Cluster effect for SNP-SNP interaction pairs for predicting complex traits.

Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP-SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP-SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP-SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP-SNP interaction detection accuracy.

Lipoprotein(a) and Risks of Peripheral Artery Disease, Abdominal Aortic Aneurysm, and Major Adverse Limb Events.

BACKGROUND: Lp(a) (lipoprotein[a])-lowering therapy to reduce cardiovascular disease is under investigation in phase 3 clinical trials. High Lp(a) may be implicated in peripheral artery disease (PAD), abdominal aortic aneurysms (AAAs), and major adverse limb events (MALE). OBJECTIVES: The authors investigated the association of high Lp(a) levels and corresponding LPA genotypes with risk of PAD, AAA, and MALE. METHODS: The authors included 108,146 individuals from the Copenhagen General Population Study. During follow-up, 2,450 developed PAD, and 1,251 AAAs. Risk of MALE was assessed in individuals with PAD at baseline and replicated in the Copenhagen City Heart Study. RESULTS: Higher Lp(a) was associated with a stepwise increase in risk of PAD and AAA (P for trend <0.001). For individuals with Lp(a) levels ≥99th (≥143 mg/dL, ≥307 nmol/L) vs <50th percentile (≤9 mg/dL, ≤17 nmol/L), multivariable-adjusted HRs were 2.99 (95% CI: 2.09-4.30) for PAD and 2.22 (95% CI: 1.21-4.07) for AAA. For individuals with PAD, the corresponding incidence rate ratio for MALE was 3.04 (95% CI: 1.55-5.98). Per 50 mg/dL (105 nmol/L) genetically higher Lp(a) risk ratios were 1.39 (95% CI: 1.24-1.56) for PAD and 1.21 (95% CI: 1.01-1.44) for AAA, consistent with observational risk ratios of 1.33 (95% CI: 1.24-1.43) and 1.27 (95% CI: 1.15-1.41), respectively. In women smokers aged 70 to 79 years with Lp(a) <50th and ≥99th percentile, absolute 10-year risks of PAD were 8% and 21%, and equivalent risks in men 11% and 29%, respectively. For AAA, corresponding risks were 2% and 4% in women, and 5% and 12% in men. CONCLUSIONS: High Lp(a) levels increased risk of PAD, AAA, and MALE by 2- to 3-fold in the general population, opening opportunities for prevention given future Lp(a)-lowering therapies.

From plasma triglycerides to triglyceride metabolism: effects on mortality in the Copenhagen General Population Study.

AIMS: It is unclear whether higher triglyceride metabolism per se contributes to mortality separate from elevated triglyceride-rich lipoproteins and body mass index. This study tested the hypotheses that higher triglyceride metabolism, measured as higher plasma glycerol and ß-hydroxybutyrate, is associated with increased all-cause, cardiovascular, cancer, and other mortality. METHODS AND RESULTS: This study included 30 000 individuals nested within 109 751 individuals from the Copenhagen General Population Study. During a median follow-up of 10.7 years, 9897 individuals died (2204 from cardiovascular, 3366 from cancer, and 2745 from other causes), while none were lost to follow-up. In individuals with glycerol >80 µmol/L (highest fourth) vs. individuals with glycerol <52 µmol/L (lowest fourth), the multivariable adjusted hazard ratio for all-cause mortality was 1.31 (95% confidence interval 1.22-1.40). In individuals with ß-hydroxybutyrate >154 µmol/L (highest fourth) vs. individuals with ß-hydroxybutyrate <91 µmol/L (lowest fourth), the multivariable adjusted hazard ratio for all-cause mortality was 1.18 (1.11-1.26). Corresponding values for higher plasma glycerol and ß-hydroxybutyrate were 1.37 (1.18-1.59) and 1.18 (1.03-1.35) for cardiovascular mortality, 1.24 (1.11-1.39) and 1.16 (1.05-1.29) for cancer mortality, and 1.45 (1.28-1.66) and 1.23 (1.09-1.39) for other mortality, respectively. Results were robust to exclusion of first years of follow-up, to stratification for covariates including plasma triglycerides and body mass index, and to further adjustments. CONCLUSION: This study observed an increased risk of all-cause, cardiovascular, cancer, and other mortality with higher triglyceride metabolism. This was not explained by higher plasma triglycerides and body mass index. The hypothesis studied in the present paper should be further validated by isotope flux studies.

