Genomic analysis of male puberty timing highlights shared genetic basis with hair colour and lifespan.

The timing of puberty is highly variable and is associated with long-term health outcomes. To date, understanding of the genetic control of puberty timing is based largely on studies in women. Here, we report a multi-trait genome-wide association study for male puberty timing with an effective sample size of 205,354 men. We find moderately strong genomic correlation in puberty timing between sexes (rg = 0.68) and identify 76 independent signals for male puberty timing. Implicated mechanisms include an unexpected link between puberty timing and natural hair colour, possibly reflecting common effects of pituitary hormones on puberty and pigmentation. Earlier male puberty timing is genetically correlated with several adverse health outcomes and Mendelian randomization analyses show a genetic association between male puberty timing and shorter lifespan. These findings highlight the relationships between puberty timing and health outcomes, and demonstrate the value of genetic studies of puberty timing in both sexes

Telomere Length Shows No Association with BRCA1 and BRCA2 Mutation Status

This study aimed to determine whether telomere length (TL) is a marker of cancer risk or genetic status amongst two cohorts of BRCA1 and BRCA2 mutation carriers and controls. The first group was a prospective set of 665 male BRCA1/2 mutation carriers and controls (mean age 53 years), all healthy at time of enrolment and blood donation, 21 of whom have developed prostate cancer whilst on study. The second group consisted of 283 female BRCA1/2 mutation carriers and controls (mean age 48 years), half of whom had been diagnosed with breast cancer prior to enrolment. TL was quantified by qPCR from DNA extracted from peripheral blood lymphocytes. Weighted and unweighted Cox regressions and linear regression analyses were used to assess whether TL was associated with BRCA1/2 mutation status or cancer risk. We found no evidence for association between developing cancer or being a BRCA1 or BRCA2 mutation carrier and telomere length. It is the first study investigating TL in a cohort of genetically predisposed males and although TL and BRCA status was previously studied in females our results don't support the previous finding of association between hereditary breast cancer and shorter TL

Breast cancer risk factors and their effects on survival: a Mendelian randomisation study

Abstract: Background: Observational studies have investigated the association of risk factors with breast cancer prognosis. However, the results have been conflicting and it has been challenging to establish causality due to potential residual confounding. Using a Mendelian randomisation (MR) approach, we aimed to examine the potential causal association between breast cancer-specific survival and nine established risk factors for breast cancer: alcohol consumption, body mass index, height, physical activity, mammographic density, age at menarche or menopause, smoking, and type 2 diabetes mellitus (T2DM). Methods: We conducted a two-sample MR analysis on data from the Breast Cancer Association Consortium (BCAC) and risk factor summary estimates from the GWAS Catalog. The BCAC data included 86,627 female patients of European ancestry with 7054 breast cancer-specific deaths during 15 years of follow-up. Of these, 59,378 were estrogen receptor (ER)-positive and 13,692 were ER-negative breast cancer patients. For the significant association, we used sensitivity analyses and a multivariable MR model. All risk factor associations were also examined in a model adjusted by other prognostic factors. Results: Increased genetic liability to T2DM was significantly associated with worse breast cancer-specific survival (hazard ratio [HR] = 1.10, 95% confidence interval [CI] = 1.03–1.17, P value [P] = 0.003). There were no significant associations after multiple testing correction for any of the risk factors in the ER-status subtypes. For the reported significant association with T2DM, the sensitivity analyses did not show evidence for violation of the MR assumptions nor that the association was due to increased BMI. The association remained significant when adjusting by other prognostic factors. Conclusions: This extensive MR analysis suggests that T2DM may be causally associated with worse breast cancer-specific survival and therefore that treating T2DM may improve prognosis

Partnering in oncogenetic research – the INHERIT BRCAs experience : opportunities and challenges

Today it is common to conduct research in collaboration with colleagues from different disciplines and institutions. The INterdisciplinary HEalth Research International Team on BReast CAncer susceptibility (INHERIT BRCAs), involves Canadian and international experts from diverse fields working with health service providers, patients and collaborators from the World Health Organization and other European networks. Evidence-based information and knowledge transfer drive our efforts to advance genomic research to understand the genetic basis of cancer susceptibility and treatment response. Several goals reveal the interdisciplinary team approach: (a) to estimate the prevalence and penetrance of BRCA1 and BRCA2 mutations and their deleterious impact upon different populations; (b) to pinpoint novel breast cancer susceptibility loci; (c) to assess the efficacy of clinical interventions; (d) to address changes in quality of life and health-related behaviour from the decision to undergo genetics testing and during follow-up; (e) to evaluate legal, social and ethical implications; and, finally; (f) to promote professional and public education by facilitating the transfer of research findings to clinical practice and informing policy makers. The lessons learned by the INHERIT research team and future challenges are presented

Pubertal development and prostate cancer risk: Mendelian randomization study in a population-based cohort.

