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Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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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...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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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...
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Unusual Results01:16

Unusual Results

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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value =...
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Odds Ratio01:09

Odds Ratio

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The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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%...
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Related Experiment Video

Updated: May 1, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

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Rare variants detection with kernel machine learning based on likelihood ratio test.

Ping Zeng1, Yang Zhao2, Liwei Zhang2

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China; Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical College, Xuzhou, Jiangsu, China.

Plos One
|March 29, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces likelihood ratio tests (LRT) and restricted likelihood ratio tests (ReLRT) for detecting rare variants linked to continuous traits using kernel machine learning. These novel methods demonstrate superior performance over SKAT in genetic association studies.

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Identifying associations between rare genetic variants and complex traits is crucial for understanding disease.
  • Kernel machine learning offers a flexible framework for analyzing high-dimensional genetic data.
  • Existing methods may have limitations in detecting associations influenced by both positive and negative variant effects.

Purpose of the Study:

  • To propose and evaluate likelihood-based tests, specifically the likelihood ratio test (LRT) and restricted likelihood ratio test (ReLRT), for detecting rare variants associated with continuous phenotypes.
  • To compare the performance of LRT and ReLRT against the existing SKAT method.
  • To investigate the application of these tests within the kernel machine learning framework and standard mixed effects models.

Main Methods:

  • Utilized likelihood ratio tests (LRT) and restricted likelihood ratio tests (ReLRT).
  • Employed eigenvalue representation to derive exact finite sample distributions via simulation.
  • Evaluated performance through numerical studies in both standard mixed effects models and kernel machine learning contexts.
  • Applied the methods to Genetic Analysis Workshop 17 exome sequencing SNP data.

Main Results:

  • LRT and ReLRT effectively control type I error rates.
  • LRT and ReLRT consistently outperformed SKAT across various scenarios, including different sample sizes and proportions of negative causal variants.
  • The proposed tests showed fewer power reductions than SKAT when both positive and negative variant effects were present.
  • Kernel machine learning-based LRT and ReLRT exhibited slightly higher statistical power compared to their standard mixed effects model counterparts.

Conclusions:

  • Likelihood-based tests (LRT and ReLRT) provide a robust and powerful approach for detecting rare variant associations with continuous phenotypes.
  • Kernel machine learning enhances the performance of these tests.
  • The developed methods offer an improvement over existing approaches like SKAT for genetic association studies involving rare variants.