Evaluating approaches for constructing polygenic risk scores for prostate cancer in men of African and European ancestry.

Genome-wide polygenic risk scores (GW-PRSs) have been reported to have better predictive ability than PRSs based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer-risk variants from multi-ancestry GWASs and fine-mapping studies (PRS269). GW-PRS models were trained with a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls that we previously used to develop the multi-ancestry PRS269. Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI = 0.635-0.677) in African and 0.844 (95% CI = 0.840-0.848) in European ancestry men and corresponding prostate cancer ORs of 1.83 (95% CI = 1.67-2.00) and 2.19 (95% CI = 2.14-2.25), respectively, for each SD unit increase in the GW-PRS. Compared to the GW-PRS, in African and European ancestry men, the PRS269 had larger or similar AUCs (AUC = 0.679, 95% CI = 0.659-0.700 and AUC = 0.845, 95% CI = 0.841-0.849, respectively) and comparable prostate cancer ORs (OR = 2.05, 95% CI = 1.87-2.26 and OR = 2.21, 95% CI = 2.16-2.26, respectively). Findings were similar in the validation studies. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the PRS269 developed from multi-ancestry GWASs and fine-mapping.

Evaluating Approaches for Constructing Polygenic Risk Scores for Prostate Cancer in Men of African and European Ancestry.

Genome-wide polygenic risk scores (GW-PRS) have been reported to have better predictive ability than PRS based on genome-wide significance thresholds across numerous traits. We compared the predictive ability of several GW-PRS approaches to a recently developed PRS of 269 established prostate cancer risk variants from multi-ancestry GWAS and fine-mapping studies (PRS 269 ). GW-PRS models were trained using a large and diverse prostate cancer GWAS of 107,247 cases and 127,006 controls used to develop the multi-ancestry PRS 269 . Resulting models were independently tested in 1,586 cases and 1,047 controls of African ancestry from the California/Uganda Study and 8,046 cases and 191,825 controls of European ancestry from the UK Biobank and further validated in 13,643 cases and 210,214 controls of European ancestry and 6,353 cases and 53,362 controls of African ancestry from the Million Veteran Program. In the testing data, the best performing GW-PRS approach had AUCs of 0.656 (95% CI=0.635-0.677) in African and 0.844 (95% CI=0.840-0.848) in European ancestry men and corresponding prostate cancer OR of 1.83 (95% CI=1.67-2.00) and 2.19 (95% CI=2.14-2.25), respectively, for each SD unit increase in the GW-PRS. However, compared to the GW-PRS, in African and European ancestry men, the PRS 269 had larger or similar AUCs (AUC=0.679, 95% CI=0.659-0.700 and AUC=0.845, 95% CI=0.841-0.849, respectively) and comparable prostate cancer OR (OR=2.05, 95% CI=1.87-2.26 and OR=2.21, 95% CI=2.16-2.26, respectively). Findings were similar in the validation data. This investigation suggests that current GW-PRS approaches may not improve the ability to predict prostate cancer risk compared to the multi-ancestry PRS 269 constructed with fine-mapping.

Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score.

BACKGROUND: Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets. METHODS: In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured. RESULTS: The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively. CONCLUSIONS: We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

KLK3 SNP-SNP interactions for prediction of prostate cancer aggressiveness.

Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP-SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P < 3.5 × 10-9) and 3145 (P < 1 × 10-5) SNP-SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene-gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP-SNP interactions were supported by gene expression and protein-protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.

Per-Particle Triglyceride-Rich Lipoproteins Imply Higher Myocardial Infarction Risk Than Low-Density Lipoproteins: Copenhagen General Population Study.

[Figure: see text].

