Faktor-Faktor Yang Mempengaruhi Pengungkapan Tanggung Jawab Sosial

Penelitian ini bertujuan untuk menentukan faktor-faktor yang mempengaruhi luasnya tingkat pengungkapan tanggung jawab sosial Perusahaan (Corporate Social Responsibility) dengan menguji pengaruh ukuran Perusahaan, profitabilitas, leverage, kepemilikan insti­tusional, ukuran dewan komisaris, ukuran dewan direksi, dan ukuran komite audit. Sampel yang digunakan adalah Perusahaan sektor pertambangan terdaftar di Bursa Efek Indonesia selama 2010-2012. Data diperoleh dari laporan keuangan auditan dan laporan tahunan serta laporan keberlanjutan (sustainability report) jika ada. Penelitian ini menggunakan pendekatan kuantitatif dengan analisis regresi linear berganda. Penelitian ini menunjukkan bahwa ukuran Perusahaan dan komite audit memiliki pengaruh positif terhadap peng­ungkapan tanggung jawab sosial. Tidak ditemukan bukti pengaruh profitabilitas, leverage, kepemilikan institusional, ukuran dewan komisaris, dan ukuran dewan direksi terhadap terhadap pengungkapan tanggung jawab sosial

The Last April

<p>Parameter settings of virtual datasets.</p

Quantitative Assessment of the Polymorphisms in the <i>HOTAIR</i> lncRNA and Cancer Risk: A Meta-Analysis of 8 Case-Control Studies

<div><p>HOX transcript antisense intergenic RNA (<i>HOTAIR</i>) is a long non-coding RNA (lncRNA) that functions as an oncogenic molecule in different cancer cells. Genetic variants of <i>HOTAIR</i> may affect the activity of certain regulatory factors and further regulate the aberrant expression of <i>HOTAIR</i>, which might be underlying mechanisms that affect tumour susceptibility and prognosis. Recently, several studies have been performed to examine the possible link between polymorphisms in <i>HOTAIR</i> and cancer risk; however, the results have been inconclusive. Therefore, we performed a meta-analysis to estimate the associations between <i>HOTAIR</i> polymorphisms (rs920778, rs4759314 and rs1899663) and cancer risk. Eight studies comprising 7,151 cases and 8,740 controls were included in our study. Overall, no significant associations between the <i>HOTAIR</i> polymorphisms (rs920778, rs4759314 and rs1899663) and cancer risk were observed. However, in further stratified analyses, the variant T allele of rs920778 exhibited a significant increased risk of developing digestive cancers (dominant model: OR = 1.44; 95% CI = 1.31–1.59). These findings provided evidence that <i>HOTAIR</i> rs920778 may modify the susceptibility to certain cancer types. Further studies incorporating subjects with different ethnic backgrounds combined with re-sequencing of the marked region and functional evaluations are warranted.</p></div

Forest plot of OR with 95%CI for the <i>HOTAIR</i> rs1899663 with cancer risk under dominant model.

<p>Forest plot of OR with 95%CI for the <i>HOTAIR</i> rs1899663 with cancer risk under dominant model.</p

Characteristics of the studies included in the meta-analysis.

<p>Characteristics of the studies included in the meta-analysis.</p

Forest plot of OR with 95%CI for the <i>HOTAIR</i> rs4759314 with cancer risk under dominant model.

<p>Forest plot of OR with 95%CI for the <i>HOTAIR</i> rs4759314 with cancer risk under dominant model.</p

Summary ORs of the <i>HOTAIR</i> rs920778 polymorphism and cancer risk.

<p>Summary ORs of the <i>HOTAIR</i> rs920778 polymorphism and cancer risk.</p

Flow diagram of the study selection process.

<p>Flow diagram of the study selection process.</p

Forest plot of OR with 95%CI for the <i>HOTAIR</i> rs920778 with cancer risk under dominant model.

<p>Forest plot of OR with 95%CI for the <i>HOTAIR</i> rs920778 with cancer risk under dominant model.</p

Weighted SNP Set Analysis in Genome-Wide Association Study

<div><p>Genome-wide association studies (GWAS) are popular for identifying genetic variants which are associated with disease risk. Many approaches have been proposed to test multiple single nucleotide polymorphisms (SNPs) in a region simultaneously which considering disadvantages of methods in single locus association analysis. Kernel machine based SNP set analysis is more powerful than single locus analysis, which borrows information from SNPs correlated with causal or tag SNPs. Four types of kernel machine functions and principal component based approach (PCA) were also compared. However, given the loss of power caused by low minor allele frequencies (MAF), we conducted an extension work on PCA and used a new method called weighted PCA (wPCA). Comparative analysis was performed for weighted principal component analysis (wPCA), logistic kernel machine based test (LKM) and principal component analysis (PCA) based on SNP set in the case of different minor allele frequencies (MAF) and linkage disequilibrium (LD) structures. We also applied the three methods to analyze two SNP sets extracted from a real GWAS dataset of non-small cell lung cancer in Han Chinese population. Simulation results show that when the MAF of the causal SNP is low, weighted principal component and weighted IBS are more powerful than PCA and other kernel machine functions at different LD structures and different numbers of causal SNPs. Application of the three methods to a real GWAS dataset indicates that wPCA and wIBS have better performance than the linear kernel, IBS kernel and PCA.</p></div