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Genetic Epidemiology

Sohee Han, Mi Yeong Hwang, Kyungheon Yoon, Yun Kyoung Kim, Young-Jin Kim, Bong-Jo Kim, Sanghoon Moon
Lipid levels in blood are widely used to diagnose and monitor chronic diseases. It is essential to identify the genetic traits involved in lipid metabolism for understanding chronic diseases. However, the influence of genetic traits varies depending on race, sex, age, and ethnicity. Therefore, research focusing on populations of individual countries is required, and the results can be used as a basis for comparison of results of other studies at the cross-racial and cross-country levels. In the present study, we selected lipid-related variants and evaluated their effects on lipid-related diseases in more than 14,000 subjects of three cohorts using the Illumina Human Exome Beadchip...
May 13, 2019: Genetic Epidemiology
Ozvan Bocher, Gaëlle Marenne, Aude Saint Pierre, Thomas E Ludwig, Stéphanie Guey, Elisabeth Tournier-Lasserve, Hervé Perdry, Emmanuelle Génin
Genetic association studies have provided new insights into the genetic variability of human complex traits with a focus mainly on continuous or binary traits. Methods have been proposed to take into account disease heterogeneity between subgroups of patients when studying common variants but none was specifically designed for rare variants. Because rare variants are expected to have stronger effects and to be more heterogeneously distributed among cases than common ones, subgroup analyses might be particularly attractive in this context...
May 13, 2019: Genetic Epidemiology
Megan Major, Malika K Freund, Kathryn S Burch, Nicholas Mancuso, Michael Ng, Dominic Furniss, Bogdan Pasaniuc, Roel A Ophoff
Dupuytren's disease is a common inherited tissue-specific fibrotic disorder, characterized by progressive and irreversible fibroblastic proliferation affecting the palmar fascia of the hand. Although genome-wide association study (GWAS) have identified 24 genomic regions associated with Dupuytrens risk, the biological mechanisms driving signal at these regions remain elusive. We identify potential biological mechanisms for Dupuytren's disease by integrating the most recent, largest GWAS (3,871 cases and 4,686 controls) with eQTLs (47 tissue panels from five consortia, total n = 3,975) to perform a transcriptome-wide association study...
May 13, 2019: Genetic Epidemiology
Jue-Sheng Ong, Stuart MacGregor
With the advent of very large scale genome-wide association studies (GWASs), the promise of Mendelian randomization (MR) has begun to be fulfilled. However, whilst GWASs have provided essential information on the single nucleotide polymorphisms (SNPs) associated with modifiable risk factors needed for MR, the availability of large numbers of SNP instruments raises issues of how best to use this information and how to deal with potential problems such as pleiotropy. Here we provide commentary on some of the recent advances in the MR analysis, including an overview of the different genetic architectures that are being uncovered for a variety of modifiable risk factors and how users ought to take that into consideration when designing MR studies...
May 2, 2019: Genetic Epidemiology
Elizabeth A Thompson
It is 100 years since R. A. Fisher proposed that a Mendelian model of genetic variant effects, additive over loci, could explain the patterns of observed phenotypic correlations between relatives. His loci were hypothetical and his model theoretical. It is only about 50 years since the first genetic markers allowed the detection of even variants with major effects on phenotype, and only 20 years since the development of single-nucleotide polymorphism technology provided dense markers over the genome. Then both mappings in defined pedigrees and population-based genome-wide association studies samples allowed the detection of multiple contributing variants of smaller effect...
May 2, 2019: Genetic Epidemiology
Mathias Gorski, Felix Günther, Thomas W Winkler, Bernhard H F Weber, Iris M Heid
While current genome-wide association analyses often rely on meta-analysis of study-specific summary statistics, individual participant data (IPD) from multiple studies increase options for modeling. When multistudy IPD is available, however, it is unclear whether this data is to be imputed and modeled across all participants (mega-imputation and mega-analysis) or study-specifically (meta-imputation and meta-analysis). Here, we investigated different approaches toward imputation and analysis using 52,189 subjects from 25 studies of the International Age-related Macular Degeneration (AMD) Genomics Consortium including, 16,144 AMD cases and 17,832 controls for association analysis...
April 23, 2019: Genetic Epidemiology
Heather E Wheeler, Sally Ploch, Alvaro N Barbeira, Rodrigo Bonazzola, Angela Andaleon, Alireza Fotuhi Sishpirani, Ashis Saha, Alexis Battle, Sushmita Roy, Hae Kyung Im
Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets...
