{4-Dimethyl­amino-N′-[1-(2-oxidophen­yl)ethyl­idene]benzohydrazidato}(methano­lato)oxidovanadium(V)

The title oxidovanadium(V) complex, [V(C17H17N3O2)(CH3O)O], was obtained by the reaction of 2-acetyl­phenol, 4-dimethyl­amino­benzohydrazide and vanadyl sulfate in methanol. The VV atom is five-coordinated by N,O,O′-donor atoms of the Schiff base ligand, one meth­oxy O atom and one oxide O atom, forming a square-pyramidal geometry

Proper Use of Allele-Specific Expression Improves Statistical Power for cis -eQTL Mapping with RNA-Seq Data

Studies of expression quantitative trait loci (eQTLs) offer insight into the molecular mechanisms of loci that were found to be associated with complex diseases and the mechanisms can be classified into cis- and trans-acting regulation. At present, high-throughput RNA sequencing (RNA-seq) is rapidly replacing expression microarrays to assess gene expression abundance. Unlike microarrays that only measure the total expression of each gene, RNA-seq also provides information on allele-specific expression (ASE), which can be used to distinguish cis-eQTLs from trans-eQTLs and, more importantly, enhance cis-eQTL mapping. However, assessing the cis-effect of a candidate eQTL on a gene requires knowledge of the haplotypes connecting the candidate eQTL and the gene, which cannot be inferred with certainty. The existing two-stage approach that first phases the candidate eQTL against the gene and then treats the inferred phase as observed in the association analysis tends to attenuate the estimated cis-effect and reduce the power for detecting a cis-eQTL. In this article, we provide a maximum-likelihood framework for cis-eQTL mapping with RNA-seq data. Our approach integrates the inference of haplotypes and the association analysis into a single stage, and is thus unbiased and statistically powerful. We also develop a pipeline for performing a comprehensive scan of all local eQTLs for all genes in the genome by controlling for false discovery rate, and implement the methods in a computationally efficient software program. The advantages of the proposed methods over the existing ones are demonstrated through realistic simulation studies and an application to empirical breast cancer data from The Cancer Genome Atlas project

Integrative analysis of sequencing and array genotype data for discovering disease associations with rare mutations

High-throughput DNA sequencing provides an unprecedented opportunity to discover rare genetic variants associated with complex diseases and traits. However, sequencing a large number of subjects is prohibitively expensive. It is common to select subjects for sequencing from the cohorts that have collected genotyping array data. We impute the sequencing data from the array data for the cohort members who are not selected for sequencing and perform gene-level association tests for rare variants by properly combining the observed genotypes for sequenced subjects and the imputed genotypes for nonsequenced subjects. This integrative analysis is substantially more powerful than the use of sequencing data alone and can accelerate the search for disease-causing mutations