scholarly journals Large-scale transcriptome-wide association study identifies new prostate cancer risk regions

2018 ◽  
Author(s):  
Nicholas Mancuso ◽  
Simon Gayther ◽  
Alexander Gusev ◽  
Wei Zheng ◽  
Kathryn L. Penney ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Association Study ◽  
Large Scale ◽  
Association Studies ◽  
Probabilistic Approach ◽  
Prostate Cancer Risk ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Genes ◽  
Credible Set

AbstractAlthough genome-wide association studies (GWAS) for prostate cancer (PrCa) have identified more than 100 risk regions, most of the risk genes at these regions remain largely unknown. Here, we integrate the largest PrCa GWAS (N=142,392) with gene expression measured in 45 tissues (N=4,458), including normal and tumor prostate, to perform a multi-tissue transcriptomewide association study (TWAS) for PrCa. We identify 235 genes at 87 independent 1Mb regions associated with PrCa risk, 9 of which are regions with no genome-wide significant SNP within 2Mb. 24 genes are significant in TWAS only for alternative splicing models in prostate tumor thus supporting the hypothesis of splicing driving risk for continued oncogenesis. Finally, we use a Bayesian probabilistic approach to estimate credible sets of genes containing the causal gene at pre-defined level; this reduced the list of 235 associations to 120 genes in the 90% credible set. Overall, our findings highlight the power of integrating expression with PrCa GWAS to identify novel risk loci and prioritize putative causal genes at known risk loci.

scholarly journals Transcriptome-wide association study of attention deficit hyperactivity disorder identifies associated genes and phenotypes

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Calwing Liao ◽  
Alexandre D. Laporte ◽  
Dan Spiegelman ◽  
Fulya Akçimen ◽  
Ridha Joober ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Association Study ◽  
Attention Deficit ◽  
Posterior Probability ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Hyperactivity Disorder ◽  
Genome Wide ◽  
Dorsolateral Prefrontal ◽  
Causal Genes

Abstract Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental psychiatric disorder. Genome-wide association studies (GWAS) have identified several loci associated with ADHD. However, understanding the biological relevance of these genetic loci has proven to be difficult. Here, we conduct an ADHD transcriptome-wide association study (TWAS) consisting of 19,099 cases and 34,194 controls and identify 9 transcriptome-wide significant hits, of which 6 genes were not implicated in the original GWAS. We demonstrate that two of the previous GWAS hits can be largely explained by expression regulation. Probabilistic causal fine-mapping of TWAS signals prioritizes KAT2B with a posterior probability of 0.467 in the dorsolateral prefrontal cortex and TMEM161B with a posterior probability of 0.838 in the amygdala. Furthermore, pathway enrichment identifies dopaminergic and norepinephrine pathways, which are highly relevant for ADHD. Overall, our findings highlight the power of TWAS to identify and prioritize putatively causal genes.

scholarly journals Transcriptome-Wide Association Study Provides Insights Into the Genetic Component of Gene Expression in Anxiety

2021 ◽  
Vol 12 ◽  
Author(s):  
Xi Su ◽  
Wenqiang Li ◽  
Luxian Lv ◽  
Xiaoyan Li ◽  
Jinfeng Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Anxiety Disorders ◽  
Association Study ◽  
Large Scale ◽  
Association Studies ◽  
Genetic Component ◽  
Genome Wide Association Studies ◽  
Risk Variants ◽  
Genome Wide ◽  
Regulated Expression

Anxiety disorders are common mental disorders that often result in disability. Recently, large-scale genome-wide association studies (GWASs) have identified several novel risk variants and loci for anxiety disorders (or anxiety traits). Nevertheless, how the reported risk variants confer risk of anxiety remains unknown. To identify genes whose cis-regulated expression levels are associated with risk of anxiety traits, we conducted a transcriptome-wide association study (TWAS) by integrating genome-wide associations from a large-scale GWAS (N = 175,163) (which evaluated anxiety traits based on Generalized Anxiety Disorder 2-item scale (GAD-2) score) and brain expression quantitative trait loci (eQTL) data (from the PsychENCODE and GTEx). We identified 19 and 17 transcriptome-wide significant (TWS) genes in the PsychENCODE and GTEx, respectively. Intriguingly, 10 genes showed significant associations with anxiety in both datasets, strongly suggesting that genetic risk variants may confer risk of anxiety traits by regulating the expression of these genes. Top TWS genes included RNF123, KANSL1-AS1, GLYCTK, CRHR1, DND1P1, MAPT and ARHGAP27. Of note, 25 TWS genes were not implicated in the original GWAS. Our TWAS identified 26 risk genes whose cis-regulated expression were significantly associated with anxiety, providing important insights into the genetic component of gene expression in anxiety disorders/traits and new clues for future drug development.

