Sub-multiplicative interaction between polygenic risk score and household coal use in relation to lung adenocarcinoma among never-smoking women in Asia.

We previously identified 10 lung adenocarcinoma susceptibility loci in a genome-wide association study (GWAS) conducted in the Female Lung Cancer Consortium in Asia (FLCCA), the largest genomic study of lung cancer among never-smoking women to date. Furthermore, household coal use for cooking and heating has been linked to lung cancer in Asia, especially in Xuanwei, China. We investigated the potential interaction between genetic susceptibility and coal use in FLCCA. We analyzed GWAS-data from Taiwan, Shanghai, and Shenyang (1472 cases; 1497 controls), as well as a separate study conducted in Xuanwei (152 cases; 522 controls) for additional analyses. We summarized genetic susceptibility using a polygenic risk score (PRS), which was the weighted sum of the risk-alleles from the 10 previously identified loci. We estimated associations between a PRS, coal use (ever/never), and lung adenocarcinoma with multivariable logistic regression models, and evaluated potential gene-environment interactions using likelihood ratio tests. There was a strong association between continuous PRS and lung adenocarcinoma among never coal users (Odds Ratio (OR) = 1.69 (95% Confidence Interval (CI) = 1.53, 1.87), p=1 × 10-26). This effect was attenuated among ever coal users (OR = 1.24 (95% CI: 1.03, 1.50), p = 0.02, p-interaction = 6 × 10-3). We observed similar attenuation among coal users from Xuanwei. Our study provides evidence that genetic susceptibility to lung adenocarcinoma among never-smoking Asian women is weaker among coal users. These results suggest that lung cancer pathogenesis may differ, at least partially, depending on exposure to coal combustion products. Notably, these novel findings are among the few instances of sub-multiplicative gene-environment interactions in the cancer literature.

Comparisons of clinical impacts on individuals with Brugada electrocardiographic patterns defined by ISHNE criteria or EHRA/HRS/APHRS criteria: a nationwide community-based study.

INTRODUCTION: Identifying Brugada electrocardiographic pattern (BrP) early is crucial to prevent sudden cardiac death. Two different diagnostic criteria proposed by International Society for Holter and Noninvasive Electrocardiography (ISHNE) and Heart Rhythm Society/European Heart Rhythm Association/Asia-Pacific Heart Rhythm Society (HRS/EHRA/APHRS) were widely used in clinical practice. The difference in prevalence and prognosis of BrP by applying the two different criteria was never studied before. METHODS: This study was prospectively conducted in a nationwide large-scale stratified random sampling community-based cohort (HALST) from Han Chinese population in Taiwan from December 2008 to December 2012. We compared the prevalence and prognosis of BrP defined by the two diagnostic criteria. RESULTS: A total of 5214 adults were enrolled (2530 men) with mean age of 69.3 years. Four had spontaneous type 1 BrP (0.077%). By the HRS/EHRA/APHRS criteria, 68 individuals have type 2 BrP (1.30%) and 101 have type 3 BrP (1.94%) whereas by the ISHNE criteria, 46 individuals exhibited type 2 BrP (0.88%). When applying the ISHNE criteria, the number of individuals with BrP decreased by 71%. However, all-cause mortality and cardiovascular mortality were not different between individuals with or without BrP, irrespective of the criteria used. CONCLUSIONS: The two different criteria may impact the diagnostic yield of individuals with BrP, but do not affect the prognosis of the individuals with BrP. Key messages Comparing with the use of HRS/EHRA/APHRS criteria, the number of individuals with Brugada ECG patterns was decreased by 71% when applying the ISHNE criteria. The prognosis of individuals with Brugada ECG patterns defined by 2012 ISHNE or 2013 HRS/EHRA/APHRS criteria were not different.

Interactions between household air pollution and GWAS-identified lung cancer susceptibility markers in the Female Lung Cancer Consortium in Asia (FLCCA).

We previously carried out a multi-stage genome-wide association study (GWAS) on lung cancer among never smokers in the Female Lung Cancer Consortium in Asia (FLCCA) (6,609 cases, 7,457 controls) that identified novel susceptibility loci at 10q25.2, 6q22.2, and 6p21.32, and confirmed two previously identified loci at 5p15.33 and 3q28. Household air pollution (HAP) attributed to solid fuel burning for heating and cooking, is the leading cause of the overall disease burden in Southeast Asia, and is known to contain lung carcinogens. To evaluate the gene-HAP interactions associated with lung cancer in loci independent of smoking, we analyzed data from studies participating in FLCCA with fuel use information available (n = 3; 1,731 cases; 1,349 controls). Coal use was associated with a 30% increased risk of lung cancer (OR 1.3, 95% CI 1.0-1.6). Among the five a priori SNPs identified by our GWAS, two showed a significant interaction with coal use (HLA Class II rs2395185, p = 0.02; TP63 rs4488809 (rs4600802), p = 0.04). The risk of lung cancer associated with coal exposure varied with the respective alleles for these two SNPs. Our observations provide evidence that genetic variation in HLA Class II and TP63 may modify the association between HAP and lung cancer risk. The roles played in the cell cycle and inflammation pathways by the proteins encoded by these two genes provide biological plausibility for these interactions; however, additional replication studies are needed in other non-smoking populations.

