The 5th CIH(LMU) Infectious Disease Symposium, Munich, Germany, March 12, 2016 brought together Tuberculosis Experts from developed and low middle-income countries to discuss the control of drug resistance Tuberculosis. The meeting featured 9 presentations: Tuberculosis history and current scenario, Tuberculosis and migration - current scenario in Germany, Mechanism of Tuberculosis resistance development, Epidemiology of resistance - transmission vs. new generation of resistance, The impact of diagnostic in patients beyond - sensitivity and specificity, The Bangladesh regimen - new hope trough old drugs, New drugs and regimens - an overview on studies and Multi and Extensively Drug Resistant Tuberculosis from Europe. Read More
Over the past 30 years, dialectical behavior therapy has been shown to be an effective treatment for adult borderline personality disorder. The adaptation of DBT for adolescents (DBT-A) in different patient groups has also led to some promising improvements of the respective psychopathology. During the second German DBT-A network meeting in 2015 in Mainz, Germany, a need for further research and innovative approaches in treatment of adolescents became apparent and resulted in controversial discussions. Read More
Primary ciliary dyskinesia (PCD) is a rare heterogenous condition that causes progressive suppurative lung disease, chronic rhinosinusitis, chronic otitis media, infertility and abnormal situs. 'Better Experimental Approaches to Treat Primary Ciliary Dyskinesia' (BEAT-PCD) is a network of scientists and clinicians coordinating research from basic science through to clinical care with the intention of developing treatments and diagnostics that lead to improved long-term outcomes for patients. BEAT-PCD activities are supported by EU Framework Programme Horizon 2020 funded COST Action (BM1407). Read More
An international conference titled "Transforming Health Care in Remote Communities" was held at the Chateau Lacombe Hotel in Edmonton, Canada, April 28-30, 2016. The event was organized by the University of Alberta's School of Public Health, in partnership with the Institute for Circumpolar Health Research in Yellowknife, Northwest Territories, and the Qaujigiartiit Health Research Centre in Iqaluit, Nunavut. There were 150 registrants from 7 countries: Canada (7 provinces and 3 territories), United States, Denmark, Iceland, Norway, Sweden, and Australia. Read More
The fifth annual meeting of the African cholera surveillance network (Africhol) took place on 10-11 June 2015 in Lomé, Togo. Together with international partners, representatives from the 11 member countries -Cameroon, Côte d'Ivoire, Democratic Republic of Congo, Guinea, Kenya, Mozambique, Nigeria, Tanzania, Togo, Uganda, Zimbabwe- and an invited country (Malawi) shared their experience. The meeting featured three sessions: i) cholera surveillance, prevention and control in participating countries, ii) cholera surveillance methodology, such as cholera mapping, cost-effectiveness studies and the issue of overlapping epidemics from different diseases, iii) cholera laboratory diagnostics tools and capacity building. Read More
In October 2016, the Global Healthcare Policy and Management Forum was held at Yonsei University, Seoul, South Korea. The goal of the forum was to discuss the role of the state in regulating and supporting the development of medical tourism. Forum attendees came from 10 countries. Read More
Given the steady rise of overdose morbidity and mortality in North America, and increasing frequency of sudden clusters of non-fatal and fatal overdoses in other jurisdictions, regional preparedness plans to respond effectively to clusters of overdoses may reduce the impact of such events on the population. On the 27th of February 2017 in Kingston, Ontario, KFL&A Public Health, in collaboration with public health partners, hosted a full-day workshop involving table-top exercises and discussions for service partners on how to prepare for, respond to, and manage a mass-casualty event secondary to opioid overdose in Southeastern Ontario. The workshop assisted in identifying the various challenges faced by service partners, provided an understanding of the roles and responsibilities of partner agencies, and helped to determine next steps in preparation to address a mass opioid overdose situation at the local level. Read More
Background: Vaccination is a complex ecosystem with several components that interact with one another and with the environment. Today's vaccine ecosystem is defined by the pursuit of polio eradication, the drive to get as many of the new vaccines to as many people as possible and the research and development against immunologically challenging diseases. Despite these successes, vaccine ecosystem is facing keys issues with regard to supply/distribution and cost/profitability asymmetry that risk slowing its global growth. Read More
Estimating the causal effect of a single nucleotide variant (SNV) on clinical phenotypes is of interest in many genetic studies. The effect estimation may be confounded by other SNVs as a result of linkage disequilibrium as well as demographic and clinical characteristics. Because a large number of these other variables, which we call potential confounders, are collected, it is challenging to select and adjust for the variables that truly confound the causal effect. Read More