scholarly journals Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

2021 ◽  
Author(s):  
Sylvan C Baca ◽  
Cassandra Singler ◽  
Soumya Zacharia ◽  
Ji-Heui Seo ◽  
Tunc Morova ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Steady State ◽  
Binding Sites ◽  
Association Studies ◽  
Prostate Cancer Risk ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Genetic Determinants ◽  
Genome Wide ◽  
Context Dependent

Methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs), are widely used to functionally annotate trait-associated variants, but they are limited in identifying context-dependent effects on transcription. To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for nominating variants that impact traits through their effects on chromatin state. CWAS associates the genetic determinants of cistromes (e.g., the genome-wide profiles of transcription factor binding sites or histone modifications) with traits using summary statistics from genome-wide association studies (GWAS). We performed CWASs of prostate cancer and androgen-related traits, using a reference panel of 307 prostate cistromes from 165 individuals. CWAS nominated susceptibility regulatory elements or androgen receptor (AR) binding sites at 52 out of 98 known prostate cancer GWAS loci and implicated an additional 17 novel loci. We functionally validated a subset of our results using CRISPRi and in vitro reporter assays. At 28 of the 52 risk loci, CWAS identified regulatory mechanisms that are not observable via eQTLs, implicating genes with complex or context-specific regulation that are overlooked by current approaches that relying on steady-state transcript measurements. CWAS genes include transcription factors that govern prostate development such as NKX3-1, HOXB13, GATA2, and KLF5. Moreover, CWAS boosts discovery power in modestly sized GWAS, identifying novel genetic associations mediated through AR binding for androgen-related phenotypes, including resistance to prostate cancer therapy. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting context-dependent transcriptional regulation.

scholarly journals Post genome-wide association studies functional characterization of prostate cancer risk loci

BMC Genomics ◽  
2013 ◽  
Vol 14 (Suppl 8) ◽  
pp. S9 ◽  
Author(s):  
Junfeng Jiang ◽  
Weirong Cui ◽  
Wanwipa Vongsangnak ◽  
Guang Hu ◽  
Bairong Shen
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Association Studies ◽  
Functional Characterization ◽  
Prostate Cancer Risk ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide

scholarly journals Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation

2021 ◽  
Author(s):  
Matthew Freedman ◽  
Sylvan Baca ◽  
Cassandra Singler ◽  
Soumya Zacharia ◽  
Ji-Heui Seo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Steady State ◽  
Binding Sites ◽  
Association Studies ◽  
Prostate Cancer Risk ◽  
Regulatory Elements ◽  
Genome Wide Association Studies ◽  
Genetic Determinants ◽  
Genome Wide ◽  
Context Dependent

Abstract Methods that link genetic variation to steady-state gene expression levels, such as expression quantitative trait loci (eQTLs), are widely used to functionally annotate trait-associated variants, but they are limited in identifying context-dependent effects on transcription. To address this challenge, we developed the cistrome-wide association study (CWAS), a framework for nominating variants that impact traits through their effects on chromatin state. CWAS associates the genetic determinants of cistromes (e.g., the genome-wide profiles of transcription factor binding sites or histone modifications) with traits using summary statistics from genome-wide association studies (GWAS). We performed CWASs of prostate cancer and androgen-related traits, using a reference panel of 307 prostate cistromes from 165 individuals. CWAS nominated susceptibility regulatory elements or androgen receptor (AR) binding sites at 52 out of 98 known prostate cancer GWAS loci and implicated an additional 17 novel loci. We functionally validated a subset of our results using CRISPRi and in vitro reporter assays. At 28 of the 52 risk loci, CWAS identified regulatory mechanisms that are not observable via eQTLs, implicating genes with complex or context-specific regulation that are overlooked by current approaches that relying on steady-state transcript measurements. CWAS genes include transcription factors that govern prostate development such as NKX3-1, HOXB13, GATA2, and KLF5. Moreover, CWAS boosts discovery power in modestly sized GWAS, identifying novel genetic associations mediated through AR binding for androgen-related phenotypes, including resistance to prostate cancer therapy. CWAS is a powerful and biologically interpretable paradigm for studying variants that influence traits by affecting context-dependent transcriptional regulation.

