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Single Nucleotide Polymorphisms-SNPs01:05

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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 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.
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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.
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Using Mendelian inheritance to improve high-throughput SNP discovery.

Nancy Chen1, Cristopher V Van Hout2, Srikanth Gottipati2

  • 1Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, New York 14853 Cornell Laboratory of Ornithology, Cornell University, Ithaca, New York 14853 nc276@cornell.edu.

Genetics
|September 7, 2014
PubMed
Summary

This study introduces MendelChecker, a new statistical method for improving the quality of genetic data from genotyping-by-sequencing (GBS). It effectively filters errors and identifies sex-linked single-nucleotide polymorphisms (SNPs) in family data.

Keywords:
Mendelian inheritanceRAD-seqSNP discoverygenotyping-by-sequencingpedigrees

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Area of Science:

  • Genetics
  • Bioinformatics
  • Population Genetics

Background:

  • Restriction site-associated DNA sequencing (GBS) enables cost-effective discovery and genotyping of numerous single-nucleotide polymorphisms (SNPs).
  • Reduced representation methods like GBS require stringent quality control to mitigate errors and biases inherent in variable genotype calls and missing data.

Purpose of the Study:

  • To develop a robust statistical framework for filtering spurious genetic loci and identifying sex-linked SNPs from GBS data.
  • To create a quality control method that accommodates variable genotype quality and missing data, common issues in GBS.

Main Methods:

  • Developed a statistical framework utilizing Mendelian inheritance patterns within nuclear families to filter erroneous loci.
  • Implemented a method to identify sex-linked SNPs, accounting for variable genotype quality and missing data.
  • Validated the framework through simulations and application to real GBS data, with a subset of high-quality SNPs confirmed.

Main Results:

  • The Mendelian inheritance filter demonstrated excellent performance in simulations and real data analysis.
  • The method successfully identified sex-linked SNPs, improving data accuracy.
  • Mendelian inheritance metric proved a powerful quality filter, complementary to existing coverage and Hardy-Weinberg filters.

Conclusions:

  • The developed statistical framework, implemented in MendelChecker software, significantly enhances SNP quality control for GBS data.
  • This method is applicable to both model and non-model organisms, improving SNP discovery and genotyping accuracy.
  • The approach effectively addresses challenges of variable genotype quality and missing data in GBS, leading to more reliable genetic analyses.