Statistical Methods Sp Gupta Pdf 83 VERIFIED
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Statistical Methods Sp Gupta Pdf 83 VERIFIED
Odds ratio with 95% credible interval is described in each column. Prokinetic agent in the top left means better efficacy and statistical validity is guaranteed when the 95% credible interval does not include 1
Genomics is the most mature of the omics fields. In the realm of medical research, genomics focuses on identifying genetic variants associated with disease, response to treatment, or future patient prognosis. GWAS is a successful approach that has been used to identify thousands of genetic variants associated with complex diseases (GWAS catalog ) in multiple human populations. In such studies, thousands of individuals are genotyped for more than a million genetic markers, and statistically significant differences in minor allele frequencies between cases and controls are considered evidence of association. GWAS studies provide an invaluable contribution to our understanding of complex phenotypes. Associated technologies include genotype arrays [111,112,113,114], NGS for whole-genome sequencing [115, 116], and exome sequencing [117].
Proteomics is used to quantify peptide abundance, modification, and interaction. The analysis and quantification of proteins has been revolutionized by MS-based methods and, recently, these have been adapted for high-throughput analyses of thousands of proteins in cells or body fluids [149, 150]. Interactions between proteins can be detected by classic unbiased methods such as phage display and yeast two-hybrid assays. Affinity purification methods, in which one molecule is isolated using an antibody or a genetic tag, can also be used. MS is then used to identify any associated proteins. Such affinity methods, sometimes coupled with chemical crosslinking, have been adapted to examine global interactions between proteins and nucleic acids (e.g., ChIP-Seq). Finally, the functions of a large fraction of proteins are mediated by post-translational modifications such as proteolysis, glycosylation, phosphorylation, nitrosylation, and ubiquitination [151, 152]. Such modifications play key roles in intracellular signaling, control of enzyme activity, protein turnover and transport, and maintaining overall cell structure [153]. MS can be used to directly measure such covalent modifications by defining the corresponding shift in the mass of the protein (in comparison to the unmodified peptide). There are efforts to develop genome-level analyses of such modifications [154]. Associated technologies include MS-based approaches to investigate global proteome interactions and quantification of post-translational modifications [155, 156].
In the past decade, high-throughput genotyping, combined with the development of a high quality reference map of the human genome, rigorous statistical tools, and large coordinated cohorts of thousands of patients, has enabled the mapping of thousands of genetic variants, both rare and common, contributing to disease [1,2,3]. However, as our power to identify genetic variants associated with complex disease increased several realizations were reached that have shaped subsequent approaches to elucidating the causes of disease. First, the loci that have been identified so far generally explain only a fraction of the heritable component for specific diseases. Second, while Mendelian diseases generally result from changes in coding regions of genes, common diseases usually result from changes in gene regulation. Third, the same genetic variants often contribute to different final outcomes, depending on the environment and genetic background. Taken together, these realizations provided a rationale for the development of systems biology technologies that involve the integration of different omics data types to identify molecular patterns associated with disease.
Compared to single omics interrogations (Box 1, Fig. 1), multi-omics can provide researchers with a greater understanding of the flow of information, from the original