April 4, 2019: Genetic Epidemiology
Xuan Bi, Long Feng, Shiying Wang, Zijie Lin, Tengfei Li, Bingxin Zhao, Hongtu Zhu, Heping Zhang
Schizophrenia is a highly heritable mental disorder and is reported to be associated with measurements in cortical regions of the human brain. In this study, we considered genome-wide association studies to uncover genetic effects on cortical regions and prodromal symptoms of schizophrenia. Specifically, area, thickness, and volume of 66 cortical regions derived from magnetic resonance imaging scans of 1,445 children and adolescents from the Philadelphia Neurodevelopmental Cohort were studied. Two common variants were identified as being associated with two prefrontal cortical regions (one significant variant rs11601331 on chromosome 11p11 for right rostral middle frontal gyral area, p = 1...
April 3, 2019: Genetic Epidemiology
Holly Trochet, Matti Pirinen, Gavin Band, Luke Jostins, Gilean McVean, Chris C A Spencer
Genome-wide association studies (GWAS) are a powerful tool for understanding the genetic basis of diseases and traits, but most studies have been conducted in isolation, with a focus on either a single or a set of closely related phenotypes. We describe MetABF, a simple Bayesian framework for performing integrative meta-analysis across multiple GWAS using summary statistics. The approach is applicable across a wide range of study designs and can increase the power by 50% compared with standard frequentist tests when only a subset of studies have a true effect...
March 28, 2019: Genetic Epidemiology
Andriy Derkach, Ruth M Pfeiffer
Meta-analysis of multiple genome-wide association studies (GWAS) is effective for detecting single- or multimarker associations with complex traits. We develop a flexible procedure (subset testing and analysis of multiple phenotypes [STAMP]) based on mixture models to perform a region-based meta-analysis of different phenotypes using data from different GWAS and identify subsets of associated phenotypes. Our model framework helps distinguish true associations from between-study heterogeneity. As a measure of association, we compute for each phenotype the posterior probability that the genetic region under investigation is truly associated...
March 28, 2019: Genetic Epidemiology
Iryna Lobach, Inyoung Kim, Alexander Alekseyenko, Siarhei Lobach, Li Zhang
Case-control genome-wide association studies (CC-GWAS) might provide valuable clues to the underlying pathophysiologic mechanisms of complex diseases, such as neurodegenerative disease and cancer. A commonly overlooked complication is that multiple distinct disease states might present with the same set of symptoms and hence share a clinical diagnosis. These disease states can only be distinguished based on a biomarker evaluation that might not be feasible in the whole set of cases in the large number of samples that are typically needed for CC-GWAS...
March 19, 2019: Genetic Epidemiology
Line Skotte, Emil Jørsboe, Thorfinn S Korneliussen, Ida Moltke, Anders Albrechtsen
During the last decade genome-wide association studies have proven to be a powerful approach to identifying disease-causing variants. However, for admixed populations, most current methods for association testing are based on the assumption that the effect of a genetic variant is the same regardless of its ancestry. This is a reasonable assumption for a causal variant but may not hold for the genetic variants that are tested in genome-wide association studies, which are usually not causal. The effects of noncausal genetic variants depend on how strongly their presence correlate with the presence of the causal variant, which may vary between ancestral populations because of different linkage disequilibrium patterns and allele frequencies...
March 18, 2019: Genetic Epidemiology
Somayeh Kafaie, Yuanzhu Chen, Ting Hu
The heritability of complex diseases including cancer is often attributed to multiple interacting genetic alterations. Such a non-linear, non-additive gene-gene interaction effect, that is, epistasis, renders univariable analysis methods ineffective for genome-wide association studies. In recent years, network science has seen increasing applications in modeling epistasis to characterize the complex relationships between a large number of genetic variations and the phenotypic outcome. In this study, by constructing a statistical epistasis network of colorectal cancer (CRC), we proposed to use multiple network measures to prioritize genes that influence the disease risk of CRC through synergistic interaction effects...
March 11, 2019: Genetic Epidemiology
Yalei Chen, Indra Adrianto, Michael C Ianuzzi, Lori Garman, Courtney G Montgomery, Benjamin A Rybicki, Albert M Levin, Jia Li
The etiology of many complex diseases involves both environmental exposures and inherited genetic predisposition as well as interactions between them. Gene-environment-wide interaction studies (GEWIS) provide a means to identify the interactions between genetic variation and environmental exposures that underlie disease risk. However, current GEWIS methods lack the capability to adjust for the potentially complex correlations in studies with varying degrees of relationships (both known and unknown) among individuals in admixed populations...
