, 95% CI = 1.15 to 1.44 and 1.19, 95% CI = 1.06 to 1.33, respectively).

Conclusions: The methylation-based measures and may provide insights into the relationship between biological aging and cancer and be useful to predict cancer risk, particularly for lung cancer.
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http://dx.doi.org/10.1093/jncics/pkaa109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7791618PMC
February 2021

Novel mammogram-based measures improve breast cancer risk prediction beyond an established mammographic density measure.

Int J Cancer 2021 May 4;148(9):2193-2202. Epub 2020 Dec 4.

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.

Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1197 matched controls; and 354 younger-diagnosis cases and 944 controls frequency-matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We estimated measure-specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen-detected and younger-diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41-2.31]) and Cirrus (1.72 [1.38-2.14]); Cirrus (1.49 [1.32-1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27-1.68]), respectively. The AUCs were: 0.73 [0.68-0.77], 0.63 [0.60-0.66], and 0.72 [0.69-0.75], respectively. Combined, our new mammogram-based measures have twice the risk gradient for screen-detected and younger-diagnosis breast cancer (P ≤ 10 ), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk-based personalised breast screening.
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http://dx.doi.org/10.1002/ijc.33396DOI Listing
May 2021

Stochastic Epigenetic Mutations Are Associated with Risk of Breast Cancer, Lung Cancer, and Mature B-cell Neoplasms.

Cancer Epidemiol Biomarkers Prev 2020 10 11;29(10):2026-2037. Epub 2020 Aug 11.

MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom.

Background: Age-related epigenetic dysregulations are associated with several diseases, including cancer. The number of stochastic epigenetic mutations (SEM) has been suggested as a biomarker of life-course accumulation of exposure-related DNA damage; however, the predictive role of SEMs in cancer has seldom been investigated.

Methods: A SEM, at a given CpG site, was defined as an extreme outlier of DNA methylation value distribution across individuals. We investigated the association of the total number of SEMs with the risk of eight cancers in 4,497 case-control pairs nested in three prospective cohorts. Furthermore, we investigated whether SEMs were randomly distributed across the genome or enriched in functional genomic regions.

Results: In the three-study meta-analysis, the estimated ORs per one-unit increase in log(SEM) from logistic regression models adjusted for age and cancer risk factors were 1.25; 95% confidence interval (CI), 1.11-1.41 for breast cancer, and 1.23; 95% CI, 1.07-1.42 for lung cancer. In the Melbourne Collaborative Cohort Study, the OR for mature B-cell neoplasm was 1.46; 95% CI, 1.25-1.71. Enrichment analyses indicated that SEMs frequently occur in silenced genomic regions and in transcription factor binding sites regulated by EZH2 and SUZ12 ( < 0.0001 and = 0.0005, respectively): two components of the polycomb repressive complex 2 (PCR2). Finally, we showed that PCR2-specific SEMs are generally more stable over time compared with SEMs occurring in the whole genome.

Conclusions: The number of SEMs is associated with a higher risk of different cancers in prediagnostic blood samples.

Impact: We identified a candidate biomarker for cancer early detection, and we described a carcinogenesis mechanism involving PCR2 complex proteins worthy of further investigations.
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http://dx.doi.org/10.1158/1055-9965.EPI-20-0451DOI Listing
October 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

Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk.

J Clin Med 2020 Feb 26;9(3). Epub 2020 Feb 26.

Centre for Epidemiology & Biostatistics/Melbourne School of Population & Global Health, Faculty of Medicine, Dentistry & Health Sciences, The University of Melbourne, Victoria 3010, Australia.

This commentary is about predicting a woman's breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.
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http://dx.doi.org/10.3390/jcm9030627DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7141100PMC
February 2020

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.

Nat Genet 2020 01 7;52(1):56-73. Epub 2020 Jan 7.

Unit of Medical Genetics, Department of Medical Oncology and Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.
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http://dx.doi.org/10.1038/s41588-019-0537-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974400PMC
January 2020

Alcohol consumption is associated with widespread changes in blood DNA methylation: Analysis of cross-sectional and longitudinal data.

