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Genetic Epidemiology

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https://www.readbyqxmd.com/read/28090672/a-powerful-statistical-framework-for-generalization-testing-in-gwas-with-application-to-the-hchs-sol
#1
Tamar Sofer, Ruth Heller, Marina Bogomolov, Christy L Avery, Mariaelisa Graff, Kari E North, Alex P Reiner, Timothy A Thornton, Kenneth Rice, Yoav Benjamini, Cathy C Laurie, Kathleen F Kerr
In genome-wide association studies (GWAS), "generalization" is the replication of genotype-phenotype association in a population with different ancestry than the population in which it was first identified. Current practices for declaring generalizations rely on testing associations while controlling the family-wise error rate (FWER) in the discovery study, then separately controlling error measures in the follow-up study. This approach does not guarantee control over the FWER or false discovery rate (FDR) of the generalization null hypotheses...
January 15, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28039885/rare-variant-association-test-with-multiple-phenotypes
#2
Selyeong Lee, Sungho Won, Young Jin Kim, Yongkang Kim, Bong-Jo Kim, Taesung Park
Although genome-wide association studies (GWAS) have now discovered thousands of genetic variants associated with common traits, such variants cannot explain the large degree of "missing heritability," likely due to rare variants. The advent of next generation sequencing technology has allowed rare variant detection and association with common traits, often by investigating specific genomic regions for rare variant effects on a trait. Although multiple correlated phenotypes are often concurrently observed in GWAS, most studies analyze only single phenotypes, which may lessen statistical power...
December 31, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/28019059/impact-of-genotyping-errors-on-statistical-power-of-association-tests-in-genomic-analyses-a-case-study
#3
Lin Hou, Ning Sun, Shrikant Mane, Fred Sayward, Nallakkandi Rajeevan, Kei-Hoi Cheung, Kelly Cho, Saiju Pyarajan, Mihaela Aslan, Perry Miller, Philip D Harvey, J Michael Gaziano, John Concato, Hongyu Zhao
A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant's DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype-phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS-based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available...
December 26, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/28019042/evidence-for-snp-snp-interaction-identified-through-targeted-sequencing-of-cleft-case-parent-trios
#4
Yanzi Xiao, Margaret A Taub, Ingo Ruczinski, Ferdouse Begum, Jacqueline B Hetmanski, Holger Schwender, Elizabeth J Leslie, Daniel C Koboldt, Jeffrey C Murray, Mary L Marazita, Terri H Beaty
Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is the most common craniofacial birth defect in humans, affecting 1 in 700 live births. This malformation has a complex etiology where multiple genes and several environmental factors influence risk. At least a dozen different genes have been confirmed to be associated with risk of NSCL/P in previous studies. However, all the known genetic risk factors cannot fully explain the observed heritability of NSCL/P, and several authors have suggested gene-gene (G × G) interaction may be important in the etiology of this complex and heterogeneous malformation...
December 26, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/28019040/a-small-sample-multivariate-kernel-machine-test-for-microbiome-association-studies
#5
Xiang Zhan, Xingwei Tong, Ni Zhao, Arnab Maity, Michael C Wu, Jun Chen
High-throughput sequencing technologies have enabled large-scale studies of the role of the human microbiome in health conditions and diseases. Microbial community level association test, as a critical step to establish the connection between overall microbiome composition and an outcome of interest, has now been routinely performed in many studies. However, current microbiome association tests all focus on a single outcome. It has become increasingly common for a microbiome study to collect multiple, possibly related, outcomes to maximize the power of discovery...
December 26, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/28019039/integration-of-gene-expression-and-methylation-to-unravel-biological-networks-in-glioblastoma-patients
#6
Francesco Gadaleta, Kyrylo Bessonov, Kristel Van Steen
The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information. In this work, we describe Regression2Net, a computational approach that is able to integrate gene expression and genomic or methylation data in two steps. First, penalized regressions are used to build Expression-Expression (EEnet) and Expression-Genomic or Expression-Methylation (EMnet) networks. Second, network theory is used to highlight important communities of genes...
December 26, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/27990689/resetting-the-bar-statistical-significance-in-whole-genome-sequencing-based-association-studies-of-global-populations
#7
Sara L Pulit, Sera A J de With, Paul I W de Bakker
Genome-wide association studies (GWAS) of common disease have been hugely successful in implicating loci that modify disease risk. The bulk of these associations have proven robust and reproducible, in part due to community adoption of statistical criteria for claiming significant genotype-phenotype associations. As the cost of sequencing continues to drop, assembling large samples in global populations is becoming increasingly feasible. Sequencing studies interrogate not only common variants, as was true for genotyping-based GWAS, but variation across the full allele frequency spectrum, yielding many more (independent) statistical tests...
