Gene-based association tests in family samples using GWAS summary statistics.

Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data. However, these association tests are restricted to unrelated individuals and thus do not apply to family samples directly. Moreover, due to its flexibility and effectiveness, the linear mixed model has been increasingly utilized in GWAS to handle correlated data, such as family samples. However, it remains unknown how to perform gene-based association tests in family samples using the GWAS summary statistics estimated from the linear mixed model. In this study, we show that, when family size is negligible compared to the total sample size, the diagonal block structure of the kinship matrix makes it possible to approximate the correlation matrix of marginal Z scores by linkage disequilibrium matrix. Based on this result, current methods utilizing summary statistics for unrelated individuals can be directly applied to family data without any modifications. Our simulation results demonstrate that this proposed strategy controls the type 1 error rate well in various situations. Finally, we exemplify the usefulness of the proposed approach with a dental caries GWAS data set.

Contaminant Organism Growth in Febrile Infants at Low Risk for Invasive Bacterial Infection.

In this multicenter, cross-sectional, secondary analysis of 4042 low-risk febrile infants, nearly 10% had a contaminated culture obtained during their evaluation (4.9% of blood cultures, 5.0% of urine cultures, and 1.8% of cerebrospinal fluid cultures). Our findings have important implications for improving sterile technique and reducing unnecessary cultures.

Racial and Ethnic Differences in Initiation of Menthol Tobacco Smoking and Subsequent Tobacco Use in the Population Assessment of Tobacco and Health Study, Waves 1-4 (2013-2018).

INTRODUCTION: Certain subpopulations in the United States are highly vulnerable to tobacco initiation and addiction, and elimination of disparities among those groups is crucial to reducing the burden of tobacco use. AIMS AND METHODS: This study evaluated the racial and ethnic differences in smoking initiation of menthol flavored cigarettes and cigars among never-users, and in subsequent tobacco use among new users of menthol-flavored products, using longitudinal data from waves 1-4 of the Population Assessment of Tobacco and Health Study. The outcomes of interest were new use of menthol-flavored products, and subsequent past 30-day and past 12-month cigarette and cigar smoking, irrespective of flavors, after initiation. RESULTS: The percentages of new users of menthol-flavored cigarettes and cigars at waves 2-4 were disproportionately higher in non-Hispanic black and Hispanic than in non-Hispanic white people. Adjusting for age and sex, black people who first used any menthol cigars had higher risk of past 30-day use of the same cigar category at the subsequent wave (adjusted risk ratio, aRR 1.48; 95% confidence interval [CI] 1.11 to 1.96) and past 12 months (aRR 1.74; 95% CI 1.55 to 2.63) compared to non-Hispanic white smokers. Black people who first used menthol-flavored cigarettes had marginally higher risk of subsequent past 30-day cigarette use (aRR 1.44; 95% CI 0.99 to 2.10) compared with their non-Hispanic white counterparts. CONCLUSIONS: This study shows that racial and ethnic differences exist in both initiation of menthol-flavored tobacco products and product-specific subsequent use after first using menthol-flavored products; black and Hispanic people have higher rates of initiation; black people also have higher rates of subsequent use. IMPLICATIONS: Use of menthol flavors in tobacco products is confirmed to be a contributor to large disparities in tobacco use; black and Hispanic people are more likely to maintain smoking through use of mentholated products than non-Hispanic white people. The findings suggest educational and regulatory actions on menthol-flavored tobacco products including restricting the selective marketing to vulnerable communities and banning characterizing flavors in cigarettes and cigars may reduce tobacco-related disparities and inform the Food And Drug Administration's evidence-based rulemaking process.

Effective Removal of Boron from Aqueous Solutions by Inorganic Adsorbents: A Review.

Increasing levels of boron in water exceeding acceptable thresholds have triggered concerns regarding environmental pollution and adverse health effects. In response, significant efforts are being made to develop new adsorbents for the removal of boron from contaminated water. Among the various materials proposed, inorganic adsorbents have emerged as promising materials due to their chemical, thermal, and mechanical stability. This review aims to comprehensively examine recent advances made in the development of inorganic adsorbents for the efficient removal of boron from water. Firstly, the adsorption performance of the most used adsorbents, such as magnesium, iron, aluminum, and individual and mixed oxides, are summarized. Subsequently, diverse functionalization methods aimed at enhancing boron adsorption capacity and selectivity are carefully analyzed. Lastly, challenges and future perspectives in this field are highlighted to guide the development of innovative high-performance adsorbents and adsorption systems, ultimately leading to a reduction in boron pollution.

