Statistical corrections of linkage data suggest predominantly regulations of gene expression-1

For each threshold of estimated using our corrected method (full pedigree analysis). The threshold for FDR = 10% is = 6.4. At this threshold we detected 22 significant linkages and expect that 2.2 are false positives. See text and Table 1 for comparison with uncorrected results.<p><b>Copyright information:</b></p><p>Taken from "Statistical corrections of linkage data suggest predominantly regulations of gene expression"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S145</p><p>BMC Proceedings 2007;1(Suppl 1):S145-S145.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367613.</p><p></p

Statistical corrections of linkage data suggest predominantly regulations of gene expression-0

T panel gives the scatter plot of the scan statistics using full pedigree and sibships. For largest values of the statistics (very likely to be true positives), most of the points are above the 45° line, which suggests that full pedigree analysis provides more power than sibship analysis to detect true linkages.<p><b>Copyright information:</b></p><p>Taken from "Statistical corrections of linkage data suggest predominantly regulations of gene expression"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S145</p><p>BMC Proceedings 2007;1(Suppl 1):S145-S145.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367613.</p><p></p

Statistical corrections of linkage data suggest predominantly regulations of gene expression-2

E findings and expected false positives using our corrected method. D, Estimated FDR curve. We identified 46 significant linkages at threshold of region-wide = 0.00036 or log() = -7.93 (FDR = 10%).<p><b>Copyright information:</b></p><p>Taken from "Statistical corrections of linkage data suggest predominantly regulations of gene expression"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S145</p><p>BMC Proceedings 2007;1(Suppl 1):S145-S145.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367613.</p><p></p

Studies of Terbium Bridge: Saturation Phenomenon, Significance of Sensitizer and Mechanisms of Energy Transfer, and Luminescence Quenching

Terbium chain in the form of S → (Tb³⁺)_<i>n</i> → A (S = Ce³⁺ or Eu²⁺, A = Eu³⁺), as a promising energy transfer (ET) approach, has been proposed to enhance Eu³⁺ emission for solid-state lighting. However, the viewpoint of ET from S to A via the terbium chain (Tb³⁺–Tb³⁺–Tb³⁺–...) is very doubtful. Here, hosts of Ba₃Ln­(PO₄)₃, LnPO₄, LnBO₃, and Na₂Ln₂B₂O₇ doped with Ce³⁺ → (Tb³⁺)_<i>n</i> → Eu³⁺ or (Tb³⁺)_<i>n</i> → Eu³⁺ are synthesized to prove the universality of S → (Tb³⁺)_<i>n</i> → A in inorganic hosts and to study the unsolved issues. Saturation distance of Tb³⁺–Eu³⁺, estimated with the empirical data of different hosts, is proposed to be a criterion for determining whether a spectral chromaticity coordinate keeps constant. A branch model is put forward to replace the chain model to explain the role of (Tb³⁺)_<i>n</i> in ET from Ce³⁺ to Eu³⁺ and the necessity of high content of Tb³⁺; the term “terbium bridge” is used to replace “terbium chain”, and the value of <i>n</i> is determined to be two or three. The intensity quenching of Eu³⁺ emission is attributed to the surface defects ascribed to the smaller particles and larger specific surface area rather than the concentration quenching of Tb³⁺. Based on the saturation distance and the mechanism of luminescence quenching, the necessary concentration of Tb³⁺ for (Tb³⁺)_<i>n</i> can be estimated as long as the cell parameters are already known and the luminescent efficiency of Eu³⁺ can be further improved by optimizing the synthesis method to decrease the quantity of surface defects

Post-Selection Inference Following Aggregate Level Hypothesis Testing in Large-Scale Genomic Data

<p>In many genomic applications, hypotheses tests are performed for powerful identification of signals by aggregating test-statistics across units within naturally defined classes. Following class-level testing, it is naturally of interest to identify the lower level units which contain true signals. Testing the individual units within a class without taking into account the fact that the class was selected using an aggregate-level test-statistic, will produce biased inference. We develop a hypothesis testing framework that guarantees control for false positive rates conditional on the fact that the class was selected. Specifically, we develop procedures for calculating unit level <i>p</i>-values that allows rejection of null hypotheses controlling for two types of conditional error rates, one relating to family-wise rate and the other relating to false discovery rate. We use simulation studies to illustrate validity and power of the proposed procedure in comparison to several possible alternatives. We illustrate the power of the method in a natural application involving whole-genome expression quantitative trait loci (eQTL) analysis across 17 tissue types using data from The Cancer Genome Atlas (TCGA) Project. Supplementary materials for this article are available online.</p

Betti Base as an Efficient Ligand for Copper-Catalyzed Ullmann Coupling of Phenol with Aryl Halides

<div><p></p><p>A simple, general, and highly efficient Betti base ligand has been developed for copper-catalyzed Ullmann coupling of phenol with aryl halides without the protection of an inert atmosphere. The reaction proceeds smoothly in the presence of K₂CO₃ as the base and dimethylsulfoxide as the solvent. The catalyst was reused several times with no evident loss of catalytic activity and is environmentally friendly.</p> </div

Associations of β-glucuronidase and β-glucosidase mean activity levels with demographic and questionnaire data of 51 study volunteers.

a<p>Beta values estimate the increase in log_e of enzymatic activity (IU/100 mg fecal protein) per unit increase in the independent variable.</p>b<p>BMI models were adjusted for gender and age.</p

Heat map of gene-level p-values on selected genes within 43 significant pathways based on the DIAGRAM and GERA studies.

<p>There are 46 unique genes in the 43 significant pathways that have their gene-level meta-analysis p-values less than 0.001. Each row in the plot represents one of 43 significant pathways. Each column represents one of the 46 unique genes. The chromosome IDs of 46 unique genes are given in parentheses. The color of each cell represents the gene-level p-value (in the −log₁₀ scale). A cell for a gene that is not included in a pathway is colored gray in the corresponding entry. The orders of genes (x-axis) and pathways (y-axis) are arranged according to their gene and pathway meta-analysis p-values.</p

Summary of 43 significant pathways detected by the pathway meta-analysis based on the DIAGRAM and GERA studies.

<p>Summary of 43 significant pathways detected by the pathway meta-analysis based on the DIAGRAM and GERA studies.</p

Comparisons of p-values from three types of pathway analyses on the GERA data.

<p>Based on the GERA data, 4,713 pathways are analyzed in three different ways. Pathway p-values obtained by ARTP using the GERA individual-level genetic data (x-axis) are compared with the ones obtained by sARTP using summary statistics in combination with the internal reference panel that consists of 500 randomly selected GERA samples (left), and the ones using the summary statistics in combination with the external reference panel that consists of 503 European subjects from the 1000 Genomes Project (right).</p