Addict Biol 2021 01 2;26(1):e12855. Epub 2019 Dec 2.

Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.

DNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart. Using the Illumina HumanMethylation450 BeadChip, DNA methylation was measured in blood samples from 5606 Melbourne Collaborative Cohort Study (MCCS) participants. For 1088 of them, these measures were repeated using blood samples collected a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models. Independent data from the London Life Sciences Prospective Population (LOLIPOP) (N = 4042) and Cooperative Health Research in the Augsburg Region (KORA) (N = 1662) cohorts were used to replicate associations discovered in the MCCS. Cross-sectional analyses identified 1414 CpGs associated with alcohol intake at P < 10 , 1243 of which had not been reported previously. Of these novel associations, 1078 were replicated (P < .05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated 403 of 518 previously reported associations. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1414 CpGs, 530 were differentially methylated (P < .05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1414 cross-sectional associations. Our study indicates that alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with alcohol consumption changes in adulthood.
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http://dx.doi.org/10.1111/adb.12855DOI Listing
January 2021

Smoking and blood DNA methylation: an epigenome-wide association study and assessment of reversibility.

Epigenetics 2020 04 25;15(4):358-368. Epub 2019 Sep 25.

Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.

We conducted a genome-wide association study of blood DNA methylation and smoking, attempted replication of previously discovered associations, and assessed the reversibility of smoking-associated methylation changes. DNA methylation was measured in baseline peripheral blood samples for 5,044 participants in the Melbourne Collaborative Cohort Study. For 1,032 participants, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. A cross-sectional analysis of the association between smoking and DNA methylation and a longitudinal analysis of changes in smoking status and changes in DNA methylation were conducted. We used our cross-sectional analysis to replicate previously reported associations for current (N = 3,327) and former (N = 172) smoking. A comprehensive smoking index accounting for the biological half-life of smoking compounds and several aspects of smoking history was constructed to assess the reversibility of smoking-induced methylation changes. This measure of lifetime exposure to smoking allowed us to detect more associations than comparing current with never smokers. We identified 4,496 cross-sectional associations at P < 10, including 3,296 annotated to 1,326 genes that were not previously implicated in smoking-associated DNA methylation changes at this significance threshold. We replicated the majority of previously reported associations (P < 10) for current and former smokers. In our data, we observed for former smokers a substantial degree of return to the methylation levels of never smokers, compared with current smokers (median: 74%, IQR = 63-86%), corresponding to small values (median: 2.75, IQR = 1.5-5.25) for the half-life parameter of the comprehensive smoking index. Longitudinal analyses identified 368 sites at which methylation changed upon smoking cessation. Our study demonstrates the usefulness of the comprehensive smoking index to detect associations between smoking and DNA methylation at CpGs across the genome, replicates the vast majority of previously reported associations, and quantifies the reversibility of smoking-induced methylation changes.
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http://dx.doi.org/10.1080/15592294.2019.1668739DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7153547PMC
April 2020

Cirrus: An Automated Mammography-Based Measure of Breast Cancer Risk Based on Textural Features.

JNCI Cancer Spectr 2018 Oct 7;2(4):pky057. Epub 2018 Dec 7.

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.

Background: We applied machine learning to find a novel breast cancer predictor based on information in a mammogram.

Methods: Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case-control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index.

Results: Combining studies, almost all textural features were associated with case-control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all <10). For the Cirrus measure derived from combining studies, the OR was 1.90 (95% confidence interval [CI] = 1.73 to 2.09), equivalent to a fourfold interquartile risk ratio, and was little attenuated after adjusting for conventional measures. In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 (95% CI = 1.08 to 1.24; =4 × 10).

Conclusions: A fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.
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http://dx.doi.org/10.1093/jncics/pky057DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6649799PMC
October 2018

Ability of known susceptibility SNPs to predict colorectal cancer risk for persons with and without a family history.

Fam Cancer 2019 10;18(4):389-397

Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3010, Australia.

