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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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RNA Splicing01:32

RNA Splicing

Splicing is the process by which eukaryotic RNA is edited before its translation into protein. The RNA strand transcribed from eukaryotic DNA is called the primary transcript. The primary transcripts that become mRNAs are called precursor messenger RNAs (pre-mRNAs). Eukaryotic pre-mRNA contains alternating sequences of exons and introns. Exons are nucleotide sequences that code for proteins, whereas introns are the non-coding regions. In RNA splicing, introns are removed and exons are bonded...
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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,...
Alternative RNA Splicing02:18

Alternative RNA Splicing

Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
There are five types of alternative RNA splicing that vary in the ways the pre-mRNA segments are removed or retained in the mature mRNA. The first...

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Related Experiment Video

Updated: May 9, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Published on: January 16, 2019

Assessing association between protein truncating variants and quantitative traits.

Manuel A Rivas1, Matti Pirinen, Matthew J Neville

  • 1Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK, Institute for Molecular Medicine Finland, University of Helsinki, Helsinki 00290, Finland, Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, Oxford OX3 7LJ, UK, NIHR Oxford Biomedical Research Centre, OUH Trust, Oxford OX3 7LE, UK and Department of Statistics, University of Oxford, Oxford OX1 3TG, UK.

Bioinformatics (Oxford, England)
|July 18, 2013
PubMed
Summary
This summary is machine-generated.

We developed a Bayesian framework to analyze protein truncating variants and quantitative traits, improving power over existing methods. This approach identified a link between APOC3 protein truncating variants and lower plasma triglyceride levels.

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Published on: August 20, 2019

Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Sequencing studies often lack power to detect associations with rare variants.
  • Combining evidence from multiple variants improves statistical power.
  • Protein truncating variants (loss of function) are increasingly recognized for their role in disease and quantitative traits.

Purpose of the Study:

  • To propose a novel Bayesian modeling framework for analyzing protein truncating variants in relation to quantitative traits.
  • To improve statistical power for detecting associations compared to existing methods.

Main Methods:

  • Developed a Bayesian modeling framework tailored for protein truncating variants.
  • Applied the framework to sequence and exome-array data.
  • Compared performance against commonly used methods via simulations.

Main Results:

  • The proposed Bayesian models demonstrated superior performance over standard approaches in simulations.
  • Analysis of real data revealed a significant association between protein truncating variants in APOC3 and reduced plasma triglyceride levels.

Conclusions:

  • The Bayesian framework offers a powerful and effective approach for studying protein truncating variants and quantitative traits.
  • This study highlights the role of APOC3 protein truncating variants in regulating plasma triglyceride levels.