ID
45744
Descrição
Principal Investigator: Francesca Luca, PhD, Wayne State University, Detroit, MI, USA MeSH: Gene-Environment Interaction https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001176 Functional variants associated with complex traits tend to fall in non-coding regions and affect regulatory mechanisms that are not yet well characterized. Furthermore, it is generally difficult to determine in which tissues and conditions they may have a functional impact. This is because the effect of a genetic variant on a molecular pathway, and ultimately on the individual's phenotype, may be modulated by "environmental" factors. We denominate such variants "gene-expression environment-specific quantitative trait nucleotides" GxE-QTNs. Achieving a better understanding of the mechanisms underlying GxE is a critical step in understanding the link between genotype and complex phenotype. It is also crucial to develop computationally efficient and statistically sound methods capable to integrate tissue/condition-specific functional genomics data to predict and validate when a sequence variant is functional. In this study we developed novel experimental and computational approaches to screen, analyze and functionally characterize genetic variants for complex traits modulated by environmental exposures. To identify and characterize genes with GxE, we analyzed allele specific gene expression in a panel of five relevant tissues (e.g. the vascular endothelium for cardiovascular diseases) under 50 controlled environmental conditions (e.g. glucocorticoids treatment, as a proxy for stress exposure). These data should be useful to develop computational tools that integrate different sources of evidence including data collected by ENCODE, RoadMap Epigenome and GTEx projects to functionally annotate GWAS variants. The experimental and computational tools developed by this project have widespread applicability, for example, can be used to tackle the functional basis of complex traits in other environmental contexts (e.g. other types of stress and hormonal levels) and genetic backgrounds. This resource represents the first comprehensive catalog of genetic variants that interact with environmental exposure in determining human complex traits.
Link
Palavras-chave
Versões (1)
- 02/06/2023 02/06/2023 - Chiara Middel
Titular dos direitos
Francesca Luca, PhD, Wayne State University, Detroit, MI, USA
Transferido a
2 de junho de 2023
DOI
Para um pedido faça login.
Licença
Creative Commons BY 4.0
Comentários do modelo :
Aqui pode comentar o modelo. Pode comentá-lo especificamente através dos balões de texto nos grupos de itens e itens.
Comentários do grupo de itens para :
Comentários do item para :
Para descarregar formulários, precisa de ter uma sessão iniciada. Por favor faça login ou registe-se gratuitamente.
dbGaP phs001176 GxE and Complex Traits
Eligibility Criteria
- StudyEvent: SEV1
- Eligibility Criteria
- The subject consent data table contains subject IDs, consent group information, and subject aliases.
- This subject sample mapping data table includes a mapping of study subject IDs to sample IDs. Samples are the final preps submitted for genotyping, sequencing, and/or expression data. For example, if one patient (subject ID) gave one sample, and that sample was processed differently to generate 2 sequencing runs, there would be two rows, both using the same subject ID, but having 2 unique sample IDs.
- This subject phenotype data table includes subject's sex and ethnicity.
- This sample attributes table includes body site where sample was collected, analyte type, histological type, tumor status, plate, barcode, and treatment IDs, sequencing step, and number of cells used in assays.
Similar models
Eligibility Criteria
- StudyEvent: SEV1
- Eligibility Criteria
- The subject consent data table contains subject IDs, consent group information, and subject aliases.
- This subject sample mapping data table includes a mapping of study subject IDs to sample IDs. Samples are the final preps submitted for genotyping, sequencing, and/or expression data. For example, if one patient (subject ID) gave one sample, and that sample was processed differently to generate 2 sequencing runs, there would be two rows, both using the same subject ID, but having 2 unique sample IDs.
- This subject phenotype data table includes subject's sex and ethnicity.
- This sample attributes table includes body site where sample was collected, analyte type, histological type, tumor status, plate, barcode, and treatment IDs, sequencing step, and number of cells used in assays.
C0680251 (UMLS CUI [1,2])
C3179121 (UMLS CUI [1,2])
C1135918 (UMLS CUI [1,3])
C0370003 (UMLS CUI [1,4])
C1265611 (UMLS CUI [1,2])
C1281286 (UMLS CUI [1,3])
C3898900 (UMLS CUI [1,4])
C0026591 (UMLS CUI [1,5])
C0200345 (UMLS CUI [2,1])
C1265611 (UMLS CUI [2,2])
C1281286 (UMLS CUI [2,3])
C0026591 (UMLS CUI [2,4])
C0205191 (UMLS CUI [2,5])
C0020538 (UMLS CUI [2,6])
C0032914 (UMLS CUI [2,7])
C0011849 (UMLS CUI [2,8])
C2909141 (UMLS CUI [2,9])
C0015934 (UMLS CUI [2,10])
C0430022 (UMLS CUI [2,11])
C0445106 (UMLS CUI [2,12])
C0205460 (UMLS CUI [3,1])
C0520510 (UMLS CUI [3,2])
C0205547 (UMLS CUI [3,3])
C4699982 (UMLS CUI [3,4])
C1516698 (UMLS CUI [4,1])
C0205163 (UMLS CUI [4,2])
C1265611 (UMLS CUI [4,3])
C1281286 (UMLS CUI [4,4])
C0681906 (UMLS CUI [4,5])
C0085756 (UMLS CUI [4,6])
C0043157 (UMLS CUI [4,7])
C0015671 (UMLS CUI [5,1])
C0445247 (UMLS CUI [5,2])
C0015031 (UMLS CUI [5,3])
C1516698 (UMLS CUI [6,1])
C0205163 (UMLS CUI [6,2])
C1265611 (UMLS CUI [6,3])
C0086582 (UMLS CUI [6,4])
C0086287 (UMLS CUI [6,5])
C3898900 (UMLS CUI [6,6])
C0021289 (UMLS CUI [6,7])
C0936012 (UMLS CUI [7,1])
C0205410 (UMLS CUI [7,2])
C1265611 (UMLS CUI [7,3])
C1881065 (UMLS CUI [7,4])
C0370003 (UMLS CUI [7,5])
C0079399 (UMLS CUI [7,6])
C3841890 (UMLS CUI [7,7])
C1274040 (UMLS CUI [7,8])
C0814897 (UMLS CUI [7,9])
C0314603 (UMLS CUI [7,10])
C0936012 (UMLS CUI [7,11])