Researchers report their genome-wide association study for age-related macular degeneration in the PNAS Early Edition this week. By interrogating 2,157 cases and 1,150 controls, the team conferred AMD susceptibility loci and genotyped 30 "promising" markers in additional cohorts of up to 7,749 cases and 4,625 controls; here, they identified another susceptibility locus near TIMP3. "Our studies extend the catalog of AMD associated loci, help identify individuals at high risk of disease, and provide clues about underlying cellular pathways that should eventually lead to new therapies," the authors conclude.
In the PNAS Early Edition, a European research team presents their computational method for the identification of potential transcription factor targets using wild-type gene expression data. "For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets," the authors write. They tested their approach using ChIP-chip and loss-of-function mutant expression data for two transcription factors that control mesoderm development in Drosophila. The team suggests that their approach is "comparable or superior to ranking based on mutant differential scores," and that "integrating complementary wild-type spatial expression data can further improve target ranking performance."
Also in the Early Edition, a team of researchers identifies seed-specific transcription factors in Arabidopsis using Affymetrix GeneChips to profile genes active in seeds throughout the plant life cycle. "Most genes active in seeds are shared by all stages of seed development, although significant quantitative changes in gene activity occur," the team writes, adding that "each stage of seed development has a small gene set that is either specific at the level of the GeneChip or up-regulated with respect to genes active at other stages, including those that encode TFs." The authors identified 289 seed-specific genes, 48 of which encode for TFs — wherein seven are known regulators of seed development. The team suggests their findings should aid in the identification of seed development regulatory networks.
A trio of researchers at Vanderbilt University presents their method of mining electronic medical records for data to support genome-wide association studies that "provably prevents this type of data linkage," in which individuals can be re-identified by their genomic sequences when linked to additional patient records. The team writes that their approach "automatically extracts potentially linkable clinical features and modifies them in a way that they can no longer be used to link a genomic sequence to a small number of patients, while preserving the associations between genomic sequences and specific sets of clinical features corresponding to GWAS-related diseases." Paper co-author Grigorios Loukides told Nature News that their system modifies the data until the minimum number of patients that should have the same set of codes, or "k," is reached. "Our method will always allow useful inferences to be made when the utility policy is satisfied — that is, when diagnosis codes are grouped as required — even when there are fewer than k patients having a diagnosis code in the original data," Loukides told Nature.