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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p ≠ 0.5.
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

Kernel machine SNP-set testing under multiple candidate kernels.

Michael C Wu1, Arnab Maity, Seunggeun Lee

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420, USA. mwu@bios.unc.edu

Genetic Epidemiology
|March 9, 2013
PubMed
Summary
This summary is machine-generated.

Kernel machine (KM) testing analyzes multiple genetic variants for complex traits. New methods using composite kernels and perturbation procedures control errors and improve power in genetic association studies.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Joint testing of multiple single-nucleotide polymorphisms (SNPs) is crucial for large-scale genetic association studies.
  • Kernel machine (KM) testing framework compares phenotypic and genotypic similarity using kernel functions for complex trait analysis.
  • Selecting the optimal kernel function a priori is challenging and can lead to inflated type I error rates.

Purpose of the Study:

  • To develop practical strategies for kernel machine (KM) testing with multiple candidate kernels.
  • To address challenges in kernel selection and protect the type I error rate in genetic association analyses.
  • To enhance statistical power in identifying associations between multiple genetic variants and complex traits.

Main Methods:

  • Proposed composite kernel construction for combining multiple candidate kernels.
  • Implemented efficient perturbation procedures for robust KM testing.
  • Utilized simulations and real genetic association data for validation.

Main Results:

  • The proposed strategies effectively protect the type I error rate.
  • Demonstrated substantially improved power compared to suboptimal kernel choices.
  • Achieved power comparable to using the best single candidate kernel.

Conclusions:

  • The developed methods offer practical solutions for kernel machine (KM) testing in the presence of multiple kernels.
  • These strategies balance type I error control with enhanced statistical power in genetic association studies.
  • The approach is effective for analyzing complex traits influenced by multiple genetic variants.