9 research outputs found
Additional file 1: Table S1. of De novo mutational profile in RB1 clarified using a mutation rate modeling algorithm
All de novo germline variants in RB1 gene of patients with RB. âgDNA positionâ is the nucleotide position in the GENBANK accession number L11910 of the gene. Table S2. All ExAC variants in RB1 gene that were considered in our analysis. âgDNA positionâ is the nucleotide position in the GENBANK accession number L11910 of the gene. Table S3. All Nonsense variants in RB1 gene from Onadim and Houdayer groups. âgDNA positionâ is the nucleotide position in the GENBANK accession number L11910 of the gene. Table S4. Comparison of observed mutations and the simulated frequency of nonsense changes per exon, to find differential pathogenicity within nonsense mutations. Analysis was performed on data from Onadim and Houdayer groups. Table S5. Comparison of observed mutations and the simulated frequency of nonsense changes to find differential pathogenicity within nonsense mutations. Data shown for all amino acids and two arginine codons (99% CI) which can change to a stop codon. Analysis was performed on data from Onadim and Houdayer groups. Table S6. Polyphen predictions on the de novo germline missense mutations or some potential variants near codon 661 in RB1 gene. âPolyphen2_formatâ is the variant format accepted by the Polyphen2 tool. âPolyphen_predictionâ is the result of Polyphen2 on the missense variant. Table S7. Comparison between observed mutations and the simulated frequency of missense changes at amino acids and codons in exon 20, to find localized pathogenicity within missense mutations. Only the significant results are reported here. Table S8. Genomic territory of RB1 gene analyzed in our study. âPosition Startâ is the start position of the entry as per GENBANK database. âPosition Endâ is the end positon of the entry as per GENBANK database. âAnnotationâ is the description of the entry. Possible keywords are exon or donor/acceptor region in essential splice or nonessential intronic region. âExonâ corresponds to exon number of the entry. (XLSX 30Â kb
Additional file 3: Figure S2. of De novo mutational profile in RB1 clarified using a mutation rate modeling algorithm
Donor splice mutations in Exons 5, 6 and 12, and their effect on codon structure. The codon structures are shown prior and after the donor splice mutation. The donor splice mutation results in exon skipping or deletion, but can also cause a frameshift mutation in certain cases. (PDF 84ĂÂ kb
Additional file 2: Figure S1. of Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype
Overlapping genes among significant pathways for hearf failure before data processing. (PDF 145ĂÂ kb
Average Ï<sup>2</sup> statistics for LT versus other approaches in simulated data.
<p>For each statistic we display average results across 1,000,000 simulations, for various effect sizes <i>Îł</i>. All statistics are Ï<sup>2</sup>(1 dof). Logistic regression with an interaction term (G+GxE) values been converted from Ï<sup>2</sup>(2 dof) to the equivalent Ï<sup>2</sup>(1 dof) value. At an effect size of 0 all statistics give the expected value under the null. OR LBMI is the odds ratio computed from cases with BMIâ=â24. OR HBMI is the odds ratio for cases with BMIâ=â35.</p
Summary statistics across all datasets.
<p>The sum of each of the test statistics across all of the SNPs in each of the diseases. LTPub vs LogR is the % increase of LTPub compared to LogR. It has a median value of 16%. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p
Inferred covariates and effect sizes on the liability scale.
<p>LT model is the liability threshold model for each disease with parameters estimated using the LTPub method. For diseases with multiple covariates, models with all covariates and each covariate separately are given. %Variance Explained is the fraction of variance explained on the liability scale in the study data for each of the covariates in each of the diseases when all covariates are used in the model, and is specific to the distribution of covariates in each particular study. BMI30 is a binary variable, which is 1 if an individual's BMI is greater than 30 and 0 otherwise. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p
Power calculations for LogR, G+GxE, and LT approaches in simulated data.
<p>For each statistic we display power to attain P<5<b>Ă</b>10<sup>â8</sup> based on 1,000,000 simulations of 3000 cases and 3000 controls, for various effect sizes <i>Îł</i>. The increase in power (ratio of y-axis values) for LT versus LogR is 22.8% for <i>Îł</i>â=â0.1, and 23.0% when computing average power across all values of <i>Îł</i>. For Îłâ=â0 the power was 5.0% for all statistics when the P-value threshold is 0.05. G+GxE performs worse due to an extra degree of freedom.</p
Illustration of liability threshold model: simulated T2D example.
<p>The posterior mean of <i>Δ</i> for low-BMI and high-BMI cases is the expected value of <i>Δ</i> given that it exceeds <u>c(t</u>â)+m. High-BMI cases have a lower posterior mean relative to low-BMI cases since they require a smaller contribution from genetics to exceed the threshold in the liability threshold model.</p
Illustration of liability threshold model: simulated T2D example.
<p>Posterior mean value of residual quantitative trait <i>Δ</i> (adjusted for BMI) as a function of BMI and case-control status. We also list allele frequencies specified in simulated genotype data.</p