PMCC PMCC

Search tips
Search criteria

Advanced
Results 1-3 (3)
 

Clipboard (0)
None
Journals
Authors
more »
Year of Publication
Document Types
1.  Testing gene-environment interactions in gene-based association studies 
BMC Proceedings  2011;5(Suppl 9):S26.
Gene-based and single-nucleotide polymorphism (SNP) set association studies provide an important complement to SNP analysis. Kernel-based nonparametric regression has recently emerged as a powerful and flexible tool for this purpose. Our goal is to explore whether this approach can be extended to incorporate and test for interaction effects, especially for genes containing rare variant SNPs. Here, we construct nonparametric regression models that can be used to include a gene-environment interaction effect under the framework of the least-squares kernel machine and examine the performance of the proposed method on the Genetic Analysis Workshop 17 unrelated individuals data set. Two hundred simulated replicates were used to explore the power for detecting interaction. We demonstrate through a genome scan of the quantitative phenotype Q1 that the simulated gene-environment interaction effect in the data can be detected with reasonable power by using the least-squares kernel machine method.
doi:10.1186/1753-6561-5-S9-S26
PMCID: PMC3287861  PMID: 22373316
2.  Defining genetic determinants of the Metabolic Syndrome in the Framingham Heart Study using association and structural equation modeling methods 
BMC Proceedings  2009;3(Suppl 7):S50.
The Metabolic Syndrome (MetSyn), which is a clustering of traits including insulin resistance, obesity, hypertension and dyslipidemia, is estimated to have a substantial genetic component, yet few specific genetic targets have been identified. Factor analysis, a sub-type of structural equation modeling (SEM), has been used to model the complex relationships in MetSyn. Therefore, we aimed to define the genetic determinants of MetSyn in the Framingham Heart Study (Offspring Cohort, Exam 7) using the Affymetrix 50 k Human Gene Panel and three different approaches: 1) an association-based "one-SNP-at-a-time" analysis with MetSyn as a binary trait using the World Health Organization criteria; 2) an association-based "one-SNP-at-a-time" analysis with MetSyn as a continuous trait using second-order factor scores derived from four first-order factors; and, 3) a multivariate SEM analysis with MetSyn as a continuous, second-order factor modeled with multiple putative genes, which were represented by latent constructs defined using multiple SNPs in each gene. Results were similar between approaches in that CSMD1 SNPs were associated with MetSyn in Approaches 1 and 2; however, the effects of CSMD1 diminished in Approach 3 when modeled simultaneously with six other genes, most notably CETP and STARD13, which were strongly associated with the Lipids and MetSyn factors, respectively. We conclude that modeling multiple genes as latent constructs on first-order trait factors, most proximal to the gene's function with limited paths directly from genes to the second-order MetSyn factor, using SEM is the most viable approach toward understanding overall gene variation effects in the presence of multiple putative SNPs.
PMCID: PMC2795950  PMID: 20018043
3.  The effect of multiple genetic variants in predicting the risk of type 2 diabetes 
BMC Proceedings  2009;3(Suppl 7):S49.
While recently performed genome-wide association studies have advanced the identification of genetic variants predisposing to type 2 diabetes (T2D), the potential application of these novel findings for disease prediction and prevention has not been well studied. Diabetes prediction and prevention have become urgent issues owing to the rapidly increasing prevalence of diabetes and its associated mortality, morbidity, and health care cost. New prediction approaches using genetic markers could facilitate early identification of high risk sub-groups of the population so that appropriate prevention methods could be effectively applied to delay, or even prevent, disease onset.
This paper assessed 18 recently identified T2D loci for their potential role in diabetes prediction. We built a new predictive genetic test for T2D using the Framingham Heart Study dataset. Using logistic regression and 15 additional loci, the new test was slightly improved over the existing test using just three loci. A formal comparison between the two tests suggests no significant improvement. We further formed a predictive genetic test for identifying early onset T2D and found higher classification accuracy for this test, not only indicating that these 18 loci have great potential for predicting early onset T2D, but also suggesting that they may play important roles in causing early-onset T2D.
To further improve the test's accuracy, we applied a newly developed nonparametric method capable of capturing high order interactions to the data, but it did not outperform a logistic regression that only considers single-locus effects. This could be explained by the absence of gene-gene interactions among the 18 loci.
PMCID: PMC2795948  PMID: 20018041

Results 1-3 (3)