Before SNP-based risk can be incorporated in colorectal cancer (CRC) screening, the ability of these SNPs to estimate CRC risk for persons with and without a family history of CRC, and the screening implications need to be determined. We estimated the association with CRC of a 45 SNP-based risk using 1181 cases and 999 controls, and its correlation with CRC risk predicted from detailed family history. We estimated the predicted change in the distribution across predefined risk categories, and implications for recommended screening commencement age, from adding SNP-based risk to family history. The inter-quintile risk ratio for colorectal cancer risk of the SNP-based risk was 3.28 (95% CI 2.54-4.22). SNP-based and family history-based risks were not correlated (r = 0.02). For persons with no first-degree relatives with CRC, screening could commence 4 years earlier for women (5 years for men) in the highest quintile of SNP-based risk. For persons with two first-degree relatives with CRC, screening could commence 16 years earlier for men and women in the highest quintile, and 7 years earlier for the lowest quintile. This 45 SNP panel in conjunction with family history, can identify people who could benefit from earlier screening. Risk reclassification by 45 SNPs could inform targeted screening for CRC prevention, particularly in clinical genetics settings when mutations in high-risk genes cannot be identified. Yet to be determined is cost-effectiveness, resources requirements, community, patient and clinician acceptance, and feasibility with potentially ethical, legal and insurance implications.
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http://dx.doi.org/10.1007/s10689-019-00136-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6785388PMC
October 2019

Genome-wide association and transcriptome studies identify target genes and risk loci for breast cancer.

Nat Commun 2019 04 15;10(1):1741. Epub 2019 Apr 15.

Molecular Oncology Laboratory, CIBERONC, Hospital Clinico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos), 28040, Madrid, Spain.

Genome-wide association studies (GWAS) have identified more than 170 breast cancer susceptibility loci. Here we hypothesize that some risk-associated variants might act in non-breast tissues, specifically adipose tissue and immune cells from blood and spleen. Using expression quantitative trait loci (eQTL) reported in these tissues, we identify 26 previously unreported, likely target genes of overall breast cancer risk variants, and 17 for estrogen receptor (ER)-negative breast cancer, several with a known immune function. We determine the directional effect of gene expression on disease risk measured based on single and multiple eQTL. In addition, using a gene-based test of association that considers eQTL from multiple tissues, we identify seven (and four) regions with variants associated with overall (and ER-negative) breast cancer risk, which were not reported in previous GWAS. Further investigation of the function of the implicated genes in breast and immune cells may provide insights into the etiology of breast cancer.
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http://dx.doi.org/10.1038/s41467-018-08053-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6465407PMC
April 2019

Genome-wide association study of germline variants and breast cancer-specific mortality.

Br J Cancer 2019 03 21;120(6):647-657. Epub 2019 Feb 21.

Lund University, Department of Cancer Epidemiology, Clinical Sciences, Lund, Sweden.

Background: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry.

Methods: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP).

Results: We did not find any variant associated with breast cancer-specific mortality at P < 5 × 10. For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10, hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84-0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10, HR = 1.27, 95% CI = 1.16-1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster.

Conclusions: We uncovered germline variants on chromosome 7 at BFDP < 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients.
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http://dx.doi.org/10.1038/s41416-019-0393-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6461853PMC
March 2019

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.

Am J Hum Genet 2019 01 13;104(1):21-34. Epub 2018 Dec 13.

Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki 00290, Finland; Department of Oncology, Örebro University Hospital, Örebro 70185, Sweden.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.
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http://dx.doi.org/10.1016/j.ajhg.2018.11.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323553PMC
January 2019

Physical Activity, Television Viewing Time, and DNA Methylation in Peripheral Blood.

Med Sci Sports Exerc 2019 03;51(3):490-498

Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, AUSTRALIA.

Introduction: Physical activity may affect health via DNA methylation. The epigenetic influences of sedentary behaviors such as television viewing are unknown. We performed a genomewide study of DNA methylation in peripheral blood in relation to physical activity and television viewing time.

