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Related Concept Videos

Pedigree Analysis01:35

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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.
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
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.
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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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Related Experiment Video

Updated: May 14, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Bayesian parentage analysis with systematic accountability of genotyping error, missing data and false matching.

Mark R Christie1, Jacob A Tennessen, Michael S Blouin

  • 1Department of Zoology, Oregon State University, Corvallis, OR 97331-2914, USA. christim@science.oregonstate.edu

Bioinformatics (Oxford, England)
|February 1, 2013
PubMed
Summary

This study introduces a new Bayesian parentage method for accurate relationship identification. It effectively minimizes incorrect assignments without needing extra data, improving parentage analysis for various species.

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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

Area of Science:

  • Population Genetics
  • Bioinformatics
  • Conservation Biology

Background:

  • Parentage analysis aims to maximize correct parent-offspring assignments while minimizing errors.
  • Current methods often require extensive demographic data, numerous genetic markers, or error rate estimations.
  • These requirements are often impractical for non-model organisms.

Purpose of the Study:

  • To develop a novel Bayesian parentage method requiring only genotype data.
  • To account for genotyping errors, missing data, and false matches within the analysis.
  • To improve the accuracy and feasibility of parentage analysis in diverse species.

Main Methods:

  • A Bayesian statistical framework was developed for parentage assignment.
  • The method inherently incorporates parameters for genotyping error, missing data, and false matches.
  • Utilized both microsatellite and single nucleotide polymorphism (SNP) genetic marker datasets for validation.

Main Results:

  • The Bayesian method effectively controlled false assignments across varying genotyping error rates.
  • It maximized correct assignments, especially when the number of genetic loci was limited, by considering allele frequencies.
  • Outperformed traditional exclusion and likelihood-based methods in an empirical salmon dataset, showing a superior correct-to-incorrect assignment ratio.

Conclusions:

  • The developed Bayesian method offers a robust and practical approach to parentage analysis.
  • It significantly improves assignment accuracy, particularly in scenarios with limited genetic markers or data.
  • This method is valuable for genetic studies of non-model organisms and conservation efforts.