We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait ...penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank (N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves from R
= 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.
Adiposity traits have been associated with risk of many cancers in observational studies, but whether these associations are causal is unclear. Mendelian randomization (MR) uses genetic predictors of ...risk factors as instrumental variables to eliminate reverse causation and reduce confounding bias. We performed MR analyses to assess the possible causal relationship of birthweight, childhood and adult body mass index (BMI), and waist-hip ratio (WHR) on the risks of breast, ovarian, prostate, colorectal and lung cancers.
We tested the association between genetic risk scores and each trait using summary statistics from published genome-wide association studies (GWAS) and from 51 537 cancer cases and 61 600 controls in the Genetic Associations and Mechanisms in Oncology (GAME-ON) Consortium.
We found an inverse association between the genetic score for childhood BMI and risk of breast cancer odds ratio (OR) = 0.71 per standard deviation (s.d.) increase in childhood BMI; 95% confidence interval (CI): 0.60, 0.80; P = 6.5 × 10(-5)). We also found the genetic score for adult BMI to be inversely associated with breast cancer risk (OR = 0.66 per s.d. increase in BMI; 95% CI: 0.57, 0.77; P = 2.5 × 10(-7)), and positively associated with ovarian cancer (OR = 1.35; 95% CI: 1.05, 1.72; P = 0.017), lung cancer (OR = 1.27; 95% CI: 1.09, 1.49; P = 2.9 × 10(-3)) and colorectal cancer (OR = 1.39; 95% CI: 1.06, 1.82, P = 0.016). The inverse association between genetically predicted adult BMI and breast cancer risk remained even after adjusting for directional pleiotropy via MR-Egger regression.
Findings from this study provide additional understandings of the complex relationship between adiposity and cancer risks. Our results for breast and lung cancer are particularly interesting, given previous reports of effect heterogeneity by menopausal status and smoking status.
The principal tool used to estimate a woman's risk of breast cancer is the Breast Cancer Risk Assessment Tool, or the Gail model, which includes the number of first-degree relatives with breast ...cancer, age at menarche, age at first live birth, and number of previous breast biopsies. In this study, the addition of data on genetic variants associated with breast cancer yielded only a minor improvement in the performance of the model.
The Breast Cancer Risk Assessment Tool includes the number of first-degree relatives with breast cancer, age at menarche, age at first live birth, and number of previous breast biopsies. In this study, the addition of data on genetic variants associated with breast cancer yielded only a minor improvement in the performance of the model.
Personalized medicine, the assignment of preventive measures or treatment interventions on the basis of individual characteristics, can result in better outcomes than the use of the same strategy for everyone. Recent changes in the U.S. Preventive Services Task Force guidelines
1
for mammographic screening raise the question of whether recommendations about age at the onset of screening and the frequency of screening can be calibrated to an individual woman's risk of breast cancer. Clinicians already use guidelines in making decisions about assessments to identify carriers of rare
BRCA1
and
BRCA2
mutations, which confer very high risks of breast cancer and ovarian . . .
BACKGROUND:Platelets have distinct roles in the vascular system in that they are the major mediator of thrombosis, critical for restoration of tissue integrity, and players in vascular inflammatory ...conditions. In close spatiotemporal proximity, the complement system acts as the first line of defense against invading microorganisms and is a key mediator of inflammation. Whereas the fluid phase cross-talk between the complement and coagulation systems is well appreciated, the understanding of the pathophysiological implications of such interactions is still scant.
METHODS:We analyzed coexpression of the anaphylatoxin receptor C3aR with activated glycoprotein IIb/IIIa on platelets of 501 patients with coronary artery disease using flow cytometry; detected C3aR expression in human or murine specimen by polymerase chain reaction, immunofluorescence, Western blotting, or flow cytometry; and examined the importance of platelet C3aR by various in vitro platelet function tests, in vivo bleeding time, and intravital microscopy. The pathophysiological relevance of C3aR was scrutinized with the use of disease models of myocardial infarction and stroke. To approach underlying molecular mechanisms, we identified the platelet small GTPase Rap1b using nanoscale liquid chromatography coupled to tandem mass spectrometry.
RESULTS:We found a strong positive correlation of platelet complement C3aR expression with activated glycoprotein IIb/IIIa in patients with coronary artery disease and coexpression of C3aR with glycoprotein IIb/IIIa in thrombi obtained from patients with myocardial infarction. Our results demonstrate that the C3a/C3aR axis on platelets regulates distinct steps of thrombus formation such as platelet adhesion, spreading, and Ca influx. Using C3aR mice or C3 mice with reinjection of C3a, we uncovered that the complement activation fragment C3a regulates bleeding time after tail injury and thrombosis. Notably, C3aR mice were less prone to experimental stroke and myocardial infarction. Furthermore, reconstitution of C3aR mice with C3aR platelets and platelet depletion experiments demonstrated that the observed effects on thrombosis, myocardial infarction, and stroke were specifically caused by platelet C3aR. Mechanistically, C3aR-mediated signaling regulates the activation of Rap1b and thereby bleeding arrest after injury and in vivo thrombus formation.
