33 research outputs found
Genome-Environmental Risk Assessment of Cocaine Dependence
Cocaine-associated biomedical and psychosocial problems are substantial twenty-first century global burdens of disease. This burden is largely driven by a cocaine dependence process that becomes engaged with increasing occasions of cocaine product use. For this reason, the development of a risk-prediction model for cocaine dependence may be of special value. Ultimately, success in building such a risk-prediction model may help promote personalized cocaine dependence prediction, prevention, and treatment approaches not presently available. As an initial step toward this goal, we conducted a genome-environmental risk-prediction study for cocaine dependence, simultaneously considering 948,658 single nucleotide polymorphisms (SNPs), six potentially cocaine-related facets of environment, and three personal characteristics. In this study, a novel statistical approach was applied to 1045 case-control samples from the Family Study of Cocaine Dependence. The results identify 330 low- to medium-effect size SNPs (i.e., those with a single-locus p-value of less than 10−4) that made a substantial contribution to cocaine dependence risk prediction (AUC = 0.718). Inclusion of six facets of environment and three personal characteristics yielded greater accuracy (AUC = 0.809). Of special importance was the joint effect of childhood abuse (CA) among trauma experiences and the GBE1 gene in cocaine dependence risk prediction. Genome-environmental risk-prediction models may become more promising in future risk-prediction research, once a more substantial array of environmental facets are taken into account, sometimes with model improvement when gene-by-environment product terms are included as part of these risk predication models
GWGGI: software for genome-wide gene-gene interaction analysis
Background: While the importance of gene-gene interactions in human diseases
has been well recognized, identifying them has been a great challenge,
especially through association studies with millions of genetic markers and
thousands of individuals. Computationally efficient and powerful tools are in
great need for the identification of new gene-gene interactions in
high-dimensional association studies. Result: We develop C++ software for
genome-wide gene-gene interaction analyses (GWGGI). GWGGI utilizes tree-based
algorithms to search a large number of genetic markers for a disease-associated
joint association with the consideration of high-order interactions, and then
uses non-parametric statistics to test the joint association. The package
includes two functions, likelihood ratio Mann-whitney (LRMW) and Tree
Assembling Mann-whitney (TAMW).We optimize the data storage and computational
efficiency of the software, making it feasible to run the genome-wide analysis
on a personal computer. The use of GWGGI was demonstrated by using two real
data-sets with nearly 500 k genetic markers. Conclusion: Through the empirical
study, we demonstrated that the genome-wide gene-gene interaction analysis
using GWGGI could be accomplished within a reasonable time on a personal
computer (i.e., ~3.5 hours for LRMW and ~10 hours for TAMW). We also showed
that LRMW was suitable to detect interaction among a small number of genetic
variants with moderate-to-strong marginal effect, while TAMW was useful to
detect interaction among a larger number of low-marginal-effect genetic
variants
A Weighted U Statistic for Genetic Association Analyses of Sequencing Data
With advancements in next generation sequencing technology, a massive amount
of sequencing data are generated, offering a great opportunity to
comprehensively investigate the role of rare variants in the genetic etiology
of complex diseases. Nevertheless, this poses a great challenge for the
statistical analysis of high-dimensional sequencing data. The association
analyses based on traditional statistical methods suffer substantial power loss
because of the low frequency of genetic variants and the extremely high
dimensionality of the data. We developed a weighted U statistic, referred to as
WU-seq, for the high-dimensional association analysis of sequencing data. Based
on a non-parametric U statistic, WU-SEQ makes no assumption of the underlying
disease model and phenotype distribution, and can be applied to a variety of
phenotypes. Through simulation studies and an empirical study, we showed that
WU-SEQ outperformed a commonly used SKAT method when the underlying assumptions
were violated (e.g., the phenotype followed a heavy-tailed distribution). Even
when the assumptions were satisfied, WU-SEQ still attained comparable
performance to SKAT. Finally, we applied WU-seq to sequencing data from the
Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very
low density lipoprotein cholesterol
A Generalized Genetic Random Field Method for the Genetic Association Analysis of Sequencing Data
With the advance of high‐throughput sequencing technologies, it has become feasible to investigate the influence of the entire spectrum of sequencing variations on complex human diseases. Although association studies utilizing the new sequencing technologies hold great promise to unravel novel genetic variants, especially rare genetic variants that contribute to human diseases, the statistical analysis of high‐dimensional sequencing data remains a challenge. Advanced analytical methods are in great need to facilitate high‐dimensional sequencing data analyses. In this article, we propose a generalized genetic random field (GGRF) method for association analyses of sequencing data. Like other similarity‐based methods (e.g., SIMreg and SKAT), the new method has the advantages of avoiding the need to specify thresholds for rare variants and allowing for testing multiple variants acting in different directions and magnitude of effects. The method is built on the generalized estimating equation framework and thus accommodates a variety of disease phenotypes (e.g., quantitative and binary phenotypes). Moreover, it has a nice asymptotic property, and can be applied to small‐scale sequencing data without need for small‐sample adjustment. Through simulations, we demonstrate that the proposed GGRF attains an improved or comparable power over a commonly used method, SKAT, under various disease scenarios, especially when rare variants play a significant role in disease etiology. We further illustrate GGRF with an application to a real dataset from the Dallas Heart Study. By using GGRF, we were able to detect the association of two candidate genes, ANGPTL 3 and ANGPTL 4, with serum triglyceride.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106664/1/gepi21790.pd
Synthesis of dendritic PdAu nanoparticles with enhanced electrocatalytic activity
A facile method for the rapid synthesis of dendritic PdAu alloyed bimetallic nanocrystals is demonstrated. The whole synthetic process is very simple just by mixing Na2PdCl4, HAuCl4, polyvinylpyrrolidone and hydroquinone and heated at 50 °C for 15 min. The as-prepared dendritic PdAu nanoparticles exhibit superior catalytic activity for methanol, ethanol and glycerol electrooxidation in alkaline solution, and their catalytic performance is composition-dependent. Keywords: Dendritic, Palladium, Gold, Electrooxidation, Catalytic activit