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BMC Proceedings

Candide Tran Ngoc, Noella Bigirimana, Derrick Muneene, Juliet Evelyn Bataringaya, Prebo Barango, Hani Eskandar, Raissah Igiribambe, Ayomide Sina-Odunsi, Jeanine Umutesi Condo, Olushayo Olu
Background: The use of digital technologies to improve access to health is gaining momentum in Africa. This is more pertinent with the increasing penetration of mobile phone technology and internet use, and calls for innovative strategies to support implementation of the health-related Sustainable Development Goals and Universal Health Coverage on the continent. However, the huge potential benefits of digital health to advance health services delivery in Africa is yet to be fully harnessed due to critical challenges such as proliferation of pilot projects, poor coordination, inadequate preparedness of the African health workforce for digital health, lack of interoperability and inadequate sustainable financing, among others...
2018: BMC Proceedings
Jacqueline Galica, Alyssandra Chee-A-Tow, Shikha Gupta, Atul Jaiswal, Andrea Monsour, Andrea C Tricco, Kelly D Cobey, Nancy J Butcher
Background and purpose: Dissemination of research results is a key component of the research continuum and is commonly achieved through publication in peer-reviewed academic journals. However, issues of poor quality reporting in the research literature are well documented. A lack of formal training in journalology (i.e., publication science) may contribute to this problem. To help address this gap in training, the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Canada Publication School was developed and facilitated by internationally-renowned faculty to train researchers and clinicians in reporting and publication best practices...
2018: BMC Proceedings
Chong Wu, Jun Young Park, Weihua Guan, Wei Pan
DNA methylation plays an important role in normal human development and disease. In epigenome-wide association studies (EWAS), a univariate test for association between a phenotype and each cytosine-phosphate-guanine (CpG) site has been widely used. Given the number of CpG sites tested in EWAS, a stringent significance cutoff is required to adjust for multiple testing; in addition, multiple nearby CpG sites may be associated with the phenotype, which is ignored by a univariate test. These two factors may contribute to the power loss of a univariate test...
2018: BMC Proceedings
Elizabeth R Piette, Jason H Moore
The Genetic Analysis Workshop (GAW) presents an opportunity to collaboratively evaluate methodology relevant to current issues in genetic epidemiology. The GAW20 data combine real clinical trial data with fictitious epigenetic drug response endpoints. Considering the evidence suggesting that networks of interactions between many genes underlie complex phenotypes, we utilize differential methylation status to identify a relevant gene set for enrichment analysis and use this to infer potential biological function underlying drug response...
2018: BMC Proceedings
Jenna Veenstra, Anya Kalsbeek, Karissa Koster, Nathan Ryder, Abbey Bos, Jordan Huisman, Lucas VanderBerg, Jason VanderWoude, Nathan L Tintle
In the search for an understanding of how genetic variation contributes to the heritability of common human disease, the potential role of epigenetic factors, such as methylation, is being explored with increasing frequency. Although standard analyses test for associations between methylation levels at individual cytosine-phosphate-guanine (CpG) sites and phenotypes of interest, some investigators have begun testing for methylation and how methylation may modulate the effects of genetic polymorphisms on phenotypes...
2018: BMC Proceedings
Yuning Chen, Gina M Peloso, Josée Dupuis
Statistical power, which is the probability of correctly rejecting a false null hypothesis, is a limitation of genome-wide association studies (GWAS). Sample size is a major component of statistical power that can be easily affected by missingness in phenotypic data and restrain the ability to detect associated single-nucleotide polymorphisms (SNPs) with small effect sizes. Although some phenotypes are hard to collect because of cost and loss to follow-up, correlated phenotypes that are easily collected can be leveraged for association analysis...
2018: BMC Proceedings
Juan M Peralta, Nicholas B Blackburn, Arthur Porto, John Blangero, Jac Charlesworth
We conducted a genome-wide linkage scan to detect loci that influence the levels of fasting triglycerides in plasma. Fasting triglyceride levels were available at 4 time points (visits), 2 pre- and 2 post-fenofibrate intervention. Multipoint identity-by-descent (MIBD) matrices were derived from genotypes using IBDLD. Variance-component linkage analyses were then conducted using SOLAR (Sequential Oligogenic Linkage Analysis Routines). We found evidence of linkage (logarithm of odds [LOD] ≥3) at 5 chromosomal regions with triglyceride levels in plasma...
