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Related Concept Videos

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%...
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,...
Genetic Drift03:33

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
Genetic Variation01:25

Genetic Variation

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.
Genes exist in different versions called alleles, which...
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...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

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Related Experiment Video

Updated: May 18, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Adaptive ridge regression for rare variant detection.

Haimao Zhan1, Shizhong Xu

  • 1Department of Botany and Plant Sciences, University of California Riverside, Riverside, California, USA.

Plos One
|September 7, 2012
PubMed
Summary

This study introduces a new adaptive ridge regression method for analyzing rare genetic variants associated with complex diseases. The method offers higher statistical power and is easier to implement than existing approaches.

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Common diseases and complex traits are influenced by both common and rare genetic variants.
  • Rare variants, in aggregate, can explain a substantial portion of trait variability.
  • High-throughput DNA sequencing enables the study of rare causal variants.

Purpose of the Study:

  • To develop a novel statistical method for analyzing the collective effects of multiple rare variants within genes or functional units.
  • To address the challenge of detecting associations between rare variants and continuous traits while accounting for covariates.
  • To provide a more powerful and user-friendly alternative to existing rare variant analysis methods.

Main Methods:

  • Developed an adaptive ridge regression model tailored for analyzing multiple rare variants simultaneously.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Last Updated: May 18, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

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10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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  • Incorporated covariate factors to adjust for potential confounding effects on continuous traits.
  • The method estimates and tests collective rare variant effects without assuming a uniform direction of effect.

    • Main Results:

    • The proposed adaptive ridge regression method demonstrated higher statistical power compared to the Bayesian hierarchical generalized linear model approach.
    • The method is computationally efficient and straightforward to implement.
    • Successfully applied to the Dallas Heart Study dataset for real-world validation.

    Conclusions:

    • The novel adaptive ridge regression method provides a powerful and practical tool for rare variant association studies.
    • This approach enhances the ability to identify genetic contributions to complex traits and diseases.
    • Facilitates the investigation of rare genetic variants using advances in DNA sequencing technology.