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Genetic Epidemiology

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https://www.readbyqxmd.com/read/28726280/an-efficient-study-design-to-test-parent-of-origin-effects-in-family-trios
#1
Xiaobo Yu, Gao Chen, Rui Feng
Increasing evidence has shown that genes may cause prenatal, neonatal, and pediatric diseases depending on their parental origins. Statistical models that incorporate parent-of-origin effects (POEs) can improve the power of detecting disease-associated genes and help explain the missing heritability of diseases. In many studies, children have been sequenced for genome-wide association testing. But it may become unaffordable to sequence their parents and evaluate POEs. Motivated by the reality, we proposed a budget-friendly study design of sequencing children and only genotyping their parents through single nucleotide polymorphism array...
July 20, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28714590/adaptive-testing-for-association-between-two-random-vectors-in-moderate-to-high-dimensions
#2
Zhiyuan Xu, Gongjun Xu, Wei Pan
Testing for association between two random vectors is a common and important task in many fields, however, existing tests, such as Escoufier's RV test, are suitable only for low-dimensional data, not for high-dimensional data. In moderate to high dimensions, it is necessary to consider sparse signals, which are often expected with only a few, but not many, variables associated with each other. We generalize the RV test to moderate-to-high dimensions. The key idea is to data adaptively weight each variable pair based on its empirical association...
July 17, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28691305/a-comparison-of-methods-for-inferring-causal-relationships-between-genotype-and-phenotype-using-additional-biological-measurements
#3
Holly F Ainsworth, So-Youn Shin, Heather J Cordell
Genome wide association studies (GWAS) have been very successful over the last decade at identifying genetic variants associated with disease phenotypes. However, interpretation of the results obtained can be challenging. Incorporation of further relevant biological measurements (e.g. 'omics' data) measured in the same individuals for whom we have genotype and phenotype data may help us to learn more about the mechanism and pathways through which causal genetic variants affect disease. We review various methods for causal inference that can be used for assessing the relationships between genetic variables, other biological measures, and phenotypic outcome, and present a simulation study assessing the performance of the methods under different conditions...
July 10, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28657194/accommodating-missingness-in-environmental-measurements-in-gene-environment-interaction-analysis
#4
Mengyun Wu, Yangguang Zang, Sanguo Zhang, Jian Huang, Shuangge Ma
For the prognosis of complex diseases, beyond the main effects of genetic (G) and environmental (E) factors, gene-environment (G-E) interactions also play an important role. Many approaches have been developed for detecting important G-E interactions, most of which assume that measurements are complete. In practical data analysis, missingness in E measurements is not uncommon, and failing to properly accommodate such missingness leads to biased estimation and false marker identification. In this study, we conduct G-E interaction analysis with prognosis data under an accelerated failure time (AFT) model...
June 28, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28657151/joint-genotype-and-ancestry-based-genome-wide-association-studies-in-admixed-populations
#5
Piotr Szulc, Malgorzata Bogdan, Florian Frommlet, Hua Tang
In genome-wide association studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand, admixture mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific ancestry). Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests...
June 28, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28657150/improving-power-for-rare-variant-tests-by-integrating-external-controls
#6
Seunggeun Lee, Sehee Kim, Christian Fuchsberger
Due to the drop in sequencing cost, the number of sequenced genomes is increasing rapidly. To improve power of rare-variant tests, these sequenced samples could be used as external control samples in addition to control samples from the study itself. However, when using external controls, possible batch effects due to the use of different sequencing platforms or genotype calling pipelines can dramatically increase type I error rates. To address this, we propose novel summary statistics based single and gene- or region-based rare-variant tests that allow the integration of external controls while controlling for type I error...
June 28, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28643332/integrative-eqtl-analysis-of-tumor-and-host-omics-data-in-individuals-with-bladder-cancer
#7
Silvia Pineda, Kristel Van Steen, Núria Malats
Integrative analyses of several omics data are emerging. The data are usually generated from the same source material (i.e., tumor sample) representing one level of regulation. However, integrating different regulatory levels (i.e., blood) with those from tumor may also reveal important knowledge about the human genetic architecture. To model this multilevel structure, an integrative-expression quantitative trait loci (eQTL) analysis applying two-stage regression (2SR) was proposed. This approach first regressed tumor gene expression levels with tumor markers and the adjusted residuals from the previous model were then regressed with the germline genotypes measured in blood...
