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Genetic Epidemiology

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https://www.readbyqxmd.com/read/28421636/leveraging-cell-type-specific-regulatory-regions-to-detect-snps-associated-with-tissue-factor-pathway-inhibitor-plasma-levels
#1
Jessica Dennis, Alejandra Medina-Rivera, Vinh Truong, Lina Antounians, Nora Zwingerman, Giovana Carrasco, Lisa Strug, Phil Wells, David-Alexandre Trégouët, Pierre-Emmanuel Morange, Michael D Wilson, France Gagnon
Tissue factor pathway inhibitor (TFPI) regulates the formation of intravascular blood clots, which manifest clinically as ischemic heart disease, ischemic stroke, and venous thromboembolism (VTE). TFPI plasma levels are heritable, but the genetics underlying TFPI plasma level variability are poorly understood. Herein we report the first genome-wide association scan (GWAS) of TFPI plasma levels, conducted in 251 individuals from five extended French-Canadian Families ascertained on VTE. To improve discovery, we also applied a hypothesis-driven (HD) GWAS approach that prioritized single nucleotide polymorphisms (SNPs) in (1) hemostasis pathway genes, and (2) vascular endothelial cell (EC) regulatory regions, which are among the highest expressers of TFPI...
April 18, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28393390/a-novel-association-test-for-multiple-secondary-phenotypes-from-a-case-control-gwas
#2
Debashree Ray, Saonli Basu
In the past decade, many genome-wide association studies (GWASs) have been conducted to explore association of single nucleotide polymorphisms (SNPs) with complex diseases using a case-control design. These GWASs not only collect information on the disease status (primary phenotype, D) and the SNPs (genotypes, X), but also collect extensive data on several risk factors and traits. Recent literature and grant proposals point toward a trend in reusing existing large case-control data for exploring genetic associations of some additional traits (secondary phenotypes, Y) collected during the study...
April 10, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28393384/binomirare-a-robust-test-of-the-association-of-a-rare-variant-with-a-disease-for-pooled-analysis-and-meta-analysis-with-application-to-the-hchs-sol
#3
Tamar Sofer
Most regression-based tests of the association between a low-count variant and a binary outcome do not protect type 1 error, especially when tests are rejected based on a very low significance threshold. Noted exception is the Firth test. However, it was recently shown that in meta-analyzing multiple studies all asymptotic, regression-based tests, including the Firth, may not control type 1 error in some settings, and the Firth test may suffer a substantial loss of power. The problem is exacerbated when the case-control proportions differ between studies...
April 10, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28378447/a-genome-wide-linkage-and-association-analysis-of-imputed-insertions-and-deletions-with-cardiometabolic-phenotypes-in-mexican-americans-the-insulin-resistance-atherosclerosis-family-study
#4
Chuan Gao, Fang-Chi Hsu, Latchezar M Dimitrov, Hayrettin Okut, Yii-Der I Chen, Kent D Taylor, Jerome I Rotter, Carl D Langefeld, Donald W Bowden, Nicholette D Palmer
Insertions and deletions (INDELs) represent a significant fraction of interindividual variation in the human genome yet their contribution to phenotypes is poorly understood. To confirm the quality of imputed INDELs and investigate their roles in mediating cardiometabolic phenotypes, genome-wide association and linkage analyses were performed for 15 phenotypes with 1,273,952 imputed INDELs in 1,024 Mexican-origin Americans. Imputation quality was validated using whole exome sequencing with an average kappa of 0...
April 5, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28370330/a-combination-test-for-detection-of-gene-environment-interaction-in-cohort-studies
#5
Brandon Coombes, Saonli Basu, Matt McGue
Identifying gene-environment (G-E) interactions can contribute to a better understanding of disease etiology, which may help researchers develop disease prevention strategies and interventions. One big criticism of studying G-E interaction is the lack of power due to sample size. Studies often restrict the interaction search to the top few hundred hits from a genome-wide association study or focus on potential candidate genes. In this paper, we test interactions between a candidate gene and an environmental factor to improve power by analyzing multiple variants within a gene...