Innovative approach to identify multigenomic and environmental interactions associated with birth defects in family-based hybrid designs.

Genes, including those with transgenerational effects, work in concert with behavioral, environmental, and social factors via complex biological networks to determine human health. Understanding complex relationships between causal factors underlying human health is an essential step towards deciphering biological mechanisms. We propose a new analytical framework to investigate the interactions between maternal and offspring genetic variants or their surrogate single nucleotide polymorphisms (SNPs) and environmental factors using family-based hybrid study design. The proposed approach can analyze diverse genetic and environmental factors and accommodate samples from a variety of family units, including case/control-parental triads, and case/control-parental dyads, while minimizing potential bias introduced by population admixture. Comprehensive simulations demonstrated that our innovative approach outperformed the log-linear approach, the best available method for case-control family data. The proposed approach had greater statistical power and was capable to unbiasedly estimate the maternal and child genetic effects and the effects of environmental factors, while controlling the Type I error rate against population stratification. Using our newly developed approach, we analyzed the associations between maternal and fetal SNPs and obstructive and conotruncal heart defects, with adjustment for demographic and lifestyle factors and dietary supplements. Fourteen and 11 fetal SNPs were associated with obstructive and conotruncal heart defects, respectively. Twenty-seven and 17 maternal SNPs were associated with obstructive and conotruncal heart defects, respectively. In addition, maternal body mass index was a significant risk factor for obstructive defects. The proposed approach is a powerful tool for interrogating the etiological mechanism underlying complex traits.

A genome-wide association study of obstructive heart defects among participants in the National Birth Defects Prevention Study.

Obstructive heart defects (OHDs) share common structural lesions in arteries and cardiac valves, accounting for ~25% of all congenital heart defects. OHDs are highly heritable, resulting from interplay among maternal exposures, genetic susceptibilities, and epigenetic phenomena. A genome-wide association study was conducted in National Birth Defects Prevention Study participants (Ndiscovery  = 3978; Nreplication  = 2507), investigating the genetic architecture of OHDs using transmission/disequilibrium tests (TDT) in complete case-parental trios (Ndiscovery_TDT  = 440; Nreplication_TDT  = 275) and case-control analyses separately in infants (Ndiscovery_CCI  = 1635; Nreplication_CCI  = 990) and mothers (case status defined by infant; Ndiscovery_CCM  = 1703; Nreplication_CCM  = 1078). In the TDT analysis, the SLC44A2 single nucleotide polymorphism (SNP) rs2360743 was significantly associated with OHD (pdiscovery  = 4.08 × 10-9 ; preplication  = 2.44 × 10-4 ). A CAPN11 SNP (rs55877192) was suggestively associated with OHD (pdiscovery  = 1.61 × 10-7 ; preplication  = 0.0016). Two other SNPs were suggestively associated (p < 1 × 10-6 ) with OHD in only the discovery sample. In the case-control analyses, no SNPs were genome-wide significant, and, even with relaxed thresholds ( × discovery < 1 × 10-5 and preplication < 0.05), only one SNP (rs188255766) in the infant analysis was associated with OHDs (pdiscovery  = 1.42 × 10-6 ; preplication  = 0.04). Additional SNPs with pdiscovery < 1 × 10-5 were in loci supporting previous findings but did not replicate. Overall, there was modest evidence of an association between rs2360743 and rs55877192 and OHD and some evidence validating previously published findings.

Decoupling the adsorption mechanisms of arsenate at molecular level on modified cube-shaped sponge loaded superparamagnetic iron oxide nanoparticles.