BACKGROUND: Epidemiological studies have observed a positive association between an earlier age at sexual development and prostate cancer, but markers of sexual maturation in boys are imprecise and observational estimates are likely to suffer from a degree of uncontrolled confounding. To obtain causal estimates, we examined the role of pubertal development in prostate cancer using genetic polymorphisms associated with Tanner stage in adolescent boys in a Mendelian randomization (MR) approach. METHODS: We derived a weighted genetic risk score for pubertal development, combining 13 SNPs associated with male Tanner stage. A higher score indicated a later puberty onset. We examined the association of this score with prostate cancer risk, stage and grade in the UK-based ProtecT case-control study (n = 2,927), and used the PRACTICAL consortium (n = 43,737) as a replication sample. RESULTS: In ProtecT, the puberty genetic score was inversely associated with prostate cancer grade (odds ratio (OR) of high- vs. low-grade cancer, per tertile of the score: 0.76; 95 % CI, 0.64-0.89). In an instrumental variable estimation of the causal OR, later physical development in adolescence (equivalent to a difference of one Tanner stage between pubertal boys of the same age) was associated with a 77 % (95 % CI, 43-91 %) reduced odds of high Gleason prostate cancer. In PRACTICAL, the puberty genetic score was associated with prostate cancer stage (OR of advanced vs. localized cancer, per tertile: 0.95; 95 % CI, 0.91-1.00) and prostate cancer-specific mortality (hazard ratio amongst cases, per tertile: 0.94; 95 % CI, 0.90-0.98), but not with disease grade. CONCLUSIONS: Older age at sexual maturation is causally linked to a reduced risk of later prostate cancer, especially aggressive disease.This work was supported by the World Cancer Research Fund (2011/419) and Cancer Research UK (C18281/A19169). The Integrative Epidemiology Unit (IEU) is supported by the MRC and the University of Bristol (G0600705, MC_UU_12013/19), and the Integrative Cancer Epidemiology Programme is supported by Cancer Research UK programme grant C18281/A19169. The National Institute for Health Research (NIHR) Bristol Nutrition Biomedical Research Unit is funded by the NIHR and is a partnership between University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The ProtecT study is supported by the UK NIHR Health Technology Assessment (HTA) Programme (HTA 96/20/99; ISRCTN20141297). Funding for PRACTICAL and the iCOGS infrastructure came from: the European Community’s Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/ A16565), the National Institutes of Health (CA128978), and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 – the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. We acknowledge support from the NIHR to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s12916-016-0602-

Investigating the possible causal role of coffee consumption with prostate cancer risk and progression using Mendelian randomization analysis.