2018: BMC Proceedings
Arthur Porto, Juan M Peralta, Nicholas B Blackburn, John Blangero
Genome-wide association studies have helped us identify a wealth of genetic variants associated with complex human phenotypes. Because most variants explain a small portion of the total phenotypic variation, however, marker-based studies remain limited in their ability to predict such phenotypes. Here, we show how modern statistical genetic techniques borrowed from animal breeding can be employed to increase the accuracy of genomic prediction of complex phenotypes and the power of genetic mapping studies. Specifically, using the triglyceride data of the GAW20 data set, we apply genomic-best linear unbiased prediction (G-BLUP) methods to obtain empirical genetic values (EGVs) for each triglyceride phenotype and each individual...
2018: BMC Proceedings
Jason Vander Woude, Jordan Huisman, Lucas Vander Berg, Jenna Veenstra, Abbey Bos, Anya Kalsbeek, Karissa Koster, Nathan Ryder, Nathan L Tintle
Although methylation data continues to rise in popularity, much is still unknown about how to best analyze methylation data in genome-wide analysis contexts. Given continuing interest in gene-based tests for next-generation sequencing data, we evaluated the performance of novel gene-based test statistics on simulated data from GAW20. Our analysis suggests that most of the gene-based tests are detecting real signals and maintaining the Type I error rate. The minimum p value and threshold-based tests performed well compared to single-marker tests in many cases, especially when the number of variants was relatively large with few true causal variants in the set...
2018: BMC Proceedings
Summaira Yasmeen, Patricia Burger, Stefanie Friedrichs, Sergi Papiol, Heike Bickeböller
In GAW20, we investigated the association of specific genetic regions of interest (ROIs) with log-transformed triglyceride (TG) levels following lipid-lowering medication using epigenetic and genetic markers. The goal was to incorporate kernels for cytosine-phosphate-guanine (CpG) markers and compare the kernels to a purely parametric model. Post-treatment TG levels were investigated for post-methylation data at CpG sites and region-specific SNPs and adjusted for pre-treatment TG levels and age, in independent individuals only (real data: n  = 150; simulated data, replicate 84: n  = 111)...
2018: BMC Proceedings
Zheng Xu, Qing Duan, Juan Cui, Yumou Qiu, Qidong Jia, Cong Wu, Jennifer Clarke
Obesity is a risk factor for heart disease, stroke, diabetes, high blood pressure, and other chronic diseases. Some drugs, including fenofibrate, are used to treat obesity or excessive weight by lowering the level of specific triglycerides. However, different groups have different drug sensitivities and, consequently, there are differences in drug effects. In this study, we assessed both genetic and nongenetic factors that influence drug responses and stratified patients into groups based on differential drug effect and sensitivity...
2018: BMC Proceedings
Jiayi Wu Cox, Devanshi Patel, Jaeyoon Chung, Congcong Zhu, Samantha Lent, Virginia Fisher, Achilleas Pitsillides, Lindsay Farrer, Xiaoling Zhang
Background: The study of DNA methylation quantitative trait loci (meQTLs) helps dissect regulatory mechanisms underlying genetic associations of human diseases. In this study, we conducted the first genome-wide examination of genetic drivers of methylation variation in response to a triglyceride-lowering treatment with fenofibrate (response-meQTL) by using an efficient analytic approach. Methods: Subjects ( n  = 429) from the GAW20 real data set with genotype and both pre- (visit 2) and post- (visit 4) fenofibrate treatment methylation measurements were included...
2018: BMC Proceedings
Rita Cantor, Linda Navarro, Calvin Pan
As part of GAW20, we analyzed the familiality and variability of methylation to identify cytosine-phosphate-guanine (CpG) sites responsive to treatment with fenofibrate. Methylation was measured at approximately 450,000 sites in pedigree members, prior to and after 3 weeks of treatment. Initially, we aimed to identify responsive sites by analyzing the pre- and posttreatment methylation changes within individuals, but these data exhibited a confounding treatment/batch effect. We applied an alternative indirect approach by searching for CpG sites whose methylation levels exhibit a genetic response to the drug...
