Publications by authors named "W Arindrarto"

Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues.

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Acute myeloid leukemia (AML) is caused by genetic aberrations that also govern the prognosis of patients and guide risk-adapted and targeted therapy. Genetic aberrations in AML are structurally diverse and currently detected by different diagnostic assays. This study sought to establish whole transcriptome RNA sequencing as single, comprehensive, and flexible platform for AML diagnostics.

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Insights into individual differences in gene expression and its heritability (h) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52,844 genes measured in whole blood in the largest twin RNA-Seq sample to date (1497 individuals including 459 monozygotic twin pairs and 150 dizygotic twin pairs) from classical twin modeling and identity-by-state-based approaches. We estimated for each gene h, composed of cis-heritability (h, the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and trans-heritability (h, the residual variance explained by all other genome-wide variants).

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Objective: Multiple single-nucleotide polymorphisms (SNPs) conferring susceptibility to osteoarthritis (OA) mark imbalanced expression of positional genes in articular cartilage, reflected by unequally expressed alleles among heterozygotes (allelic imbalance [AI]). We undertook this study to explore the articular cartilage transcriptome from OA patients for AI events to identify putative disease-driving genetic variation.

Methods: AI was assessed in 42 preserved and 5 lesioned OA cartilage samples (from the Research Arthritis and Articular Cartilage study) for which RNA sequencing data were available.

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Article Synopsis
  • Understanding the causal relationships in gene regulation is key to grasping gene functions.
  • We created a method to infer gene-gene interactions from observational population genomics data, effectively handling genetic variations and their effects.
  • Our analysis of data from over 3000 individuals revealed 49 key genes influencing gene expression changes, highlighting new potential functions for specific genes like SENP7 and BCL2A1.
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