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Yashin, A.

Publications and source records attributed to Yashin, A..

2 recordsLinked to original sources

A Novel Gene ARHGAP44 for Longitudinal Changes in Glycated Hemoglobin (HbA1c) in Subjects without Type 2 Diabetes: Evidence from the Long Life Family Study (LLFS) and the Framingham Offspring Study (FOS)

Glycated hemoglobin (HbA1c) indicates average glucose levels over three months and is associated with insulin resistance and type 2 diabetes (T2D). Longitudinal changes in HbA1c ({Delta}HbA1c) are also associated with aging processes, cognitive performance, and mortality. We analyzed {Delta}HbA1c in 1,886 non-diabetic Europeans from the Long Life Family Study to uncover gene variants influencing {Delta}HbA1c. Using growth curve modeling adjusted for multiple covariates, we derived {Delta}HbA1c and conducted linkage-guided sequence analysis. Our genome-wide linkage scan identified a significant locus on 17p12. In-depth analysis of this locus revealed a variant rs56340929 (explaining 27% of the linkage peak) in the ARHGAP44 gene that was significantly associated with {Delta}HbA1c. RNA transcription of ARHGAP44 was associated with {Delta}HbA1c. The Framingham Offspring Study data further supported these findings on the gene level. Together, we found a novel gene ARHGAP44 for {Delta}HbA1c in family members without T2D. Follow-up studies using longitudinal omics data in large independent cohorts are warranted.

genetics↗

MXP: Modular eXpandable framework for building bioinformatics Pipelines

BackgroundPipelines are a natural tool in bioinformatics applications. Virtually any meaningful processing of biological data involves the execution of multiple software tools, and this execution must be arranged in a coherent manner. Many tools for the building of pipelines were developed over time and used to facilitate work with increasing volume of bioinformatics data. Here we present a flexible and expandable framework for building pipelines, MXP, which we hope will find its own niche in bioinformatics applications.\n\nResultsWe developed MXP and tested it on various tasks in our organization, primarily for building pipelines for GWAS (Genome-Wide Association Studies) and post-GWAS analysis. It was proven to be sufficiently flexible and useful.\n\nConclusionsMXP implements a number of novel features which, from our point of view, make it to be more suitable and more convenient for building bioinformatics pipelines.

bioinformatics↗