Prevalence and prognosis of Brugada electrocardiogram patterns in an elderly Han Chinese population: a nation-wide community-based study (HALST cohort).

AIMS: The exact world-wide prevalence of Brugada electrocardiogram (ECG) pattern is still unclear, especially in adults aged 55 years and older. METHODS AND RESULTS: The study was conducted as part of the Healthy Aging Longitudinal Study in Taiwan (HALST). Using a stratified random sampled method, a sample of community-dwelling subjects was recruited from seven community-based regions across Taiwan. All enrolled subjects were follow-up annually and cause of death was documented by citizen death records. A total of 5214 subjects were enrolled (male/female: 2530/2684) with a mean age of 69 ± 8 years. The overall prevalence of Brugada ECG patterns was 3.32%. Four subjects carried spontaneous Type 1 Brugada ECG pattern, 68 carried Type 2, and 101 carried Type 3. Compared with the world-wide average prevalence of Brugada ECG patterns, the prevalence of spontaneous Type 1 Brugada ECG pattern in subjects from the HALST cohort was similar (0.077 vs. 0.07%) and the combined prevalence of Types 2 and 3 Brugada ECG pattern was 10 times higher (3.24 vs. 0.28%) even the mean age of study subjects was significantly higher (69 ± 8 vs. 35 ± 8, P < 0.001). However, all-cause mortality and cardiac mortality rates were not significantly different between subjects with and without Brugada ECG patterns during the 4-year follow-up (log-rank test, P = 0.21, 0.32, respectively). CONCLUSION: The prevalence of Brugada ECG pattern in adults aged 55 years and older in Taiwan was higher than the average world-wide prevalence but was not associated with increased mortality.

Draft Genome Sequence of NDM-1-Producing Klebsiella pneumoniae Clinical Isolate 303K.

Multidrug-resistant New Delhi metallo-ß-lactamase 1 (NDM-1)-producing bacteria have spread globally and become a major clinical and public health threat. We report here the draft genome sequence of the Klebsiella pneumoniae clinical isolate 303K, harboring an NDM-1 coding sequence.

Genetic variant in TP63 on locus 3q28 is associated with risk of lung adenocarcinoma among never-smoking females in Asia.

A recent genome-wide association study (GWAS) of subjects from Japan and South Korea reported a novel association between the TP63 locus on chromosome 3q28 and risk of lung adenocarcinoma (p = 7.3 × 10(-12)); however, this association did not achieve genome-wide significance (p ≤ 10(-7)) among never-smoking males or females. To determine if this association with lung cancer risk is independent of tobacco use, we genotyped the TP63 SNPs reported by the previous GWAS (rs10937405 and rs4488809) in 3,467 never-smoking female lung cancer cases and 3,787 never-smoking female controls from 10 studies conducted in Taiwan, Mainland China, South Korea, and Singapore. Genetic variation in rs10937405 was associated with risk of lung adenocarcinoma [n = 2,529 cases; p = 7.1 × 10(-8); allelic risk = 0.80, 95% confidence interval (CI) = 0.74-0.87]. There was also evidence of association with squamous cell carcinoma of the lung (n = 302 cases; p = 0.037; allelic risk = 0.82, 95% CI = 0.67-0.99). Our findings provide strong evidence that genetic variation in TP63 is associated with the risk of lung adenocarcinoma among Asian females in the absence of tobacco smoking.

Selection of effective features for ECG beat recognition based on nonlinear correlations.

