Genome-wide association study and meta-analysis in multiple populations identifies new loci for peanut allergy and establishes C11orf30/EMSY as a genetic risk factor for food allergy.

BACKGROUND: Peanut allergy (PA) is a complex disease with both environmental and genetic risk factors. Previously, PA loci were identified in filaggrin (FLG) and HLA in candidate gene studies, and loci in HLA were identified in a genome-wide association study and meta-analysis. OBJECTIVE: We sought to investigate genetic susceptibility to PA. METHODS: Eight hundred fifty cases and 926 hyper-control subjects and more than 7.8 million genotyped and imputed single nucleotide polymorphisms (SNPs) were analyzed in a genome-wide association study to identify susceptibility variants for PA in the Canadian population. A meta-analysis of 2 phenotypes (PA and food allergy) was conducted by using 7 studies from the Canadian, American (n = 2), Australian, German, and Dutch (n = 2) populations. RESULTS: An SNP near integrin α6 (ITGA6) reached genome-wide significance with PA (P = 1.80 × 10-8), whereas SNPs associated with Src kinase-associated phosphoprotein 1 (SKAP1), matrix metallopeptidase 12 (MMP12)/MMP13, catenin α3 (CTNNA3), rho GTPase-activating protein 24 (ARHGAP24), angiopoietin 4 (ANGPT4), chromosome 11 open reading frame (C11orf30/EMSY), and exocyst complex component 4 (EXOC4) reached a threshold suggestive of association (P ≤ 1.49 × 10-6). In the meta-analysis of PA, loci in or near ITGA6, ANGPT4, MMP12/MMP13, C11orf30, and EXOC4 were significant (P ≤ 1.49 × 10-6). When a phenotype of any food allergy was used for meta-analysis, the C11orf30 locus reached genome-wide significance (P = 7.50 × 10-11), whereas SNPs associated with ITGA6, ANGPT4, MMP12/MMP13, and EXOC4 and additional C11orf30 SNPs were suggestive (P ≤ 1.49 × 10-6). Functional annotation indicated that SKAP1 regulates expression of CBX1, which colocalizes with the EMSY protein coded by C11orf30. CONCLUSION: This study identifies multiple novel loci as risk factors for PA and food allergy and establishes C11orf30 as a risk locus for both PA and food allergy. Multiple genes (C11orf30/EMSY, SKAP1, and CTNNA3) identified by this study are involved in epigenetic regulation of gene expression.

MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms.

BACKGROUND: Molecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. One solution is to combine independent studies into a single integrative analysis, additionally increasing sample size. However, the different protocols and technological platforms across transcriptomic studies produce unwanted systematic variation that strongly confounds the integrative analysis results. When studies aim to discriminate an outcome of interest, the common approach is a sequential two-step procedure; unwanted systematic variation removal techniques are applied prior to classification methods. RESULTS: To limit the risk of overfitting and over-optimistic results of a two-step procedure, we developed a novel multivariate integration method, MINT, that simultaneously accounts for unwanted systematic variation and identifies predictive gene signatures with greater reproducibility and accuracy. In two biological examples on the classification of three human cell types and four subtypes of breast cancer, we combined high-dimensional microarray and RNA-seq data sets and MINT identified highly reproducible and relevant gene signatures predictive of a given phenotype. MINT led to superior classification and prediction accuracy compared to the existing sequential two-step procedures. CONCLUSIONS: MINT is a powerful approach and the first of its kind to solve the integrative classification framework in a single step by combining multiple independent studies. MINT is computationally fast as part of the mixOmics R CRAN package, available at http://www.mixOmics.org/mixMINT/ and http://cran.r-project.org/web/packages/mixOmics/ .

[Effects of extended aromatase inhibitors in women with hormone-dependent breast cancer who have already received five years of adjuvant hormone therapy: A systematic review and meta-analysis]. / Analyse des effets des inhibiteurs de l'aromatase en traitement prolongé chez les femmes ménopausées atteintes d'un cancer du sein non métastatique hormonodépendant ayant déjà reçu cinq ans d'hormonothérapie adjuvante : revue systématique et méta-analyse.

