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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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Genome-wide Association Studies-GWAS01:11

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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.
GWAS does not require the identification of the target gene involved in...
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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.
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%...
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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TheSNPpit-A High Performance Database System for Managing Large Scale SNP Data.

Eildert Groeneveld1, Helmut Lichtenberg1

  • 1Institute of Farm Animal Genetics, Friedrich-Loeffler-Institute, 31535 Mariensee, Germany.

Plos One
|October 26, 2016
PubMed
Summary
This summary is machine-generated.

TheSNPpit database efficiently manages large-scale genotype data, offering compressed storage and rapid data manipulation for genetic studies. This system handles millions of samples and trillions of single nucleotide polymorphisms (SNPs), overcoming previous data handling limitations.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput genotyping generates massive datasets, posing significant data management challenges.
  • Traditional database designs struggle with the scale of modern genetic data, particularly with hundreds of thousands of individuals.

Purpose of the Study:

  • To introduce TheSNPpit, a novel database system designed for efficient management of large-scale multi-panel SNP genotype data.
  • To address the limitations of existing systems in handling extensive genetic datasets from various genotyping platforms.

Main Methods:

  • Implemented highly compressed vector storage within a relational database (PostgreSQL).
  • Utilized set-based data manipulation and a fast C-based export utility.
  • Developed a novel subset system for creating named subsets based on SNP and sample filtering with minimal storage overhead.
  • Ensured compatibility with PLINK ped and map file formats for input and output.

Main Results:

  • TheSNPpit achieves high data compression, using only 2 bits per SNP, enabling storage of 4 million SNPs per 1MB.
  • Demonstrated linear scalability with database size, handling over 18.5 million samples and 3.4 trillion SNPs.
  • Achieved high import (6 million SNPs/sec) and export (60-120 million SNPs/sec) speeds, largely independent of database size.

Conclusions:

  • TheSNPpit provides a robust and scalable solution for managing large-scale SNP genotype data, essential for fields like animal/plant breeding and human genetics.
  • Its efficient storage, rapid processing, and flexible subset system overcome critical data handling bottlenecks in modern genetic research.
  • The system's command-line interface facilitates integration into automated bioinformatics pipelines.