scholarly journals Assessment of possible association between rs378854 and prostate cancer risk in the Serbian population

2013 ◽  
Vol 65 (2) ◽  
pp. 475-486
Author(s):  
G. Brajuskovic ◽  
Zorana Nikolic ◽  
A. Kojic ◽  
Dusanka Savic-Pavicevic ◽  
Snezana Cerovic ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Association Studies ◽  
Prostate Cancer Risk ◽  
Quality Analysis ◽  
Control Group ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Study Population ◽  
Hardy Weinberg Equilibrium

Prostate cancer (PCa) is the second most commonly diagnosed cancer among men worldwide. Despite its high incidence rate, the molecular basis of PCa onset and its progression remains little understood. Genome-wide association studies (GWAS) have greatly contributed to the identification of single nucleotide polymorphisms (SNP) associated with PCa risk. Several GWAS identified 8q24 as one of the most significant PCa-associated regions. The aim of this study was to evaluate the association of SNP rs378854 at 8q24 with PCa risk in the Serbian population. The study population included 261 individuals diagnosed with PCa, 257 individuals diagnosed with benign prostatic hyperplasia (BPH) and 106 healthy controls. Data quality analysis yielded results showing deviations from Hardy-Weinberg equilibrium in groups of PCa patients and BPH patients as well as in the control group. There was no significant association between alleles and genotypes of the genetic variant rs378854 and PCa risk in the Serbian population.

scholarly journals Untargeted metabolome- and transcriptome-wide association study identifies causal genes modulating metabolite concentrations in urine

2020 ◽  
Author(s):  
Reyhan Sönmez Flitman ◽  
Bita Khalili ◽  
Zoltan Kutalik ◽  
Rico Rueedi ◽  
Sven Bergmann
Keyword(s):  
Gene Expression ◽  
Association Study ◽  
Association Studies ◽  
Urine Concentration ◽  
Resonance Spectroscopy ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Rnaseq Data ◽  
Metabolite Concentrations ◽  
Causal Genes

SummaryIn this study we investigate the results of a metabolome- and transcriptome-wide association study to identify genes influencing the human metabolome. We used RNAseq data from lymphoblastoid cell lines (LCLs) derived from 555 Caucasian individuals to characterize their transcriptome. As for the metabolome we took an untargeted approach using binned features from 1H nuclear magnetic resonance spectroscopy (NMR) of urine samples from the same subjects allowing for data-driven discovery of associated compounds (rather than working with a limited set of quantified metabolites).Using pairwise linear regression we identified 21 study-wide significant associations between metabolome features and gene expression levels. We observed the most significant association between the gene ALMS1 and two adjacent metabolome features at 2.0325 and 2.0375 ppm. By using our previously developed metabomatching methodology, we found N-Acetylaspartate (NAA) as the potential underlying metabolite whose urine concentration is correlated with ALMS1 expression. Indeed, a number of metabolome- and genome-wide association studies (mGWAS) had already suggested the locus of this gene to be involved in regulation of N-acetylated compounds, yet were not able to identify unambiguously the exact metabolite, nor to disambiguate between ALMS1 and NAT8, another gene found in the same locus as the mediator gene. The second highest significant association was observed between HPS1 and two metabolome features at 2.8575 and 2.8725 ppm. Metabomatching of the association profile of HPS1 with all metabolite features pointed at trimethylamine (TMA) as the most likely underlying metabolite. mGWAS had previously implicated a locus containing HPS1 to be associated with TMA concentrations in urine but could not disambiguate this association signal from PYROXD2, a gene in the same locus. We used Mendelian randomization to show for both ALMS1 and HPS1 that their expression is causally linked to the respective metabolite concentrations.Our study provides evidence that the integration of metabolomics with gene expression data can support mQTL analysis, helping to identify the most likely gene involved in the modulation of the metabolite concentration.

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