Methods: DNA methylation was measured using the Illumina Infinium HumanMethylation450K BeadChip array in blood samples collected at baseline (N = 5513) and follow-up (N = 1249) from participants in the Melbourne Collaborative Cohort Study. At baseline, times per week of leisure-time physical activity were self-reported. At follow-up, the International Physical Activity Questionnaire was used to assess MET-hours per week of total and leisure-time physical activity and hours per day of television viewing time. Linear mixed models were used to assess associations between physical activity and television viewing measures and DNA methylation at individual CpG sites, adjusted for potential confounders and batch effects.

Results: At follow-up, total physical activity was associated with DNA methylation at cg10266336 (P = 6.0 × 10), annotated to the SAA2 gene. Weaker evidence of associations (P < 1.0 × 10) were observed for an additional 14 CpG sites with total physical activity, for 7 CpG sites with leisure-time physical activity, and for 9 CpG sites with television viewing time. Changes in leisure-time physical activity between baseline and follow-up were associated with methylation changes (P < 0.05) at four of the seven CpG sites with weaker evidence of cross-sectional associations with leisure-time physical activity.

Conclusion: Physical activity and television viewing may be associated with blood DNA methylation, a potential pathway to chronic disease development. Further research using accelerometer data and larger sample sizes is warranted.
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http://dx.doi.org/10.1249/MSS.0000000000001827DOI Listing
March 2019

Heritable methylation marks associated with breast and prostate cancer risk.

Prostate 2018 09 29;78(13):962-969. Epub 2018 May 29.

Genetic Epidemiology Laboratory, Department of Clinical Pathology, The University of Melbourne, Victoria, Australia.

Background: DNA methylation can mimic the effects of germline mutations in cancer predisposition genes. Recently, we identified twenty-four heritable methylation marks associated with breast cancer risk. As breast and prostate cancer share genetic risk factors, including rare, high-risk mutations (eg, in BRCA2), we hypothesized that some of these heritable methylation marks might also be associated with the risk of prostate cancer.

Methods: We studied 869 incident prostate cancers (430 aggressive and 439 non-aggressive) and 869 matched controls nested within a prospective cohort study. DNA methylation was measured in pre-diagnostic blood samples using the Illumina Infinium HM450K BeadChip. Conditional logistic regression models, adjusted for prostate cancer risk factors and blood cell composition, were used to estimate odds ratios and 95% confidence intervals for the association between the 24 methylation marks and the risk of prostate cancer.

Results: Five methylation marks within the VTRNA2-1 promoter region (cg06536614, cg00124993, cg26328633, cg25340688, and cg26896946), and one in the body of CLGN (cg22901919) were associated with the risk of prostate cancer. In stratified analyses, the five VTRNA2-1 marks were associated with the risk of aggressive prostate cancer.

Conclusions: This work highlights a potentially important new area of investigation for prostate cancer susceptibility and adds to our knowledge about shared risk factors for breast and prostate cancer.
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http://dx.doi.org/10.1002/pros.23654DOI Listing
September 2018

Dietary intake of one-carbon metabolism nutrients and DNA methylation in peripheral blood.

Am J Clin Nutr 2018 09;108(3):611-621

Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.

Background: Folate and other one-carbon metabolism nutrients are essential to enable DNA methylation to occur, but the extent to which their dietary intake influences methylation in adulthood is unclear.

Objective: We assessed associations between dietary intake of these nutrients and DNA methylation in peripheral blood, overall and at specific genomic locations.

