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Comparing Copy Number Variations and SNPs

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-SNPs

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,...

Genome-wide Association Studies-GWAS

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...

Non-nuclear Inheritance

Non-nuclear Inheritance

Most DNA resides in the nucleus of a cell. However, some organelles in the cell cytoplasm⁠—such as chloroplasts and mitochondria⁠—also have their own DNA. These organelles replicate their DNA independently of the nuclear DNA of the cell in which they reside. Non-nuclear inheritance describes the inheritance of genes from structures other than the nucleus.

Non-nuclear Inheritance

Non-nuclear Inheritance

Most DNA resides in the nucleus of a cell. However, some organelles in the cell cytoplasm⁠—such as chloroplasts and mitochondria⁠—also have their own DNA. These organelles replicate their DNA independently of the nuclear DNA of the cell in which they reside. Non-nuclear inheritance describes the inheritance of genes from structures other than the nucleus.

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Updated: May 21, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Detecting rare variants for quantitative traits using nuclear families.

Wei Guo¹, Yin Yao Shugart

¹Division of Intramural Division Program, National Institute of Mental Health, National Institute of Health, Bethesda, MD 20892, USA.

|June 16, 2012

Summary

New statistical methods using beta-determined weights improve the detection of rare genetic variants for complex disorders in families. These novel approaches outperform existing collapsing methods in family-based association tests.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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Last Updated: May 21, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Personal genome sequencing generates vast rare variant data for complex human disorders.
Existing statistical methods for rare variant analysis in family-based designs are limited.
Novel statistical approaches are crucial for powerful rare variant detection.

Purpose of the Study:

Introduce three new beta-determined weight tests for detecting rare variants in nuclear families for quantitative traits.
Evaluate the performance of these new methods against existing FBAT (Family-Based Association Test) rare variant tests.
Compare different beta-determined weight strategies and existing collapsing methods.

Main Methods:

Development of three novel beta-determined weight tests for rare variant analysis.
Simulation studies to assess test performance under various conditions, including linkage disequilibrium.
Comparison with two existing FBAT rare variant tests and collapsing methods.

Main Results:

The four beta-determined weight tests demonstrated superior performance compared to the two FBAT collapsing methods (-v0 and -v1).
A linear combination method and a multiple regression method (LASSO/Ridge) showed better performance than other proposed beta-determined weight tests.
The developed methods were submitted to CRAN for public accessibility.

Conclusions:

Beta-determined weight tests offer a statistically powerful approach for analyzing rare variants in family-based studies.
The linear combination and multiple regression methods represent advanced strategies for rare variant detection.
The availability of these methods via CRAN facilitates broader research in genetic complex disorders.