Coffee consumption has been shown in some studies to be associated with lower risk of prostate cancer. However, it is unclear if this association is causal or due to confounding or reverse causality. We conducted a Mendelian randomisation analysis to investigate the causal effects of coffee consumption on prostate cancer risk and progression. We used two genetic variants robustly associated with caffeine intake (rs4410790 and rs2472297) as proxies for coffee consumption in a sample of 46,687 men of European ancestry from 25 studies in the PRACTICAL consortium. Associations between genetic variants and prostate cancer case status, stage and grade were assessed by logistic regression and with all-cause and prostate cancer-specific mortality using Cox proportional hazards regression. There was no clear evidence that a genetic risk score combining rs4410790 and rs2472297 was associated with prostate cancer risk (OR per additional coffee increasing allele: 1.01, 95% CI: 0.98,1.03) or having high-grade compared to low-grade disease (OR: 1.01, 95% CI: 0.97,1.04). There was some evidence that the genetic risk score was associated with higher odds of having nonlocalised compared to localised stage disease (OR: 1.03, 95% CI: 1.01, 1.06). Amongst men with prostate cancer, there was no clear association between the genetic risk score and all-cause mortality (HR: 1.00, 95% CI: 0.97,1.04) or prostate cancer-specific mortality (HR: 1.03, 95% CI: 0.98,1.08). These results, which should have less bias from confounding than observational estimates, are not consistent with a substantial effect of coffee consumption on reducing prostate cancer incidence or progression.British Heart Foundation, Cancer Research UK, Economic and Social Research Council, Medical Research Council, and the National Institute for Health Research, under the auspices of the UK Clinical Research Collaboration Cancer Research UK. Grant Number: C18281/A19169 RMM and Caroline Relton (Integrative Cancer Epidemiology Programme) Canadian Institutes of Health Research the European Commission's Seventh Framework Programme. Grant Numbers: 223175, HEALTH-F2-2009-223175 Cancer Research UK. Grant Numbers: C5047/A7357, C1287/A10118, C5047/A3354, C5047/A10692, C16913/A6135 National Institute of Health (NIH) Cancer Post-Cancer GWAS. Grant Number: 1 U19 CA 148537-01 the GAME-ON initiative the European Community's Seventh Framework Programme. Grant Numbers: 223175, HEALTH-F2-2009-223175 Cancer Research UK. Grant Numbers: C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692 the National Institutes of Health. Grant Number: CA128978 Post-Cancer GWAS initiative. Grant Numbers: 1U19 CA148537, 1U19 CA148065, 1U19 CA148112 the GAME-ON initiative the Department of Defence. Grant Number: W81XWH-10-1-0341 the Canadian Institutes of Health Research (CIHR) CIHR Team in Familial Risks of Breast Cancer Komen Foundation for the Cure Breast Cancer Research Foundation. Grant Number: Ovarian Cancer Research Fund VicHealth and Cancer Council Victoria Australian NHMRC. Grant Numbers: 209057, 251553, 504711 Cancer Council Victoria Australian Institute of Health and Welfare (AIHW) National Death Index and the Australian Cancer Database U.K. Health Technology Assessment (HTA) Programme of the NIH Research. Grant Numbers: HTA 96/20/99, ISRCTN20141297 Prodigal study and the ProMPT (Prostate Mechanisms of Progression and Treatment) National Cancer Research Institute (NCRI) Department of Health, the Medical Research Council and Cancer Research UK. Grant Number: G0500966/75466 Cancer Research UK. Grant Number: C5047/A7357 NIHR Biomedical Research Centre at The Institute of Cancer Research and Royal Marsden NHS Foundation Trust National Institute for Health Research Bristol Nutrition Biomedical Research Unit based at University Hospitals Bristol NHS Foundation Trust and the University of Bristol FCH, DEN and JLD are NIHR Senior Investigators MRC and the University of Bristol. Grant Numbers: G0600705, MC_UU_12013/6This is the final version of the article. It first appeared from Wiley via https://doi.org/10.1002/ijc.3046

The effects of height and BMI on prostate cancer incidence and mortality:a Mendelian randomization study in 20,848 cases and 20,214 controls from the PRACTICAL consortium

Background\ud \ud Epidemiological studies suggest a potential role for obesity and determinants of adult stature in prostate cancer risk and mortality, but the relationships described in the literature are complex. To address uncertainty over the causal nature of previous observational findings, we investigated associations of height- and adiposity-related genetic variants with prostate cancer risk and mortality.\ud \ud Methods\ud \ud We conducted a case–control study based on 20,848 prostate cancers and 20,214 controls of European ancestry from 22 studies in the PRACTICAL consortium. We constructed genetic risk scores that summed each man’s number of height and BMI increasing alleles across multiple single nucleotide polymorphisms robustly associated with each phenotype from published genome-wide association studies.\ud \ud Results\ud \ud The genetic risk scores explained 6.31 and 1.46 % of the variability in height and BMI, respectively. There was only weak evidence that genetic variants previously associated with increased BMI were associated with a lower prostate cancer risk (odds ratio per standard deviation increase in BMI genetic score 0.98; 95 % CI 0.96, 1.00; p = 0.07). Genetic variants associated with increased height were not associated with prostate cancer incidence (OR 0.99; 95 % CI 0.97, 1.01; p = 0.23), but were associated with an increase (OR 1.13; 95 % CI 1.08, 1.20) in prostate cancer mortality among low-grade disease (p heterogeneity, low vs. high grade <0.001). Genetic variants associated with increased BMI were associated with an increase (OR 1.08; 95 % CI 1.03, 1.14) in all-cause mortality among men with low-grade disease (p heterogeneity = 0.03).\ud \ud Conclusions\ud \ud We found little evidence of a substantial effect of genetically elevated height or BMI on prostate cancer risk, suggesting that previously reported observational associations may reflect common environmental determinants of height or BMI and prostate cancer risk. Genetically elevated height and BMI were associated with increased mortality (prostate cancer-specific and all-cause, respectively) in men with low-grade disease, a potentially informative but novel finding that requires replication

Blood lipids and prostate cancer: a Mendelian randomization analysis.