Design: We conducted a cross-sectional study using baseline data and samples from 5186 adult participants in the Melbourne Collaborative Cohort Study (MCCS). Nutrient intake was estimated from a food-frequency questionnaire. DNA methylation was measured by using the Illumina Infinium HumanMethylation450 BeadChip array (HM450K). We assessed associations of intakes of folate, riboflavin, vitamins B-6 and B-12, methionine, choline, and betaine with methylation at individual cytosine-guanine dinucleotides (CpGs), and with median (genome-wide) methylation across all CpGs, CpGs in gene bodies, and CpGs in gene promoters. We also assessed associations with methylation at long interspersed nuclear element 1 (LINE-1), satellite 2 (Sat2), and Arthrobacter luteus restriction endonuclease (Alu) repetitive elements for a subset of participants. We used linear mixed regression, adjusting for age, sex, country of birth, smoking, energy intake from food, alcohol intake, Mediterranean diet score, and batch effects to assess log-linear associations with dietary intake of each nutrient. In secondary analyses, we assessed associations with low or high intakes defined by extreme quintiles.

Results: No evidence of log-linear association was observed at P < 10-7 between the intake of one-carbon metabolism nutrients and methylation at individual CpGs. Low intake of riboflavin was associated with higher methylation at CpG cg21230392 in the first exon of PROM1 (P = 5.0 × 10-8). No consistent evidence of association was observed with genome-wide or repetitive element measures of methylation.

Conclusion: Our findings suggest that dietary intake of one-carbon metabolism nutrients in adulthood, as measured by a food-frequency questionnaire, has little association with blood DNA methylation. An association with low intake of riboflavin requires replication in independent cohorts. This study was registered at http://www.clinicaltrials.gov as NCT03227003.
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http://dx.doi.org/10.1093/ajcn/nqy119DOI Listing
September 2018

Genetic susceptibility markers for a breast-colorectal cancer phenotype: Exploratory results from genome-wide association studies.

PLoS One 2018 26;13(4):e0196245. Epub 2018 Apr 26.

Department of Gastroenterology, Hepatology and Nutrition, The University of Texas, MD, Anderson Cancer Center, Houston, United States of America.

Background: Clustering of breast and colorectal cancer has been observed within some families and cannot be explained by chance or known high-risk mutations in major susceptibility genes. Potential shared genetic susceptibility between breast and colorectal cancer, not explained by high-penetrance genes, has been postulated. We hypothesized that yet undiscovered genetic variants predispose to a breast-colorectal cancer phenotype.

Methods: To identify variants associated with a breast-colorectal cancer phenotype, we analyzed genome-wide association study (GWAS) data from cases and controls that met the following criteria: cases (n = 985) were women with breast cancer who had one or more first- or second-degree relatives with colorectal cancer, men/women with colorectal cancer who had one or more first- or second-degree relatives with breast cancer, and women diagnosed with both breast and colorectal cancer. Controls (n = 1769), were unrelated, breast and colorectal cancer-free, and age- and sex- frequency-matched to cases. After imputation, 6,220,060 variants were analyzed using the discovery set and variants associated with the breast-colorectal cancer phenotype at P<5.0E-04 (n = 549, at 60 loci) were analyzed for replication (n = 293 cases and 2,103 controls).

Results: Multiple correlated SNPs in intron 1 of the ROBO1 gene were suggestively associated with the breast-colorectal cancer phenotype in the discovery and replication data (most significant; rs7430339, Pdiscovery = 1.2E-04; rs7429100, Preplication = 2.8E-03). In meta-analysis of the discovery and replication data, the most significant association remained at rs7429100 (P = 1.84E-06).

Conclusion: The results of this exploratory analysis did not find clear evidence for a susceptibility locus with a pleiotropic effect on hereditary breast and colorectal cancer risk, although the suggestive association of genetic variation in the region of ROBO1, a potential tumor suppressor gene, merits further investigation.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196245PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5919670PMC
August 2018

An open-source, integrated pedigree data management and visualization tool for genetic epidemiology.

Int J Epidemiol 2018 08;47(4):1034-1039

Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.