June 23, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28636232/a-genetic-stochastic-process-model-for-genome-wide-joint-analysis-of-biomarker-dynamics-and-disease-susceptibility-with-longitudinal-data
#8
Liang He, Ilya Zhbannikov, Konstantin G Arbeev, Anatoliy I Yashin, Alexander M Kulminski
Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases...
June 21, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28626864/detecting-genetic-association-through-shortest-paths-in-a-bidirected-graph
#9
Masao Ueki, Yoshinori Kawasaki, Gen Tamiya
Genome-wide association studies (GWASs) commonly use marginal association tests for each single-nucleotide polymorphism (SNP). Because these tests treat SNPs as independent, their power will be suboptimal for detecting SNPs hidden by linkage disequilibrium (LD). One way to improve power is to use a multiple regression model. However, the large number of SNPs preclude simultaneous fitting with multiple regression, and subset regression is infeasible because of an exorbitant number of candidate subsets. We therefore propose a new method for detecting hidden SNPs having significant yet weak marginal association in a multiple regression model...
September 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28580727/integrative-gene-set-enrichment-analysis-utilizing-isoform-specific-expression
#10
Lie Li, Xinlei Wang, Guanghua Xiao, Adi Gazdar
Gene set enrichment analysis (GSEA) aims at identifying essential pathways, or more generally, sets of biologically related genes that are involved in complex human diseases. In the past, many studies have shown that GSEA is a very useful bioinformatics tool that plays critical roles in the innovation of disease prevention and intervention strategies. Despite its tremendous success, it is striking that conclusions of GSEA drawn from isolated studies are often sparse, and different studies may lead to inconsistent and sometimes contradictory results...
September 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28580640/region-based-association-tests-for-sequencing-data-on-survival-traits
#11
Li-Chu Chien, Donald W Bowden, Yen-Feng Chiu
Family-based designs enriched with affected subjects and disease associated variants can increase statistical power for identifying functional rare variants. However, few rare variant analysis approaches are available for time-to-event traits in family designs and none of them applicable to the X chromosome. We developed novel pedigree-based burden and kernel association tests for time-to-event outcomes with right censoring for pedigree data, referred to FamRATS (family-based rare variant association tests for survival traits)...
September 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28480976/polygenic-scores-via-penalized-regression-on-summary-statistics
#12
Timothy Shin Heng Mak, Robert Milan Porsch, Shing Wan Choi, Xueya Zhou, Pak Chung Sham
Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses...
September 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28560825/phredem-a-phred-score-informed-genotype-calling-approach-for-next-generation-sequencing-studies
#13
Peizhou Liao, Glen A Satten, Yi-Juan Hu
A fundamental challenge in analyzing next-generation sequencing (NGS) data is to determine an individual's genotype accurately, as the accuracy of the inferred genotype is essential to downstream analyses. Correctly estimating the base-calling error rate is critical to accurate genotype calls. Phred scores that accompany each call can be used to decide which calls are reliable. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores...
July 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28464407/conditional-analysis-of-multiple-quantitative-traits-based-on-marginal-gwas-summary-statistics
#14
Yangqing Deng, Wei Pan
There has been an increasing interest in joint association testing of multiple traits for possible pleiotropic effects. However, even in the presence of pleiotropy, most of the existing methods cannot distinguish direct and indirect effects of a genetic variant, say single-nucleotide polymorphism (SNP), on multiple traits, and a conditional analysis of a trait adjusting for other traits is perhaps the simplest and most common approach to addressing this question. However, without individual-level genotypic and phenotypic data but with only genome-wide association study (GWAS) summary statistics, as typical with most large-scale GWAS consortium studies, we are not aware of any existing method for such a conditional analysis...
