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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...
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%...
Exon Recombination02:32

Exon Recombination

The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes. 
Exon shuffling follows “splice frame rules.” Each exon has three reading...

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Related Experiment Video

Updated: May 24, 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

SNP set analysis for detecting disease association using exon sequence data.

Ru Wang1, Jie Peng, Pei Wang

  • 1Department of Statistics, University of California, Davis, CA 95616, USA. ruwang@ucdavis.edu.

BMC Proceedings
|March 1, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces new methods to analyze common and rare genetic variants simultaneously, improving disease gene discovery. Logistic kernel machine models proved more powerful than traditional logistic regression for identifying disease-associated genes.

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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
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Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Related Experiment Videos

Last Updated: May 24, 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

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Rare genetic variants are increasingly recognized for their role in disease etiology.
  • High-throughput sequencing facilitates the comprehensive analysis of both common and rare genetic variants.
  • Existing methods often analyze common and rare variants separately, potentially missing combined effects.

Purpose of the Study:

  • To develop and evaluate novel statistical approaches for simultaneously testing common and rare variants within single-nucleotide polymorphism (SNP) sets.
  • To incorporate gene-environment and SNP-SNP interactions into these models.
  • To assess the performance of these methods using real-world genetic data.

Main Methods:

  • Development of logistic regression and logistic kernel machine models for joint variant analysis.
  • Inclusion of interaction terms for gene-environment and SNP-SNP effects.
  • Application of methods to unrelated individuals data from the Genetic Analysis Workshop 17 (GAW17).

Main Results:

  • Consistent identification of three true disease genes (FLT1, PIK3C3, KDR) across proposed methods.
  • Logistic kernel machine models demonstrated superior power compared to standard logistic regression, likely due to regularization.
  • A preliminary screening step effectively reduced false-positive findings in association studies.

Conclusions:

  • Simultaneous analysis of common and rare variants offers a powerful approach for genetic association studies.
  • Logistic kernel machine models provide enhanced power for detecting genetic associations.
  • Implementing screening steps is crucial for mitigating false positives in genetic analyses.