December 18, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/27943406/fast-genome-wide-qtl-association-mapping-on-pedigree-and-population-data
#8
Hua Zhou, John Blangero, Thomas D Dyer, Kei-Hang K Chan, Kenneth Lange, Eric M Sobel
Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects...
December 12, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/27917519/leveraging-population-information-in-family-based-rare-variant-association-analyses-of-quantitative-traits
#9
Yu Jiang, Yunqi Ji, Alexander B Sibley, Yi-Ju Li, Andrew S Allen
Confounding due to population substructure is always a concern in genetic association studies. Although methods have been proposed to adjust for population stratification in the context of common variation, it is unclear how well these approaches will work when interrogating rare variation. Family-based association tests can be constructed that are robust to population stratification. For example, when considering a quantitative trait, a linear model can be used that decomposes genetic effects into between- and within-family components and a test of the within-family component is robust to population stratification...
December 5, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/27900789/a-general-approach-to-testing-for-pleiotropy-with-rare-and-common-variants
#10
Sharon M Lutz, Tasha E Fingerlin, John E Hokanson, Christoph Lange
Through genome-wide association studies, numerous genes have been shown to be associated with multiple phenotypes. To determine the overlap of genetic susceptibility of correlated phenotypes, one can apply multivariate regression or dimension reduction techniques, such as principal components analysis, and test for the association with the principal components of the phenotypes rather than the individual phenotypes. However, as these approaches test whether there is a genetic effect for at least one of the phenotypes, a significant test result does not necessarily imply pleiotropy...
November 30, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/27885705/multiple-linear-combination-mlc-regression-tests-for-common-variants-adapted-to-linkage-disequilibrium-structure
#11
Yun Joo Yoo, Lei Sun, Julia G Poirier, Andrew D Paterson, Shelley B Bull
By jointly analyzing multiple variants within a gene, instead of one at a time, gene-based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive...
November 25, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/27813156/low-high-coverage-and-two-stage-dna-sequencing-in-the-design-of-the-genetic-association-study
#12
Chao Xu, Kehao Wu, Ji-Gang Zhang, Hui Shen, Hong-Wen Deng
Next-generation sequencing-based genetic association study (GAS) is a powerful tool to identify candidate disease variants and genomic regions. Although low-coverage sequencing offers low cost but inadequacy in calling rare variants, high coverage is able to detect essentially every variant but at a high cost. Two-stage sequencing may be an economical way to conduct GAS without losing power. In two-stage sequencing, an affordable number of samples are sequenced at high coverage as the reference panel, then to impute in a larger sample is sequenced at low coverage...
November 4, 2016: Genetic Epidemiology
https://www.readbyqxmd.com/read/27917522/genome-wide-survey-in-african-americans-demonstrates-potential-epistasis-of-fitness-in-the-human-genome
#13
Heming Wang, Yoonha Choi, Bamidele Tayo, Xuefeng Wang, Nathan Morris, Xiang Zhang, Uli Broeckel, Craig Hanis, Sharon Kardia, Susan Redline, Richard S Cooper, Hua Tang, Xiaofeng Zhu
The role played by epistasis between alleles at unlinked loci in shaping population fitness has been debated for many years and the existing evidence has been mainly accumulated from model organisms. In model organisms, fitness epistasis can be systematically inferred by detecting nonindependence of genotypic values between loci in a population and confirmed through examining the number of offspring produced in two-locus genotype groups. No systematic study has been conducted to detect epistasis of fitness in humans owing to experimental constraints...
February 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/27917525/a-comparison-study-of-multivariate-fixed-models-and-gene-association-with-multiple-traits-gamut-for-next-generation-sequencing
#14
Chi-Yang Chiu, Jeesun Jung, Yifan Wang, Daniel E Weeks, Alexander F Wilson, Joan E Bailey-Wilson, Christopher I Amos, James L Mills, Michael Boehnke, Momiao Xiong, Ruzong Fan
In this paper, extensive simulations are performed to compare two statistical methods to analyze multiple correlated quantitative phenotypes: (1) approximate F-distributed tests of multivariate functional linear models (MFLM) and additive models of multivariate analysis of variance (MANOVA), and (2) Gene Association with Multiple Traits (GAMuT) for association testing of high-dimensional genotype data. It is shown that approximate F-distributed tests of MFLM and MANOVA have higher power and are more appropriate for major gene association analysis (i...
