Statistical Methods Sp Gupta Pdf 83 ((FULL))

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 cause of disease (genetic, environmental, or developmental) to the functional consequences or relevant interactions [4, 5]. Omics studies, by their nature, rely on large numbers of comparisons, tailored statistical analyses, and a considerable investment of time, skilled manpower, and money. Therefore, careful planning and execution are required. In this section, we discuss general experimental parameters that should be considered when planning an omics study.

Omics approaches generate data to provide biological insight based on statistical inference from datasets that are typically large. As such, the power to detect associations or the flow of information strongly depends on effect size, heterogeneity of the background noise, and sample size, with the latter often being the only parameter controlled by researchers. Unfortunately, human studies are affected by a multitude of confounding factors that are difficult or impossible to control for (e.g., diet and lifestyle choices). Thus, the ability of omics approaches to produce meaningful insight into human disease is very much dependent on available sample sizes, and in many settings an underpowered study may not only be a shot in the dark, missing true signals, but it is also more likely to produce false positive results. This issue is well illustrated in the earlier days of candidate gene studies for complex diseases, where lack of appreciation of these factors led to many publications of non-reproducible genetic associations. An initial power calculation to ensure sufficient sample size and variation in outcomes is increasingly necessary in large-scale studies.

Another potential pitfall of omics approaches is insufficient attention to data analysis requirements, before and during data collection. General analytical pipelines for each type of omics data are available (Box 1); however, most omics fields have not yet developed an agreed gold standard. Moreover, these datasets are often large and complex, and require tailoring of the general statistical approach to the specific dataset. An important aspect of all omics study designs, to make sure that the collected data meet analysis requirements, is to envision the main goal of analysis and the analytical approach, before collecting the data. For example, a common consideration when planning RNA-Seq experiments would be the allocation of financial resources to balance the number of samples with depth of coverage. To identify differentially expressed genes between the cases and controls, the power provided by more samples is generally preferable to the increased accuracy provided by higher depth of sequencing. However, if the main purpose of the analysis is to identify new transcripts, or examine allele-specific expression, the higher depth of coverage is desirable [7,8,9] ( _standards_v1_2011_May.pdf). In addition to financial limitations, data analysis should guide data collection to avoid or minimize technical artifacts, such as batch effects that could be introduced during all steps of sample processing and data acquisition [10,11,12,13]. In large studies, some technical artifacts cannot be avoided, and in these cases it is crucial to understand to what extent those artifacts limit our ability to draw conclusions from observations, and possibly introduce controls that would be able to quantify its effect.

The third type of study design involves statistical modeling of metabolite fluxes in response to specific substrates. For example, the integration of bibliographic, metabolomic, and genomic data have been used to reconstruct the dynamic range of metabolome flow of organisms, first performed in Escherichia coli [35] and since extended to yeast [36, 37] and to individual tissues in mice [38] and humans [39]. Other applications have explored various connections between metabolome models and other layers of information, including the transcriptome [40] and proteome [41,42,43]. Refinement of these techniques and subsequent application to larger population-wide datasets will likely lead to elucidation of novel key regulatory nodes in metabolite control.

As the cost of omics analyses continues to decrease, more types of high throughput data can guide individualized treatment regimens and be integrated into the clinic. However, such undertaking also poses significant challenges. The ever-growing amount and sophistication of our knowledge, combined with the sheer quantity of data and technical expertise required for comprehensive collection and analysis of multi-omics data, are far from trivial. No one research group on their own can handle multi-scale omics data generation, development of analytical methodology, adaptation of those methods to specific disease, and functional follow-up, let alone repeating this process for multiple diseases and integrating between them. To be efficient and translatable in the clinic, such undertakings necessitate coordinated efforts of many groups, each providing its own expertise or resource, as reflected by the formation of large consortia. Some consortia efforts (e.g., ENCODE) focus on investigating a series of omic data on coordinated sets of samples, providing invaluable insight into the basic biological properties reflected by these data, and development of rigorous analytical frameworks that can be then applied or adapted to other datasets. Other consortia may focus on tissue specificity [101], particular disease, or resource development.

This study was performed to evaluate the spatial and temporal distribution of major ions in water samples of a newly designated Ramsar site, namely Kabar Tal (KT) wetland of Bihar. Samples were collected during summer, monsoon, and winter seasons. The analytical and GIS results show that concentration of electrical conductivity, chloride, and nitrate are higher in summer than monsoon and winter. However, the concentration of major cations such as sodium, potassium, calcium, and magnesium are higher in winter than monsoon and summer. In addition, major anions like sulphate and phosphate concentration is higher during monsoon than summer and winter. Multivariate statistical tool (discriminant analysis) results suggest that temperature, pH, electrical conductivity, sulphate, and potassium are the major parameters distinguishing the water quality in different seasons. The study confirms that seasonal variations are playing a major role in the hydrochemistry of KT wetland. Overall, this work outlines the approach towards proper conservation and utilization of wetlands and to assess the quality of surface water for determining its suitability for agricultural purposes. Overall, this work highlights the approach towards estimating the seasonal dynamics of chemical species in KT wetland and its suitability for irrigation purposes. 2b1af7f3a8