Polygenic hazard score is associated with prostate cancer in multi-ethnic populations.

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10-180). Comparing the 80th/20th PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset.

Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer.

BACKGROUND: Polygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46). MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy. RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer. CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

Very Low-Density Lipoprotein Cholesterol May Mediate a Substantial Component of the Effect of Obesity on Myocardial Infarction Risk: The Copenhagen General Population Study.

BACKGROUND: Individuals with obesity have higher concentrations of very low-density lipoprotein (VLDL) cholesterol and increased risk of myocardial infarction. We hypothesized that VLDL cholesterol explains a fraction of the excess myocardial infarction risk in individuals with obesity. METHODS: We included 29 010 individuals free of myocardial infarction at baseline, nested within 109 751 individuals from the Copenhagen General Population Study. During 10 years of follow-up, 2306 individuals developed myocardial infarction. Cholesterol content in large and small VLDLs, in intermediate-density lipoprotein (IDL), and in LDL was measured directly with nuclear magnetic resonance spectroscopy. RESULTS: Median concentrations of cholesterol in large and small VLDLs were 0.12 mmol/L (interquartile range [IQR], 0.07-0.20 mmol/L; 4.5 mg/dL [IQR, 2.6-6.9 mg/dL]) and 0.6 mmol/L (IQR, 0.5-0.8 mmol/L; 25 mg/dL [IQR, 20-30 mg/dL]) in individuals with obesity vs 0.06 mmol/L (IQR, 0.03-0.1 mmol/L; 2.2 mg/dL [IQR, 1.1-3.8 mg/dL]), and 0.5 mmol/L (IQR, 0.4-0.6 mmol/L; 20 mg/dL (IQR, 16-25 mg/dL]) in individuals with normal weight; in contrast, concentrations of IDL and LDL cholesterol were similar across body mass index (BMI) categories. Cholesterol in large and small VLDLs combined explained 40% (95% CI, 27%-53%) of the excess risk of myocardial infarction associated with higher BMI. In contrast, IDL and LDL cholesterol did not explain excess risk of myocardial infarction, whereas systolic blood pressure explained 17% (11%-23%) and diabetes mellitus explained 8.6% (3.2%-14%). CONCLUSIONS: VLDL cholesterol explains a large fraction of excess myocardial infarction risk in individuals with obesity. These novel findings support a focus on cholesterol in VLDL for prevention of myocardial infarction and atherosclerotic cardiovascular disease in individuals with obesity.

Complement C3 and allergic asthma: a cohort study of the general population.

Complement C3 plays a role in asthma development and severity. We tested the hypothesis that high plasma complement C3 concentration is associated with high risks of asthma hospitalisation and exacerbation.We prospectively assessed the risk of asthma hospitalisation in 101 029 individuals from the Copenhagen General Population Study with baseline measurements of plasma complement C3, and genotyped for rs1065489, rs429608 and rs448260 determining levels of complement C3. Risk of asthma exacerbation was further assessed in 2248 individuals with allergic asthma.The multivariable adjusted hazard ratio of asthma hospitalisation was 1.23 (95% CI 1.04-1.45) for individuals in the highest tertile (>1.19 g·L-1) of plasma complement C3 compared with those in the lowest tertile (<1.03 g·L-1). The C3 rs448260 genotype was associated with risk of asthma hospitalisation with an observed hazard ratio of 1.17 (95% CI 1.06-1.28) for the CC genotype compared with the AA genotype. High plasma complement C3 was associated with high levels of blood eosinophils and IgE (p for trends ≤6×10-9), but only the SKIV2L rs429608 genotype was positively associated with blood eosinophil count (p=3×10-4) and IgE level (p=3×10-4). In allergic asthma, the multivariable adjusted incidence rate ratio for risk of exacerbation was 1.69 (95% CI 1.06-2.72) for individuals in the highest plasma complement C3 tertile (>1.24 g·L-1) versus the lowest (<1.06 g·L-1).In conclusion, a high concentration of plasma complement C3 was associated with a high risk of asthma hospitalisation in the general population and with a high risk of asthma exacerbation in individuals with allergic asthma. Our findings support a causal role of the complement system in asthma severity.

