scholarly journals Integrative Genomic Analysis for the Discovery of Biomarkers in Prostate Cancer

2014 ◽  
Vol 9 ◽  
pp. BMI.S13729 ◽  
Author(s):  
Chindo Hicks ◽  
Tejaswi Koganti ◽  
Shankar Giri ◽  
Memory Tekere ◽  
Ritika Ramani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Gene Expression Data ◽  
Genetic Variants ◽  
Association Studies ◽  
Biological Pathways ◽  
Great Success ◽  
Genome Wide Association Studies ◽  
Expression Data ◽  
Increased Risk

Genome-wide association studies (GWAS) have achieved great success in identifying single nucleotide polymorphisms (SNPs, herein called genetic variants) and genes associated with risk of developing prostate cancer. However, GWAS do not typically link the genetic variants to the disease state or inform the broader context in which the genetic variants operate. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to infer the causal association between gene expression and the disease and to identify the network states and biological pathways enriched for genetic variants. We identified gene regulatory networks and biological pathways enriched for genetic variants, including the prostate cancer, IGF-1, JAK2, androgen, and prolactin signaling pathways. The integration of GWAS information with gene expression data provides insights about the broader context in which genetic variants associated with an increased risk of developing prostate cancer operate.

scholarly journals An Integrative Genomics Approach to Biomarker Discovery in Breast Cancer

2011 ◽  
Vol 10 ◽  
pp. CIN.S6837 ◽  
Author(s):  
Chindo Hicks ◽  
Rozana Asfour ◽  
Antonio Pannuti ◽  
Lucio Miele
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Genetic Variants ◽  
Association Studies ◽  
Biological Pathways ◽  
Genome Wide Association Studies ◽  
Expression Data ◽  
Integrative Genomics ◽  
Novel Genes

Genome-wide association studies (GWAS) have successfully identified genetic variants associated with risk for breast cancer. However, the molecular mechanisms through which the identified variants confer risk or influence phenotypic expression remains poorly understood. Here, we present a novel integrative genomics approach that combines GWAS information with gene expression data to assess the combined contribution of multiple genetic variants acting within genes and putative biological pathways, and to identify novel genes and biological pathways that could not be identified using traditional GWAS. The results show that genes containing SNPs associated with risk for breast cancer are functionally related and interact with each other in biological pathways relevant to breast cancer. Additionally, we identified novel genes that are co-expressed and interact with genes containing SNPs associated with breast cancer. Integrative analysis combining GWAS information with gene expression data provides functional bridges between GWAS findings and biological pathways involved in breast cancer.

scholarly journals An Integrative Genomics Approach for Associating GWAS Information with Triple-Negative Breast Cancer

2013 ◽  
Vol 12 ◽  
pp. CIN.S10413 ◽  
Author(s):  
Chindo Hicks ◽  
Ranjit Kumar ◽  
Antonio Pannuti ◽  
Kandis Backus ◽  
Alexandra Brown ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Triple Negative Breast Cancer ◽  
Genetic Variants ◽  
Triple Negative ◽  
Genome Wide Association Studies ◽  
Expression Data ◽  
Integrative Genomics ◽  
Increased Risk

Genome-wide association studies (GWAS) have identified genetic variants associated with an increased risk of developing breast cancer. However, the association of genetic variants and their associated genes with the most aggressive subset of breast cancer, the triple-negative breast cancer (TNBC), remains a central puzzle in molecular epidemiology. The objective of this study was to determine whether genes containing single nucleotide polymorphisms (SNPs) associated with an increased risk of developing breast cancer are connected to and could stratify different subtypes of TNBC. Additionally, we sought to identify molecular pathways and networks involved in TNBC. We performed integrative genomics analysis, combining information from GWAS studies involving over 400,000 cases and over 400,000 controls, with gene expression data derived from 124 breast cancer patients classified as TNBC (at the time of diagnosis) and 142 cancer-free controls. Analysis of GWAS reports produced 500 SNPs mapped to 188 genes. We identified a signature of 159 functionally related SNP-containing genes which were significantly ( P < 10−5) associated with and stratified TNBC. Additionally, we identified 97 genes which were functionally related to, and had similar patterns of expression profiles, SNP-containing genes. Network modeling and pathway prediction revealed multi-gene pathways including p53, NFkB, BRCA, apoptosis, DNA repair, DNA mismatch, and excision repair pathways enriched for SNPs mapped to genes significantly associated with TNBC. The results provide convincing evidence that integrating GWAS information with gene expression data provides a unified and powerful approach for biomarker discovery in TNBC.

scholarly journals MixMir: microRNA motif discovery from gene expression data using mixed linear models

2014 ◽  
Author(s):  
LIYANG Diao ◽  
Antoine Marcais ◽  
Scott Norton ◽  
Kevin C. Chen
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Motif Discovery ◽  
Linear Models ◽  
Developmental Stages ◽  
Sequence Similarity ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Expression Data ◽  
Mixed Linear Models

MicroRNAs (miRNAs) are a class of ~22nt non-coding RNAs that potentially regulate over 60% of human protein-coding genes. MiRNA activity is highly specific, differing between cell types, developmental stages and environmental conditions, so the identification of active miRNAs in a given sample is of great interest. Here we present a novel computational approach for analyzing both mRNA sequence and gene expression data, called MixMir. Our method corrects for 3' UTR background sequence similarity between transcripts, which is known to correlate with mRNA transcript abundance. We demonstrate that after accounting for kmer sequence similarities in 3' UTRs, a statistical linear model based on motif presence/absence can effectively discover active miRNAs in a sample. MixMir utilizes fast software implementations for solving mixed linear models which are widely-used in genome-wide association studies (GWAS). Essentially we use 3' UTR sequence similarity in place of population cryptic relatedness in the GWAS problem. Compared to similar methods such as miREDUCE, Sylamer and cWords, we found that MixMir performed better at discovering true miRNA motifs in Dicer knockout CD4+ T-cells, as well as protein and mRNA expression data obtained from miRNA transfection experiments in human cell lines. MixMir can be freely downloaded from https://github.com/ldiao/MixMir.

scholarly journals Novel Integrative Genomics Approach for Associating GWAS Information with Intrinsic Subtypes of Breast Cancer

2013 ◽  
Vol 12 ◽  
pp. CIN.S11452 ◽  
Author(s):  
Chindo Hicks ◽  
Tejaswi Koganti ◽  
Alexandra S. Brown ◽  
Jesus Monico ◽  
Kandis Backus ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genetic Variants ◽  
Association Studies ◽  
Great Success ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Integrative Genomics ◽  
Intrinsic Subtypes ◽  
Increased Risk ◽  
Distinct Disease

Genome-wide association studies (GWAS) have achieved great success in identifying common variants associated with increased risk of developing breast cancer. However, GWAS do not typically provide information about the broader context in which genetic variants operate in different subtypes of breast cancer. The objective of this study was to determine whether genes containing single nucleotide polymorphisms (SNPs, herein called genetic variants) are associated with different subtypes of breast cancer. Additionally, we sought to identify gene regulator networks and biological pathways enriched for these genetic variants. Using supervised analysis, we identified 201 genes that were significantly associated with the six intrinsic subtypes of breast cancer. The results demonstrate that integrative genomics analysis is a powerful approach for linking GWAS information to distinct disease states and provide insights about the broader context in which genetic variants operate in different subtypes of breast cancer.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

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