A central goal in the biomedical and biological sciences is to link variation in quantitative traits to locations along the genome (single nucleotide polymorphisms). Sequencing technology has rapidly advanced in recent decades, along with the statistical methodology to analyze genetic data. Two classes of association mapping methods exist: those that account for the evolutionary relatedness among individuals, and those that ignore the evolutionary relationships among individuals. Read More
The relationship between genetic variability and individual phenotypes is usually investigated by testing for association relying on called genotypes. Allele counts obtained from next-generation sequence data could be used for this purpose too. Genetic association can be examined by treating alternative allele counts (AACs) as the response variable in negative binomial regression. Read More
Availability of genomic sequence data provides opportunities to study the role of low-frequency and rare variants in the etiology of complex disease. In this study, we conduct association analyses of hypertension status in the cohort of 1943 unrelated Mexican Americans provided by Genetic Analysis Workshop 19, focusing on exonic variants in MAP4 on chromosome 3. Our primary interest is to compare the performance of standard and sparse-data approaches for single-variant tests and variant-collapsing tests for sets of rare and low-frequency variants. Read More
In this study, the effects of (a) the minor allele frequency of the single nucleotide variant (SNV), (b) the degree of departure from normality of the trait, and (c) the position of the SNVs on type I error rates were investigated in the Genetic Analysis Workshop (GAW) 19 whole exome sequence data. To test the distribution of the type I error rate, 5 simulated traits were considered: standard normal and gamma distributed traits; 2 transformed versions of the gamma trait (log10 and rank-based inverse normal transformations); and trait Q1 provided by GAW 19. Each trait was tested with 313,340 SNVs. Read More
Background: Recent advances in next-generation sequencing technologies have made it possible to generate large amounts of sequence data with rare variants in a cost-effective way. Yet, the statistical aspect of testing disease association of rare variants is quite challenging as the typical assumptions fail to hold owing to low minor allele frequency (<0.5 or 1 %). Read More
Background: Nearly half of adults in the United States who are diagnosed with hypertension use blood-pressure-lowering medications. Yet there is a large interindividual variability in the response to these medications. Two complementary gene-environment interaction methods have been published and incorporated into publicly available software packages to examine interaction effects, including whether genetic variants modify the association between medication use and blood pressure. Read More
Several variants have been implicated earlier on ULK4 and MAP4 genes on chromosome 3 to be associated with hypertension. As a natural follow-up step, we explore association of haplotypes in those genes. We consider the Genetic Analysis Workshop 19 real data on unrelated individuals and analyze haplotype blocks of 5 single-nucleotide polymorphisms through a sliding window approach. Read More
Background: Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model. Read More
The aggregation of functionally associated variants given a priori biological information can aid in the discovery of rare variants associated with complex diseases. Many methods exist that aggregate rare variants into a set and compute a single p value summarizing association between the set of rare variants and a phenotype of interest. These methods are often called gene-based, rare variant tests of association because the variants in the set are often all contained within the same gene. Read More
Background: Whereas genome-wide association study (GWAS) has proven to be an important tool for discovery of variants influencing many human diseases and traits, unfortunately its performance has not been much of all-around success for some complex conditions, for example, hypertension. Because some of the existing effective pharmacotherapeutic agents act by targeting known biological pathways, pathway-based analytical approaches could lead to more success in discovery of disease-associated variants. The objective of the present study was to identify functional variants associated with blood pressure in the aldosterone-regulated sodium reabsorption pathway using the simulated and real blood pressure phenotypes provided for Genetic Analysis Workshop 19. Read More
Background: The application of pathway and gene-set based analyses to high-throughput data is increasingly common and represents an effort to understand underlying biology where single-gene or single-marker analyses have failed. Many such analyses rely on the a priori identification of genes associated with the trait of interest. In contrast, this variance-component-based approach creates a similarity matrix of individuals based on the expression of genes in each pathway. Read More
Interactions between genes are an important part of the genetic architecture of complex diseases. In this paper, we use literature-guided individual genes known to be associated with type 2 diabetes (referred to as "seed genes") to create a larger list of genes that share implied or direct networks with these seed genes. This larger list of genes are known to interact with each other, but whether they interact in ways to influence hypertension in individuals presents an interesting question. Read More
BMC Proc 2016 18;10(Suppl 7):329-332. Epub 2016 Oct 18.