CONCLUSIONS:Overall, our findings uncover a novel function of the anaphylatoxin C3a for platelet function and thrombus formation, highlighting a detrimental role of imbalanced complement activation in cardiovascular diseases.
Inference of ancestry using genetic data is motivated by applications in genetic association studies, population genetics and personal genomics. Here, we provide methods and software for improved ...ancestry inference using genome-wide single nucleotide polymorphism (SNP) weights from external reference panels. This approach makes it possible to leverage the rich ancestry information that is available from large external reference panels, without the administrative and computational complexities of re-analyzing the raw genotype data from the reference panel in subsequent studies.
We extensively validate our approach in multiple African American, Latino American and European American datasets, making use of genome-wide SNP weights derived from large reference panels, including HapMap 3 populations and 6546 European Americans from the Framingham Heart Study. We show empirically that our approach provides much greater accuracy than either the prevailing ancestry-informative marker (AIM) approach or the analysis of genome-wide target genotypes without a reference panel. For example, in an independent set of 1636 European American genome-wide association study samples, we attained prediction accuracy (R(2)) of 1.000 and 0.994 for the first two principal components using our method, compared with 0.418 and 0.407 using 150 published AIMs or 0.955 and 0.003 by applying principal component analysis directly to the target samples. We finally show that the higher accuracy in inferring ancestry using our method leads to more effective correction for population stratification in association studies.
The SNPweights software is available online at http://www.hsph.harvard.edu/faculty/alkes-price/software/.
Supplementary data are available at Bioinformatics online.
Abstract
Background
Evidence linking breast size to breast cancer risk has been inconsistent, and its interpretation is often hampered by confounding factors such as body mass index (BMI). Here, we ...used linkage disequilibrium score regression and two-sample Mendelian randomization (MR) to examine the genetic associations between BMI, breast size and breast cancer risk.
Methods
Summary-level genotype data from 23andMe, Inc (breast size, n = 33 790), the Breast Cancer Association Consortium (breast cancer risk, n = 228 951) and the Genetic Investigation of ANthropometric Traits (BMI, n = 183 507) were used for our analyses. In assessing causal relationships, four complementary MR techniques inverse variance weighted (IVW), weighted median, weighted mode and MR-Egger regression were used to test the robustness of the results.
Results
The genetic correlation (rg) estimated between BMI and breast size was high (rg = 0.50, P = 3.89x10−43). All MR methods provided consistent evidence that higher genetically predicted BMI was associated with larger breast size odds ratio (ORIVW): 2.06 (1.80–2.35), P = 1.38x10−26 and lower overall breast cancer risk ORIVW: 0.81 (0.74–0.89), P = 9.44x10−6. No evidence of a relationship between genetically predicted breast size and breast cancer risk was found except when using the weighted median and weighted mode methods, and only with oestrogen receptor (ER)-negative risk. There was no evidence of reverse causality in any of the analyses conducted (P > 0.050).
Conclusion
Our findings indicate a potential positive causal association between BMI and breast size and a potential negative causal association between BMI and breast cancer risk. We found no clear evidence for a direct relationship between breast size and breast cancer risk.
Abstract
Background
Observational studies have suggested an association between circulating vitamin D concentrations 25(OH)D and risk of breast and prostate cancer, which was not supported by a ...recent Mendelian randomization (MR) analysis comprising 15 748 breast and 22 898 prostate-cancer cases. Demonstrating causality has proven challenging and one common limitation of MR studies is insufficient power.
Methods
We aimed to determine whether circulating concentrations of vitamin D are causally associated with the risk of breast and prostate cancer, by using summary-level data from the largest ever genome-wide association studies conducted on vitamin D (N = 73 699), breast cancer (Ncase = 122 977) and prostate cancer (Ncase = 79 148). We constructed a stronger instrument using six common genetic variants (compared with the previous four variants) and applied several two-sample MR methods.
Results
We found no evidence to support a causal association between 25(OH)D and risk of breast cancer OR per 25 nmol/L increase, 1.02 (95% confidence interval: 0.97–1.08), P = 0.47, oestrogen receptor (ER)+ 1.00 (0.94–1.07), P = 0.99 or ER− 1.02 (0.90–1.16), P = 0.75 subsets, prostate cancer 1.00 (0.93–1.07), P = 0.99 or the advanced subtype 1.02 (0.90–1.16), P = 0.72 using the inverse-variance-weighted method. Sensitivity analyses did not reveal any sign of directional pleiotropy.
