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Related Concept Videos

Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.In the early 20th century,...
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...
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...
Genetic Drift03:33

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...
What is Population Genetics?01:25

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.While some alleles of a given gene might be observed commonly, other variants...
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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Incorporating genotyping uncertainty in haplotype frequency estimation in pedigree studies.

Wen-Sheng Zhu1, Wing-Kam Fung, Jianhua Guo

  • 1Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, SAR, China.

Human Heredity
|May 31, 2007
PubMed
Summary

Estimating haplotype frequencies is crucial for human genetics. A new algorithm, GS-PEM, improves accuracy by accounting for genotyping errors in family data, reducing their impact on results.

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Infinium Assay for Large-scale SNP Genotyping Applications
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Area of Science:

  • Human genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Haplotype frequency estimation is vital for human genetics research.
  • Existing methods often assume error-free genotype data, which is unrealistic for large datasets.
  • Genotyping errors can significantly distort haplotype frequency estimates.

Purpose of the Study:

  • To develop a novel algorithm for haplotype frequency estimation that accounts for genotyping uncertainty.
  • To address the limitations of existing methods in handling dependent pedigree data.
  • To improve the accuracy of haplotype frequency estimation in the presence of genotyping errors.

Main Methods:

  • Introduced a new Expectation-Maximization (EM) algorithm named GS-PEM.
  • GS-PEM utilizes all possible multilocus genotypes (GenoSpectrum) for each individual within pedigrees.
  • The algorithm incorporates the dependence information among relatives to enhance estimation.

Main Results:

  • Evaluated the performance of GS-PEM through simulation studies.
  • Demonstrated that GS-PEM effectively reduces the impact of genotyping errors on haplotype frequency estimation.
  • The new algorithm shows improved robustness compared to methods assuming error-free data.

Conclusions:

  • GS-PEM provides a more accurate method for haplotype frequency estimation, especially in pedigree studies.
  • The algorithm's ability to handle genotyping errors makes it a valuable tool for human genetics research.
  • This work advances statistical genetics by offering a robust approach to a common data quality challenge.