- Learning multi-way epistatic interactions between polymorphisms that modulate the current and future risk of in ILD
- DIrectly incorprate the role of environmental modifiers with polymorphism data (heterogenous varaibles in teh modeling framerwork)
- Use the notion of learning 'digital twins' of the genetric interaction and disease fate, vi ateh Gibbs net framework.
A genome-wide association study (abbreviated GWAS) is a research approach used to identify genomic variants that are statistically associated with a risk for a disease or a particular trait. The method involves surveying the genomes of many people, looking for genomic variants that occur more frequently in those with a specific disease or trait compared to those without the disease or trait. Once such genomic variants are identified, they are typically used to search for nearby variants that contribute directly to the disease or trait.
The methods and results of GWAS have informed other applications in applied epidemiologic research such as gene environment studies, Mendelian randomization studies, and polygenic risk score approaches. As I noted, GWAS focus on statistical associations. They inform us of correlation not causation. A major challenge posed by GWAS is the exploration of the functional consequences of identified variants which will provide insights into the biology of disease.
Epistasis is the phenomenon whereby the effect of one gene is influenced by the presence or absence of one or more other genes. In other words, the expression or function of one gene can be modified by the presence or absence of another gene. This can occur through various mechanisms, such as the interaction of proteins encoded by different genes, or through regulatory mechanisms that control gene expression. Epistasis can have important implications for genetic inheritance and the evolution of species, as it can lead to complex patterns of inheritance that are not easily predicted based on the inheritance of individual genes. Epistasis is an important concept in the fields of genetics and evolutionary biology, and it has significant implications for understanding the genetic basis of complex traits and diseases.
Allosteric interactions occur when the binding of a molecule to one site on a protein (called the allosteric site) affects the activity of the protein at a different site (called the active site). Allosteric interactions can either enhance or inhibit the activity of the protein, depending on the nature of the allosteric molecule and the type of allosteric interaction that occurs.
Allosteric interactions are an important mechanism by which proteins can be regulated in cells. For example, enzymes often have allosteric sites that bind small molecules called allosteric effectors, which can either stimulate or inhibit the activity of the enzyme. This type of regulation allows cells to fine-tune the activity of enzymes in response to changes in the cell's environment or internal signaling pathways.
Allosteric interactions are also important in other types of proteins, such as receptors, which bind specific molecules (called ligands) and transmit signals into cells. The binding of ligands to allosteric sites on receptors can alter the activity of the receptor and influence downstream signaling pathways.
Overall, allosteric interactions are an important mechanism for the regulation of protein function and play a key role in many biological processes.
It is possible to infer multi-way epistatic interactions from polymorphism data, although it can be challenging due to the complexity of such interactions and the limitations of current statistical methods.
Polymorphism data refers to the presence of multiple alleles (variants) of a gene within a population. By analyzing the frequency and distribution of these alleles in different individuals, it is possible to infer the patterns of inheritance and the genetic basis of traits and diseases.
Multi-way epistatic interactions involve the combined effect of three or more genes on a trait or disease. These interactions can be difficult to detect and quantify due to their complexity and the large number of possible combinations of alleles that can contribute to an observed phenotype.
Current statistical methods for analyzing polymorphism data can account for some types of epistatic interactions, such as pairwise interactions between two genes. However, methods for detecting and quantifying multi-way epistatic interactions are still under development and may require larger sample sizes or more sophisticated statistical approaches.
Overall, while it is possible to learn about multi-way epistatic interactions from polymorphism data, it can be challenging due to the complexity of these interactions and the limitations of current statistical methods.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4552232/
The first step in a data analysis plan is to describe the data collected in the study. This can be done using figures to give a visual presentation of the data and statistics to generate numeric descriptions of the data.
The first step in identifying relevant inferential statistics for a study is to consider the type of research question being asked.
A relational question seeks information about the relationship among variables; in this situation, investigators will be interested in determining whether there is an association. A causal question seeks information about the effect of an intervention on an outcome; in this situation, the investigator will be interested in determining whether there is a difference.