In this study, a commercial cube-shaped open-celled cellulose sponge adsorbent was modified by in-situ co-precipitation of superparamagnetic iron oxide nanoparticles (SPION) and used to remove As(V) from aqueous solutions. Fe K-edge X-ray absorption spectroscopy (XAS) and TEM identified maghemite as the main iron phase of the SPION nanoparticles with an average size 13 nm. Batch adsorption experiments at 800 mg/L showed a 63% increase of adsorption capacity when loading 2.6 wt.% mass fraction of SPION in the cube-sponge. Experimental determination of the adsorption thermodynamic parameters indicated that the As(V) adsorption on the composite material is a spontaneous and exothermic process. As K-edge XAS results confirmed that the adsorption enhancement on the composite can be attributed to the nanoparticles loaded. In addition, adsorbed As(V) did not get reduced to more toxic As(III) and formed a binuclear corner-sharing complex with SPION. The advantageous cube-shape of the sponge-loaded SPION composite together with its high affinity and good adsorption capacity for As(V), good regeneration capability and the enhanced-diffusion attributed to its open-celled structure make this adsorbent a good candidate for industrial applications.

Generalized multifactor dimensionality reduction approaches to identification of genetic interactions underlying ordinal traits.

The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as disease severity are measured on an ordinal scale with more than two categories. A proportional odds model can improve statistical power for these outcomes, when compared to a logit model either collapsing the categories into two mutually exclusive groups or limiting the analysis to pairs of categories. In this study, we propose a proportional odds model-based generalized multifactor dimensionality reduction (GMDR) method for detection of interactions underlying polytomous ordinal phenotypes. Computer simulations demonstrated that this new GMDR method has a higher power and more accurate predictive ability than the GMDR methods based on a logit model and a multinomial logit model. We applied this new method to the genetic analysis of low-density lipoprotein (LDL) cholesterol, a causal risk factor for coronary artery disease, in the Multi-Ethnic Study of Atherosclerosis, and identified a significant joint action of the CELSR2, SERPINA12, HPGD, and APOB genes. This finding provides new information to advance the limited knowledge about genetic regulation and gene interactions in metabolic pathways of LDL cholesterol. In conclusion, the proportional odds model-based GMDR is a useful tool that can boost statistical power and prediction accuracy in studying multifactor interactions underlying ordinal traits.

Effects of growth hormone therapy in pediatric patients with growth hormone deficiency and Chiari I malformation: a retrospective study.

PURPOSE: The safety and efficacy of growth hormone replacement therapy (GHRT) on pediatric patients with growth hormone deficiency (GHD) and Chiari I malformation (CIM) are not well investigated within the current body of literature. With no clear indication of the effects of GHRT on CIM disease progression, we sought to determine the effect of GHRT on tonsillar herniation and progression of CIM symptomatology. METHODS: From a previously established database of 465 patients with radiologically confirmed CIM defined as > 5 mm of tonsillar descent on head magnetic resonance imaging (MRI), we identified 20 patients who also had GHD. Using the imaging analysis software package, ANALYZE, the degree of change in tonsillar herniation was documented between initial and final MRI measurements. The radiologic and clinical changes over time were examined via a proportional odds model, Student's t test, Mann-Whitney test, or a mixed model corresponding to the outcomes measured either on an ordinal scale or on a quantitative scale. RESULTS: Incidence of GHD in our CIM population was 4.3%. There was no significant effect of GHRT on the degree of tonsillar herniation in patients with GHD and CIM. No patient became symptomatic, developed syringomyelia, or required surgical intervention for CIM. CONCLUSION: Based on our findings with a larger sample size, along with recent reports, the incidence of patients with CIM and GHD we reported (0.86-5%) is likely more indicative of the actual incidence of GHD and CIM than the prior findings within the literature (9.1-20%). We also suggest that GHRT does not significantly affect CIM morphology or symptomatology. Therefore, neurosurgeons should have no hesitation clearing these patients for GHRT.

