Systematical Assessment of Rare Variant Association Tests via Simulations
Mentor 1
Shengtong Han
Start Date
16-4-2021 12:00 AM
Description
Rare variant association methods are used to detect rare variants associated with human complex traits or disease. A number of factors could influence the power of the test such as sample sizes in cases and control. There is lack of literature about the comparison of rare variant association test methods in different scenarios. However, studying the strengths and weaknesses of each method helps determine which test is the best one to use for each situation. This makes the process of research easier when studying the rare variant associations in the genome. The program, Rmarkdown, gives a place to run the code of each rare variant association method. In this research, we start with six tests which are Burden tests, Adaptive Burden tests, Variance Component tests, Combined tests and EC tests and they have subcategories which are methods that analyze the data more closely which consist of CMC method, aSum test, SKAT, c-alpha, O-SKAT and EC test. The methodology to this research is to begin by looking into each method and becoming comfortable working with Rmarkdown. Then, research and download all the needed packages in order for each method to work. Next, find examples of inputs to plug into Rmarkdown and understand the meaning of each output. Finally, begin using real data/scenarios to test which method(s) is/are the best to use and study the statistical significance of the data sets. Bioinformatics is a growing field and it’s important to find out the variants associated with disease. These findings from this research can be helpful to researchers when they choose appropriate methods to conduct rare variant association tests.
Systematical Assessment of Rare Variant Association Tests via Simulations
Rare variant association methods are used to detect rare variants associated with human complex traits or disease. A number of factors could influence the power of the test such as sample sizes in cases and control. There is lack of literature about the comparison of rare variant association test methods in different scenarios. However, studying the strengths and weaknesses of each method helps determine which test is the best one to use for each situation. This makes the process of research easier when studying the rare variant associations in the genome. The program, Rmarkdown, gives a place to run the code of each rare variant association method. In this research, we start with six tests which are Burden tests, Adaptive Burden tests, Variance Component tests, Combined tests and EC tests and they have subcategories which are methods that analyze the data more closely which consist of CMC method, aSum test, SKAT, c-alpha, O-SKAT and EC test. The methodology to this research is to begin by looking into each method and becoming comfortable working with Rmarkdown. Then, research and download all the needed packages in order for each method to work. Next, find examples of inputs to plug into Rmarkdown and understand the meaning of each output. Finally, begin using real data/scenarios to test which method(s) is/are the best to use and study the statistical significance of the data sets. Bioinformatics is a growing field and it’s important to find out the variants associated with disease. These findings from this research can be helpful to researchers when they choose appropriate methods to conduct rare variant association tests.
