8 research outputs found
Genome-Wide Association Study for Type 2 Diabetes in Indians Identifies a New Susceptibility Locus at 2q21
Indians undergoing socioeconomic and lifestyle transitions will
be maximally affected by epidemic of type 2 diabetes (T2D). We
conducted a two-stage genome-wide association study of T2D in
12,535 Indians, a less explored but high-risk group. We identified
a new type 2 diabetes–associated locus at 2q21, with the lead
signal being rs6723108 (odds ratio 1.31; P = 3.32 3 1029
). Imputation
analysis refined the signal to rs998451 (odds ratio 1.56;
P = 6.3 3 10212) within TMEM163 that encodes a probable vesicular
transporter in nerve terminals. TMEM163 variants also
showed association with decreased fasting plasma insulin and
homeostatic model assessment of insulin resistance, indicating
a plausible effect through impaired insulin secretion. The 2q21
region also harbors RAB3GAP1 and ACMSD; those are involved
in neurologic disorders. Forty-nine of 56 previously reported signals
showed consistency in direction with similar effect sizes in
Indians and previous studies, and 25 of them were also associated
(P , 0.05). Known loci and the newly identified 2q21 locus altogether
explained 7.65% variance in the risk of T2D in Indians. Our
study suggests that common susceptibility variants for T2D are
largely the same across populations, but also reveals a population-specific
locus and provides further insights into genetic architecture
and etiology of T2D
Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes
Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology.
Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions.
Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions.
Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs.
Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies