1.
1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.
Gorski, M, van der Most, PJ, Teumer, A, Chu, AY, Li, M, Mijatovic, V, Nolte, IM, Cocca, M, Taliun, D, Gomez, F, et al
Scientific reports. 2017;:45040
Abstract
HapMap imputed genome-wide association studies (GWAS) have revealed >50 loci at which common variants with minor allele frequency >5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 × 10-8 previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until whole-genome sequencing becomes feasible in large samples.
2.
Genevestigator transcriptome meta-analysis and biomarker search using rice and barley gene expression databases.
Zimmermann, P, Laule, O, Schmitz, J, Hruz, T, Bleuler, S, Gruissem, W
Molecular plant. 2008;(5):851-7
Abstract
The wide-spread use of microarray technologies to study plant transcriptomes has led to important discoveries and to an accumulation of profiling data covering a wide range of different tissues, developmental stages, perturbations, and genotypes. Querying a large number of microarray experiments can provide insights that cannot be gained by analyzing single experiments. However, such a meta-analysis poses significant challenges with respect to data comparability and normalization, systematic sample annotation, and analysis tools. Genevestigator addresses these issues using a large curated expression database and a set of specifically developed analysis tools that are accessible over the internet. This combination has already proven to be useful in the area of plant research based on a large set of Arabidopsis data (Grennan, 2006). Here, we present the release of the Genevestigator rice and barley gene expression databases that contain quality-controlled and well annotated microarray experiments using ontologies. The databases currently comprise experiments from pathology, plant nutrition, abiotic stress, hormone treatment, genotype, and spatial or temporal analysis, but are expected to cover a broad variety of research areas as more experimental data become available. The transcriptome meta-analysis of the model species rice and barley is expected to deliver results that can be used for functional genomics and biotechnological applications in cereals.