Multivariate analysis of complex gene expression and clinical phenotypes with genetic marker data

2007 ◽  
Vol 31 (S1) ◽  
pp. S103-S109 ◽  
Author(s):  
Joseph Beyene ◽  
David Tritchler ◽  
Keyword(s):  
Gene Expression ◽  
Multivariate Analysis ◽  
Genetic Marker ◽  
Clinical Phenotypes ◽  
Marker Data

scholarly journals The Functional False Discovery Rate with Applications to Genomics

2017 ◽  
Author(s):  
Xiongzhi Chen ◽  
David G. Robinson ◽  
John D. Storey
Keyword(s):  
Gene Expression ◽  
False Discovery Rate ◽  
Genetic Marker ◽  
Read Depth ◽  
False Discovery Rates ◽  
Additional Information ◽  
False Discovery ◽  
Gene Expression Trait ◽  
Genetics Of Gene Expression ◽  
False Discoveries

AbstractThe false discovery rate measures the proportion of false discoveries among a set of hypothesis tests called significant. This quantity is typically estimated based on p-values or test statistics. In some scenarios, there is additional information available that may be used to more accurately estimate the false discovery rate. We develop a new framework for formulating and estimating false discovery rates and q-values when an additional piece of information, which we call an “informative variable”, is available. For a given test, the informative variable provides information about the prior probability a null hypothesis is true or the power of that particular test. The false discovery rate is then treated as a function of this informative variable. We consider two applications in genomics. Our first is a genetics of gene expression (eQTL) experiment in yeast where every genetic marker and gene expression trait pair are tested for associations. The informative variable in this case is the distance between each genetic marker and gene. Our second application is to detect differentially expressed genes in an RNA-seq study carried out in mice. The informative variable in this study is the per-gene read depth. The framework we develop is quite general, and it should be useful in a broad range of scientific applications.

Abstract 19055: MicroRNA Gene Expression of Heart Transplant Endomyocardial Biopsy

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Eleanor Chang ◽  
Gregory Fishbein ◽  
Maral Bakir ◽  
Galyna Bondar ◽  
Nicholas Jackson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endomyocardial Biopsy ◽  
Allograft Rejection ◽  
Empirical Bayes ◽  
Expression Profiles ◽  
Target Prediction ◽  
Cardiac Allograft ◽  
Protein Kinase Activity ◽  
Cardiac Allograft Rejection ◽  
Clinical Phenotypes

Introduction Endomyocardial biopsy is the standard surveillance method to detect cardiac allograft rejection. While microRNAs (miRNA) play a major role in regulating mRNA, their nature and role in the biology is not well understood. We hypothesized that specific mRNA-miRNA networks can be identified underlying the clinical phenotypes of different forms of cardiac allograft rejection. Method Twenty one tissue samples from 14 post-HTx patients were subjected to genome wide miRNA sequencing. A non-parametric empirical Bayes framework removed batch effect and filtered genes with low variability. Weighted Gene Correlation Network Analysis (WGCNA) clustered genes into related eigengene modules based on their gene expression. Identified miRNAs were subjected to target prediction and compared with mRNA expression profiles previously identified on the same biopsies. Gene Ontology (GO) was used for biological interpretation of selected genes. Results 1270 miRNAs were used to construct 9 eigengene modules. Module-Trait relationship were then investigated as shown in Figure. The top ten miRNA probe sets filtered by the highest intra-module correlation and statistical significance were hsa-miR-141-3p, hsa-miR-150-5p, hsa-miR-605, hsa-miR-582-5p, hsa-miR-3150b-3p, hsa-miR-508-3p, hsa-miR-652-5p, hsa-miR-26a-1-3p, hsa-miR-3667-3p and hsa-miR-3911. Target prediction analysis resulted in 724 gene targets. GO analysis revealed 184 categories enriched by these genes including regulation of protein kinase activity, cardiac muscle cell differentiation and epithelial cell migration among others. Compared to mRNA previously identified in the same heart biopsies showed 685 overlapping gene targets. Conclusion WGCNA identified miRNA modules correlated with different clinical phenotypes of rejection. MRNA-miRNA pairs were identified to help understand the biology of rejection and as interesting candidates for diagnostic or therapeutic applications.

A multivariate analysis approach to the integration of proteomic and gene expression data

PROTEOMICS ◽  
2007 ◽  
Vol 7 (13) ◽  
pp. 2162-2171 ◽  
Author(s):  
Ailís Fagan ◽  
Aedín C. Culhane ◽  
Desmond G. Higgins
Keyword(s):  
Gene Expression ◽  
Multivariate Analysis ◽  
Gene Expression Data ◽  
Analysis Approach ◽  
Expression Data

scholarly journals The clinical utility of circulating neuroendocrine gene transcript analysis in well-differentiated paragangliomas and pheochromocytomas

2017 ◽  
Vol 176 (2) ◽  
pp. 143-157 ◽  
Author(s):  
M Pęczkowska ◽  
J Cwikla ◽  
M Kidd ◽  
A Lewczuk ◽  
A Kolasinska-Ćwikła ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multivariate Analysis ◽  
Clinical Utility ◽  
Progressive Disease ◽  
Somatostatin Receptor ◽  
Blood Analysis ◽  
Receptor Expression ◽  
Gene Transcript ◽  
Transcript Analysis ◽  
Well Differentiated