With advances in genetic epidemiology, increasingly large amounts of pedigree-related information are being collected by family studies, including twin studies. To date, biomedical data management systems that cater for family data have usually done so as part of their standard (non-family-centric) data model. Consequently, data managers with computing expertise are needed to extract family datasets and perform family-centric operations. We present a robust approach to handling large family datasets. Our approach is implemented as a new module which extends the capabilities of The Ark, an open-source web-based biomedical data management tool. Using an algorithm designed by the authors, the pedigree module dynamically infers family relationships for any selected subject (not necessarily the proband). A web interface allows researchers to create, update, delete and navigate parental and twin relationships between subjects, and bulk import/export pedigrees. Consanguineous relationships can be captured, and configurable pedigree visualizations generated. A web services interface provides interoperability.
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http://dx.doi.org/10.1093/ije/dyy049DOI Listing
August 2018

DNA methylation-based biological aging and cancer risk and survival: Pooled analysis of seven prospective studies.

Int J Cancer 2018 04 18;142(8):1611-1619. Epub 2017 Dec 18.

Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.

The association between aging and cancer is complex. Recent studies have developed measures of biological aging based on DNA methylation and called them "age acceleration." We aimed to assess the associations of age acceleration with risk of and survival from seven common cancers. Seven case-control studies of DNA methylation and colorectal, gastric, kidney, lung, prostate and urothelial cancer and B-cell lymphoma nested in the Melbourne Collaborative Cohort Study were conducted. Cancer cases, vital status and cause of death were ascertained through linkage with cancer and death registries. Conditional logistic regression and Cox models were used to estimate odds ratios (OR) and hazard ratios (HR) and 95% confidence intervals (CI) for associations of five age acceleration measures derived from the Human Methylation 450 K Beadchip assay with cancer risk (N = 3,216 cases) and survival (N = 1,726 deaths), respectively. Epigenetic aging was associated with increased cancer risk, ranging from 4% to 9% per five-year age acceleration for the 5 measures considered. Heterogeneity by study was observed, with stronger associations for risk of kidney cancer and B-cell lymphoma. An associated increased risk of death following cancer diagnosis ranged from 2% to 6% per five-year age acceleration, with no evidence of heterogeneity by cancer site. Cancer risk and mortality were increased by 15-30% for the fourth versus first quartile of age acceleration. DNA methylation-based measures of biological aging are associated with increased cancer risk and shorter cancer survival, independently of major health risk factors.
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http://dx.doi.org/10.1002/ijc.31189DOI Listing
April 2018

Association analysis identifies 65 new breast cancer risk loci.

Nature 2017 11 23;551(7678):92-94. Epub 2017 Oct 23.

Department of Clinical Genetics, VU University Medical Center, Amsterdam, The Netherlands.

Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.
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http://dx.doi.org/10.1038/nature24284DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798588PMC
November 2017

Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer.

Nat Genet 2017 Dec 23;49(12):1767-1778. Epub 2017 Oct 23.

Department of Epidemiology, University of California, Irvine, Irvine, California, USA.

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.
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http://dx.doi.org/10.1038/ng.3785DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5808456PMC
December 2017

Association of DNA Methylation-Based Biological Age With Health Risk Factors and Overall and Cause-Specific Mortality.

Am J Epidemiol 2018 03;187(3):529-538

Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia.

Measures of biological age based on blood DNA methylation, referred to as age acceleration (AA), have been developed. We examined whether AA was associated with health risk factors and overall and cause-specific mortality. At baseline (1990-1994), blood samples were drawn from 2,818 participants in the Melbourne Collaborative Cohort Study (Melbourne, Victoria, Australia). DNA methylation was determined using the Infinium HumanMethylation450 BeadChip array (Illumina Inc., San Diego, California). Mixed-effects models were used to examine the association of AA with health risk factors. Cox models were used to assess the association of AA with mortality. A total of 831 deaths were observed during a median 10.7 years of follow-up. Associations of AA were observed with male sex, Greek nationality (country of birth), smoking, obesity, diabetes, lower education, and meat intake. AA measures were associated with increased mortality, and this was only partly accounted for by known determinants of health (hazard ratios were attenuated by 20%-40%). Weak evidence of heterogeneity in the association was observed by sex (P = 0.06) and cause of death (P = 0.07) but not by other factors. DNA-methylation-based AA measures are associated with several major health risk factors, but these do not fully explain the association between AA and mortality. Future research should investigate what genetic and environmental factors determine AA.
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http://dx.doi.org/10.1093/aje/kwx291DOI Listing
March 2018

Genome-Wide Measures of Peripheral Blood Dna Methylation and Prostate Cancer Risk in a Prospective Nested Case-Control Study.

