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Genome-wide association studies have been able to identify disease associations with many common variants; however most of the estimated genetic contribution explained by these variants appears to be very modest. Rare variants are thought to have larger effect sizes compared to common SNPs but effects of rare variants cannot be tested in the GWAS setting. Here we propose a novel method to test for association of rare variants obtained by sequencing in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency SNPs that may be enriched in functional variants can be upweighted compared to common variants. Using simulations we show that the family-based methods perform at par with the population-based methods under no population stratification. By construction, family-based tests are completely robust to population stratification; we show that our proposed methods remain valid even when population stratification is present.

Although obstructive sleep apnea (OSA) is known to have a strong familial basis, no genetic polymorphisms influencing apnea risk have been identified in cross-cohort analyses. We utilized the National Heart, Lung, and Blood Institute (NHLBI) Candidate Gene Association Resource (CARe) to identify sleep apnea susceptibility loci. Using a panel of 46,449 polymorphisms from roughly 2,100 candidate genes on a customized Illumina iSelect chip, we tested for association with the apnea hypopnea index (AHI) as well as moderate to severe OSA (AHI≥15) in 3,551 participants of the Cleveland Family Study and two cohorts participating in the Sleep Heart Health Study.Among 647 African-Americans, rs11126184 in the pleckstrin (PLEK) gene was associated with OSA while rs7030789 in the lysophosphatidic acid receptor 1 (LPAR1) gene was associated with AHI using a chip-wide significance threshold of p-value<2×10(-6). Among 2,904 individuals of European ancestry, rs1409986 in the prostaglandin E2 receptor (PTGER3) gene was significantly associated with OSA. Consistency of effects between rs7030789 and rs1409986 in LPAR1 and PTGER3 and apnea phenotypes were observed in independent clinic-based cohorts.Novel genetic loci for apnea phenotypes were identified through the use of customized gene chips and meta-analyses of cohort data with replication in clinic-based samples. The identified SNPs all lie in genes associated with inflammation suggesting inflammation may play a role in OSA pathogenesis.

Purpose Preventive health-care behavior (PHB) adoption as a primordial prevention to stay healthy and avoid lifestyle disease risk is a global trend. This paper aims to use the PHB model and stimulus-organism-response theory to empirically examine the role of individual and technological factors in influencing primordial PHB. Design/methodology/approach A sequential mixed-method was adopted to identify the primordial PHB adoption and propose a conceptual framework. The identified determinants and the hypothesized relationships were empirically tested using a convenience sample of 406 urban Indians. Partial least square structural equation modeling is used for data analysis. Findings The derived conceptual framework was empirically tested to assess the role of health literacy (HL), health value (HV) and digital health information seeking (DHIS) on primordial PHB. Findings confirmed the significant influence of DHIS on HL, HL on HV and PHB and HV on PHB. The direct effects of DHIS on PHB and HV were insignificant. HL solely mediated the indirect effect of DHIS on PHB, while the mediation of HV was insignificant. HL and HV fully mediated the relationship between DHIS and PHB. Research limitations/implications The impact of DHIS on PHB adoption and the serial multiple mediating roles of HL and HV are significant in understanding primordial PHB adoption for both academic theory and practice. However, the cross-sectional study on urban Indians needs further validation across geographies. Originality/value To the best of the authors’ knowledge, this pioneering study is among the first to propose and validate a comprehensive model of primordial PHB adoption.

Objective: To assess treatment patterns, health care resource utilization (HRU), and costs among previously stimulant-treated children and adolescents with attention-deficit hyperactivity disorder (ADHD) receiving atypical antipsychotic (AAP) prescriptions in Quebec. Methods: Health care claims data extracted from Quebec's provincial health plan database between March 2007 and February 2012 were analyzed. Children and adolescents (6 to 17 years) with ADHD who were taking a stimulant and either switched to, or augmented with, an AAP (with the first AAP defined as the index AAP) without a documented diagnosis for which AAPs are Health Canada–approved were included. Discontinuation, augmentation, and switching of the index AAP during the 12-month, follow-up period were estimated using Kaplan–Meier survival analysis. HRU and costs for the 6 months before (baseline period) and after initiation of the index AAP were compared. Results: A total of 453 children and adolescents with ADHD, mostly male (74.6%) and aged 6 to 12 years (73.7%), met the inclusion criteria. The 12-month discontinuation, augmentation, and switching rates were 45.5%, 68.2%, and 80.7%, respectively. Patients had, on average, more all-cause prescription fills (22.2, compared with 13.3) and incurred more all-cause pharmacy ($889, compared with $710), total medical ($1096, compared with $644), and total health care ($1985, compared with $1354) costs during the 6-month study period than during the 6-month baseline period (all P < 0.05). Similarly, ADHD-related total health care costs were higher during the study period ($1269, compared with $835; P < 0.05); all-cause and ADHD-related total health care costs increased by 46.6% and 52.0%, respectively. Conclusion: Use of an AAP among stimulant-treated children and adolescents with ADHD in Quebec was associated with high rates of therapy changes and increased HRU and costs.

