DataSheet_1_Bidirectional two-sample Mendelian randomization study of causality between rheumatoid arthritis and myocardial infarction.zip

BackgroundEpidemiological evidence suggests an association between rheumatoid arthritis (RA) and myocardial infarction (MI). However, causality remains uncertain. Therefore, this study aimed to explore the causal association between RA and MI.MethodsUsing publicly available genome-wide association study summary datasets, bidirectional two-sample Mendelian randomization (TSMR) was performed using inverse-variance weighted (IVW), weighted median, MR-Egger regression, simple mode, and weighted mode methods.ResultsThe MR results for the causal effect of RA on MI (IVW, odds ratio [OR] = 1.041, 95% confidence interval [CI]: 1.007–1.076, P = 0.017; weighted median, OR = 1.027, 95% CI: 1.006–1.049, P = 0.012) supported a causal association between genetic susceptibility to RA and an increased risk of MI. MR results for the causal effect of MI on RA (IVW, OR = 1.012, 95% CI: 0.807–1.268, P = 0.921; weighted median, OR = 1.069, 95% CI: 0.855–1.338, P = 0.556) indicated that there was no causal association between genetic susceptibility to MI and an increased risk of RA.ConclusionBidirectional TSMR analysis supports a causal association between genetic susceptibility to RA and an increased risk of MI but does not support a causal association between genetic susceptibility to MI and an increased risk of RA.</p

Additional file 9: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

Comparison of tissue-specific GRNs and atlas GRNs on percentage of overlap between GRN predicted targets and ChIP-Seq identified targets. Leaf, root, SAM and seed GRNs are networks in this study. mRNA and protein networks were constructed by Walley et al. Medium networks (light grey) are the targets within top 1 million edges. Small networks (dark grey) are the targets within top 100,000 edges. (PDF 77 kb

Additional file 8: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

Predicted TF target overlap with ChIP-Seq confirmed binding genes for KN1, FEA4 and O2. The leaf, root, SAM and seed refer to our tissue GRNs. “Protein” and “RNA” refer to protein GRN and RNA GRN from Walley et al. (2016) dataset. “Large network” used top 10 million edges in KN1, FEA4 and O2 networks. “Medium network” used top 1 million edges, while “Small network” used top 100,000 edges. “Atlas GRN medium” used top 1 million edges from Walley et al. (2016) dataset while “Atlas GRN small” used top 100,000 edges. (XLSX 13 kb

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Additional file 6: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

GO enrichment analysis for 1657 conserved targets in four tissue GRNs. (XLSX 22 kb

Legislative Documents

Also, variously referred to as: House bills; House documents; House legislative documents; legislative documents; General Court documents

Additional file 3: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

Evaluation of tissue GRNs generated by MRNET and CLR. (XLSX 9 kb

Additional file 2: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

GO enrichment analysis for tissue-specific genes in four tissue GRNs. (XLSX 23 kb

Additional file 4: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

A Venn diagram showing the overlap among top 1 million edges of each tissue-specific GRN. (PDF 55 kb

Additional file 7: of Distinct tissue-specific transcriptional regulation revealed by gene regulatory networks in maize

GO enrichment analysis for KN1, FEA4 and O2 targets in four tissue GRNs. (XLSX 19 kb