Context Paragangliomas and pheochromocytomas (PPGLs) exhibit variable malignancy, which is difficult to determine by histopathology, amine measurements or tissue genetic analyses. Objective To evaluate whether a 51-neuroendocrine gene blood analysis has clinical utility as a diagnostic and prognostic marker. Design Prospective cohort study. Well-differentiated PPGLs (n = 32), metastatic (n = 4); SDHx mutation (n = 25); 12 biochemically active, Lanreotide treated (n = 4). Nine patients had multiple sampling. Age- and gender-matched controls and GEP-NETs (comparators). Methods Circulating neuroendocrine tumor mRNA measured (qPCR) with multianalyte algorithmic analysis. Metabolic, epigenomic and proliferative genes as well as somatostatin receptor expression were assessed (averaged, normalized gene expression: mean ± s.e.m.). Amines were measured by HPLC and chromogranin A by ELISA. Analyses (2-tailed): Fisher’s test, non-parametric (Mann–Whitney), receiver-operator curve (ROC) and multivariate analysis (MVA). All data are presented as mean ± s.e.m. Results PPGL were NETest positive (100%). All exhibited higher scores than controls (55 ± 5% vs 8 ± 1%, P = 0.0001), similar to GEP-NETs (47 ± 5%). ROC analysis area under curve was 0.98 for differentiating PPGLs/controls (cut-off for normal: 26.7%). Mutation status was not directly linked to NETest. Genetic and molecular clustering was associated (P < 0.04) with NETest scores. Metastatic (80 ± 9%) and multicentric (64 ± 9%) disease had significantly (P < 0.04) higher scores than localized disease (43 ± 7%). Progressive disease (PD) had the highest scores (86 ± 2%) vs stable (SD, 41 ± 2%) (P < 0.0001). The area under the curve for PD from SD was 0.93 (cut-off for PD: 53%). Proliferation, epigenetic and somatostatin receptor gene expression was elevated (P < 0.03) in PD. Metabolic gene expression was decreased in SDHx mutations. Repeat NETest measurements defined clinical status in the 9 patients (6 SD and 3 PD). Amine measurement was non-informative. Multivariate analysis identified NETest >53% as an independent prognostic factor. Conclusion Circulating NET transcript analysis is positive (100% diagnostic) in well-differentiated PCC/PGL, scores were elevated in progressive disease irrespective of mutation or biochemical activity and elevated levels were prognostic.

Familial lipoid adrenal hyperplasia: Genetic marker data and an approach to prenatal diagnosis

1986 ◽  
Vol 25 (2) ◽  
pp. 319-325 ◽  
Author(s):  
Moshe Frydman ◽  
Arieh Kauschansky ◽  
Rina Zamir ◽  
Batsheva Bonné-Tamir ◽  
John M. Opitz ◽  
...  
Keyword(s):  
Prenatal Diagnosis ◽  
Genetic Marker ◽  
Adrenal Hyperplasia ◽  
Marker Data

Extensive dispersal of Roanoke logperch (Percina rex ) inferred from genetic marker data

2014 ◽  
Vol 25 (1) ◽  
pp. 1-16 ◽  
Author(s):  
James H. Roberts ◽  
Paul L. Angermeier ◽  
Eric M. Hallerman
Keyword(s):  
Genetic Marker ◽  
Marker Data ◽  
Roanoke Logperch

scholarly journals Leveraging collective regulatory effects of long-range DNA methylations to predict gene expressions and estimate their effects on phenotypes in cancer

2018 ◽  
Author(s):  
Soyeon Kim ◽  
Hyun Jung Park ◽  
Xiangqin Cui ◽  
Degui Zhi
Keyword(s):  
Gene Expression ◽  
Long Range ◽  
Estrogen Receptor Status ◽  
The Cancer Genome Atlas ◽  
Collective Effects ◽  
Statistical Machine Learning ◽  
Promoter Regions ◽  
Gene Expressions ◽  
Cancer Data ◽  
Clinical Phenotypes

ABSTRACTDNA methylation of various genomic regions plays an important role in regulating gene expression in diverse biological contexts. However, most genome-wide studies have focused on the effect of 1) methylation in cis, not in trans and 2) a single CpG, not the collective effects of multiple CpGs, on gene expression. In this study, we developed a statistical machine learning model, geneEXPLORER (geneexpression prediction by long-range epigenetic regulation), that quantifies the collective effects of both cis- and trans- methylations on gene expression. By applying geneEXPLORER to The Cancer Genome Atlas (TCGA) breast and lung cancer data, we found that most genes are affected by methylations of as much as 10Mb from promoter regions or more, and the long-range methylation explains 50% of the variation in gene expression on average, far greater than cis-methylation. The highly predictive genes are related to breast cancer, especially oncogenes and suppressor genes. Further, the predicted gene expressions could predict clinical phenotypes such as breast tumor status and estrogen receptor status (AUC=0.999, 0.94 respectively) as accurately as the measured gene expression levels. These results suggest that geneEXPLORER provides a means for accurate imputation of gene expression, which can be further used to predict clinical phenotypes.

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