The neutrophil chemoattractant peptide proline-glycine-proline is associated with acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS) is characterized by unrelenting polymorphonuclear neutrophil (PMN) inflammation and vascular permeability. The matrikine proline-glycine-proline (PGP) and acetylated PGP (Ac-PGP) have been shown to induce PMN inflammation and endothelial permeability in vitro and in vivo. In this study, we investigated the presence and role of airway PGP peptides in acute lung injury (ALI)/ARDS. Pseudomonas aeruginosa-derived lipopolysaccharide (LPS) was instilled intratracheally in mice to induce ALI, and increased Ac-PGP with neutrophil inflammation was noted. The PGP inhibitory peptide, arginine-threonine-arginine (RTR), was administered (it) 30 min before or 6 h after LPS injection. Lung injury was evaluated by detecting neutrophil infiltration and permeability changes in the lung. Pre- and posttreatment with RTR significantly inhibited LPS-induced ALI by attenuating lung neutrophil infiltration, pulmonary permeability, and parenchymal inflammation. To evaluate the role of PGP levels in ARDS, minibronchoalveolar lavage was collected from nine ARDS, four cardiogenic edema, and five nonlung disease ventilated patients. PGP levels were measured and correlated with Acute Physiology and Chronic Health Evaluation (APACHE) score, PaO2 to FIO2 (P/F), and ventilator days. PGP levels in subjects with ARDS were significantly higher than cardiogenic edema and nonlung disease ventilated patients. Preliminary examination in both ARDS and non-ARDS populations demonstrated PGP levels significantly correlated with P/F ratio, APACHE score, and duration on ventilator. These results demonstrate an increased burden of PGP peptides in ARDS and suggest the need for future studies in ARDS cohorts to examine correlation with key clinical parameters.

Doxycycline improves clinical outcomes during cystic fibrosis exacerbations.

Matrix metalloprotease-9 (MMP-9) plays a role in progression of cystic fibrosis, and doxycycline can reduce MMP-9 in vitro Here, we explore the effect of doxycycline during cystic fibrosis exacerbation treatment on MMP-9 related readouts and clinical end-points.This randomised, double-blind, placebo-controlled study enrolled hospitalised patients with cystic fibrosis undergoing exacerbation. In total, 20 participants were given doxycycline and 19 participants were given placebo over an 8-day period during hospitalisation. Biospecimens were collected at the beginning and the end of the study period. Primary end-points were total MMP-9 levels in the sputum and safety/tolerability. Secondary end-points included change in lung function, time to next exacerbation, and markers of MMP-9-related protease activity (active MMP-9 and TIMP-1). Nonparametric testing was used for within-group and between-group analyses.Doxycycline was well tolerated, with no treatment discontinuations or serious adverse events. Doxycycline reduced total sputum MMP-9 levels by 63.2% (p<0.05), and was also associated with a 56.5% reduction in active MMP-9 levels (p<0.05), a 1.6-fold increase in sputum TIMP-1 (p<0.05), improvement in forced expiratory volume in 1 s (p<0.05), and an increase in time to next exacerbation (p<0.01).Adjunctive use of doxycycline improved dysregulated MMP-9 levels in sputum, along with biomarkers consistent with a reduced proteolytic pulmonary environment. Improvement in clinical outcome measures suggests an important therapeutic benefit of doxycycline for individuals with cystic fibrosis.

GMDR: Versatile Software for Detecting Gene-Gene and Gene-Environ- ment Interactions Underlying Complex Traits.

Identification of multifactor gene-gene (G×G) and gene-environment (G×E) interactions underlying complex traits poses one of the great challenges to today's genetic study. Development of the generalized multifactor dimensionality reduction (GMDR) method provides a practicable solution to problems in detection of interactions. To exploit the opportunities brought by the availability of diverse data, it is in high demand to develop the corresponding GMDR software that can handle a breadth of phenotypes, such as continuous, count, dichotomous, polytomous nominal, ordinal, survival and multivariate, and various kinds of study designs, such as unrelated case-control, family-based and pooled unrelated and family samples, and also allows adjustment for covariates. We developed a versatile GMDR package to implement this serial of GMDR analyses for various scenarios (e.g., unified analysis of unrelated and family samples) and large-scale (e.g., genome-wide) data. This package includes other desirable features such as data management and preprocessing. Permutation testing strategies are also built in to evaluate the threshold or empirical p values. In addition, its performance is scalable to the computational resources. The software is available at http://www.soph.uab.edu/ssg/software or http://ibi.zju.edu.cn/software.

Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.

