Browsing by Subject "expression quantitative trait loci"
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Item Open Access Association of genetic variants of TMEM135 and PEX5 in the peroxisome pathway with cutaneous melanoma-specific survival.(Annals of translational medicine, 2021-03) Wang, Haijiao; Liu, Hongliang; Dai, Wei; Luo, Sheng; Amos, Christopher I; Lee, Jeffrey E; Li, Xin; Yue, Ying; Nan, Hongmei; Wei, QingyiBackground
Peroxisomes are ubiquitous and dynamic organelles that are involved in the metabolism of reactive oxygen species (ROS) and lipids. However, whether genetic variants in the peroxisome pathway genes are associated with survival in patients with melanoma has not been established. Therefore, our aim was to identify additional genetic variants in the peroxisome pathway that may provide new prognostic biomarkers for cutaneous melanoma (CM).Methods
We assessed the associations between 8,397 common single-nucleotide polymorphisms (SNPs) in 88 peroxisome pathway genes and CM disease-specific survival (CMSS) in a two-stage analysis. For the discovery, we extracted the data from a published genome-wide association study from The University of Texas MD Anderson Cancer Center (MDACC). We then replicated the results in another dataset from the Nurse Health Study (NHS)/Health Professionals Follow-up Study (HPFS).Results
Overall, 95 (11.1%) patients in the MDACC dataset and 48 (11.7%) patients in the NHS/HPFS dataset died of CM. We found 27 significant SNPs in the peroxisome pathway genes to be associated with CMSS in both datasets after multiple comparison correction using the Bayesian false-discovery probability method. In stepwise Cox proportional hazards regression analysis, with adjustment for other covariates and previously published SNPs in the MDACC dataset, we identified 2 independent SNPs (TMEM135 rs567403 C>G and PEX5 rs7969508 A>G) that predicted CMSS (P=0.003 and 0.031, respectively, in an additive genetic model). The expression quantitative trait loci analysis further revealed that the TMEM135 rs567403 GG and PEX5 rs7969508 GG genotypes were associated with increased and decreased levels of mRNA expression of their genes, respectively.Conclusions
Once our findings are replicated by other investigators, these genetic variants may serve as novel biomarkers for the prediction of survival in patients with CM.Item Open Access Efficient analysis of complex, multimodal genomic data(2016) Acharya, Chaitanya RamanujOur primary goal is to better understand complex diseases using statistically disciplined approaches. As multi-modal data is streaming out of consortium projects like Genotype-Tissue Expression (GTEx) project, which aims at collecting samples from various tissue sites in order to understand tissue-specific gene regulation, new approaches are needed that can efficiently model groups of data with minimal loss of power. For example, GTEx project delivers RNA-Seq, Microarray gene expression and genotype data (SNP Arrays) from a vast number of tissues in a given individual subject. In order to analyze this type of multi-level (hierarchical) multi-modal data, we proposed a series of efficient-score based tests or score tests and leveraged groups of tissues or gene isoforms in order map genomic biomarkers. We model group-specific variability as a random effect within a mixed effects model framework. In one instance, we proposed a score-test based approach to map expression quantitative trait loci (eQTL) across multiple-tissues. In order to do that we jointly model all the tissues and make use of all the information available to maximize the power of eQTL mapping and investigate an overall shift in the gene expression combined with tissue-specific effects due to genetic variants. In the second instance, we showed the flexibility of our model framework by expanding it to include tissue-specific epigenetic data (DNA methylation) and map eQTL by leveraging both tissues and methylation. Finally, we also showed that our methods are applicable on different data type such as whole transcriptome expression data, which is designed to analyze genomic events such alternative gene splicing. In order to accomplish this, we proposed two different models that exploit gene expression data of all available gene-isoforms within a gene to map biomarkers of interest (either genes or gene-sets) in paired early-stage breast tumor samples before and after treatment with external beam radiation. Our efficient score-based approaches have very distinct advantages. They have a computational edge over existing methods because they do not need parameter estimation under the alternative hypothesis. As a result, model parameters only have to be estimated once per genome, significantly decreasing computation time. Also, the efficient score is the locally most powerful test and is guaranteed a theoretical optimality over all other approaches in a neighborhood of the null hypothesis. This theoretical performance is born out in extensive simulation studies which show that our approaches consistently outperform existing methods both in statistical power and computational speed. We applied our methods to publicly available datasets. It is important to note that all of our methods also accommodate the analysis of next-generation sequencing data.
Item Open Access Novel Genetic Variants of ALG6 and GALNTL4 of the Glycosylation Pathway Predict Cutaneous Melanoma-Specific Survival.(Cancers, 2020-01-24) Zhou, Bingrong; Zhao, Yu Chen; Liu, Hongliang; Luo, Sheng; Amos, Christopher I; Lee, Jeffrey E; Li, Xin; Nan, Hongmei; Wei, QingyiBecause aberrant glycosylation is known to play a role in the progression of melanoma, we hypothesize that genetic variants of glycosylation pathway genes are associated with the survival of cutaneous melanoma (CM) patients. To test this hypothesis, we used a Cox proportional hazards regression model in a single-locus analysis to evaluate associations between 34,096 genetic variants of 227 glycosylation pathway genes and CM disease-specific survival (CMSS) using genotyping data from two previously published genome-wide association studies. The discovery dataset included 858 CM patients with 95 deaths from The University of Texas MD Anderson Cancer Center, and the replication dataset included 409 CM patients with 48 deaths from Harvard University nurse/physician cohorts. In the multivariable Cox regression analysis, we found that two novel single-nucleotide polymorphisms (SNPs) (ALG6 rs10889417 G>A and GALNTL4 rs12270446 G>C) predicted CMSS, with an adjusted hazards ratios of 0.60 (95% confidence interval = 0.44-0.83 and p = 0.002) and 0.66 (0.52-0.84 and 0.004), respectively. Subsequent expression quantitative trait loci (eQTL) analysis revealed that ALG6 rs10889417 was associated with mRNA expression levels in the cultured skin fibroblasts and whole blood cells and that GALNTL4 rs12270446 was associated with mRNA expression levels in the skin tissues (all p < 0.05). Our findings suggest that, once validated by other large patient cohorts, these two novel SNPs in the glycosylation pathway genes may be useful prognostic biomarkers for CMSS, likely through modulating their gene expression.