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Related Concept Videos

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...
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Genetic Screens02:46

Genetic Screens

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Related Experiment Video

Updated: May 13, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Simpute: an efficient solution for dense genotypic data.

Yen-Jen Lin1, Chun-Tien Chang, Chuan Yi Tang

  • 1Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan.

Biomed Research International
|March 20, 2013
PubMed
Summary
This summary is machine-generated.

Missing single nucleotide polymorphism (SNP) data can skew genetic association analyses. A new, efficient imputation algorithm, Simpute, offers a valuable tool for handling missing SNP data without reference panels, especially in large-scale genotyping projects.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

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Last Updated: May 13, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Single nucleotide polymorphism (SNP) data, crucial for genetic studies, frequently contain missing values due to technological limitations.
  • Missing SNP data can introduce bias into association analyses, potentially leading to inaccurate genetic findings.
  • Imputation methods are commonly used to infer and fill in missing genotypes, maximizing data utility.

Purpose of the Study:

  • To evaluate and compare the imputation performance of existing tools: BEAGLE, IMPUTE, BIMBAM, SNPMStat, MACH, and PLINK.
  • To introduce and assess a novel imputation algorithm, simple imputation (Simpute), designed for efficiency and high-resolution SNP data.

Main Methods:

  • Performance comparison of six imputation tools using masked genotype data from the International HapMap Phase III project.
  • Development and evaluation of the Simpute algorithm, which does not require external reference data and leverages high-density SNP array information.

Main Results:

  • The study systematically compared the accuracy and efficiency of various imputation methods.
  • Simpute demonstrated significant computational efficiency, with a time complexity of O(mw + n), making it suitable for large datasets.
  • Simpute's ability to function without reference data is a key advantage for certain applications.

Conclusions:

  • Simpute offers an efficient and effective solution for imputing missing SNP data, particularly in large-scale genotyping projects where computational resources are a concern.
  • The proposed Simpute algorithm provides a valuable alternative for handling missing genotype data, complementing existing imputation tools.
  • Efficient imputation of missing SNPs is critical for robust genetic association studies.