March 31, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28318110/on-the-association-analysis-of-genome-sequencing-data-a-spatial-clustering-approach-for-partitioning-the-entire-genome-into-nonoverlapping-windows
#6
Heide Loehlein Fier, Dmitry Prokopenko, Julian Hecker, Michael H Cho, Edwin K Silverman, Scott T Weiss, Rudolph E Tanzi, Christoph Lange
For the association analysis of whole-genome sequencing (WGS) studies, we propose an efficient and fast spatial-clustering algorithm. Compared to existing analysis approaches for WGS data, that define the tested regions either by sliding or consecutive windows of fixed sizes along variants, a meaningful grouping of nearby variants into consecutive regions has the advantage that, compared to sliding window approaches, the number of tested regions is likely to be smaller. In comparison to consecutive, fixed-window approaches, our approach is likely to group nearby variants together...
March 20, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28300291/are-rare-variants-really-independent
#7
Asuman Turkmen, Shili Lin
Recent advances in genotyping with high-density markers allow researchers access to genomic variants including rare ones. Linkage disequilibrium (LD) is widely used to provide insight into evolutionary history. It is also the basis for association mapping in humans and other species. Better understanding of the genomic LD structure may lead to better-informed statistical tests that can improve the power of association studies. Although rare variant associations with common diseases (RVCD) have been extensively studied recently, there is very limited understanding, and even controversial view of LD structures among rare variants and between rare and common variants...
March 16, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28198095/genetic-risk-models-influence-of-model-size-on-risk-estimates-and-precision
#8
Ying Shan, Gerard Tromp, Helena Kuivaniemi, Diane T Smelser, Shefali S Verma, Marylyn D Ritchie, James R Elmore, David J Carey, Yvette P Conley, Michael B Gorin, Daniel E Weeks
Disease risk estimation plays an important role in disease prevention. Many studies have found that the ability to predict risk improves as the number of risk single-nucleotide polymorphisms (SNPs) in the risk model increases. However, the width of the confidence interval of the risk estimate is often not considered in the evaluation of the risk model. Here, we explore how the risk and the confidence interval width change as more SNPs are added to the model in the order of decreasing effect size, using both simulated data and real data from studies of abdominal aortic aneurysms and age-related macular degeneration...
February 15, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28393391/inclusion-of-biological-knowledge-in-a-bayesian-shrinkage-model-for-joint-estimation-of-snp-effects
#9
Miguel Pereira, John R Thompson, Christian X Weichenberger, Duncan C Thomas, Cosetta Minelli
With the aim of improving detection of novel single-nucleotide polymorphisms (SNPs) in genetic association studies, we propose a method of including prior biological information in a Bayesian shrinkage model that jointly estimates SNP effects. We assume that the SNP effects follow a normal distribution centered at zero with variance controlled by a shrinkage hyperparameter. We use biological information to define the amount of shrinkage applied on the SNP effects distribution, so that the effects of SNPs with more biological support are less shrunk toward zero, thus being more likely detected...
May 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28317167/semiparametric-methods-for-estimation-of-a-nonlinear-exposure-outcome-relationship-using-instrumental-variables-with-application-to-mendelian-randomization
#10
James R Staley, Stephen Burgess
Mendelian randomization, the use of genetic variants as instrumental variables (IV), can test for and estimate the causal effect of an exposure on an outcome. Most IV methods assume that the function relating the exposure to the expected value of the outcome (the exposure-outcome relationship) is linear. However, in practice, this assumption may not hold. Indeed, often the primary question of interest is to assess the shape of this relationship. We present two novel IV methods for investigating the shape of the exposure-outcome relationship: a fractional polynomial method and a piecewise linear method...
May 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28211093/gsskat-rapid-gene-set-analysis-and-multiple-testing-correction-for-rare-variant-association-studies-using-weighted-linear-kernels
#11
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 E Bailey-Wilson, Olivier Cussenot, Geraldine Cancel-Tassin, Graham G Giles, Robert J 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
Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data...
May 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28191685/gene-based-segregation-method-for-identifying-rare-variants-in-family-based-sequencing-studies
#12
Dandi Qiao, Christoph Lange, Nan M Laird, Sungho Won, Craig P Hersh, Jarrett Morrow, Brian D Hobbs, Sharon M Lutz, Ingo Ruczinski, Terri H Beaty, Edwin K Silverman, Michael H Cho
Whole-exome sequencing using family data has identified rare coding variants in Mendelian diseases or complex diseases with Mendelian subtypes, using filters based on variant novelty, functionality, and segregation with the phenotype within families. However, formal statistical approaches are limited. We propose a gene-based segregation test (GESE) that quantifies the uncertainty of the filtering approach. It is constructed using the probability of segregation events under the null hypothesis of Mendelian transmission...
