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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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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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How imputation can mitigate SNP ascertainment Bias.

Johannes Geibel1,2, Christian Reimer3,4, Torsten Pook3,4

  • 1Department of Animal Sciences, Animal Breeding and Genetics Group, University of Goettingen, Albrecht-Thaer-Weg 3, 37075, Göttingen, Germany. johannes.geibel@uni-goettingen.de.

BMC Genomics
|May 13, 2021
PubMed
Summary

This study introduces imputation as a method to correct for SNP ascertainment bias in population genetics. Using sequenced individuals as a reference set effectively mitigates bias without needing array design details.

Keywords:
ChickensImputationPopulation geneticsSNP ascertainment bias

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Area of Science:

  • Population genetics
  • Genomics
  • Bioinformatics

Background:

  • Genotyping arrays use non-randomly selected single nucleotide polymorphisms (SNPs), leading to ascertainment bias in population genetic studies.
  • This bias affects allele frequency spectra, heterozygosity, and genetic distance estimates compared to whole genome sequencing (WGS) data.
  • Correcting this bias typically requires detailed array design information, which is often unavailable.

Purpose of the Study:

  • To propose and evaluate an alternative method for mitigating SNP ascertainment bias using imputation.
  • To assess the effectiveness of using a small set of sequenced individuals to correct bias in a large genotyped population.
  • To investigate the impact of reference set composition on bias correction and imputation accuracy.

Main Methods:

  • Simulated ascertainment bias in 1566 chickens genotyped on a 580k array.
  • Utilized imputation with varying reference sets (balanced and unbalanced) of sequenced individuals.
  • Compared array genotype imputation to WGS data to evaluate bias reduction and accuracy.

Main Results:

  • Imputation accuracy was higher when reference populations were included in the original SNP discovery.
  • Balanced reference sets (one individual per population) effectively corrected ascertainment bias for heterozygosity and genetic distances.
  • Imputation to WGS showed reduced bias, with a balanced reference panel yielding better results than an unbalanced one, though a larger panel was generally favored for WGS imputation.

Conclusions:

  • Imputation offers a viable strategy for mitigating SNP ascertainment bias in population genetic analyses.
  • The composition of the reference set is crucial; unbiased and balanced sets are necessary for accurate bias correction.
  • Further research may be needed to optimize reference panel size and composition for imputing to whole genome sequencing data.