July 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28464328/inferring-gene-regulatory-relationships-with-a-high-dimensional-robust-approach
#15
Yangguang Zang, Qing Zhao, Qingzhao Zhang, Yang Li, Sanguo Zhang, Shuangge Ma
Gene expression (GE) levels have important biological and clinical implications. They are regulated by copy number alterations (CNAs). Modeling the regulatory relationships between GEs and CNAs facilitates understanding disease biology and can also have values in translational medicine. The expression level of a gene can be regulated by its cis-acting as well as trans-acting CNAs, and the set of trans-acting CNAs is usually not known, which poses a high-dimensional selection and estimation problem. Most of the existing studies share a common limitation in that they cannot accommodate long-tailed distributions or contamination of GE data...
July 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28421636/leveraging-cell-type-specific-regulatory-regions-to-detect-snps-associated-with-tissue-factor-pathway-inhibitor-plasma-levels
#16
Jessica Dennis, Alejandra Medina-Rivera, Vinh Truong, Lina Antounians, Nora Zwingerman, Giovana Carrasco, Lisa Strug, Phil Wells, David-Alexandre Trégouët, Pierre-Emmanuel Morange, Michael D Wilson, France Gagnon
Tissue factor pathway inhibitor (TFPI) regulates the formation of intravascular blood clots, which manifest clinically as ischemic heart disease, ischemic stroke, and venous thromboembolism (VTE). TFPI plasma levels are heritable, but the genetics underlying TFPI plasma level variability are poorly understood. Herein we report the first genome-wide association scan (GWAS) of TFPI plasma levels, conducted in 251 individuals from five extended French-Canadian Families ascertained on VTE. To improve discovery, we also applied a hypothesis-driven (HD) GWAS approach that prioritized single nucleotide polymorphisms (SNPs) in (1) hemostasis pathway genes, and (2) vascular endothelial cell (EC) regulatory regions, which are among the highest expressers of TFPI...
July 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28393390/a-novel-association-test-for-multiple-secondary-phenotypes-from-a-case-control-gwas
#17
RANDOMIZED CONTROLLED TRIAL
Debashree Ray, Saonli Basu
In the past decade, many genome-wide association studies (GWASs) have been conducted to explore association of single nucleotide polymorphisms (SNPs) with complex diseases using a case-control design. These GWASs not only collect information on the disease status (primary phenotype, D) and the SNPs (genotypes, X), but also collect extensive data on several risk factors and traits. Recent literature and grant proposals point toward a trend in reusing existing large case-control data for exploring genetic associations of some additional traits (secondary phenotypes, Y) collected during the study...
July 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28393384/binomirare-a-robust-test-of-the-association-of-a-rare-variant-with-a-disease-for-pooled-analysis-and-meta-analysis-with-application-to-the-hchs-sol
#18
Tamar Sofer
Most regression-based tests of the association between a low-count variant and a binary outcome do not protect type 1 error, especially when tests are rejected based on a very low significance threshold. Noted exception is the Firth test. However, it was recently shown that in meta-analyzing multiple studies all asymptotic, regression-based tests, including the Firth, may not control type 1 error in some settings, and the Firth test may suffer a substantial loss of power. The problem is exacerbated when the case-control proportions differ between studies...
July 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28370330/a-combination-test-for-detection-of-gene-environment-interaction-in-cohort-studies
#19
Brandon Coombes, Saonli Basu, Matt McGue
Identifying gene-environment (G-E) interactions can contribute to a better understanding of disease etiology, which may help researchers develop disease prevention strategies and interventions. One big criticism of studying G-E interaction is the lack of power due to sample size. Studies often restrict the interaction search to the top few hundred hits from a genome-wide association study or focus on potential candidate genes. In this paper, we test interactions between a candidate gene and an environmental factor to improve power by analyzing multiple variants within a gene...
July 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28393391/inclusion-of-biological-knowledge-in-a-bayesian-shrinkage-model-for-joint-estimation-of-snp-effects
#20
Miguel Pereira, John R Thompson, Christian X Weichenberger, Duncan C Thomas, Cosetta Minelli
With the aim of improving detection of novel single-nucleotide polymorphisms (SNPs) in genetic association studies, we propose a method of including prior biological information in a Bayesian shrinkage model that jointly estimates SNP effects. We assume that the SNP effects follow a normal distribution centered at zero with variance controlled by a shrinkage hyperparameter. We use biological information to define the amount of shrinkage applied on the SNP effects distribution, so that the effects of SNPs with more biological support are less shrunk toward zero, thus being more likely detected...
May 2017: Genetic Epidemiology
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