Biomedical subjects

David L Corcoran

Publications and source records attributed to David L Corcoran.

5 recordsLinked to original sources

PubMed2025

An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference.

Polygenic indexes (PGIs) - DNA-based predictors of individual phenotypes - have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGI's predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repository's scope, and introduce novel resources that reduce confounding bias and improve interpretability.

Journal Article↗

PubMed2005

Deregulation of common genes by c-Myc and its direct target, MT-MC1.

In addition to its role in cancer, the c-Myc oncoprotein controls many normal cellular processes as a consequence of its function as a basic helix-loop-helix leucine zipper transcription factor. Determining which of the myriad genes under c-Myc control are relevant for these various roles is thus a major challenge. mt-mc1 is a direct c-Myc target gene whose overexpression recapitulates multiple c-Myc phenotypes, including transformation. Using transcriptional profiling, we now show that MT-MC1-overexpressing myeloid cells misregulate a total of 47 distinct transcripts, a large proportion of which are involved in signal transduction and/or cancer. Analysis of these genes reveals a consensus promoter structure consisting of multiple, often closely spaced c-Myc binding sites and three additional Wilm's tumor and Egr1-like motifs. More than one-third of MT-MC1 target genes are also clustered on six cancer-associated chromosomal loci. Most surprisingly, all of the transcripts examined also are regulated by c-Myc. Finally, an estrogen receptor-MT-MC1 fusion protein was used to establish that all examined transcripts were regulated directly by the chimeric protein. Our results thus indicate that MT-MC1 target genes largely comprise a subset of those regulated by c-Myc. We propose that the properties imparted by MT-MC1 are the result of its control of a small and select c-Myc target gene population.

Amino Acid Motifs↗

PubMed2005

FOOTER: a web tool for finding mammalian DNA regulatory regions using phylogenetic footprinting.

FOOTER is a newly developed algorithm that analyzes homologous mammalian promoter sequences in order to identify transcriptional DNA regulatory 'signals'. FOOTER uses prior knowledge about the binding site preferences of the transcription factors (TFs) in the form of position-specific scoring matrices (PSSMs). The PSSM models are generated from known mammalian binding sites from the TRANSFAC database. In a test set of 72 confirmed binding sites (most of them not present in TRANSFAC) of 19 TFs, it exhibited 83% sensitivity and 72% specificity. FOOTER is accessible over the web at http://biodev.hgen.pitt.edu/Footer/.

Algorithms↗

PubMed2005

enoLOGOS: a versatile web tool for energy normalized sequence logos.

enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the available web servers are able to create logo images from a set of aligned sequences, but none of them generates weighted sequence logos directly from energy measurements or other sources. With the advent of high-throughput technologies for estimating the contact energy of different DNA sequences, tools that can create logos directly from binding affinity data are useful to researchers. enoLOGOS generates sequence logos from a variety of input data, including energy measurements, probability matrices, alignment matrices, count matrices and aligned sequences. Furthermore, enoLOGOS can represent the mutual information of different positions of the consensus sequence, a unique feature of this tool. Another web interface for our software, C2H2-enoLOGOS, generates logos for the DNA-binding preferences of the C2H2 zinc-finger transcription factor family members. enoLOGOS and C2H2-enoLOGOS are accessible over the web at http://biodev.hgen.pitt.edu/enologos/.

Amino Acids↗

PubMed2005

Footer: a quantitative comparative genomics method for efficient recognition of cis-regulatory elements.

The search for mammalian DNA regulatory regions poses a challenging problem in computational biology. The short length of the DNA patterns compared with the size of the promoter regions and the degeneracy of the patterns makes their identification difficult. One way to overcome this problem is to use evolutionary information to reduce the number of false-positive predictions. We developed a novel method for pattern identification that compares a pair of putative binding sites in two species (e.g., human and mouse) and assigns two probability scores based on the relative position of the sites in the promoter and their agreement with a known model of binding preferences. We tested the algorithm's ability to predict known binding sites on various promoters. Overall, it exhibited 83% sensitivity and the specificity was 72%, which is a clear improvement over existing methods. Our algorithm also successfully predicted two novel NF-kappaB binding sites in the promoter region of the mouse autotaxin gene (ATX, ENPP2), which we were able to verify by using chromatin immunoprecipitation assay coupled with quantitative real-time PCR.

Algorithms↗