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IntroductionCirculating microRNAs are promising biomarkers for diagnosis, predication and prognostication of diseases. Lung cancer is the cancer disease accountable for most cancer deaths, largely due to being diagnosed at late stages. Therefore, diagnosing lung cancer patients at an early stage is crucial for improving the outcome. The purpose of this study was to identify circulating microRNAs for detection of early stage lung cancer, capable of discriminating lung cancer patients from those with chronic obstructive pulmonary disease (COPD) and healthy volunteers.ResultsWe identified 7 microRNAs separating lung cancer patients from controls. By using RT-qPCR, we validated 6 microRNAs (miR-429, miR-205, miR-200b, miR-203, miR-125b and miR-34b) with a significantly higher abundance in serum from NSCLC patients. Furthermore, the 6 miRNAs were validated in a different dataset, revealing an area under the receiver operating characteristic curve of 0.89 for stage I-IV and 0.88 for stage I/II.Materials and MethodsWe profiled the expression of 754 unique microRNAs by TaqMan Low Density Arrays, and analyzed serum from 38 patients with NSCLC, 16 patients suffering from COPD and 16 healthy volunteers from Norway, to explore their potential as diagnostic biomarkers. For validation, we analyzed serum collected from high-risk individuals enrolled in the Valencia branch of the International Early Lung Cancer Action Program screening trial (n=107) in addition to 51 lung cancer patients.ConclusionConsidering the accessibility and stability of circulating miRNAs, these 6 microRNAs are promising biomarkers as a supplement in future screening studies.

BackgroundThere is considerable evidence that many complex traits have a partially shared genetic basis, termed pleiotropy. It is therefore useful to consider integrating genome-wide association study (GWAS) data across several traits, usually at the summary statistic level. A major practical challenge arises when these GWAS have overlapping subjects. This is particularly an issue when estimating pleiotropy using methods that condition the significance of one trait on the signficance of a second, such as the covariate-modulated false discovery rate (cmfdr).ResultsWe propose a method for correcting for sample overlap at the summary statistic level. We quantify the expected amount of spurious correlation between the summary statistics from two GWAS due to sample overlap, and use this estimated correlation in a simple linear correction that adjusts the joint distribution of test statistics from the two GWAS. The correction is appropriate for GWAS with case-control or quantitative outcomes. Our simulations and data example show that without correcting for sample overlap, the cmfdr is not properly controlled, leading to an excessive number of false discoveries and an excessive false discovery proportion. Our correction for sample overlap is effective in that it restores proper control of the false discovery rate, at very little loss in power.ConclusionsWith our proposed correction, it is possible to integrate GWAS summary statistics with overlapping samples in a statistical framework that is dependent on the joint distribution of the two GWAS.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4859-7) contains supplementary material, which is available to authorized users.

Rationale Coronary Artery Disease (CAD) is a critical determinant of morbidity and mortality. Previous studies have identified several cardiovascular disease (CVD) risk factors, which may partly arise from a shared genetic basis with CAD, and thus be useful for discovery of CAD genes. Objective We aimed to improve discovery of CAD genes, and inform the etiologic relationship between CAD and several CVD risk factors using a shared polygenic signal-informed statistical framework. Methods and Results Using genome-wide association studies (GWAS) summary statistics and shared polygenic pleiotropy-informed conditional and conjunctional false discovery rate (FDR) methodology, we systematically investigated genetic overlap between CAD and 8 traits related to CVD risk factors: low density lipoprotein (LDL) cholesterol, high density lipoprotein (HDL) cholesterol, triglycerides (TG), type 2 diabetes (T2D), C-reactive protein (CRP), body mass index (BMI), systolic blood pressure (SBP) and type 1 diabetes (T1D). We found significant enrichment of single nucleotide polymorphisms (SNPs) associated with CAD as a function of their association with LDL, HDL, TG, T2D, CRP, BMI, SBP and T1D. Applying the conditional FDR method to the enriched phenotypes, we identified 67 novel loci associated with CAD (overall conditional FDR < 0.01). Further, we identified 53 loci with significant effects in both CAD and at least one of LDL, HDL, TG, T2D, CRP, SBP and T1D. Conclusions The observed polygenic overlap between CAD and cardio-metabolic risk factors indicates an etiological relation that warrants further investigation. The new gene loci identified implicate novel genetic mechanisms related to CAD.

