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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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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.
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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,...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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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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Genetic Variation01:25

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare variant testing across methods and thresholds using the multi-kernel sequence kernel association test (MK-SKAT).

Eugene Urrutia1, Seunggeun Lee2, Arnab Maity3

  • 1Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.

Statistics and Its Interface
|January 8, 2016
PubMed
Summary
This summary is machine-generated.

Multi-Kernel SKAT (MK-SKAT) addresses challenges in rare genetic variant analysis by testing multiple variant groupings and association tests simultaneously. This approach enhances statistical power and controls errors for complex trait association studies.

Keywords:
PerturbationRare variantsSequence kernel association testSequencing association studies

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

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Region-based analysis of rare genetic variants is crucial for understanding complex traits.
  • Challenges include selecting appropriate statistical tests and defining variant subsets within genomic regions.
  • These choices are often dependent on unknown biological factors.

Purpose of the Study:

  • To develop a robust framework, Multi-Kernel SKAT (MK-SKAT), for analyzing rare genetic variants.
  • To overcome the limitations of choosing specific tests and variant groupings in region-based analyses.
  • To provide a method that adapts to the unknown true biological state.

Main Methods:

  • MK-SKAT is presented as a generalization of the Sequence Kernel Association Test (SKAT).
  • It unifies various rare variant tests and variant grouping strategies by framing them as kernel choices.
  • The method employs perturbation to systematically test across a spectrum of kernels, representing different tests and groupings.

Main Results:

  • Simulations and real data analyses demonstrate that MK-SKAT effectively controls Type I error rates.
  • The framework maintains high statistical power across diverse analytical scenarios.
  • While specific kernel choices might offer slightly more power in ideal situations, MK-SKAT provides superior power compared to suboptimal selections.

Conclusions:

  • MK-SKAT offers a flexible and powerful approach to region-based rare variant association analysis.
  • It mitigates the need for pre-specifying statistical tests or variant sets, improving discoverability of trait associations.
  • The method enhances the reliability and power of genetic studies investigating complex traits influenced by rare variants.