Alignment of retention time obtained from multicapillary column gas chromatography used for VOC analysis with ion mobility spectrometry

Multicapillary column (MCC) ion mobility spectrometers (IMS) are increasingly in demand for medical diagnosis, biological applications and process control. In a MCC-IMS, volatile compounds are differentiated by specific retention time and ion mobility when rapid preseparation techniques are applied, e.g. for the analysis of complex and humid samples. Therefore, high accuracy in the determination of both parameters is required for reliable identification of the signals. The retention time in the MCC is the subject of the present investigation because, for such columns, small deviations in temperature and flow velocity may cause significant changes in retention time. Therefore, a universal correction procedure would be a helpful tool to increase the accuracy of the data obtained from a gas-chromatographic preseparation. Although the effect of the carrier gas flow velocity and temperature on retention time is not linear, it could be demonstrated that a linear alignment can compensate for the changes in retention time due to common minor deviations of both the carrier gas flow velocity and the column temperature around the MCC-IMS standard operation conditions. Therefore, an effective linear alignment procedure for the correction of those deviations has been developed from the analyses of defined gas mixtures under various experimental conditions. This procedure was then applied to data sets generated from real breath analyses obtained in clinical studies using different instruments at different measuring sites for validation. The variation in the retention time of known signals, especially for compounds with higher retention times, was significantly improved. The alignment of the retention time—an indispensable procedure to achieve a more precise identification of analytes—using the proposed method reduces the random error caused by small accidental deviations in column temperature and flow velocity significantly

Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use

BACKGROUND: Smoking and alcohol use have been associated with common genetic variants in multiple loci. Rare variants within these loci hold promise in the identification of biological mechanisms in substance use. Exome arrays and genotype imputation can now efficiently genotype rare nonsynonymous and loss of function variants. Such variants are expected to have deleterious functional consequences and to contribute to disease risk. METHODS: We analyzed similar to 250,000 rare variants from 16 independent studies genotyped with exome arrays and augmented this dataset with imputed data from the UK Biobank. Associations were tested for five phenotypes: cigarettes per day, pack-years, smoking initiation, age of smoking initiation, and alcoholic drinks per week. We conducted stratified heritability analyses, single-variant tests, and gene-based burden tests of nonsynonymous/loss-of-function coding variants. We performed a novel fine-mapping analysis to winnow the number of putative causal variants within associated loci. RESULTS: Meta-analytic sample sizes ranged from 152,348 to 433,216, depending on the phenotype. Rare coding variation explained 1.1% to 2.2% of phenotypic variance, reflecting 11% to 18% of the total single nucleotide polymorphism heritability of these phenotypes. We identified 171 genome-wide associated loci across all phenotypes. Fine mapping identified putative causal variants with double base-pair resolution at 24 of these loci, and between three and 10 variants for 65 loci. Twenty loci contained rare coding variants in the 95% credible intervals. CONCLUSIONS: Rare coding variation significantly contributes to the heritability of smoking and alcohol use. Fine-mapping genome-wide association study loci identifies specific variants contributing to the biological etiology of substance use behavior.Peer reviewe

Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci

Smoking is a major heritable and modifiable risk factor for many diseases, including cancer, common respiratory disorders and cardiovascular diseases. Fourteen genetic loci have previously been associated with smoking behaviour-related traits. We tested up to 235,116 single nucleotide variants (SNVs) on the exome-array for association with smoking initiation, cigarettes per day, pack-years, and smoking cessation in a fixed effects meta-analysis of up to 61 studies (up to 346,813 participants). In a subset of 112,811 participants, a further one million SNVs were also genotyped and tested for association with the four smoking behaviour traits. SNV-trait associations withP <5 x 10(-8)in either analysis were taken forward for replication in up to 275,596 independent participants from UK Biobank. Lastly, a meta-analysis of the discovery and replication studies was performed. Sixteen SNVs were associated with at least one of the smoking behaviour traits (P <5 x 10(-8)) in the discovery samples. Ten novel SNVs, including rs12616219 nearTMEM182, were followed-up and five of them (rs462779 inREV3L, rs12780116 inCNNM2, rs1190736 inGPR101, rs11539157 inPJA1, and rs12616219 nearTMEM182) replicated at a Bonferroni significance threshold (P <4.5 x 10(-3)) with consistent direction of effect. A further 35 SNVs were associated with smoking behaviour traits in the discovery plus replication meta-analysis (up to 622,409 participants) including a rare SNV, rs150493199, inCCDC141and two low-frequency SNVs inCEP350andHDGFRP2. Functional follow-up implied that decreased expression ofREV3Lmay lower the probability of smoking initiation. The novel loci will facilitate understanding the genetic aetiology of smoking behaviour and may lead to the identification of potential drug targets for smoking prevention and/or cessation.Peer reviewe

Формирование эмоциональной культуры как компонента инновационной культуры студентов

Homozygosity has long been associated with rare, often devastating, Mendelian disorders1 and Darwin was one of the first to recognise that inbreeding reduces evolutionary fitness2. However, the effect of the more distant parental relatedness common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity, ROH), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power3,4. Here we use ROH to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts and find statistically significant associations between summed runs of homozygosity (SROH) and four complex traits: height, forced expiratory lung volume in 1 second (FEV1), general cognitive ability (g) and educational attainment (nominal p<1 × 10−300, 2.1 × 10−6, 2.5 × 10−10, 1.8 × 10−10). In each case increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing convincing evidence for the first time that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples5,6, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein (LDL) cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection7, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been

An epigenome-wide association study meta-analysis of educational attainment

The epigenome is associated with biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption and body mass index. Although there is a widespread perception that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here we provide evidence on the associations between epigenetic modifications-in our case, CpG methylation -and educational attainment (EA), a biologically distal environmental factor that is arguably among the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10 767 individuals. We find nine CpG probes significantly associated with EA. However, robustness analyses show that all nine probes have previously been found to be associated with smoking. Only two associations remain when we perform a sensitivity analysis in the subset of never-smokers, and these two probes are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, the effect sizes of the associations with EA are far smaller than the known associations with the biologically proximal environmental factors alcohol consumption, body mass index, smoking and maternal smoking during pregnancy. Follow-up analyses that combine the effects of many probes also point to small methylation associations with EA that are highly correlated with the combined effects of smoking. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome

Genetic variants linked to education predict longevity

Educational attainment is associated with many health outcomes, including longevity. It is also known to be substantially heritable. Here, we used data from three large genetic epidemiology cohort studies (Generation Scotland, n = ∼17,000; UK Biobank, n = ∼115,000; and the Estonian Biobank, n = ∼6,000) to test whether education-linked genetic variants can predict lifespan length. We did so by using cohort members’ polygenic profile score for education to predict their parents’ longevity. Across the three cohorts, meta-analysis showed that a 1 SD higher polygenic education score was associated with ∼2.7% lower mortality risk for both mothers (total ndeaths = 79,702) and ∼2.4% lower risk for fathers (total ndeaths = 97,630). On average, the parents of offspring in the upper third of the polygenic score distribution lived 0.55 y longer compared with those of offspring in the lower third. Overall, these results indicate that the genetic contributions to educational attainment are useful in the prediction of human longevity.</p