May 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28191669/adaptive-testing-for-multiple-traits-in-a-proportional-odds-model-with-applications-to-detect-snp-brain-network-associations
#13
Junghi Kim, Wei Pan
There has been increasing interest in developing more powerful and flexible statistical tests to detect genetic associations with multiple traits, as arising from neuroimaging genetic studies. Most of existing methods treat a single trait or multiple traits as response while treating an SNP as a predictor coded under an additive inheritance mode. In this paper, we follow an earlier approach in treating an SNP as an ordinal response while treating traits as predictors in a proportional odds model (POM). In this way, it is not only easier to handle mixed types of traits, e...
April 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28176359/detecting-association-of-rare-and-common-variants-based-on-cross-validation-prediction-error
#14
Xinlan Yang, Shuaichen Wang, Shuanglin Zhang, Qiuying Sha
Despite the extensive discovery of disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants may explain additional disease risk or trait variability. Although sequencing technology provides a supreme opportunity to investigate the roles of rare variants in complex diseases, detection of these variants in sequencing-based association studies presents substantial challenges. In this article, we propose novel statistical tests to test the association between rare and common variants in a genomic region and a complex trait of interest based on cross-validation prediction error (PE)...
April 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28090672/a-powerful-statistical-framework-for-generalization-testing-in-gwas-with-application-to-the-hchs-sol
#15
Tamar Sofer, Ruth Heller, Marina Bogomolov, Christy L Avery, Mariaelisa Graff, Kari E North, Alex P Reiner, Timothy A Thornton, Kenneth Rice, Yoav Benjamini, Cathy C Laurie, Kathleen F Kerr
In genome-wide association studies (GWAS), "generalization" is the replication of genotype-phenotype association in a population with different ancestry than the population in which it was first identified. Current practices for declaring generalizations rely on testing associations while controlling the family-wise error rate (FWER) in the discovery study, then separately controlling error measures in the follow-up study. This approach does not guarantee control over the FWER or false discovery rate (FDR) of the generalization null hypotheses...
April 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28039885/rare-variant-association-test-with-multiple-phenotypes
#16
Selyeong Lee, Sungho Won, Young Jin Kim, Yongkang Kim, Bong-Jo Kim, Taesung Park
Although genome-wide association studies (GWAS) have now discovered thousands of genetic variants associated with common traits, such variants cannot explain the large degree of "missing heritability," likely due to rare variants. The advent of next generation sequencing technology has allowed rare variant detection and association with common traits, often by investigating specific genomic regions for rare variant effects on a trait. Although multiple correlated phenotypes are often concurrently observed in GWAS, most studies analyze only single phenotypes, which may lessen statistical power...
April 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28019042/evidence-for-snp-snp-interaction-identified-through-targeted-sequencing-of-cleft-case-parent-trios
#17
Yanzi Xiao, Margaret A Taub, Ingo Ruczinski, Ferdouse Begum, Jacqueline B Hetmanski, Holger Schwender, Elizabeth J Leslie, Daniel C Koboldt, Jeffrey C Murray, Mary L Marazita, Terri H Beaty
Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is the most common craniofacial birth defect in humans, affecting 1 in 700 live births. This malformation has a complex etiology where multiple genes and several environmental factors influence risk. At least a dozen different genes have been confirmed to be associated with risk of NSCL/P in previous studies. However, all the known genetic risk factors cannot fully explain the observed heritability of NSCL/P, and several authors have suggested gene-gene (G × G) interaction may be important in the etiology of this complex and heterogeneous malformation...
April 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/27943406/fast-genome-wide-qtl-association-mapping-on-pedigree-and-population-data
#18
Hua Zhou, John Blangero, Thomas D Dyer, Kei-Hang K Chan, Kenneth Lange, Eric M Sobel
Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects...
April 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/27813156/low-high-coverage-and-two-stage-dna-sequencing-in-the-design-of-the-genetic-association-study
#19
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...
April 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28019059/impact-of-genotyping-errors-on-statistical-power-of-association-tests-in-genomic-analyses-a-case-study
#20
Lin Hou, Ning Sun, Shrikant Mane, Fred Sayward, Nallakkandi Rajeevan, Kei-Hoi Cheung, Kelly Cho, Saiju Pyarajan, Mihaela Aslan, Perry Miller, Philip D Harvey, J Michael Gaziano, John Concato, Hongyu Zhao
A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant's DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype-phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS-based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available...
February 2017: Genetic Epidemiology
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