Background: Sarcopenia is a skeletal muscle disease of clinical importance that occurs commonly in old age and in various disease sub-categories. Widening the scope of knowledge of the genetics of muscle mass and strength is important because it may allow to identify patients with an increased risk to develop a specific musculoskeletal disease or condition such as sarcopenia based on genetic markers. Methods: We used bioinformatics tools to identify gene loci responsible for regulating muscle strength and lean mass, which can then be a target for downstream lab experimentation validation. Single nuclear polymorphisms (SNPs) associated with various disease traits of muscles and specific genes were chosen according to their muscle phenotype association p-value, as traditionally done in Genome Wide Association Studies, GWAS. We've developed and applied a combination of expression quantitative trait loci (eQTLs) and GWAS summary information, to prioritize causative SNP and point out the unique genes associated in the tissues of interest (muscle). Results: We found NUDT3 and KLF5 for lean mass and HLA-DQB1-AS1 for hand grip strength as candidate genes to target for these phenotypes. The associated regulatory SNPs are rs464553, rs1028883 and rs3129753 respectively. Conclusion: Transcriptome Wide Association Studies, TWAS, approaches of combining GWAS and eQTL summary statistics proved helpful in statistically prioritizing genes and their associated SNPs for the disease phenotype of study, in this case, Sarcopenia. Potentially regulatory SNPs associated with these genes, and the genes further prioritized by a scoring system, can be then wet lab verified, depending on the phenotype it is hypothesized to affect.
Background: Sarcopenia is a skeletal muscle disease of clinical importance that occurs commonly in old age and in various disease sub-categories. Widening the scope of knowledge of the genetics of muscle mass and strength is important because it may allow to identify patients with an increased risk to develop a specific musculoskeletal disease or condition such as sarcopenia based on genetic markers. We used bioinformatics tools to identify gene loci responsible for regulating muscle strength and lean mass, which can then be a target for downstream lab experimentation validation. Single nuclear polymorphisms (SNPs) associated with various disease traits of muscles and specific genes were chosen according to their muscle phenotype association p-value, as traditionally done in Genome Wide Association Studies, GWAS. We've developed and applied a combination of expression quantitative trait loci (eQTLs) and GWAS summary information, to prioritize causative SNP and point out the unique genes associated in the tissues of interest (muscle). Results: We found NUDT3 and KLF5 for lean mass and HLA-DQB1-AS1 for hand grip strength as candidate genes to target for these phenotypes. The associated regulatory SNPs are rs464553, rs1028883 and rs3129753 respectively. Conclusion: Transcriptome Wide Association Studies, TWAS, approaches of combining GWAS and eQTL summary statistics proved helpful in statistically prioritizing genes and their associated SNPs for the disease phenotype of study, in this case, Sarcopenia. Potentially regulatory SNPs associated with these genes can be then wet-lab verified, depending on the phenotype it is hypothesized to affect.
Background: Genome Wide Analytics Studies with regard to structural variations is a key component in phenome association. Here we analyze a family trio of father, mother and children for scientific discovery purpose. Methods: Structural variations, SVs, with size 1 base-pair to several 1000s of base-pairs with their precise breakpoints and single-nucleotide polymorphisms, SNPs, were determined for members of a family of four. The method involved optimal genome assembly and mapping to reference genome. Results: It is discovered that the mitochondrial DNA is less prone to SVs re-arrangements than SNPs and can possibly have paternal leakage of inheritance or high mutation in maternal inheritance. Sex determination of an individual is found to be strongly confirmed by means of calls of nucleotide bases of SVs to the Y chromosome. Conclusion: mtDNA inheritance pattern proposes concerns for determining ancestry and divergence between races and species. These in silico techniques for analysis would become such a widespread application that a total transformation of the bio-and-medical industry would go through, as is currently with genome wide analytics and association studies. SVs would serve as fingerprint of an individual contributing to his traits and drug responses, more strongly than SNPs.
Graph network science is becoming increasingly popular, notably in big-data perspective where understanding individual entities for individual functional roles is complex and time consuming. It is likely when a set of genes are regulated by a set of genetic variants, the genes set is recruited for a common or related functional purpose. Grouping and extracting communities from network of associations becomes critical to understand system complexity, thus prioritizing genes for disease and functional associations. Workload is reduced when studying entities one at a time. For this, we present GraphBreak, a suite of tools for community detection application, such as for gene co-expression, protein interaction, regulation network, etc.Although developed for use case of eQTLs regulatory genomic network community study- results shown with our analysis with sample eQTL data-Graphbreak can be deployed for other studies if input data has been fed in requisite format, including but not limited to gene co-expression networks, protein-protein interaction network, signaling pathway and metabolic network. GraphBreak showed critical use case value in its downstream analysis for disease association of communities detected. If all independent steps of community detection and analysis are a step-by-step sub-part of the algorithm, GraphBreak can be considered a new algorithm for community based functional characterization. Combination of various algorithmic implementation modules into a single script for this purpose illustrates GraphBreak novelty. Compared to other similar tools, with GraphBreak we can better detect communities with overrepresentation of its member genes for statistical association with diseases, therefore target genes which can be prioritized for drug-positioning or drug-repositioning as the case be.
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