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Genetic Epidemiology

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https://www.readbyqxmd.com/read/29124805/identification-of-16q21-as-a-modifier-of-nonsyndromic-orofacial-cleft-phenotypes
#1
Jenna C Carlson, Jennifer Standley, Aline Petrin, John R Shaffer, Azeez Butali, Carmen J Buxó, Eduardo Castilla, Kaare Christensen, Frederic W-D Deleyiannis, Jacqueline T Hecht, L Leigh Field, Ariuntuul Garidkhuu, Lina M Moreno Uribe, Natsume Nagato, Ieda M Orioli, Carmencita Padilla, Fernando Poletta, Satoshi Suzuki, Alexandre R Vieira, George L Wehby, Seth M Weinberg, Terri H Beaty, Eleanor Feingold, Jeffrey C Murray, Mary L Marazita, Elizabeth J Leslie
Orofacial clefts (OFCs) are common, complex birth defects with extremely heterogeneous phenotypic presentations. Two common subtypes-cleft lip alone (CL) and CL plus cleft palate (CLP)-are typically grouped into a single phenotype for genetic analysis (i.e., CL with or without cleft palate, CL/P). However, mounting evidence suggests there may be unique underlying pathophysiology and/or genetic modifiers influencing expression of these two phenotypes. To this end, we performed a genome-wide scan for genetic modifiers by directly comparing 450 CL cases with 1,692 CLP cases from 18 recruitment sites across 13 countries from North America, Central or South America, Asia, Europe, and Africa...
November 10, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29119601/an-integrative-approach-to-assess-x-chromosome-inactivation-using-allele-specific-expression-with-applications-to-epithelial-ovarian-cancer
#2
Nicholas B Larson, Zachary C Fogarty, Melissa C Larson, Kimberly R Kalli, Kate Lawrenson, Simon Gayther, Brooke L Fridley, Ellen L Goode, Stacey J Winham
X-chromosome inactivation (XCI) epigenetically silences transcription of an X chromosome in females; patterns of XCI are thought to be aberrant in women's cancers, but are understudied due to statistical challenges. We develop a two-stage statistical framework to assess skewed XCI and evaluate gene-level patterns of XCI for an individual sample by integration of RNA sequence, copy number alteration, and genotype data. Our method relies on allele-specific expression (ASE) to directly measure XCI and does not rely on male samples or paired normal tissue for comparison...
November 8, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29114920/integrative-sparse-principal-component-analysis-of-gene-expression-data
#3
Mengque Liu, Xinyan Fan, Kuangnan Fang, Qingzhao Zhang, Shuangge Ma
In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis...
November 8, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29114909/an-ancestry-based-approach-for-detecting-interactions
#4
Danny S Park, Itamar Eskin, Eun Yong Kang, Eric R Gamazon, Celeste Eng, Christopher R Gignoux, Joshua M Galanter, Esteban Burchard, Chun J Ye, Hugues Aschard, Eleazar Eskin, Eran Halperin, Noah Zaitlen
BACKGROUND: Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies...
November 8, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29110346/on-meta-and-mega-analyses-for-gene-environment-interactions
#5
Jing Huang, Yulun Liu, Steve Vitale, Trevor M Penning, Alexander S Whitehead, Ian A Blair, Anil Vachani, Margie L Clapper, Joshua E Muscat, Philip Lazarus, Paul Scheet, Jason H Moore, Yong Chen
Gene-by-environment (G × E) interactions are important in explaining the missing heritability and understanding the causation of complex diseases, but a single, moderately sized study often has limited statistical power to detect such interactions. With the increasing need for integrating data and reporting results from multiple collaborative studies or sites, debate over choice between mega- versus meta-analysis continues. In principle, data from different sites can be integrated at the individual level into a "mega" data set, which can be fit by a joint "mega-analysis...
November 7, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29110330/multiethnic-polygenic-risk-scores-improve-risk-prediction-in-diverse-populations
#6
Carla Márquez-Luna, Po-Ru Loh, Alkes L Price
Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population...
November 7, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29082545/testing-for-the-indirect-effect-under-the-null-for-genome-wide-mediation-analyses
#7
Richard Barfield, Jincheng Shen, Allan C Just, Pantel S Vokonas, Joel Schwartz, Andrea A Baccarelli, Tyler J VanderWeele, Xihong Lin
Mediation analysis helps researchers assess whether part or all of an exposure's effect on an outcome is due to an intermediate variable. The indirect effect can help in designing interventions on the mediator as opposed to the exposure and better understanding the outcome's mechanisms. Mediation analysis has seen increased use in genome-wide epidemiological studies to test for an exposure of interest being mediated through a genomic measure such as gene expression or DNA methylation (DNAm). Testing for the indirect effect is challenged by the fact that the null hypothesis is composite...
