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BMC Proceedings

Chi Wang, Jinpeng Liu, David W Fardo
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. The Bayesian adjustment for confounding (BAC) method has been proposed as a general method to estimate the average causal effect in the presence of a large number of potential confounders under the assumption of no unmeasured confounders...
2016: BMC Proceedings
Katherine L Thompson, David W Fardo
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. While the former methods more fully use implicit information in the data, the latter methods are more flexible in the types of data they can handle...
2016: BMC Proceedings
Rosa González Silos, Özge Karadag, Barbara Peil, Christine Fischer, Maria Kabisch, Carine Legrand, Justo Lorenzo Bermejo
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. AACs from sequence data often contain an excess of zeros, thus motivating the use of Hurdle and zero-inflated models. Here we examine rough type I error rates and the ability to pick out variants with small probability values for 7 different testing approaches that incorporate AACs as an explanatory or as a response variable...
2016: BMC Proceedings
Ji-Hyung Shin, Ruiyang Yi, Shelley B Bull
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...
2016: BMC Proceedings
Tae-Hwi Schwantes-An, Heejong Sung, Jeremy A Sabourin, Cristina M Justice, Alexa J M Sorant, Alexander F Wilson
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...
2016: BMC Proceedings
Cheongeun Oh
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 %). METHODS: I present a Bayesian variable selection approach to detect associations with both rare and common genetic variants for quantitative traits simultaneously...
2016: BMC Proceedings
Lindsay Fernández-Rhodes, Chani J Hodonsky, Mariaelisa Graff, Shelly-Ann M Love, Annie Green Howard, Amanda A Seyerle, Christy L Avery, Geetha Chittoor, Nora Franceschini, V Saroja Voruganti, Kristin Young, Jeffrey R O'Connell, Kari E North, Anne E Justice
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. The first approach uses a gene-environment interaction term to measure the change in outcome when both the genetic marker and medication are present (the "interaction model")...
2016: BMC Proceedings
Ananda S Datta, Yuan Zhang, Lei Zhang, Swati Biswas
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. We apply 4 haplotype association methods-haplo.score, haplo.glm, hapassoc, and logistic Bayesian LASSO (LBL)-and for comparison, sequence kernel association test (SKAT) and its variants...
2016: BMC Proceedings
Elizabeth M Blue, Lisa A Brown, Matthew P Conomos, Jennifer L Kirk, Alejandro Q Nato, Alice B Popejoy, Jesse Raffa, John Ranola, Ellen M Wijsman, Timothy Thornton
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. RESULTS: We found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations...
2016: BMC Proceedings
Alessandra Valcarcel, Kelsey Grinde, Kaitlyn Cook, Alden Green, Nathan Tintle
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. A reasonable extension of these approaches involves aggregating variants across an even larger set of variants (eg, all variants contained in genes within a pathway)...
2016: BMC Proceedings
Bamidele O Tayo, Liping Tong, Richard S Cooper
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...
2016: BMC Proceedings
Ellen E Quillen, John Blangero, Laura Almasy
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. METHODS: We compared 16 methods of calculating similarity for positive control matrices based on probes for the genes used to model the simulated Genetic Analysis Workshop phenotypes...
2016: BMC Proceedings
Adeline Lo, Michael Agne, Jonathan Auerbach, Rachel Fan, Shaw-Hwa Lo, Pei Wang, Tian Zheng
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. Using Genetic Analysis Workshop data on individuals with diabetes, for which only case-control labels of hypertension are known, we offer a foray into identification of diabetes-related gene interactions that are associated with hypertension...
2016: BMC Proceedings
Phillip E Melton, Juan M Peralta, Laura Almasy
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. This method simultaneously accounts for all repeat measurements of a phenotype in families...
2016: BMC Proceedings
Anne E Justice, Annie Green Howard, Geetha Chittoor, Lindsay Fernandez-Rhodes, Misa Graff, V Saroja Voruganti, Guoqing Diao, Shelly-Ann M Love, Nora Franceschini, Jeffrey R O'Connell, Christy L Avery, Kristin L Young, Kari E North
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...
2016: BMC Proceedings
Yen-Feng Chiu, Chun-Yi Lee, Fang-Chi Hsu
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...
2016: BMC Proceedings
Jianping Sun, Sahir R Bhatnagar, Karim Oualkacha, Antonio Ciampi, Celia M T Greenwood
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...
2016: BMC Proceedings
Yeunjoo E Song, Nathan J Morris, Catherine M Stein
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. The quantitative blood pressure (BP) traits, systolic BP (SBP) and diastolic BP (DBP) were analyzed as the main traits of interest with age, sex, and smoking status as covariates...
2016: BMC Proceedings
Mohamad Saad, Alejandro Q Nato, Fiona L Grimson, Steven M Lewis, Lisa A Brown, Elizabeth M Blue, Timothy A Thornton, Elizabeth A Thompson, Ellen M Wijsman
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. Apart from imputation, the use of IBD information is also common for several types of genetic analysis, including pedigree-based linkage analysis...
2016: BMC Proceedings
Stefan Konigorski, Yildiz E Yilmaz, Tobias Pischon
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...
2016: BMC Proceedings
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