INTRODUCTION: Evaluating the benefits and risks of prolonged hormonal treatment with aromatase inhibitors (AIs) for treating hormone-dependent breast cancer. METHODS: A systematic review and meta-analysis was conducted. Studies reporting on randomized clinical trials concerning prolongating hormonal therapy with AIs as compared to a placebo or no prolongation, after an initial five years of hormonal therapy, were eligible. RESULTS: Seven clinical trials were included. Prolonged AI therapy was associated with a statistically significant improvement in disease-free survival (RR=0.70, 95% CI 0.60 to 0.80). A statistically significant increase was observed for osteoporosis (RR=1.17, 95% CI 1.03 to 1.33), hot flushes/flashes (RR=1.27, 95% CI 1.08 to 1.49), myalgia (RR=1.23, 95% CI 1.09 to 1.39), fractures (RR=1.26, 95% CI 1.09 to 1.45) and arthralgia (RR=1.17, 95% CI 1.10 to 1.25). However, no statistically significant association was observed between prolonged AI therapy and overall survival, cardiovascular events, and bone pain. DISCUSSION: Prolonged AI therapy has significant benefits in terms of disease-free survival in women with hormone-dependent breast cancer. However, adverse effects and a lack of evidence for a benefit on overall survival must be considered in the decision-making process regarding adjuvant hormone therapy extension.

Polygenic inheritance and its interplay with smoking history in predicting lung cancer diagnosis: a French-Canadian case-control cohort.

BACKGROUND: The most near-term clinical application of genome-wide association studies in lung cancer is a polygenic risk score (PRS). METHODS: A case-control dataset was generated consisting of 4002 lung cancer cases from the LORD project and 20,010 ethnically matched controls from CARTaGENE. A genome-wide PRS including >1.1 million genetic variants was derived and validated in UK Biobank (n = 5419 lung cancer cases). The predictive ability and diagnostic discrimination performance of the PRS was tested in LORD/CARTaGENE and benchmarked against previous PRSs from the literature. Stratified analyses were performed by smoking status and genetic risk groups defined as low (<20th percentile), intermediate (20-80th percentile) and high (>80th percentile) PRS. FINDINGS: The phenotypic variance explained and the effect size of the genome-wide PRS numerically outperformed previous PRSs. Individuals with high genetic risk had a 2-fold odds of lung cancer compared to low genetic risk. The PRS was an independent predictor of lung cancer beyond conventional clinical risk factors, but its diagnostic discrimination performance was incremental in an integrated risk model. Smoking increased the odds of lung cancer by 7.7-fold in low genetic risk and by 11.3-fold in high genetic risk. Smoking with high genetic risk was associated with a 17-fold increase in the odds of lung cancer compared to individuals who never smoked and with low genetic risk. INTERPRETATION: Individuals at low genetic risk are not protected against the smoking-related risk of lung cancer. The joint multiplicative effect of PRS and smoking increases the odds of lung cancer by nearly 20-fold. FUNDING: This work was supported by the CQDM and the IUCPQ Foundation owing to a generous donation from Mr. Normand Lord.

Contribution of Lipoprotein(a) to Polygenic Risk Prediction of Coronary Artery Disease: A Prospective UK Biobank Analysis.

BACKGROUND: Lp(a) (lipoprotein[a]) is a highly atherogenic lipoprotein subfraction that may contribute to polygenic risk of coronary artery disease (CAD), but the extent of this contribution is unknown. Our objective was to estimate the contribution of Lp(a) to polygenic risk of CAD and to evaluate the respective contributions of Lp(a) and a CAD polygenic risk score (PRS) to CAD. METHODS: A total of 372 385 UK Biobank participants of European ancestry free of CAD at baseline were included. Plasma Lp(a) levels were measured and a CAD-PRS was calculated using the LDpred2 algorithm. Over the median follow-up of 12.6 years, 13 538 participants had incident CAD (myocardial infarction, coronary artery bypass grafting, or coronary angioplasty). RESULTS: The LPA region contribution to the CAD-PRS-mediated CAD risk was modest (7.2% [95% CI, 6.1-8.3]). Lp(a) levels significantly increased the predictive performance of a CAD-PRS including age and sex in Cox regression (C statistic 0.751 versus 0.746, difference, 0.005 [95% CI, 0.004-0.006]). Compared with participants in the bottom CAD-PRS quintile with Lp(a) levels <25 nmol/L (CAD event rate, 1.4%), the hazard ratio for incident CAD in participants in the top CAD-PRS quintile with Lp(a) levels ≥125 nmol/L was 5.45 (95% CI, 4.93-6.03; P=9.35×10-242, CAD event rate 6.6%). CONCLUSIONS: Compared with individuals with a low genetic risk of CAD (low CAD-PRS and low Lp[a] levels), those with a high genetic risk (high CAD-PRS and high Lp[a] levels) had a 5-fold higher CAD risk. These results highlight a substantial contribution of genetic risk factors to CAD and that accurate estimation of genetic risk of CAD may need to consider blood levels of Lp(a).

Polygenic Risk Score for Coronary Artery Disease Improves the Prediction of Early-Onset Myocardial Infarction and Mortality in Men.

