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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...
September 26, 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)...
September 9, 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...
August 3, 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
H Robert Frost, Li Shen, Andrew J Saykin, Scott M Williams, Jason H Moore
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...
November 2016: Genetic Epidemiology
Huilin Li, Jinbo Chen
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...
November 2016: Genetic Epidemiology
Rui Sun, Haoyi Weng, Inchi Hu, Junfeng Guo, William K K Wu, Benny Chung-Ying Zee, Maggie Haitian Wang
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 samples of the data, and allows for fast and accurate P-value calculation without the need of permutations...
November 2016: Genetic Epidemiology
Yulun Liu, Yong Chen, Paul Scheet
With varying, but substantial, proportions of heritability remaining unexplained by summaries of single-SNP genetic variation, there is a demand for methods that extract maximal information from genetic association studies. One source of variation that is difficult to assess is genetic interactions. A major challenge for naive detection methods is the large number of possible combinations, with a requisite need to correct for multiple testing. Assumptions of large marginal effects, to reduce the search space, may be restrictive and miss higher order interactions with modest marginal effects...
November 2016: Genetic Epidemiology
Su Yon Jung, Eric M Sobel, Jeanette C Papp, Carolyn J Crandall, Alan N Fu, Zuo-Feng Zhang
PURPOSE: Impaired glucose metabolism-related genetic variants likely interact with obesity-modifiable factors in response to glucose intolerance, yet their interconnected pathways have not been fully characterized. METHODS: With data from 1,027 postmenopausal participants of the Genomics and Randomized Trials Network study and 15 single-nucleotide polymorphisms (SNPs) associated with glucose homeostasis, we assessed whether obesity, physical activity, and high dietary fat intake interact with the SNP-glucose variations...
September 2016: Genetic Epidemiology
Sungkyoung Choi, Sungyoung Lee, Dandi Qiao, Megan Hardin, Michael H Cho, Edwin K Silverman, Taesung Park, Sungho Won
Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation...
September 2016: Genetic Epidemiology
Yalu Wen, Qing Lu
Although compelling evidence suggests that the genetic etiology of complex diseases could be heterogeneous in subphenotype groups, little attention has been paid to phenotypic heterogeneity in genetic association analysis of complex diseases. Simply ignoring phenotypic heterogeneity in association analysis could result in attenuated estimates of genetic effects and low power of association tests if subphenotypes with similar clinical manifestations have heterogeneous underlying genetic etiologies. To facilitate the family-based association analysis allowing for phenotypic heterogeneity, we propose a clustered multiclass likelihood-ratio ensemble (CMLRE) method...
September 2016: Genetic Epidemiology
Longfei Wang, Sungyoung Lee, Jungsoo Gim, Dandi Qiao, Michael Cho, Robert C Elston, Edwin K Silverman, Sungho Won
Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT)...
September 2016: Genetic Epidemiology
Nicholas B Larson, Shannon McDonnell, Lisa Cannon Albright, Craig Teerlink, Janet Stanford, Elaine A Ostrander, William B Isaacs, Jianfeng Xu, Kathleen A Cooney, Ethan Lange, Johanna Schleutker, John D Carpten, Isaac Powell, Joan Bailey-Wilson, Olivier Cussenot, Geraldine Cancel-Tassin, Graham Giles, Robert MacInnis, Christiane Maier, Alice S Whittemore, Chih-Lin Hsieh, Fredrik Wiklund, William J Catolona, William Foulkes, Diptasri Mandal, Rosalind Eeles, Zsofia Kote-Jarai, Michael J Ackerman, Timothy M Olson, Christopher J Klein, Stephen N Thibodeau, Daniel J Schaid
Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association...
September 2016: Genetic Epidemiology
Miaoyan Wang, Johanna Jakobsdottir, Albert V Smith, Mary Sara McPeek
In a large-scale genetic association study, the number of phenotyped individuals available for sequencing may, in some cases, be greater than the study's sequencing budget will allow. In that case, it can be important to prioritize individuals for sequencing in a way that optimizes power for association with the trait. Suppose a cohort of phenotyped individuals is available, with some subset of them possibly already sequenced, and one wants to choose an additional fixed-size subset of individuals to sequence in such a way that the power to detect association is maximized...
September 2016: Genetic Epidemiology
Tamar Sofer, John R Shaffer, Mariaelisa Graff, Qibin Qi, Adrienne M Stilp, Stephanie M Gogarten, Kari E North, Carmen R Isasi, Cathy C Laurie, Adam A Szpiro
Investigators often meta-analyze multiple genome-wide association studies (GWASs) to increase the power to detect associations of single nucleotide polymorphisms (SNPs) with a trait. Meta-analysis is also performed within a single cohort that is stratified by, e.g., sex or ancestry group. Having correlated individuals among the strata may complicate meta-analyses, limit power, and inflate Type 1 error. For example, in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), sources of correlation include genetic relatedness, shared household, and shared community...
September 2016: Genetic Epidemiology
Akram Yazdani, Azam Yazdani, Xiaoming Liu, Eric Boerwinkle
We use whole genome sequence data and rare variant analysis methods to investigate a subset of the human serum metabolome, including 16 carnitine-related metabolites that are important components of mammalian energy metabolism. Medium pass sequence data consisting of 12,820,347 rare variants and serum metabolomics data were available on 1,456 individuals. By applying a penalization method, we identified two genes FGF8 and MDGA2 with significant effects on lysine and cis-4-decenoylcarnitine, respectively, using Δ-AIC and likelihood ratio test statistics...
September 2016: Genetic Epidemiology
Elisabeth A Rosenthal, Vahagn Makaryan, Amber A Burt, David R Crosslin, Daniel Seung Kim, Joshua D Smith, Deborah A Nickerson, Alex P Reiner, Stephen S Rich, Rebecca D Jackson, Santhi K Ganesh, Linda M Polfus, Lihong Qi, David C Dale, Gail P Jarvik
Neutrophils are a key component of innate immunity. Individuals with low neutrophil count are susceptible to frequent infections. Linkage and association between congenital neutropenia and a single rare missense variant in TCIRG1 have been reported in a single family. Here, we report on nine rare missense variants at evolutionarily conserved sites in TCIRG1 that are associated with lower absolute neutrophil count (ANC; p = 0.005) in 1,058 participants from three cohorts: Atherosclerosis Risk in Communities (ARIC), Coronary Artery Risk Development in Young Adults (CARDIA), and Jackson Heart Study (JHS) of the NHLBI Grand Opportunity Exome Sequencing Project (GO ESP)...
September 2016: Genetic Epidemiology
Evangelina López de Maturana, Sílvia Pineda, Angela Brand, Kristel Van Steen, Núria Malats
Primary and secondary prevention can highly benefit a personalized medicine approach through the accurate discrimination of individuals at high risk of developing a specific disease from those at moderate and low risk. To this end precise risk prediction models need to be built. This endeavor requires a precise characterization of the individual exposome, genome, and phenome. Massive molecular omics data representing the different layers of the biological processes of the host and the nonhost will enable to build more accurate risk prediction models...
July 18, 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...
July 7, 2016: Genetic Epidemiology
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