PreCimp: Pre ‐collapsing imputation approach increases imputation accuracy of rare variants in terms of collapsed variables
ABSTRACT
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. Following imputation...
Source: Genetic Epidemiology - November 9, 2016 Category: Epidemiology Authors: Young Jin Kim, Juyoung Lee, Bong ‐Jo Kim, , Taesung Park Tags: Research Article Source Type: research
Exome copy number variation detection: Use of a pool of unrelated healthy tissue as reference sample
ABSTRACT
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 ...
Source: Genetic Epidemiology - November 9, 2016 Category: Epidemiology Authors: Stephane Wenric, Tiberio Sticca, Jean ‐Hubert Caberg, Claire Josse, Corinne Fasquelle, Christian Herens, Mauricette Jamar, Stéphanie Max, André Gothot, Jo Caers, Vincent Bours Tags: Research Article Source Type: research
Mendelian randomization analysis of a time ‐varying exposure for binary disease outcomes using functional data analysis methods
ABSTRACT
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 exposur...
Source: Genetic Epidemiology - November 3, 2016 Category: Epidemiology Authors: Ying Cao, Suja S. Rajan, Peng Wei Tags: Research Article Source Type: research
Practical aspects of gene regulatory inference via conditional inference forests from expression data
ABSTRACT
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 performa...
Source: Genetic Epidemiology - October 31, 2016 Category: Epidemiology Authors: Kyrylo Bessonov, Kristel Steen Tags: Research Article Source Type: research
Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis
ABSTRACT
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. This proposed method not only allows us to address...
Source: Genetic Epidemiology - October 31, 2016 Category: Epidemiology Authors: Yan Zhou, Pei Wang, Xianlong Wang, Ji Zhu, Peter X. ‐K. Song Tags: Research Article Source Type: research
Longitudinal SNP ‐set association analysis of quantitative phenotypes
ABSTRACT
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. We propose Longitudinal SNP‐set/sequence kernel association test (LS...
Source: Genetic Epidemiology - October 31, 2016 Category: Epidemiology Authors: Zhong Wang, Ke Xu, Xinyu Zhang, Xiaowei Wu, Zuoheng Wang Tags: Research Article Source Type: research
Low ‐, high‐coverage, and two‐stage DNA sequencing in the design of the genetic association study
ABSTRACT
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. As unit sequencing costs continue to dec...
Source: Genetic Epidemiology - October 31, 2016 Category: Epidemiology Authors: Chao Xu, Kehao Wu, Ji ‐Gang Zhang, Hui Shen, Hong‐Wen Deng Tags: Research Article Source Type: research
Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - October 17, 2016 Category: Epidemiology Tags: Issue Information Source Type: research
The 2016 Annual Meeting of the International Genetic Epidemiology Society
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - August 31, 2016 Category: Epidemiology Tags: Abstracts Source Type: research
Validity of using ad hoc methods to analyze secondary traits in case ‐control association studies
ABSTRACT
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 mod...
Source: Genetic Epidemiology - August 31, 2016 Category: Epidemiology Authors: Godwin Yung, Xihong Lin Tags: Research Article Source Type: research
Bias due to participant overlap in two ‐sample Mendelian randomization
ABSTRACT
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 bia...
Source: Genetic Epidemiology - August 31, 2016 Category: Epidemiology Authors: Stephen Burgess, Neil M. Davies, Simon G. Thompson Tags: Research Article Source Type: research
When do myopia genes have their effect? Comparison of genetic risks between children and adults
We examined the lead SNP at all 39 currently known genetic loci for refractive error identified from genome‐wide association studies (GWAS), as well as a combined genetic risk score (GRS). The beta coefficient for association between SNP genotype or GRS versus AL/CR was compared across the three age groups, adjusting for age, sex, and principal components. Analyses were Bonferroni‐corrected. In the age group <10 years, three loci (GJD2, CHRNG, ZIC2) were associated with AL/CR. In the age group 10–25 years, four loci (BMP2, KCNQ5, A2BP1, CACNA1D) were associated; and in adults 20 loci were associated. Association w...
Source: Genetic Epidemiology - August 31, 2016 Category: Epidemiology Authors: J. Willem L. Tideman, Qiao Fan, Jan Roelof Polling, Xiaobo Guo, Seyhan Yazar, Anthony Khawaja, Ren é Höhn, Yi Lu, Vincent W.V. Jaddoe, Kenji Yamashiro, Munemitsu Yoshikawa, Aslihan Gerhold‐Ay, Stefan Nickels, Tanja Zeller, Mingguang He, Thibaud Boutin Tags: Research Article Source Type: research
Identifying significant gene ‐environment interactions using a combination of screening testing and hierarchical false discovery rate control
ABSTRACT
Although gene‐environment (G× E) interactions play an important role in many biological systems, detecting these interactions within genome‐wide data can be challenging due to the loss in statistical power incurred by multiple hypothesis correction. To address the challenge of poor power and the limitations of existing multistage methods, we recently developed a screening‐testing approach for G× E interaction detection that combines elastic net penalized regression with joint estimation to support a single omnibus test for the presence of G× E interactions. In our original work on this technique, however,...
Source: Genetic Epidemiology - August 31, 2016 Category: Epidemiology Authors: H. Robert Frost, Li Shen, Andrew J. Saykin, Scott M. Williams, Jason H. Moore, Tags: Research Article Source Type: research
Efficient unified rare variant association test by modeling the population genetic distribution in case ‐control studies
ABSTRACT
Recent advancements in next‐generation DNA sequencing technologies have made it plausible to study the association of rare variants with complex diseases. Due to the low frequency, rare variants need to be aggregated in association tests to achieve adequate power with reasonable sample sizes. Hierarchical modeling/kernel machine methods have gained popularity among many available methods for testing a set of rare variants collectively. Here, we propose a new score statistic based on a hierarchical model by additionally modeling the distribution of rare variants under the case‐control study design. Results from...
Source: Genetic Epidemiology - August 23, 2016 Category: Epidemiology Authors: Huilin Li, Jinbo Chen Tags: Research Article Source Type: research
A W ‐test collapsing method for rare‐variant association testing in exome sequencing data
ABSTRACT
Advancement in sequencing technology enables the study of association between complex disorder phenotypes and single‐nucleotide polymorphisms with rare mutations. However, the rare genetic variant has extremely small variance and impairs testing power of traditional statistical methods. We introduce a W‐test collapsing method to evaluate rare‐variant association by measuring the distributional differences between cases and controls through combined log of odds ratio within a genomic region. The method is model‐free and inherits chi‐squared distribution with degrees of freedom estimated from bootstrapped ...
Source: Genetic Epidemiology - August 17, 2016 Category: Epidemiology Authors: Rui Sun, Haoyi Weng, Inchi Hu, Junfeng Guo, William K. K. Wu, Benny Chung ‐Ying Zee, Maggie Haitian Wang Tags: Research Article Source Type: research
