Evidence for Correlations Between BMI-Associated SNPs and circRNAs
- 1. Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
- 2. Institute of Human Genetics, University Hospital Essen, Essen, Germany
- 3. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany
- 4. Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany
Description
The datasets provided here are part of the study "Evidence for Correlations Between BMI-Associated SNPs and circRNAs" by Rajcsanyi et al.
Abstract of the study:
Circular RNAs (circRNAs) are regulators of processes like adipogenesis. Their expression can be modulated by SNPs. We analysed links between BMI-associated SNPs and circRNAs. First, we detected an enrichment of BMI-associated SNPs on circRNA genomic loci in comparison to non-significant variants. Analysis of sex-stratified GWAS data revealed that circRNA genomic loci encompassed more genome-wide significant BMI-SNPs in females than in males. To explore if the enrichment is restricted to BMI, we investigated nine additional GWAS studies. We showed an enrichment of trait-associated SNPs in circRNAs for four analysed phenotypes (body height, chronic kidney disease, anorexia nervosa and autism spectrum disorder). To analyse the influence of BMI-affecting SNPs on circRNA levels in vitro, we examined rs4752856 located on hsa_circ_0022025. The analysis of heterozygous individuals revealed an increased level of circRNA derived from the BMI-increasing SNP allele. We conclude that genetic variation may affect the BMI partly through circRNAs.
Information regarding the datasets:
The data provided represents the analysed as well as generated data throughout the study. For further information about the datasets used, processed and generated, please see the study by Rajcsanyi et al.
circRNA datasets:
The analysed circRNA datasets were extracted from four circRNA databases (circAtlas v2.0, circBase, CIRCpediaV2 and circVAR) and were further processed to exclude internal duplicates and circRNAs derived from sex chromosomes. These original datasets have been downloaded from the following websites:
circAtlas v2.0: http://159.226.67.237:8080/new/links.php
circBase: http://www.circbase.org/cgi-bin/downloads.cgi
CIRCpediaV2: http://yang-laboratory.com/circpedia/download
circVAR: http://soft.bioinfo-minzhao.org/circvar/
GWAS datasets:
The original genome-wide association study (GWAS) summary statistics dataset of the BMI GWAS by Yengo et al. (2018) were classified into significant (P < 5*10-8) and non-significant (P >= 5*10-8) SNPs. A subsequent sensitivty analysis adjusted the P-value threshold of the non-significant SNPs to either P >= 5*10-5, P >= 5*10-6 or 5*10-7.
Please note that these datasets are not provided in this repository, as these were solely classified and divided based on the SNPs' P-value. The same applied for all additional GWAS data solely divided based on the P-value (GWAS data for Anorexia nervosa, Autism spectrum disorder, etc.). The original and complete summary statistcs data can be obtained in the stated references below for each GWAS. Yet, the datasets generated for an approximation of the linkage disequilibrium based on the GWAS data by Yengo et al. (2018, BMI) are provided in this repository.
BMI and body height: Yengo et al. (2018)
BMI sex-stratified: Pulit et al. (2019)
Anorexia nervosa: Watson et al. (2018)
Amyotropic lateral scerlosis: Iacoangeli et al. (2020)
Autism spectrum disorder: Grove et al. (2019)
Chronic kidney disease: Wuttke et al. 2019
Epilepsy: ILAE consortium et al. 2018
Heart Failure: Shah et al. 2020
Pernicious anemia: Glanville et al. 2021
Ulcerative Colitis: de Lange et al. 2017
Generated data:
The unprocessed output data of the study produced by the custom R script is provided here. Please note that the amount of information (rsID, P-value, Beta-value, allele frequency, circRNA_ID, circRNA strand, etc.) can vary between the output files due to differences in the data included in each circRNA and GWAS dataset. These dataset represent the raw and thus unprocessed output data. The results/counts presented in the study were obtained by further processing these output files. Further, the data produced by the SNaPshot assay are provided as well.
Files
dataset_circAtlas_hg19.txt
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Additional details
References
- Wu, W., Ji, P. & Zhao, F. CircAtlas: an integrated resource of one million highly accurate circular RNAs from 1070 vertebrate transcriptomes. Genome Biol 21, 101 (2020).
- Glažar, P., Papavasileiou, P. & Rajewsky, N. circBase: a database for circular RNAs. RNA 20, 1666-70 (2014).
- Dong, R., Ma, X.K., Li, G.W. & Yang, L. CIRCpedia v2: An Updated Database for Comprehensive Circular RNA Annotation and Expression Comparison. Genomics Proteomics Bioinformatics 16, 226-233 (2018).
- Zhao, M. & Qu, H. circVAR database: genome-wide archive of genetic variants for human circular RNAs. BMC Genomics 21, 750 (2020).
- Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in approximately 700000 individuals of European ancestry. Hum Mol Genet 27, 3641-3649 (2018).
- Iacoangeli, A. et al. Genome-wide Meta-analysis Finds the ACSL5-ZDHHC6 Locus Is Associated with ALS and Links Weight Loss to the Disease Genetics. Cell Rep 33, 108323 (2020).
- International League Against Epilepsy Consortium on Complex, E. Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies. Nat Commun 9, 5269 (2018).
- Wuttke, M. et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet 51, 957-972 (2019).
- Shah, S. et al. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nat Commun 11, 163 (2020).
- Glanville, K.P., Coleman, J.R.I., O'Reilly, P.F., Galloway, J. & Lewis, C.M. Investigating Pleiotropy Between Depression and Autoimmune Diseases Using the UK Biobank. Biol Psychiatry Glob Open Sci 1, 48-58 (2021).
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- Meyer, H.V. plinkQC: Genotype quality control in genetic association studies. (2020)
- Pulit, S.L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet 28, 166-174 (2019).
- Li, J.H., Liu, S., Zhou, H., Qu, L.H. & Yang, J.H. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res 42, D92-7 (2014).