Prostate 2017 04 24;77(5):471-478. Epub 2017 Jan 24.

Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Parkville, VIC, Australia.

Background: Global measures of peripheral blood DNA methylation have been associated with risk of some malignancies, including breast, bladder, and gastric cancer. Here, we examined genome-wide measures of peripheral blood DNA methylation in prostate cancer and its non-aggressive and aggressive disease forms.

Methods: We used a matched, case-control study of 687 incident prostate cancer samples, nested within a larger prospective cohort study. DNA methylation was measured in pre-diagnostic, peripheral blood samples using the Illumina Infinium HM450K BeadChip. Genome-wide measures of DNA methylation were computed as the median M-value of all CpG sites and according to CpG site location and regulatory function. We used conditional logistic regression to test for associations between genome-wide measures of DNA methylation and risk of prostate cancer and its subtypes, and by time between blood draw and diagnosis.

Results: We observed no associations between the genome-wide measure of DNA methylation based on all CpG sites and risk of prostate cancer or aggressive disease. Risk of non-aggressive disease was associated with higher methylation of CpG islands (OR = 0.80; 95%CI = 0.68-0.94), promoter regions (OR = 0.79; 95%CI = 0.66-0.93), and high density CpG regions (OR = 0.80; 95%CI = 0.68-0.94). Additionally, higher methylation of all CpGs (OR = 0.66; 95%CI = 0.48-0.89), CpG shores (OR = 0.62; 95%CI = 0.45-0.84), and regulatory regions (OR = 0.68; 95% CI = 0.51-0.91) was associated with a reduced risk of overall prostate cancer within 5 years of blood draw but not thereafter.

Conclusions: A reduced risk of overall prostate cancer within 5 years of blood draw and non-aggressive prostate cancer was associated with higher genome-wide methylation of peripheral blood DNA. While these data have no immediate clinical utility, with further work they may provide insight into the early events of prostate carcinogenesis. Prostate 77:471-478, 2017. © 2017 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/pros.23289DOI Listing
April 2017

Genome-wide analysis identifies 12 loci influencing human reproductive behavior.

Nat Genet 2016 12 31;48(12):1462-1472. Epub 2016 Oct 31.

Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands.

The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individuals for NEB. We identified 12 independent loci that are significantly associated with AFB and/or NEB in a SNP-based genome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.
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http://dx.doi.org/10.1038/ng.3698DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695684PMC
December 2016

Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.

Cancer Epidemiol Biomarkers Prev 2016 12 18;25(12):1619-1624. Epub 2016 Aug 18.

Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.

Background: We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e.g., SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalized regression, and decision trees. Unlike marginal regression, which considers each SNP individually, the key idea behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Our aim was to compare the performance of DEPTH with that of standard logistic regression analysis.

Methods: We selected 1,854 prostate cancer cases and 1,894 controls from the UK for whom 541,129 SNPs were measured using the Illumina Infinium HumanHap550 array. Confirmation was sought using 4,152 cases and 2,874 controls, ascertained from the UK and Australia, for whom 211,155 SNPs were measured using the iCOGS Illumina Infinium array.

Results: From the DEPTH analysis, we identified 14 regions associated with prostate cancer risk that had been reported previously, five of which would not have been identified by conventional logistic regression. We also identified 112 novel putative susceptibility regions.

Conclusions: DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs.

Impact: This study demonstrates that the DEPTH algorithm could identify additional genetic susceptibility regions that merit further investigation. Cancer Epidemiol Biomarkers Prev; 25(12); 1619-24. ©2016 AACR.
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http://dx.doi.org/10.1158/1055-9965.EPI-16-0301DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5232414PMC
December 2016
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