South Texas Diabetes and Obesity Institute, University of Texas Health Science Center, San Antonio, TX 78229 USA ; Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104 USA ; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA.
Background: The incorporation of longitudinal data into genetic epidemiological studies has the potential to provide valuable information regarding the effect of time on complex disease etiology. Yet, the majority of research focuses on variables collected from a single time point. This aim of this study was to test for main effects on a quantitative trait across time points using a constrained maximum-likelihood measured genotype approach. Read More
Background: There is great interindividual variation in systolic blood pressure (SBP) as a result of the influences of several factors, including sex, ancestry, smoking status, medication use, and, especially, age. The majority of genetic studies have examined SBP measured cross-sectionally; however, SBP changes over time, and not necessarily in a linear fashion. Therefore, this study conducted a genome-wide association (GWA) study of SBP change trajectories using data available through the Genetic Analysis Workshop 19 (GAW19) of 959 individuals from 20 extended Mexican American families from the San Antonio Family Studies with up to 4 measures of SBP. Read More
It is essential to develop adequate statistical methods to fully utilize information from longitudinal family studies. We extend our previous multipoint linkage disequilibrium approach-simultaneously accounting for correlations between markers and repeat measurements within subjects, and the correlations between subjects in families-to detect loci relevant to disease through gene-based analysis. Estimates of disease loci and their genetic effects along with their 95 % confidence intervals (or significance levels) are reported. Read More
BMC Proc 2016 18;10(Suppl 7):309-313. Epub 2016 Oct 18.
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2 Canada ; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2 Canada ; Department of Oncology, McGill University, Montreal, QC H2W 1S6 Canada ; Department of Human Genetics, McGill University, Montreal, QC H3A 1B1 Canada.
Introduction: Large-scale sequencing studies often measure many related phenotypes in addition to the genetic variants. Joint analysis of multiple phenotypes in genetic association studies may increase power to detect disease-associated loci.
Methods: We apply a recently developed multivariate rare-variant association test to the Genetic Analysis Workshop 19 data in order to test associations between genetic variants and multiple blood pressure phenotypes simultaneously. Read More
BMC Proc 2016 18;10(Suppl 7):303-307. Epub 2016 Oct 18.
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106 USA ; Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106 USA.
Structural equation modeling (SEM) has been used in a wide range of applied sciences including genetic analysis. The recently developed R package, strum, implements a framework for SEM for general pedigree data. We explored different SEM techniques using strum to analyze the multivariate longitudinal data and to ultimately test the association of genotypes on blood pressure traits. Read More
BMC Proc 2016 18;10(Suppl 7):295-301. Epub 2016 Oct 18.
Department of Biostatistics, University of Washington, Seattle, WA USA ; Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA USA ; Department of Genome Sciences, University of Washington, Seattle, WA USA.
Background: In the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been proposed. With the recent surge of family-based designs, family-based imputation has become more important. Imputation methods for both designs are based on identity-by-descent (IBD) information. Read More
Recent work on genetic association studies suggests that much of the heritable variation in complex traits is unexplained, which indicates a need for using more biologically meaningful modeling approaches and appropriate statistical methods. In this study, we propose a biological framework and a corresponding statistical model incorporating multilevel biological measures, and illustrate it in the analysis of the real data provided by the Genetic Analysis Workshop (GAW) 19, which contains whole genome sequence (WGS), gene expression (GE), and blood pressure (BP) data. We investigate the direct effect of single-nucleotide variants (SNVs) on BP and GE, while considering the non-directional dependence between BP and GE, by using copula functions to jointly model BP and GE conditional on SNVs. Read More
BMC Proc 2016 18;10(Suppl 7):283-288. Epub 2016 Oct 18.
Unit on Statistical Genomics, Division of Intramural Research Programs, National Institute of Mental Health, National Institutes of Health, Building 35, Room 3A 1000, 35 Convent Drive, Bethesda, MD 20892 USA.
Background: Although many genes have been implicated as hypertension candidates, to date, few studies have integrated different types of genomic data for the purpose of biomarker selection.
Methods: Applying a newly proposed sparse representation based variable selection (SRVS) method to the Genetic Analysis Workshop19 data, we analyzed a combined data set consisting of 11522 gene expressions and 354893 single-nucleotide polymorphisms (SNPs) from 397 subjects (case/control: 151/246), with the aim to identify potential biomarkers for blood pressure using both gene expression measures and SNP data.