Conclusions
Despite its almost five-fold augmented sample size and substantially improved statistical power, our MR analysis does not support a causal effect of circulating 25(OH)D concentrations on breast- or prostate-cancer risk. However, we can still not exclude a modest or non-linear effect of vitamin D. Future studies may be designed to understand the effect of vitamin D in subpopulations with a profound deficiency.
Both depression and breast cancer (BC) contribute to a substantial global burden of morbidity and mortality among women, and previous studies have observed a potential depression-BC link. We aimed to ...comprehensively characterize the phenotypic and genetic relationships between depression and BC.
We first evaluated phenotypic association using longitudinal follow-up data from the UK Biobank (N = 250,294). We then investigated genetic relationships leveraging summary statistics from the hitherto largest genome-wide association study of European individuals conducted for depression (N = 500,199), BC (N = 247,173), and its subtypes based on the status of estrogen receptor (ER + : N = 175,475; ER - : N = 127,442).
Observational analysis suggested an increased hazard of BC in depression patients (HR = 1.10, 95%CIs = 0.95-1.26). A positive genetic correlation between depression and overall BC was observed (Formula: see text = 0.08, P = 3.00 × 10
), consistent across ER + (Formula: see text = 0.06, P = 6.30 × 10
) and ER - subtypes (Formula: see text = 0.08, P = 7.20 × 10
). Several specific genomic regions showed evidence of local genetic correlation, including one locus at 9q31.2, and four loci at, or close, to 6p22.1. Cross-trait meta-analysis identified 17 pleiotropic loci shared between depression and BC. TWAS analysis revealed five shared genes. Bi-directional Mendelian randomization suggested risk of depression was causally associated with risk of overall BC (OR = 1.12, 95%Cis = 1.04-1.19), but risk of BC was not causally associated with risk of depression.
Our work demonstrates a shared genetic basis, pleiotropic loci, and a putative causal relationship between depression and BC, highlighting a biological link underlying the observed phenotypic relationship; these findings may provide important implications for future studies aimed reducing BC risk.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In many applications of instrumental variable (IV) methods, the treatments of interest are intrinsically time-varying and outcomes of interest are failure time outcomes. A common example is Mendelian ...randomization (MR), which uses genetic variants as proposed IVs. In this article, we present a novel application of g-estimation of structural nested cumulative failure models (SNCFTMs), which can accommodate multiple measures of a time-varying treatment when modelling a failure time outcome in an IV analysis.
A SNCFTM models the ratio of two conditional mean counterfactual outcomes at time k under two treatment strategies which differ only at an earlier time m. These models can be extended to accommodate inverse probability of censoring weights, and can be applied to case-control data. We also describe how the g-estimates of the SNCFTM parameters can be used to calculate marginal cumulative risks under nondynamic treatment strategies. We examine the performance of this method using simulated data, and present an application of these models by conducting an MR study of alcohol intake and endometrial cancer using longitudinal observational data from the Nurses' Health Study.
Our simulations found that estimates from SNCFTMs which used an IV approach were similar to those obtained from SNCFTMs which adjusted for confounders, and similar to those obtained from the g-formula approach when the outcome was rare. In our data application, the cumulative risk of endometrial cancer from age 45 to age 72 under the "never drink" strategy (4.0%) was similar to that under the "always ½ drink per day" strategy (4.3%).
SNCFTMs can be used to conduct MR and other IV analyses with time-varying treatments and failure time outcomes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background
Statins, 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors, are common lipid-lowering agents and may reduce the risk of several cancer types including pancreatic cancer. ...However, the association between statin use and pancreatic cancer risk has not been fully evaluated in prospective studies.
Methods
We studied the association between statin use and incident pancreatic cancer in 113,059 participants from the prospective Nurses’ Health Study and Health Professionals Follow-up Study. Statin use was self-reported via study questionnaires and updated biennially. Hazard ratios (HRs) and 95% confidence intervals (CIs) for incidence of pancreatic cancer were estimated using multivariable Cox proportional hazards models with adjustment for potential confounders.
Results
In total, 583 participants developed incident pancreatic cancer during 1.4 million person-years of follow-up. No difference was identified in pancreatic cancer risk for regular versus non-regular statin users (multivariable-adjusted HR 0.98; 95% CI 0.82–1.16). There was no significant heterogeneity in the association of statin use with pancreatic cancer risk between the cohorts. Similarly, longer duration of regular statin use was not associated with decreased risk of pancreatic cancer (
P
trend
= 0.65). The results remained similar when we examined statin use status at baseline or accounting for 4-year latency period. We observed no statistically significant effect modification for the association of statin use with pancreatic cancer risk by body mass index, smoking status, or diabetes mellitus status (all
P
interaction
> 0.21).
Conclusions
Regular statin use was not associated with pancreatic cancer risk in two large prospective cohort studies in the U.S.
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