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Related Concept Videos

Dihybrid Crosses01:18

Dihybrid Crosses

Overview
Law of Segregation01:49

Law of Segregation

When crossing pea plants, Mendel noticed that one of the parental traits would sometimes disappear in the first generation of offspring, called the F1 generation, and could reappear in the next generation (F2). He concluded that one of the traits must be dominant over the other, thereby causing masking of one trait in the F1 generation. When he crossed the F1 plants, he found that 75% of the offspring in the F2 generation had the dominant phenotype, while 25% had the recessive phenotype.
Test Cross01:39

Test Cross

Alleles are different forms of the same gene. Humans and other diploid organisms inherit two alleles of every gene, one from each parent.
Dihybrid Crosses01:18

Dihybrid Crosses

Overview
Trihybrid Crosses02:27

Trihybrid Crosses

Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal chance to...
Chi-square Analysis02:46

Chi-square Analysis

The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...

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BEST: Barcode Enabled Sequencing of Tetrads
12:59

BEST: Barcode Enabled Sequencing of Tetrads

Published on: May 1, 2014

A bivalent polyploid model for linkage analysis in outcrossing tetraploids.

Rongling Wu1, Chang-Xing Ma, George Casella

  • 1Department of Statistics, University of Florida, Gainesville, Florida 32611, USA. rwu@stat.u-fl.edu

Theoretical Population Biology
|August 9, 2002
PubMed
Summary
This summary is machine-generated.

This study introduces a statistical method for linkage analysis in bivalent polyploids, accounting for preferential chromosome pairing. The method accurately estimates recombination fractions and pairing factors in tetraploids.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Polyploids are classified by origin (allopolyploids, autopolyploids) but can be functionally described by meiotic pairing (bivalent, multivalent, general).
  • Linkage analysis in polyploids is complicated by complex chromosome pairing behaviors during meiosis.

Purpose of the Study:

  • To develop a statistical method for linkage analysis in bivalent polyploids, specifically addressing preferential bivalent pairing in tetraploids.
  • To estimate recombination fractions and the preferential pairing factor in tetraploid species.

Main Methods:

  • Developed a statistical method for linkage analysis in bivalent polyploids.
  • Incorporated preferential bivalent pairing, where homologous chromosomes pair more frequently than homoeologous ones.
  • Utilized a maximum likelihood method with the Expectation-Maximization (EM) algorithm for estimating linkage and parental phases.

Main Results:

  • The proposed method accurately estimates the recombination fraction between marker types in bivalent tetraploids.
  • The method effectively estimates the preferential pairing factor, crucial for understanding segregation patterns.
  • Simulation studies validated the method's performance across various marker cross types.

Conclusions:

  • The developed statistical method provides a robust tool for genetic mapping in bivalent polyploids, particularly tetraploids with preferential pairing.
  • This approach has significant implications for genome projects and genetic studies in polyploid species.
  • Accurate linkage analysis is essential for understanding genome organization and facilitating breeding in polyploid crops and organisms.