Genetic risk scores were used as unconfounded instruments for specific lipid traits (Mendelian randomization) to assess whether circulating lipids causally influence prostate cancer risk. Data from 22,249 prostate cancer cases and 22,133 controls from 22 studies within the international PRACTICAL consortium were analyzed. Allele scores based on single nucleotide polymorphisms (SNPs) previously reported to be uniquely associated with each of low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglyceride (TG) levels, were first validated in an independent dataset, and then entered into logistic regression models to estimate the presence (and direction) of any causal effect of each lipid trait on prostate cancer risk. There was weak evidence for an association between the LDL genetic score and cancer grade: the odds ratio (OR) per genetically instrumented standard deviation (SD) in LDL, comparing high- (≥7 Gleason score) versus low-grade (<7 Gleason score) cancers was 1.50 (95% CI: 0.92, 2.46; P = 0.11). A genetically instrumented SD increase in TGs was weakly associated with stage: the OR for advanced versus localized cancer per unit increase in genetic risk score was 1.68 (95% CI: 0.95, 3.00; P = 0.08). The rs12916-T variant in 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) was inversely associated with prostate cancer (OR: 0.97; 95% CI: 0.94, 1.00; P = 0.03). In conclusion, circulating lipids, instrumented by our genetic risk scores, did not appear to alter prostate cancer risk. We found weak evidence that higher LDL and TG levels increase aggressive prostate cancer risk, and that a variant in HMGCR (that mimics the LDL lowering effect of statin drugs) reduces risk. However, inferences are limited by sample size and evidence of pleiotropy.C. J. B. is funded by the Wellcome Trust 4-year studentship WT083431MA. The Integrative Cancer Epidemiology Programme is supported by Cancer Research UK programme grant C18281/A19169. The MRC IEU is supported by the Medical Research Council and the University of Bristol (MC_UU_12013/1-9). The NIHR Bristol Nutrition Biomedical Research Unit is funded by the National Institute for Health Research (NIHR) and is a partnership between University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The CRUK study and PRACTICAL consortium is supported by the Canadian Institutes of Health Research, European Commission’s Seventh Framework Programme grant agreement no. 223175 (HEALTH-F2-2009-223175), Cancer Research UK Grants C5047/A7357, C1287/A10118, C5047/A3354, C5047/A10692, and C16913/ A6135. The National Institutes of Health (NIH) Cancer Post-Cancer GWAS initiative grant no. 1 U19 CA 148537-01 (the GAME-ON initiative) and NIHR support to the Biomedical Research Centre and The Institute of Cancer Research and Royal Marsden NHS Foundation Trust.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/cam4.69

AA9int: SNP interaction pattern search using non-hierarchical additive model set.

MOTIVATION: The use of single nucleotide polymorphism (SNP) interactions to predict complex diseases is getting more attention during the past decade, but related statistical methods are still immature. We previously proposed the SNP Interaction Pattern Identifier (SIPI) approach to evaluate 45 SNP interaction patterns/patterns. SIPI is statistically powerful but suffers from a large computation burden. For large-scale studies, it is necessary to use a powerful and computation-efficient method. The objective of this study is to develop an evidence-based mini-version of SIPI as the screening tool or solitary use and to evaluate the impact of inheritance mode and model structure on detecting SNP-SNP interactions. RESULTS: We tested two candidate approaches: the 'Five-Full' and 'AA9int' method. The Five-Full approach is composed of the five full interaction models considering three inheritance modes (additive, dominant and recessive). The AA9int approach is composed of nine interaction models by considering non-hierarchical model structure and the additive mode. Our simulation results show that AA9int has similar statistical power compared to SIPI and is superior to the Five-Full approach, and the impact of the non-hierarchical model structure is greater than that of the inheritance mode in detecting SNP-SNP interactions. In summary, it is recommended that AA9int is a powerful tool to be used either alone or as the screening stage of a two-stage approach (AA9int+SIPI) for detecting SNP-SNP interactions in large-scale studies. AVAILABILITY AND IMPLEMENTATION: The 'AA9int' and 'parAA9int' functions (standard and parallel computing version) are added in the SIPI R package, which is freely available at https://linhuiyi.github.io/LinHY_Software/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online