February 22, 2019: Genetic Epidemiology
Rounak Dey, Jonas B Nielsen, Lars G Fritsche, Wei Zhou, Huanhuan Zhu, Cristen J Willer, Seunggeun Lee
With the availability of large-scale biobanks, genome-wide scale phenome-wide association studies are being instrumental in discovering novel genetic variants associated with clinical phenotypes. As increasing number of such association results from different biobanks become available, methods to meta-analyse those association results is of great interest. Because the binary phenotypes in biobank-based studies are mostly unbalanced in their case-control ratios, very few methods can provide well-calibrated tests for associations...
February 22, 2019: Genetic Epidemiology
Ji Eun Lim, Hye Ok Kim, Sang Youl Rhee, Mi Kyung Kim, Yeon-Jung Kim, Bermseok Oh
Hypertension is a complex disorder caused by genetic and environmental risk factors. Recently, genome-wide association studies (GWASs) identified more than 100 genetic variants for blood pressure traits and hypertension. However, the interactions between these genetic variants and environmental factors have not been systematically investigated. Therefore, we examined the interaction between genetic and environmental risk factors in blood pressure traits using the genetic risk score (GRS). Two Korean community-based cohorts, Cohort I (KARE; N = 8,840) and Cohort II (CAVAS; N = 9,599), were used for this study, and GRSs were calculated from 42 GWAS single-nucleotide polymorphisms (SNPs) that were validated for their association in these cohorts...
February 15, 2019: Genetic Epidemiology
Jie Ren, Yinhao Du, Shaoyu Li, Shuangge Ma, Yu Jiang, Cen Wu
In cancer genomic studies, an important objective is to identify prognostic markers associated with patients' survival. Network-based regularization has achieved success in variable selections for high-dimensional cancer genomic data, because of its ability to incorporate the correlations among genomic features. However, as survival time data usually follow skewed distributions, and are contaminated by outliers, network-constrained regularization that does not take the robustness into account leads to false identifications of network structure and biased estimation of patients' survival...
February 11, 2019: Genetic Epidemiology
Daniel J Schaid, Shannon K McDonnell, Stephen N Thibodeau
The familial recurrence risk is the probability a person will have disease, given a reported family history. When family histories are obtained as simple counts of disease among family members, as often obtained in cancer registries or surveys, we propose methods to estimate recurrence risks based on truncated binomial distributions. By this approach, we are able to obtain unbiased estimates of risk for a person with at least k-affected relatives, where k can be specified to determine how risk varies with k...
February 11, 2019: Genetic Epidemiology
Margaret M Parker, Sharon M Lutz, Brian D Hobbs, Robert Busch, MerryLynn N McDonald, Peter J Castaldi, Terri H Beaty, John E Hokanson, Edwin K Silverman, Michael H Cho
Genetic association studies have increasingly recognized variant effects on multiple phenotypes. Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with environmental and genetic causes. Multiple genetic variants have been associated with COPD, many of which show significant associations to additional phenotypes. However, it is unknown if these associations represent biological pleiotropy or if they exist through correlation of related phenotypes ("mediated pleiotropy"). Using 6,670 subjects from the COPDGene study, we describe the association of known COPD susceptibility loci with other COPD-related phenotypes and distinguish if these act directly on the phenotypes (i...
February 11, 2019: Genetic Epidemiology
Sara Lindström, Jennifer A Brody, Constance Turman, Marine Germain, Traci M Bartz, Erin N Smith, Ming-Huei Chen, Marja Puurunen, Daniel Chasman, Jeffrey Hassler, Nathan Pankratz, Saonli Basu, Weihua Guan, Beata Gyorgy, Manal Ibrahim, Jean-Philippe Empana, Robert Olaso, Rebecca Jackson, Sigrid K Braekkan, Barbara McKnight, Jean-Francois Deleuze, Cristopher J O'Donnell, Xavier Jouven, Kelly A Frazer, Bruce M Psaty, Kerri L Wiggins, Kent Taylor, Alexander P Reiner, Susan R Heckbert, Charles Kooperberg, Paul Ridker, John-Bjarne Hansen, Weihong Tang, Andrew D Johnson, Pierre-Emmanuel Morange, David A Trégouët, Peter Kraft, Nicholas L Smith, Christopher Kabrhel
Although recent Genome-Wide Association Studies have identified novel associations for common variants, there has been no comprehensive exome-wide search for low-frequency variants that affect the risk of venous thromboembolism (VTE). We conducted a meta-analysis of 11 studies comprising 8,332 cases and 16,087 controls of European ancestry and 382 cases and 1,476 controls of African American ancestry genotyped with the Illumina HumanExome BeadChip. We used the seqMeta package in R to conduct single variant and gene-based rare variant tests...
January 19, 2019: Genetic Epidemiology
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