The CHEK2 Variant C.349A>G Is Associated with Prostate Cancer Risk and Carriers Share a Common Ancestor.

The identification of recurrent founder variants in cancer predisposing genes may have important implications for implementing cost-effective targeted genetic screening strategies. In this study, we evaluated the prevalence and relative risk of the CHEK2 recurrent variant c.349A>G in a series of 462 Portuguese patients with early-onset and/or familial/hereditary prostate cancer (PrCa), as well as in the large multicentre PRACTICAL case-control study comprising 55,162 prostate cancer cases and 36,147 controls. Additionally, we investigated the potential shared ancestry of the carriers by performing identity-by-descent, haplotype and age estimation analyses using high-density SNP data from 70 variant carriers belonging to 11 different populations included in the PRACTICAL consortium. The CHEK2 missense variant c.349A>G was found significantly associated with an increased risk for PrCa (OR 1.9; 95% CI: 1.1-3.2). A shared haplotype flanking the variant in all carriers was identified, strongly suggesting a common founder of European origin. Additionally, using two independent statistical algorithms, implemented by DMLE+2.3 and ESTIAGE, we were able to estimate the age of the variant between 2300 and 3125 years. By extending the haplotype analysis to 14 additional carrier families, a shared core haplotype was revealed among all carriers matching the conserved region previously identified in the high-density SNP analysis. These findings are consistent with CHEK2 c.349A>G being a founder variant associated with increased PrCa risk, suggesting its potential usefulness for cost-effective targeted genetic screening in PrCa families.

Association of LPA Variants With Risk of Coronary Disease and the Implications for Lipoprotein(a)-Lowering Therapies: A Mendelian Randomization Analysis.

Importance: Human genetic studies have indicated that plasma lipoprotein(a) (Lp[a]) is causally associated with the risk of coronary heart disease (CHD), but randomized trials of several therapies that reduce Lp(a) levels by 25% to 35% have not provided any evidence that lowering Lp(a) level reduces CHD risk. Objective: To estimate the magnitude of the change in plasma Lp(a) levels needed to have the same evidence of an association with CHD risk as a 38.67-mg/dL (ie, 1-mmol/L) change in low-density lipoprotein cholesterol (LDL-C) level, a change that has been shown to produce a clinically meaningful reduction in the risk of CHD. Design, Setting, and Participants: A mendelian randomization analysis was conducted using individual participant data from 5 studies and with external validation using summarized data from 48 studies. Population-based prospective cohort and case-control studies featured 20 793 individuals with CHD and 27 540 controls with individual participant data, whereas summarized data included 62 240 patients with CHD and 127 299 controls. Data were analyzed from November 2016 to March 2018. Exposures: Genetic LPA score and plasma Lp(a) mass concentration. Main Outcomes and Measures: Coronary heart disease. Results: Of the included study participants, 53% were men, all were of white European ancestry, and the mean age was 57.5 years. The association of genetically predicted Lp(a) with CHD risk was linearly proportional to the absolute change in Lp(a) concentration. A 10-mg/dL lower genetically predicted Lp(a) concentration was associated with a 5.8% lower CHD risk (odds ratio [OR], 0.942; 95% CI, 0.933-0.951; P = 3 × 10-37), whereas a 10-mg/dL lower genetically predicted LDL-C level estimated using an LDL-C genetic score was associated with a 14.5% lower CHD risk (OR, 0.855; 95% CI, 0.818-0.893; P = 2 × 10-12). Thus, a 101.5-mg/dL change (95% CI, 71.0-137.0) in Lp(a) concentration had the same association with CHD risk as a 38.67-mg/dL change in LDL-C level. The association of genetically predicted Lp(a) concentration with CHD risk appeared to be independent of changes in LDL-C level owing to genetic variants that mimic the relationship of statins, PCSK9 inhibitors, and ezetimibe with CHD risk. Conclusions and Relevance: The clinical benefit of lowering Lp(a) is likely to be proportional to the absolute reduction in Lp(a) concentration. Large absolute reductions in Lp(a) of approximately 100 mg/dL may be required to produce a clinically meaningful reduction in the risk of CHD similar in magnitude to what can be achieved by lowering LDL-C level by 38.67 mg/dL (ie, 1 mmol/L).