October 29, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29076270/rare-variant-association-tests-in-longitudinal-studies-with-an-application-to-the-multi-ethnic-study-of-atherosclerosis-mesa
#8
Zihuai He, Seunggeun Lee, Min Zhang, Jennifer A Smith, Xiuqing Guo, Walter Palmas, Sharon L R Kardia, Iuliana Ionita-Laza, Bhramar Mukherjee
Over the past few years, an increasing number of studies have identified rare variants that contribute to trait heritability. Due to the extreme rarity of some individual variants, gene-based association tests have been proposed to aggregate the genetic variants within a gene, pathway, or specific genomic region as opposed to a one-at-a-time single variant analysis. In addition, in longitudinal studies, statistical power to detect disease susceptibility rare variants can be improved through jointly testing repeatedly measured outcomes, which better describes the temporal development of the trait of interest...
October 27, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29071737/on-the-testing-of-hardy-weinberg-proportions-and-equality-of-allele-frequencies-in-males-and-females-at-biallelic-genetic-markers
#9
Jan Graffelman, Bruce S Weir
Standard statistical tests for equality of allele frequencies in males and females and tests for Hardy-Weinberg equilibrium are tightly linked by their assumptions. Tests for equality of allele frequencies assume Hardy-Weinberg equilibrium, whereas the usual chi-square or exact test for Hardy-Weinberg equilibrium assume equality of allele frequencies in the sexes. In this paper, we propose ways to break this interdependence in assumptions of the two tests by proposing an omnibus exact test that can test both hypotheses jointly, as well as a likelihood ratio approach that permits these phenomena to be tested both jointly and separately...
October 25, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29071735/impact-of-sample-collection-participation-on-the-validity-of-estimated-measures-of-association-in-the-national-birth-defects-prevention-study-when-assessing-gene-environment-interactions
#10
Mary M Jenkins, Jennita Reefhuis, Amy H Herring, Margaret A Honein
To better understand the impact that nonresponse for specimen collection has on the validity of estimates of association, we examined associations between self-reported maternal periconceptional smoking, folic acid use, or pregestational diabetes mellitus and six birth defects among families who did and did not submit buccal cell samples for DNA following a telephone interview as part of the National Birth Defects Prevention Study (NBDPS). Analyses included control families with live born infants who had no birth defects (N = 9,465), families of infants with anorectal atresia or stenosis (N = 873), limb reduction defects (N = 1,037), gastroschisis (N = 1,090), neural tube defects (N = 1,764), orofacial clefts (N = 3,836), or septal heart defects (N = 4,157)...
October 25, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29034560/estimation-of-a-significance-threshold-for-epigenome-wide-association-studies
#11
Ayden Saffari, Matt J Silver, Patrizia Zavattari, Loredana Moi, Amedeo Columbano, Emma L Meaburn, Frank Dudbridge
Epigenome-wide association studies (EWAS) are designed to characterise population-level epigenetic differences across the genome and link them to disease. Most commonly, they assess DNA-methylation status at cytosine-guanine dinucleotide (CpG) sites, using platforms such as the Illumina 450k array that profile a subset of CpGs genome wide. An important challenge in the context of EWAS is determining a significance threshold for declaring a CpG site as differentially methylated, taking multiple testing into account...
October 15, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/29023970/phenotype-validation-in-electronic-health-records-based-genetic-association-studies
#12
Lu Wang, Scott M Damrauer, Hong Zhang, Alan X Zhang, Rui Xiao, Jason H Moore, Jinbo Chen
The linkage between electronic health records (EHRs) and genotype data makes it plausible to study the genetic susceptibility of a wide range of disease phenotypes. Despite that EHR-derived phenotype data are subjected to misclassification, it has been shown useful for discovering susceptible genes, particularly in the setting of phenome-wide association studies (PheWAS). It is essential to characterize discovered associations using gold standard phenotype data by chart review. In this work, we propose a genotype stratified case-control sampling strategy to select subjects for phenotype validation...
October 11, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28944551/mendelian-randomization-with-fine-mapped-genetic-data-choosing-from-large-numbers-of-correlated-instrumental-variables
#13
Stephen Burgess, Verena Zuber, Elsa Valdes-Marquez, Benjamin B Sun, Jemma C Hopewell
Mendelian randomization uses genetic variants to make causal inferences about the effect of a risk factor on an outcome. With fine-mapped genetic data, there may be hundreds of genetic variants in a single gene region any of which could be used to assess this causal relationship. However, using too many genetic variants in the analysis can lead to spurious estimates and inflated Type 1 error rates. But if only a few genetic variants are used, then the majority of the data is ignored and estimates are highly sensitive to the particular choice of variants...