Summary Background Loss‐of‐function mutations in the skin barrier gene filaggrin (FLG) increase the risk of atopic dermatitis (AD), but their role in skin barrier function, dry skin and eczema in infancy is unclear. Objectives To determine the role of FLG mutations in impaired skin barrier function, dry skin, eczema and AD at 3 months of age and throughout infancy. Methods FLG mutations were analysed in 1836 infants in the Scandinavian population‐based PreventADALL study. Transepidermal water loss (TEWL), dry skin, eczema and AD were assessed at 3, 6 and 12 months of age. Results FLG mutations were observed in 166 (9%) infants. At 3 months, carrying FLG mutations was not associated with impaired skin barrier function (TEWL > 11·3 g m−2 h−1) or dry skin, but was associated with eczema [odds ratio (OR) 2·89, 95% confidence interval (CI) 1·95–4·28; P < 0·001]. At 6 months, mutation carriers had significantly higher TEWL than nonmutation carriers [mean 9·68 (95% CI 8·69–10·68) vs. 8·24 (95% CI 7·97–8·15), P < 0·01], and at 3 and 6 months mutation carriers had an increased risk of dry skin on the trunk (OR 1·87, 95% CI 1·25–2·80; P = 0·002 and OR 2·44, 95% CI 1·51–3·95; P < 0·001) or extensor limb surfaces (OR 1·52, 95% CI 1·04–2·22; P = 0·028 and OR 1·74, 95% CI 1·17–2·57; P = 0·005). FLG mutations were associated with eczema and AD in infancy. Conclusions FLG mutations were not associated with impaired skin barrier function or dry skin in general at 3 months of age, but increased the risk for eczema, and for dry skin on the trunk and extensor limb surfaces at 3 and 6 months.

We identified four novel loci associated with neurocognitive function and one novel epistatic interaction. The findings should be replicated in independent samples, but indicate a role of PTPRO in learning and memory, WDR72 with executive functioning, and an interaction between FOXQ1 and SUMO1P1 for psychomotor speed.

Background: Prenatal maternal stress increases the risk of offspring developmental and psychological difficulties. The biological mechanisms behind these associations are mostly unknown. One explanation suggests that exposure of the fetus to maternal stress may influence DNA methylation. However, this hypothesis is largely based on animal studies, and human studies of candidate genes from single timepoints. Aim: The aim of this study was to investigate if prenatal maternal stress, in the form of maternal depressive symptoms, was associated with variation in genome-wide DNA methylation at two timepoints. Methods: One-hundred and eighty-four mother-child dyads were selected from a population of pregnant women in the Little-in-Norway study. The Edinburgh Postnatal Depression Scale (EPDS) measured maternal depressive symptoms. It was completed by the pregnant mothers between weeks 17 and 32 of gestation. DNA was obtained from infant saliva cells at two timepoints (age 6 weeks and 12 months). DNA methylation was measured in 274 samples from 6 weeks (n ¼ 146) and 12 months (n ¼ 128) using the Illumina Infinium HumanMethylation 450 BeadChip. Linear regression analyses of prenatal maternal depressive symptoms and infant methylation were performed at 6 weeks and 12 months separately, and for both timepoints together using a mixed model. Results: The analyses revealed no significant genome-wide association between maternal depressive symptoms and infant DNA methylation in the separate analyses and for both timepoints together. Conclusions: This sample of pregnant women and their infants living in Norway did not reveal associations between maternal depressive symptoms and infant DNA methylation.