Results: Among the top 1000 variables (SNPs/gene expressions = 575/425) selected, the bioinformatics analysis showed that 302 were plausibly associated with blood pressure. Read More
Statistical association studies are an important tool in detecting novel disease genes. However, for sequencing data, association studies confront the challenge of low power because of relatively small data sample size and rare variants. Incorporating biological information that reflects disease mechanism is likely to strengthen the association evidence of disease genes, and thus increase the power of association studies. Read More
We used our extension of the kernel score test to family data to analyze real and simulated baseline systolic blood pressure in extended pedigrees. We compared the power for different kernels and for different weightings of genetic markers. Moreover, we compared the power of rare and common markers with 3 strategies for joint testing and on marker panels with different densities. Read More
Population-based identity by descent (IBD) mapping is a statistical method for detection of genetic loci that share an ancestral segment among "unrelated" pairs of individuals for a disease. As a complementary method to genome-wide association studies, IBD mapping is robust to allelic heterogeneity and may identify rare inherited variants when combined with sequence data. Our objective is to identify the causal genes for diastolic blood pressure (DBP). Read More
With the rapidly decreasing cost of the next-generation sequencing technology, a large number of whole genome sequences have been generated, enabling researchers to survey rare variants in the protein-coding and regulatory regions of the genome. However, it remains a daunting task to identify functional variants associated with complex diseases from whole genome sequencing (WGS) data because of the millions of candidate variants and yet moderate sample size. We propose to incorporate the Encyclopedia of DNA Elements (ENCODE) information in the association analysis of WGS data to boost the statistical power. Read More
Background: Genetic association studies aim to test for disease or trait association with genetic variants, either throughout the human genome or in regions of interest. However, for most diseases and traits, the combined effects of associated genetic variants explain only a small proportion of the genetic variation. This "missing heritability" may be a result of the small effects of common variants considered in the genetic association studies. Read More
The new generation of whole genome sequencing platforms offers great possibilities and challenges for dissecting the genetic basis of complex traits. With a very high number of sequence variants, a naïve multiple hypothesis threshold correction hinders the identification of reliable associations by the overreduction of statistical power. In this report, we examine 2 alternative approaches to improve the statistical power of a whole genome association study to detect reliable genetic associations. Read More
BMC Proc 2016 18;10(Suppl 7):239-244. Epub 2016 Oct 18.
Department of Human Genetics, University of California, Los Angeles, CA 90095 USA ; Department of Biomathematics, University of California, Los Angeles, CA 90095 USA ; Department of Statistics, University of California, Los Angeles, CA 90095 USA.
Pedigree genome-wide association studies (GWAS) (Option 29) in the current version of the Mendel software is an optimized subroutine for performing large-scale genome-wide quantitative trait locus (QTL) analysis. This analysis (a) works for random sample data, pedigree data, or a mix of both; (b) is highly efficient in both run time and memory requirement; (c) accommodates both univariate and multivariate traits; (d) works for autosomal and x-linked loci; (e) correctly deals with missing data in traits, covariates, and genotypes; (f) allows for covariate adjustment and constraints among parameters; (g) uses either theoretical or single nucleotide polymorphism (SNP)-based empirical kinship matrix for additive polygenic effects; (h) allows extra variance components such as dominant polygenic effects and household effects; (i) detects and reports outlier individuals and pedigrees; and (j) allows for robust estimation via the t-distribution. This paper assesses these capabilities on the genetics analysis workshop 19 (GAW19) sequencing data. Read More
BMC Proc 2016 18;10(Suppl 7):233-237. Epub 2016 Oct 18.
Division of Epidemiology and Biostatistics of Mel and Enid Zuckerman College of Public Health, University of Arizona, 1295 N. Martin Ave., Campus PO Box: 245211, Drachman Hall A242, Tucson, AZ 85724 USA.