January 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/27910131/whole-exome-association-of-rare-deletions-in-multiplex-oral-cleft-families
#15
Jack Fu, Terri H Beaty, Alan F Scott, Jacqueline Hetmanski, Margaret M Parker, Joan E Bailey Wilson, Mary L Marazita, Elisabeth Mangold, Hasan Albacha-Hejazi, Jeffrey C Murray, Alexandre Bureau, Jacob Carey, Stephen Cristiano, Ingo Ruczinski, Robert B Scharpf
By sequencing the exomes of distantly related individuals in multiplex families, rare mutational and structural changes to coding DNA can be characterized and their relationship to disease risk can be assessed. Recently, several rare single nucleotide variants (SNVs) were associated with an increased risk of nonsyndromic oral cleft, highlighting the importance of rare sequence variants in oral clefts and illustrating the strength of family-based study designs. However, the extent to which rare deletions in coding regions of the genome occur and contribute to risk of nonsyndromic clefts is not well understood...
January 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/27873357/use-of-big-data-for-drug-development-and-for-public-and-personal-health-and-care
#16
Lada Leyens, Matthias Reumann, Nuria Malats, Angela Brand
The use of data analytics across the entire healthcare value chain, from drug discovery and development through epidemiology to informed clinical decision for patients or policy making for public health, has seen an explosion in the recent years. The increase in quantity and variety of data available together with the improvement of storing capabilities and analytical tools offer numerous possibilities to all stakeholders (manufacturers, regulators, payers, healthcare providers, decision makers, researchers) but most importantly, it has the potential to improve general health outcomes if we learn how to exploit it in the right way...
January 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/27862229/sparse-multivariate-factor-analysis-regression-models-and-its-applications-to-integrative-genomics-analysis
#17
Yan Zhou, Pei Wang, Xianlong Wang, Ji Zhu, Peter X-K Song
The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors...
January 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/27862228/exome-copy-number-variation-detection-use-of-a-pool-of-unrelated-healthy-tissue-as-reference-sample
#18
Stephane Wenric, Tiberio Sticca, Jean-Hubert Caberg, Claire Josse, Corinne Fasquelle, Christian Herens, Mauricette Jamar, Stéphanie Max, André Gothot, Jo Caers, Vincent Bours
An increasing number of bioinformatic tools designed to detect CNVs (copy number variants) in tumor samples based on paired exome data where a matched healthy tissue constitutes the reference have been published in the recent years. The idea of using a pool of unrelated healthy DNA as reference has previously been formulated but not thoroughly validated. As of today, the gold standard for CNV calling is still aCGH but there is an increasing interest in detecting CNVs by exome sequencing. We propose to design a metric allowing the comparison of two CNV profiles, independently of the technique used and assessed the validity of using a pool of unrelated healthy DNA instead of a matched healthy tissue as reference in exome-based CNV detection...
January 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/27859682/a-method-for-integrating-neuroimaging-into-genetic-models-of-learning-performance
#19
Chintan M Mehta, Jeffrey R Gruen, Heping Zhang
Specific learning disorders (SLD) are an archetypal example of how clinical neuropsychological (NP) traits can differ from underlying genetic and neurobiological risk factors. Disparate environmental influences and pathologies impact learning performance assessed through cognitive examinations and clinical evaluations, the primary diagnostic tools for SLD. We propose a neurobiological risk for SLD with neuroimaging biomarkers, which is integrated into a genome-wide association study (GWAS) of learning performance in a cohort of 479 European individuals between 8 and 21 years of age...
January 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/27859628/longitudinal-snp-set-association-analysis-of-quantitative-phenotypes
#20
Zhong Wang, Ke Xu, Xinyu Zhang, Xiaowei Wu, Zuoheng Wang
Many genetic epidemiological studies collect repeated measurements over time. This design not only provides a more accurate assessment of disease condition, but allows us to explore the genetic influence on disease development and progression. Thus, it is of great interest to study the longitudinal contribution of genes to disease susceptibility. Most association testing methods for longitudinal phenotypes are developed for single variant, and may have limited power to detect association, especially for variants with low minor allele frequency...
January 2017: Genetic Epidemiology
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