OBJECTIVE: The objective of this study is to develop feature selectors based on nonlinear correlations in order to select the most effective and least redundant features from an ECG beat classification system based on higher order statistics of subband components and a feed-forward back-propagation neural network, denoted as HOS-DWT-FFBNN. METHODS AND MATERIALS: Three correlation-based filters (NCBFs) are proposed. Two of them, NCBF1 and NCBF2, apply feature-feature correlation to remove redundant features prior to the feature selection process based on feature-class correlation. The other, SUFCO, skips the redundancy reduction process and selects features based only on feature-class correlation. The performance of these filters is compared to another commonly used nonlinear feature selection method, Relief-F. The discriminality and redundancy of the retained features are evaluated quantitatively. The performance of the most effective NCBF is compared with that of the linear correlation-based filter (LCBF) and other representative heartbeat classifiers in the literature. RESULTS: The results demonstrate that the two NCBFs based on both feature-feature and feature-class correlation methods, i.e. NCBF1 and NCBF2, outperform the other two methods, i.e. SUFCO and Relief-F. An accuracy of as high as 96.34% can be attained with as few as eight features. When tested with statistical methods, the retained features selected by the NCBF1/NCBF2 approach are demonstrated to be more discriminative and less redundant when compared with those features selected by other methods. When compared with LCBF and other heartbeat classifiers in the literature, the proposed NCBF1/NCBF2 approach in conjunction with the HOS-DWT-FFBNN structure outperform them with improved performance that allows discrimination of more beat types and fewer feature dimensions. CONCLUSION: This study demonstrates the effectiveness and superiority of the proposed approach for ECG beat recognition.

Noise-tolerant electrocardiogram beat classification based on higher order statistics of subband components.

OBJECTIVE: This paper presents a noise-tolerant electrocardiogram (ECG) beat classification method based on higher order statistics (HOS) of subband components. METHODS AND MATERIAL: Five levels of discrete wavelet transform (DWT) were applied to decompose the signal into six subband components. Higher order statistics proceeded to calculate four sets of HOS features from the three midband components, which together with three RR interval-related features constructed the primary feature set. A feature selection algorithm based on correlation coefficient and Fisher discriminality was then exploited to eliminate redundant features from the primary feature set. A feedforward backpropagation neural network (FFBNN) was employed as the classifier. Two sample selection strategies and four categories of noise artifacts were utilized to justify the capacity of the method. RESULTS: More than 97.5% discrimination rate was achieved, no matter which of the two sampling selection strategies was used. By using the feature selection method, the feature dimension can be readily reduced from 30 to 18 with negligible decrease in accuracy. Compared with other method in the literature, the proposed method improves the sensitivities of most beat types, resulting in an elevated average accuracy. The proposed method is tolerant to environmental noises; as high as 91% accuracies were retained even when contaminated with serious noises, 10 dB signal-to-noise ration (SNR), of different kinds. CONCLUSION: The results demonstrate the effectiveness and noise-tolerant capacities of the proposed method in ECG beat classification.

Subband features based on higher order statistics for ECG beat classification.

A novel feature extraction method, which uses subband features calculated from higher order statistics, is proposed for ECG beat classification. Five levels of discrete wavelet transformation (DWT) are applied to decompose the signal into six subband signals with different frequency distribution. Higher order statistics proceeds to calculate valuable features from the three midband signals. Three RR interval-related features are added to build a feature vector of 30 features. The feature extraction paradigm cooperates with the typical feedforward backpropagation neural network (FFBNN) to discriminate seven ECG beat types. Two signal selection profiles are also proposed for different experimental design. Between them, the 2nd profile which selected different beat types from each record was determined to be more appropriate and was used as a test bench for comparing with the other methods. The proposed method demonstrates a promising accuracy of 97.53%. Comparing to the method which used higher order statistics solely to the original signal, the proposed method elevates the sensitivities of most beat types. Especially in the three beat types: RBBB, VEB, and VFW, more than 4% elevations in sensitivity are observed. This study demonstrates the effectiveness of applying higher order statistics to subband signals for characterizing ECG beats for computer-aided diagnosis of heart diseases.

Comparison of different wavelet subband features in the classification of ECG beats using probabilistic neural network.

In this paper, an electrocardiogram (ECG) beat classification system based on wavelet transformation and probabilistic neural network (PNN) is proposed to discriminate six ECG beat types. The effects of two wavelet decomposition structures, the two-stage two-band and the two-stage full binary decomposition structures, in the recognition of ECG beat types are studied. The ECG beat signals are first decomposed into components in different subbands using discrete wavelet transformation. Three statistical features of each decomposed subband signals as well as the AC power and instantaneous RR interval of the original signal are exploited to characterize the ECG signals. A PNN then follows to classify the feature vectors. The result shows that features extracted from the decomposed signals based on the two-stage two-band structure outperform the two-stage full binary structure. A promising accuracy of 99.65%, with equally well recognition rates of over 99% throughout all type of ECG beats, has been achieved using the optimal feature set. Only 11 features are needed to attain this performance. The results demonstrate the effectiveness and efficiency of the proposed method for the computer-aided diagnosis of heart diseases based on ECG signals.