BACKGROUND: Several risk factors for coronary artery disease (CAD) have been described, some of which are genetically determined. The use of a polygenic risk score (PRS) could improve CAD risk assessment, but predictive accuracy according to age and sex is not well established. METHODS: A PRSCAD including the weighted effects of >1.14 million single nucleotide polymorphisms associated with CAD was calculated in UK Biobank (n=408 422), using LDpred. Cox regressions were performed, stratified by age quartiles and sex, for incident myocardial infarction (MI) and mortality, with a median follow-up of 11.0 years. Improvement in risk prediction of MI was assessed by comparing PRSCAD to the pooled cohort equation with categorical net reclassification index using a 2% threshold (NRI0.02) and continuous NRI (NRI>0). RESULTS: From 7746 incident MI cases and 393 725 controls, hazard ratio for MI reached 1.53 (95% CI, 1.49-1.56; P=2.69×10-296) per SD increase of PRSCAD. PRSCAD was significantly associated with MI in both sexes, with a stronger association in men (interaction P=0.002), particularly in those aged between 40 and 51 years (hazard ratio, 2.00 [95% CI, 1.86-2.16], P=1.93×10-72). This group showed the highest reclassification improvement, mainly driven by the up-classification of cases (NRI0.02, 0.199 [95% CI, 0.157-0.248] and NRI>0, 0.602 [95% CI, 0.525-0.683]). From 23 982 deaths, hazard ratio for mortality was 1.08 (95% CI, 1.06-1.09; P=5.46×10-30) per SD increase of PRSCAD, with a stronger association in men (interaction P=1.60×10-6). CONCLUSIONS: Our PRSCAD predicts MI incidence and all-cause mortality, especially in men aged between 40 and 51 years. PRS could optimize the identification and management of individuals at risk for CAD.

Prioritization of candidate causal genes for asthma in susceptibility loci derived from UK Biobank.

To identify candidate causal genes of asthma, we performed a genome-wide association study (GWAS) in UK Biobank on a broad asthma definition (n = 56,167 asthma cases and 352,255 controls). We then carried out functional mapping through transcriptome-wide association studies (TWAS) and Mendelian randomization in lung (n = 1,038) and blood (n = 31,684) tissues. The GWAS reveals 72 asthma-associated loci from 116 independent significant variants (PGWAS < 5.0E-8). The most significant lung TWAS gene on 17q12-q21 is GSDMB (PTWAS = 1.42E-54). Other TWAS genes include TSLP on 5q22, RERE on 1p36, CLEC16A on 16p13, and IL4R on 16p12, which all replicated in GTEx lung (n = 515). We demonstrate that the largest fold enrichment of regulatory and functional annotations among asthma-associated variants is in the blood. We map 485 blood eQTL-regulated genes associated with asthma and 50 of them are causal by Mendelian randomization. Prioritization of druggable genes reveals known (IL4R, TSLP, IL6, TNFSF4) and potentially new therapeutic targets for asthma.

Comprehensive sensory and chemical data on the flavor of 16 red wines from two varieties: Sensory descriptive analysis, HS-SPME-GC-MS volatile compounds quantitative analysis, and odor-active compounds identification by HS-SPME-GC-MS-O.

This paper describes data collected on 2 sets of 8 French red wines from two grape varieties: Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Headspace Solid Phase Micro-Extraction - Gas Chromatography - Mass Spectrometry (HS-SPME-GC-MS) and (iii) odor-active compounds identification by Headspace Solid Phase Micro-Extraction - Gas Chromatography - Mass Spectrometry - Olfactometry (HS-SPME-GC-MS-O). The raw data are hosted on an open-access research data repository [1].

Assessing the structure of the CAST (Cannabis Abuse Screening Test) in 13 European countries using multigroup analyses.

Our aims are to describe and explain the structure of the Cannabis Abuse Screening Test (CAST) across countries. Standard statistical analyses fail to describe and explain several variables simultaneously while taking account of the group structure of individuals. The 2011 European School Survey Project on Alcohol and other Drugs (ESPAD): 5204 last-year cannabis users aged 15-16 from 13 European countries. Multigroup principal component analysis (mgPCA) and multigroup partial least squares (mgPLS). MgPCA shows that the CAST has a two-dimensional structure (frequency of use/problems and non-recreational use/dependency symptoms). All the countries present a good concordance with the common structure, except Kosovo, Lichtenstein and Romania. MgPLS shows that three explanative variables (in a total of eight) are mainly related with the CAST (the frequencies of cannabis use in the last 12 months and in the last 30 days and the age at first cannabis use) while Kosovo, Lichtenstein and Romania also present specificities. The CAST structure appears stable in the 13 countries except for Kosovo, Lichtenstein and Romania that also show specific relationships between the CAST variables and their determinants.