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Genetic Epidemiology

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...
December 1, 2016: Genetic Epidemiology
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
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
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...
November 21, 2016: Genetic Epidemiology
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...
November 18, 2016: Genetic Epidemiology
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...
November 10, 2016: Genetic Epidemiology
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...
November 10, 2016: Genetic Epidemiology
Young Jin Kim, Juyoung Lee, Bong-Jo Kim, Taesung Park
Imputation is widely used for obtaining information about rare variants. However, one issue concerning imputation is the low accuracy of imputed rare variants as the inaccurate imputed rare variants may distort the results of region-based association tests. Therefore, we developed a pre-collapsing imputation method (PreCimp) to improve the accuracy of imputation by using collapsed variables. Briefly, collapsed variables are generated using rare variants in the reference panel, and a new reference panel is constructed by inserting pre-collapsed variables into the original reference panel...
November 10, 2016: Genetic Epidemiology
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...
November 9, 2016: Genetic Epidemiology
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
Kyrylo Bessonov, Kristel Van Steen
Gene regulatory network (GRN) inference is an active area of research that facilitates understanding the complex interplays between biological molecules. We propose a novel framework to create such GRNs, based on Conditional Inference Forests (CIFs) as proposed by Strobl et al. Our framework consists of using ensembles of Conditional Inference Trees (CITs) and selecting an appropriate aggregation scheme for variant selection prior to network construction. We show on synthetic microarray data that taking the original implementation of CIFs with conditional permutation scheme (CIFcond ) may lead to improved performance compared to Breiman's implementation of Random Forests (RF)...
December 2016: Genetic Epidemiology
Ying Cao, Suja S Rajan, Peng Wei
A Mendelian randomization (MR) analysis is performed to analyze the causal effect of an exposure variable on a disease outcome in observational studies, by using genetic variants that affect the disease outcome only through the exposure variable. This method has recently gained popularity among epidemiologists given the success of genetic association studies. Many exposure variables of interest in epidemiological studies are time varying, for example, body mass index (BMI). Although longitudinal data have been collected in many cohort studies, current MR studies only use one measurement of a time-varying exposure variable, which cannot adequately capture the long-term time-varying information...
December 2016: Genetic Epidemiology
Godwin Yung, Xihong Lin
Case-control association studies often collect from their subjects information on secondary phenotypes. Reusing the data and studying the association between genes and secondary phenotypes provide an attractive and cost-effective approach that can lead to discovery of new genetic associations. A number of approaches have been proposed, including simple and computationally efficient ad hoc methods that ignore ascertainment or stratify on case-control status. Justification for these approaches relies on the assumption of no covariates and the correct specification of the primary disease model as a logistic model...
December 2016: Genetic Epidemiology
J Willem L Tideman, Qiao Fan, Jan Roelof Polling, Xiaobo Guo, Seyhan Yazar, Anthony Khawaja, René Höhn, Yi Lu, Vincent W V Jaddoe, Kenji Yamashiro, Munemitsu Yoshikawa, Aslihan Gerhold-Ay, Stefan Nickels, Tanja Zeller, Mingguang He, Thibaud Boutin, Goran Bencic, Veronique Vitart, David A Mackey, Paul J Foster, Stuart MacGregor, Cathy Williams, Seang Mei Saw, Jeremy A Guggenheim, Caroline C W Klaver
Previous studies have identified many genetic loci for refractive error and myopia. We aimed to investigate the effect of these loci on ocular biometry as a function of age in children, adolescents, and adults. The study population consisted of three age groups identified from the international CREAM consortium: 5,490 individuals aged <10 years; 5,000 aged 10-25 years; and 16,274 aged >25 years. All participants had undergone standard ophthalmic examination including measurements of axial length (AL) and corneal radius (CR)...
December 2016: Genetic Epidemiology
Qianchuan He, Tianxi Cai, Yang Liu, Ni Zhao, Quaker E Harmon, Lynn M Almli, Elisabeth B Binder, Stephanie M Engel, Kerry J Ressler, Karen N Conneely, Xihong Lin, Michael C Wu
Kernel machine learning methods, such as the SNP-set kernel association test (SKAT), have been widely used to test associations between traits and genetic polymorphisms. In contrast to traditional single-SNP analysis methods, these methods are designed to examine the joint effect of a set of related SNPs (such as a group of SNPs within a gene or a pathway) and are able to identify sets of SNPs that are associated with the trait of interest. However, as with many multi-SNP testing approaches, kernel machine testing can draw conclusion only at the SNP-set level, and does not directly inform on which one(s) of the identified SNP set is actually driving the associations...
December 2016: Genetic Epidemiology
Hugues Aschard
The identification of gene-gene and gene-environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of the inherent characteristics of standard regression-based interaction analyses. Here, I revisit and untangle major theoretical aspects of interaction tests in the special case of linear regression; in particular, I discuss variables coding scheme, interpretation of effect estimate, statistical power, and estimation of variance explained in regard of various hypothetical interaction patterns...
December 2016: Genetic Epidemiology
H Robert Frost, Christopher I Amos, Jason H Moore
Statistical interactions between markers of genetic variation, or gene-gene interactions, are believed to play an important role in the etiology of many multifactorial diseases and other complex phenotypes. Unfortunately, detecting gene-gene interactions is extremely challenging due to the large number of potential interactions and ambiguity regarding marker coding and interaction scale. For many data sets, there is insufficient statistical power to evaluate all candidate gene-gene interactions. In these cases, a global test for gene-gene interactions may be the best option...
December 2016: Genetic Epidemiology
Ruzong Fan, Chi-Yang Chiu, Jeesun Jung, Daniel E Weeks, Alexander F Wilson, Joan E Bailey-Wilson, Christopher I Amos, Zhen Chen, James L Mills, Momiao Xiong
In association studies of complex traits, fixed-effect regression models are usually used to test for association between traits and major gene loci. In recent years, variance-component tests based on mixed models were developed for region-based genetic variant association tests. In the mixed models, the association is tested by a null hypothesis of zero variance via a sequence kernel association test (SKAT), its optimal unified test (SKAT-O), and a combined sum test of rare and common variant effect (SKAT-C)...
December 2016: Genetic Epidemiology
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November 2016: Genetic Epidemiology
Stephen Burgess, Neil M Davies, Simon G Thompson
Mendelian randomization analyses are often performed using summarized data. The causal estimate from a one-sample analysis (in which data are taken from a single data source) with weak instrumental variables is biased in the direction of the observational association between the risk factor and outcome, whereas the estimate from a two-sample analysis (in which data on the risk factor and outcome are taken from non-overlapping datasets) is less biased and any bias is in the direction of the null. When using genetic consortia that have partially overlapping sets of participants, the direction and extent of bias are uncertain...
November 2016: Genetic Epidemiology
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