Next-generation sequencing technology makes directly testing rare variants possible. However, existing statistical methods to detect common variants may not be optimal for testing rare variants because of allelic heterogeneity as well as the extreme rarity of individual variants. Recently, several statistical methods to detect associations of rare variants were developed, including population-based and family-based methods. Read More
Background: The advent of affordable sequencing has enabled researchers to discover many variants contributing to disease, including rare variants. There are methods for determining the most informative individuals for sequencing, but the application of these methods is more complex when working with families. Sets of large families can be beneficial in finding rare variants, but it may be unfeasible to sequence all members of these family sets. Read More
We propose a novel LASSO (least absolute shrinkage and selection operator) penalized regression method used to analyze samples consisting of (potentially) related individuals. Developed in the context of linear mixed models, our method models the relatedness of individuals in the sample through a random effect whose covariance structure is a linear function of known matrices with elements combinations of the condensed coefficients of identity between the individuals in the sample. We implement our method to analyze the simulated family data provided by the 19th Genetic Analysis Workshop in an effort to identify loci regulating the simulated trait of systolic blood pressure. Read More
Statistical association tests for rare variants can be classified as the burden approach and the sequence kernel association test (SKAT) approach. The burden and SKAT approaches, originally developed for case-control analysis, have also been extended to family-based tests. In the presence of both case-control and family data for a study, joint analysis for the combined data set can increase the statistical power. Read More
Background: Recent focus on studying rare variants makes imputation accuracy of rare variants an important issue. Many approaches have been proposed to increase imputation accuracy among rare variants, from reference panel selection to combinations of existing methods to multistage analyses. We aimed to bring the strengths of these new approaches together with our proposed two-stage imputation for family data. Read More
Background: Advances in whole genome sequencing have enabled the investigation of rare variants, which could explain some of the missing heritability that genome-wide association studies are unable to detect. Most methods to detect associations with rare variants are developed for unrelated individuals; however, several methods exist that utilize family studies and could have better power to detect such associations.
Methods: Using whole genome sequencing data and simulated phenotypes provided by the organizers of the Genetic Analysis Workshop 19 (GAW19), we compared family-based methods that test for associations between rare and common variants with a quantitative trait. Read More
BMC Proc 2016 18;10(Suppl 7):197-202. Epub 2016 Oct 18.
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, 1020 Pine Avenue West, Montreal, QC H3A 1A2 Canada ; Department of Psychiatry, McGill University, Montreal, QC Canada.
A statistical departure from Mendel's law of segregation is known as transmission ratio distortion. Although well documented in many other organisms, the extent of transmission ratio distortion and its influence in the human genome remains incomplete. Using Genetic Analysis Workshop 19 whole genome sequence data from 20 large Mexican American pedigrees, our goal was to identify potentially distorted regions in the genome using family-based association methods such as the transmission disequilibrium test, the pedigree disequilibrium test, and the family-based association test. Read More
Both population-based and family-based designs are commonly used in genetic association studies to identify rare variants that underlie complex diseases. For any type of study design, the statistical power will be improved if rare variants can be enriched in the samples. Family-based designs, with ascertainment based on phenotype, may enrich the sample for causal rare variants and thus can be more powerful than population-based designs. Read More
Background: Meta-analysis has been widely used in genetic association studies to increase sample size and to improve power, both in the context of single-variant analysis, as well as for gene-based tests. Meta-analysis approaches for haplotype analysis have not been extensively developed and used, and have not been compared with other ways of jointly analysing multiple genetic variants.
Methods: We propose a novel meta-analysis approach for a gene-based haplotype association test, and compare it with an existing meta-analysis approach of the sequence kernel association test (SKAT), using the unrelated samples and family samples of the Genetic Analysis Workshop 19 data sets. Read More
BMC Proc 2016 18;10(Suppl 7):181-186. Epub 2016 Oct 18.
Interdisciplinary Program in bioinformatics, Seoul National University, Seoul, 151-742 Korea ; Department of Public Health Science, Seoul National University, Seoul, 151-742 Korea ; Institute of Health Environment, Seoul National University, Seoul, 151-742 Korea.
Background: It has been repeatedly stressed that family-based samples suffer less from genetic heterogeneity and that association analyses with family-based samples are expected to be powerful for detecting susceptibility loci for rare disease. Various approaches for rare-variant analysis with family-based samples have been proposed.
Methods: In this report, performances of the existing methods were compared with the simulated data set provided as part of Genetic Analysis Workshop 19 (GAW19). Read More
Several statistical group-based approaches have been proposed to detect effects of variation within a gene for each of the population- and family-based designs. However, unified tests to combine gene-phenotype associations obtained from these 2 study designs are not yet well established. In this study, we investigated the efficient combination of population-based and family-based sequencing data to evaluate best practices using the Genetic Analysis Workshop 19 (GAW19) data set. Read More
Background: Genome-wide association studies have made substantial progress in identifying common variants associated with human diseases. Despite such success, a large portion of heritability remains unexplained. Evolutionary theory and empirical studies suggest that rare mutations could play an important role in human diseases, which motivates comprehensive investigation of rare variants in sequencing studies. Read More