September 25, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28944497/evolutionarily-derived-networks-to-inform-disease-pathways
#14
Britney E Graham, Christian Darabos, Minjun Huang, Louis J Muglia, Jason H Moore, Scott M Williams
Methods to identify genes or pathways associated with complex diseases are often inadequate to elucidate most risk because they make implicit and oversimplified assumptions about underlying models of disease etiology. These can lead to incomplete or inadequate conclusions. To address this, we previously developed human phenotype networks (HPN), linking phenotypes based on shared biology. However, such visualization alone is often uninterpretable, and requires additional filtering. Here, we expand the HPN to include another method, evolutionary triangulation (ET)...
September 25, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28913944/the-more-you-test-the-more-you-find-the-smallest-p-values-become-increasingly-enriched-with-real-findings-as-more-tests-are-conducted
#15
Olga A Vsevolozhskaya, Chia-Ling Kuo, Gabriel Ruiz, Luda Diatchenko, Dmitri V Zaykin
The increasing accessibility of data to researchers makes it possible to conduct massive amounts of statistical testing. Rather than follow specific scientific hypotheses with statistical analysis, researchers can now test many possible relationships and let statistics generate hypotheses for them. The field of genetic epidemiology is an illustrative case, where testing of candidate genetic variants for association with an outcome has been replaced by agnostic screening of the entire genome. Poor replication rates of candidate gene studies have improved dramatically with the increase in genomic coverage, due to factors such as adoption of better statistical practices and availability of larger sample sizes...
September 14, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28913902/analysis-of-cancer-gene-expression-data-with-an-assisted-robust-marker-identification-approach
#16
Hao Chai, Xingjie Shi, Qingzhao Zhang, Qing Zhao, Yuan Huang, Shuangge Ma
Gene expression (GE) studies have been playing a critical role in cancer research. Despite tremendous effort, the analysis results are still often unsatisfactory, because of the weak signals and high data dimensionality. Analysis is often further challenged by the long-tailed distributions of the outcome variables. In recent multidimensional studies, data have been collected on GEs as well as their regulators (e.g., copy number alterations (CNAs), methylation, and microRNAs), which can provide additional information on the associations between GEs and cancer outcomes...
September 14, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28875524/iterative-hard-thresholding-for-model-selection-in-genome-wide-association-studies
#17
Kevin L Keys, Gary K Chen, Kenneth Lange
A genome-wide association study (GWAS) correlates marker and trait variation in a study sample. Each subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here, we assume that subjects are randomly collected unrelateds and that trait values are normally distributed or can be transformed to normality. Over the past decade, geneticists have been remarkably successful in applying GWAS analysis to hundreds of traits. The massive amount of data produced in these studies present unique computational challenges...
September 6, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28872698/a-multivariate-distance-based-analytic-framework-for-microbial-interdependence-association-test-in-longitudinal-study
#18
Yilong Zhang, Sung Won Han, Laura M Cox, Huilin Li
Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high-throughput sequencing and analytical tools, we are able to probe the interdependent relationship among microbial species through longitudinal study...
September 5, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28861891/improving-power-of-association-tests-using-multiple-sets-of-imputed-genotypes-from-distributed-reference-panels
#19
Wei Zhou, Lars G Fritsche, Sayantan Das, He Zhang, Jonas B Nielsen, Oddgeir L Holmen, Jin Chen, Maoxuan Lin, Maiken B Elvestad, Kristian Hveem, Goncalo R Abecasis, Hyun Min Kang, Cristen J Willer
The accuracy of genotype imputation depends upon two factors: the sample size of the reference panel and the genetic similarity between the reference panel and the target samples. When multiple reference panels are not consented to combine together, it is unclear how to combine the imputation results to optimize the power of genetic association studies. We compared the accuracy of 9,265 Norwegian genomes imputed from three reference panels-1000 Genomes phase 3 (1000G), Haplotype Reference Consortium (HRC), and a reference panel containing 2,201 Norwegian participants from the population-based Nord Trøndelag Health Study (HUNT) from low-pass genome sequencing...
September 1, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28850771/a-functional-u-statistic-method-for-association-analysis-of-sequencing-data
#20
Sneha Jadhav, Xiaoran Tong, Qing Lu
Although sequencing studies hold great promise for uncovering novel variants predisposing to human diseases, the high dimensionality of the sequencing data brings tremendous challenges to data analysis. Moreover, for many complex diseases (e.g., psychiatric disorders) multiple related phenotypes are collected. These phenotypes can be different measurements of an underlying disease, or measurements characterizing multiple related diseases for studying common genetic mechanism. Although jointly analyzing these phenotypes could potentially increase the power of identifying disease-associated genes, the different types of phenotypes pose challenges for association analysis...
August 29, 2017: Genetic Epidemiology
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