OBJECTIVE: The kernel machine (KM) test reportedly performs well in the set-based association test of rare variants. Many studies have been conducted to measure phenotypes at multiple time points, but the standard KM methodology has only been available for phenotypes at a single time point. In addition, family-based designs have been widely used in genetic association studies; therefore, the data analysis method used must appropriately handle familial relatedness. A rare-variant test does not currently exist for longitudinal data from family samples. Therefore, in this paper, we aim to introduce an association test for rare variants, which includes multiple longitudinal phenotype measurements for either population or family samples. METHODS: This approach uses KM regression based on the linear mixed model framework and is applicable to longitudinal data from either population (L-KM) or family samples (LF-KM). RESULTS: In our population-based simulation studies, L-KM has good control of Type I error rate and increased power in all the scenarios we considered compared with other competing methods. Conversely, in the family-based simulation studies, we found an inflated Type I error rate when L-KM was applied directly to the family samples, whereas LF-KM retained the desired Type I error rate and had the best power performance overall. Finally, we illustrate the utility of our proposed LF-KM approach by analyzing data from an association study between rare variants and blood pressure from the Genetic Analysis Workshop 18 (GAW18). CONCLUSION: We propose a method for rare-variant association testing in population and family samples using phenotypes measured at multiple time points for each subject. The proposed method has the best power performance compared to competing approaches in our simulation study.

A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model.

OBJECTIVE: The existing methods for identifying multiple rare variants underlying complex diseases in family samples are underpowered. Therefore, we aim to develop a new set-based method for an association study of dichotomous traits in family samples. METHODS: We introduce a framework for testing the association of genetic variants with diseases in family samples based on a generalized linear mixed model. Our proposed method is based on a kernel machine regression and can be viewed as an extension of the sequence kernel association test (SKAT and famSKAT) for application to family data with dichotomous traits (F-SKAT). RESULTS: Our simulation studies show that the original SKAT has inflated type I error rates when applied directly to family data. By contrast, our proposed F-SKAT has the correct type I error rate. Furthermore, in all of the considered scenarios, F-SKAT, which uses all family data, has higher power than both SKAT, which uses only unrelated individuals from the family data, and another method, which uses all family data. CONCLUSION: We propose a set-based association test that can be used to analyze family data with dichotomous phenotypes while handling genetic variants with the same or opposite directions of effects as well as any types of family relationships.

Kernel-machine testing coupled with a rank-truncation method for genetic pathway analysis.

Traditional genome-wide association studies (GWASs) usually focus on single-marker analysis, which only accesses marginal effects. Pathway analysis, on the other hand, considers biological pathway gene marker hierarchical structure and therefore provides additional insights into the genetic architecture underlining complex diseases. Recently, a number of methods for pathway analysis have been proposed to assess the significance of a biological pathway from a collection of single-nucleotide polymorphisms. In this study, we propose a novel approach for pathway analysis that assesses the effects of genes using the sequence kernel association test and the effects of pathways using an extended adaptive rank truncated product statistic. It has been increasingly recognized that complex diseases are caused by both common and rare variants. We propose a new weighting scheme for genetic variants across the whole allelic frequency spectrum to be analyzed together without any form of frequency cutoff for defining rare variants. The proposed approach is flexible. It is applicable to both binary and continuous traits, and incorporating covariates is easy. Furthermore, it can be readily applied to GWAS data, exome-sequencing data, and deep resequencing data. We evaluate the new approach on data simulated under comprehensive scenarios and show that it has the highest power in most of the scenarios while maintaining the correct type I error rate. We also apply our proposed methodology to data from a study of the association between bipolar disorder and candidate pathways from Wellcome Trust Case Control Consortium (WTCCC) to show its utility.

Transcriptome analysis of Brassica napus pod using RNA-Seq and identification of lipid-related candidate genes.