2018: BMC Proceedings
Sarmistha Das, Pronoy Kanti Mondal, Saurabh Ghosh, Indranil Mukhopadhyay
In this paper we analyzed whole-genome genetic information provided by GAW20 from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study for family data. Lipid levels such as triglycerides (TGs) and high-density lipoprotein (HDL) are measured at different time points before and after administration of an anti-inflammatory drug fenofibrate. Apart from that, the data contain some covariates and whole-genome genotype information. We propose 2 novel approaches based on Henderson's iterative mixed model to identify associated loci corresponding to (a) inflammatory biomarkers like TGs and HDLs together over time, and (b) the response to fenofibrate treatment...
2018: BMC Proceedings
Wenda Zhou, Shaw-Hwa Lo
In this paper, we consider the use of the least absolute shrinkage and selection operator (LASSO)-type regression techniques to detect important genetic or epigenetic loci in genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS). We demonstrate how these techniques can be adapted to provide quantifiable uncertainty using stability selection, including explicit control of the family-wise error rate. We also consider variants of the LASSO, such as the group LASSO, to study genetic and epigenetic interactions...
2018: BMC Proceedings
Hemant Kulkarni, Indranil Mukhopadhyay, Saurabh Ghosh
Complex genetic traits are often characterized by multiple quantitative phenotypes. Because values of such phenotypes vary over time, it is thought that analyses of longitudinal data on the phenotypes may lead to increased power in detecting genetic association. In this paper, we extend a transmission-based association test applying quasi-likelihood that has been developed by us to the longitudinal framework and to carry out a genome-wide association analysis of triglyceride levels based on the data provided in GAW20...
2018: BMC Proceedings
Svetlana Cherlin, Richard A J Howey, Heather J Cordell
Background: In a typical genome-enabled prediction problem there are many more predictor variables than response variables. This prohibits the application of multiple linear regression, because the unique ordinary least squares estimators of the regression coefficients are not defined. To overcome this problem, penalized regression methods have been proposed, aiming at shrinking the coefficients toward zero. Methods: We explore prediction of phenotype from single nucleotide polymorphism (SNP) data in the GAW20 data set using a penalized regression approach (LASSO [least absolute shrinkage and selection operator] regression)...
2018: BMC Proceedings
Ke Hu, Jing Li
DNA methylation levels at cytosine-phosphate-guanine (CpG) sites with multimodal distributions among different samples have been reported recently. One possible explanation for such variability is that genetic variants might affect epigenetic variation. One obvious case is that mutations such as single-nucleotide polymorphisms (SNPs) interrupt CpG sites, resulting in different DNA methylation levels for different genotypes. However, the relationship between genetic variations and epigenetic differences has not been studied thoroughly, partially because of the lack of powerful and robust methods to survey genome-wide CpG sites with multimodal methylation level distributions (mmCpGs)...
2018: BMC Proceedings
Stella Aslibekyan, Laura Almasy, Michael A Province, Devin M Absher, Donna K Arnett
GAW20 provided participants with an opportunity to comprehensively examine genetic and epigenetic variation among related individuals in the context of drug treatment response. GAW20 used data from 188 families ( N  = 1105) participating in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study ( identifier NCT00083369), which included CD4+ T-cell DNA methylation at 463,995 cytosine-phosphate-guanine (CpG) sites measured before and after a 3-week treatment with fenofibrate, single-nucleotide variation at 906,600 loci, metabolic syndrome components ascertained before and after the drug intervention, and relevant covariates...
2018: BMC Proceedings
Angga M Fuady, Renaud L M Tissier, Jeanine J Houwing-Duistermaat
The main goal of this paper is to estimate the effect of triglyceride levels on methylation of cytosine-phosphate-guanine (CpG) sites in multiple-case families. These families are selected because they have 2 or more cases of metabolic syndrome (primary phenotype). The methylations at the CpG sites are the secondary phenotypes. Ascertainment corrections are needed when there is an association between the primary and secondary phenotype. We will apply the newly developed secondary phenotype analysis for multiple-case family studies to identify CpG sites where methylations are influenced by triglyceride levels...
2018: BMC Proceedings
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