Blood eosinophil count and risk of pneumonia hospitalisations in individuals with COPD.

Blood eosinophil count in chronic obstructive pulmonary disease (COPD) is associated with higher exacerbation rate and favourable response to corticosteroids; however, frequent exacerbations and use of inhaled corticosteroids could elevate pneumonia risk. We tested the hypothesis that high blood eosinophil counts are associated with high risk of pneumonia in individuals with severe COPD from the general population.We included 7180 individuals with COPD from the Copenhagen General Population Study, including 643 with forced expiratory volume in 1 s (FEV1) <50% predicted between 2003 and 2011. All primary discharge diagnoses of pneumonia during follow-up were recorded.Among individuals with COPD and FEV1 <50% pred, the multivariable adjusted incidence rate ratio was 2.17 (95% CI 1.31-3.58) for pneumonia comparing individuals with blood eosinophil counts ≥0.34×109 cells·L-1versus <0.34×109 cells·L-1 In individuals with clinical COPD, defined by recent exacerbation, ≥10 pack-years of smoking and FEV1 <70% pred, the corresponding risk was 4.52 (2.11-9.72). Risk of pneumonia did not differ by blood eosinophil count in individuals with COPD and FEV1 ≥50% pred.In individuals with COPD and FEV1 <50% pred, blood eosinophil count ≥0.34×109 cells·L-1 was associated with high risk of hospitalisation due to pneumonia.

Publisher Correction: Evidence that breast cancer risk at the 2q35 locus is mediated through IGFBP5 regulation.

This corrects the article DOI: 10.1038/ncomms5999.

Polygenic hazard score to guide screening for aggressive prostate cancer: development and validation in large scale cohorts.

OBJECTIVES: To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age. DESIGN: Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa. SETTING: Multiple institutions that were members of international PRACTICAL consortium. PARTICIPANTS: All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men. MAIN OUTCOME MEASURES: Prediction with hazard score of age of onset of aggressive cancer in validation set. RESULTS: In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P<10-16). When men in the validation set with high scores (>98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score. CONCLUSIONS: Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

Plasma urate, lung function and chronic obstructive pulmonary disease: a Mendelian randomisation study in 114 979 individuals from the general population.

BACKGROUND: Urate is a strong antioxidant in plasma and may protect against lung function impairment. We tested the hypothesis that high plasma urate is causally associated with better lung function and low risk of respiratory symptoms and COPD. METHODS: We measured lung function and plasma urate in 114 979 individuals from the Copenhagen City Heart Study and the Copenhagen General Population Study and genotyped for SLC2A9 rs7442295 and ABCG2 rs2231142 variants, previously associated with high plasma urate, in 110 152 individuals. RESULTS: In the two studies combined, multivariable-adjusted 100 µmol/L higher plasma urate was associated with -1.54% (95% CI -1.67 to -1.40) lower FEV1 % predicted and -1.57% (95% CI -1.69 to -1.44) lower FVC % predicted observationally; the corresponding estimates for genetically determined 100 µmol/L higher plasma urate were -0.46% (95% CI -1.17 to 0.25) and -0.40% (95% CI -1.03 to 0.23). High plasma urate was also associated with higher risk of respiratory symptoms; however, genetically determined high plasma urate was not associated with respiratory symptoms. Finally, we identified 14 151 individuals with COPD and found ORs of 1.08 (95% CI 1.06 to 1.11) for COPD observationally and 1.01 (95% CI 0.88 to 1.15) genetically per 100 µmol/L higher plasma urate. CONCLUSION: High plasma urate was associated with worse lung function and higher risk of respiratory symptoms and COPD in observational analyses; however, genetically high plasma urate was not associated with any of these outcomes. Thus, our data do not support a direct causal relationship.