BACKGROUND: Brassica napus is an important oilseed crop. Dissection of the genetic architecture underlying oil-related biological processes will greatly facilitates the genetic improvement of rapeseed. The differential gene expression during pod development offers a snapshot on the genes responsible for oil accumulation in. To identify candidate genes in the linkage peaks reported previously, we used RNA sequencing (RNA-Seq) technology to analyze the pod transcriptomes of German cultivar Sollux and Chinese inbred line Gaoyou. METHODS: The RNA samples were collected for RNA-Seq at 5-7, 15-17 and 25-27 days after flowering (DAF). Bioinformatics analysis was performed to investigate differentially expressed genes (DEGs). Gene annotation analysis was integrated with QTL mapping and Brassica napus pod transcriptome profiling to detect potential candidate genes in oilseed. RESULTS: Four hundred sixty five and two thousand, one hundred fourteen candidate DEGs were identified, respectively, between two varieties at the same stages and across different periods of each variety. Then, 33 DEGs between Sollux and Gaoyou were identified as the candidate genes affecting seed oil content by combining those DEGs with the quantitative trait locus (QTL) mapping results, of which, one was found to be homologous to Arabidopsis thaliana lipid-related genes. DISCUSSION: Intervarietal DEGs of lipid pathways in QTL regions represent important candidate genes for oil-related traits. Integrated analysis of transcriptome profiling, QTL mapping and comparative genomics with other relative species leads to efficient identification of most plausible functional genes underlying oil-content related characters, offering valuable resources for bettering breeding program of Brassica napus. CONCLUSIONS: This study provided a comprehensive overview on the pod transcriptomes of two varieties with different oil-contents at the three developmental stages.

Multiple comparisons in genetic association studies: a hierarchical modeling approach.

Multiple comparisons or multiple testing has been viewed as a thorny issue in genetic association studies aiming to detect disease-associated genetic variants from a large number of genotyped variants. We alleviate the problem of multiple comparisons by proposing a hierarchical modeling approach that is fundamentally different from the existing methods. The proposed hierarchical models simultaneously fit as many variables as possible and shrink unimportant effects towards zero. Thus, the hierarchical models yield more efficient estimates of parameters than the traditional methods that analyze genetic variants separately, and also coherently address the multiple comparisons problem due to largely reducing the effective number of genetic effects and the number of statistically "significant" effects. We develop a method for computing the effective number of genetic effects in hierarchical generalized linear models, and propose a new adjustment for multiple comparisons, the hierarchical Bonferroni correction, based on the effective number of genetic effects. Our approach not only increases the power to detect disease-associated variants but also controls the Type I error. We illustrate and evaluate our method with real and simulated data sets from genetic association studies. The method has been implemented in our freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).

Haplotype kernel association test as a powerful method to identify chromosomal regions harboring uncommon causal variants.

For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome-wide association studies is minor. Although the so-called "rare variants" (minor allele frequency [MAF] < 1%) have attracted increasing attention, they are unlikely to account for much of the "missing heritability" because very few people may carry these rare variants. The genetic variants that are likely to fill in the "missing heritability" include uncommon causal variants (MAF < 5%), which are generally untyped in association studies using tagging single-nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome-wide or exome-wide next-generation sequencing. In this work, we propose a haplotype kernel association test (HKAT) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of HKAT to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, HKAT is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants.

A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence.

Gene-gene and gene-environment interactions govern a substantial portion of the variation in complex traits and diseases. In convention, a set of either unrelated or family samples are used in detection of such interactions; even when both kinds of data are available, the unrelated and the family samples are analyzed separately, potentially leading to loss in statistical power. In this report, to detect gene-gene interactions we propose a generalized multifactor dimensionality reduction method that unifies analyses of nuclear families and unrelated subjects within the same statistical framework. We used principal components as genetic background controls against population stratification, and when sibling data are included, within-family control were used to correct for potential spurious association at the tested loci. Through comprehensive simulations, we demonstrate that the proposed method can remarkably increase power by pooling unrelated and offspring's samples together as compared with individual analysis strategies and the Fisher's combining p value method while it retains a controlled type I error rate in the presence of population structure. In application to a real dataset, we detected one significant tetragenic interaction among CHRNA4, CHRNB2, BDNF, and NTRK2 associated with nicotine dependence in the Study of Addiction: Genetics and Environment sample, suggesting the biological role of these genes in nicotine dependence development.

Malachite green: a long-buried water-soluble AIEgen with near-infrared fluorescence for living cell nucleus staining.

Fluorescent imaging probes are crucial for exploring nucleus-related cellular events in live cells. Ideal probes should be photostable, small-sized, highly contrasted, and low in background. Here, we discovered that malachite green is a water-soluble near-infrared luminogen with aggregation-induced emission properties. Importantly, it can be used for living cell nucleus staining in a wash-free manner.