To assess treatment patterns, health care resource utilization (HRU), and costs among previously stimulant-treated children and adolescents with attention-deficit hyperactivity disorder (ADHD) receiving atypical antipsychotic (AAP) prescriptions in Quebec. Health care claims data extracted from Quebec's provincial health plan database between March 2007 and February 2012 were analyzed. Children and adolescents (6 to 17 years) with ADHD who were taking a stimulant and either switched to, or augmented with, an AAP (with the first AAP defined as the index AAP) without a documented diagnosis for which AAPs are Health Canada-approved were included. Discontinuation, augmentation, and switching of the index AAP during the 12-month, follow-up period were estimated using Kaplan-Meier survival analysis. HRU and costs for the 6 months before (baseline period) and after initiation of the index AAP were compared. A total of 453 children and adolescents with ADHD, mostly male (74.6%) and aged 6 to 12 years (73.7%), met the inclusion criteria. The 12-month discontinuation, augmentation, and switching rates were 45.5%, 68.2%, and 80.7%, respectively. Patients had, on average, more all-cause prescription fills (22.2, compared with 13.3) and incurred more all-cause pharmacy ($889, compared with $710), total medical ($1096, compared with $644), and total health care ($1985, compared with $1354) costs during the 6-month study period than during the 6-month baseline period (all P < 0.05). Similarly, ADHD-related total health care costs were higher during the study period ($1269, compared with $835; P < 0.05); all-cause and ADHD-related total health care costs increased by 46.6% and 52.0%, respectively. Use of an AAP among stimulant-treated children and adolescents with ADHD in Quebec was associated with high rates of therapy changes and increased HRU and costs.

The classical analysis of variance (ANOVA) compares the means of different groups under the assumption that the variances within each of the groups are equal. However, for genetic studies of complex disorders, it is not reasonable to assume that variance of a quantitative trait within each genotype at the trait locus will be equal. Thus, the use of ANOVA may lead to misleading association inferences. In this article, we perform a simulation-based study to assess the rate of false positives and the power of ANOVA under various probability distributions of the quantitative trait and different genetic parameters such as allele frequencies and coefficient of linkage disequilibrium.

A complex combination of numerous genetic and environmental factors is responsible for causing a complex disease. Estimation of such gene-environment interaction may help build better predictive model for these diseases. In chapter one we evaluate different methods of estimation of gene-environment interaction parameters for family-based designs. Another important problem in family-based design is analysis of low frequency variants or rare variants which needs separate treatment from common variants. In chapter two and three we make novel contribution to methodology related to testing association for rare variants in context of family-based designs. In chapter one, we suggest using information from unaffected offspring to estimate relative risk parameters for gene-environment interaction for unascertained family-based samples. We develop the method initially for trios and then suggest extension for general family structure. We evaluate the performance of our method, comparing it against the traditional affected offspring only analysis, using asymptotic relative efficiency as a measure. Finally the methodology is applied to an unascertained family-based samples from two Malaria-infested villages in Senegal. In chapter two, we propose a novel method to test for association of rare variants in family-based samples by collapsing the standard family-based association test (FBAT) statistic over a region of interest. We also propose a suitable weighting scheme so that low frequency single nucleotide polymorphisms (SNP) that are enriched in functional variants can be upweighted compared to common variants. We compare our method with traditional methods for analyzing rare variants in population-based design. In chapter three, we extend the collapsed sum FBAT statistic by introducing use of a threshold to identify the subset of rare variants to be analyzed. We compare the fixed and variable threshold methods with the no threshold weighted method. In this context we also introduce two resampling-based testing strategy - one based on permutation of the phenotype and the other based on bootstrapping - for family-based designs to get the distribution of the variable threshold test statistic. Both methods from chapter two and three are applied on simulated dataset. Method in chapter two has also been included as an additional functionality in the FBAT software.

Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees ( n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotide polymorphisms for four genes ( KDR , VEGFA , VEGFC , and FLT1 ). Our results suggest that both linkage and association tests with families show promise for identifying rare variants.

Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees (n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotide polymorphisms for four genes (KDR, VEGFA, VEGFC, and FLT1). Our results suggest that both linkage and association tests with families show promise for identifying rare variants.

A very aggressive and deadly brain cancer, glioblastoma multiforme (GBM) poses formidable obstacles to effective therapy. Despite advancements in conventional therapies like surgery, chemotherapy, and radiation therapy, the prognosis for GBM patients remains poor, with limited survival outcomes. Nanotechnology is gaining popularity as a promising platform for managing GBM, offering targeted drug delivery, improved therapeutic efficacy, and reduced systemic toxicity. This review offers a comprehensive analysis of the current therapeutic approach for GBM using nanotechnology-based interventions. This study explored various nanocarrier (NC) systems like polymeric nanoparticles, liposomes, dendrimers, polymeric micelles, and mesoporous silica nanoparticles for improved precision as well as efficacy in encapsulating and delivering therapeutic agents to GBM tumors. Methods for improving drug delivery into GBM cells are described in this study, including novel delivery modalities such as convection-enhanced delivery, intranasal administration, magnetic hyperthermia, peptide-guided nanoparticles, and immune liposomes. It also explores the influence of diabetes and obesity on GBM prognosis and survival rates, suggesting that managing glucose levels and using metformin may improve patient outcomes. The discussion focuses on the advancements in nanotechnology-enabled GBM therapy, highlighting the challenges and opportunities in implementing these promising technologies in clinical practice. The study highlights the potential of nanotechnology and metabolic modulation in transforming GBM treatment strategies. To further understand how these factors impact GBM patients and develop innovative nanotechnology-based treatments for GBM and diabetes mellitus, more study is necessary.