Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Dihybrid Crosses01:18

Dihybrid Crosses

78.3K
Overview
78.3K
Law of Independent Assortment02:03

Law of Independent Assortment

60.0K
While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
60.0K
Trihybrid Crosses02:27

Trihybrid Crosses

24.5K
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...
24.5K
Law of Segregation01:49

Law of Segregation

74.4K
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.
74.4K
Chi-square Analysis02:46

Chi-square Analysis

41.1K
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...
41.1K
Punnett Squares01:00

Punnett Squares

120.4K
Overview
120.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Market introduction of plant varieties and products with gene-edited traits.

GM crops & food·2026
Same author

A k-mer-based genome-wide association study approach empowering gene mining in polyploids.

Nature genetics·2026
Same author

Integrated Optimized HPLC-MS/MS Profiling and GWAS Uncover Candidate Genes for Folate Content in Cucumber Fruits.

Journal of agricultural and food chemistry·2026
Same author

Influence of gRNA efficiency and inversion size on the frequency of CRISPR/Cas9-induced chromosomal inversions in tomato protoplasts.

BMC plant biology·2026
Same author

AxioSAFE: an accessible, semi-automatic filtering tool for the curation of genotyping datasets.

Bioinformatics advances·2026
Same author

Spotibot: Rapid scoring of <i>B</i> <i>otrytis</i> lesions on rose petals using deep learning and mobile computing.

Plant phenomics (Washington, D.C.)·2025

Related Experiment Video

Updated: Nov 4, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

4.5K

Using probabilistic genotypes in linkage analysis of polyploids.

Yanlin Liao1, Roeland E Voorrips1, Peter M Bourke1

  • 1Wageningen University and Research Plant Breeding, P.O. Box 386, Wageningen, AJ, 6700, The Netherlands.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|May 25, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces probabilistic genotypes for polyploid linkage mapping, improving marker identification. This method enhances the accuracy and completeness of genetic maps in complex polyploid genomes.

More Related Videos

FISH for Pre-implantation Genetic Diagnosis
07:34

FISH for Pre-implantation Genetic Diagnosis

Published on: February 23, 2011

37.4K
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

10.4K

Related Experiment Videos

Last Updated: Nov 4, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

4.5K
FISH for Pre-implantation Genetic Diagnosis
07:34

FISH for Pre-implantation Genetic Diagnosis

Published on: February 23, 2011

37.4K
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

10.4K

Area of Science:

  • Genetics
  • Bioinformatics
  • Plant Breeding

Background:

  • Linkage mapping in polyploids traditionally uses discrete allele dosage scores.
  • Discrete genotypes can introduce uncertainty, especially with sequencing data.
  • Accurate genotype calling is crucial for robust genetic map construction.

Purpose of the Study:

  • To present and validate a novel approach for polyploid linkage map construction using probabilistic marker dosages.
  • To compare the performance of probabilistic versus discrete dosage methods for mapping.
  • To assess the utility of probabilistic genotypes for handling uncertainty in polyploid data.

Main Methods:

  • Development of a linkage mapping approach utilizing probabilistic genotypes.
  • Comparison with discrete dosage methods using simulated SNP array and sequence read data.
  • Validation with experimental SNP array data from a potato (Solanum tuberosum L.) mapping population.

Main Results:

  • The probabilistic dosage method successfully mapped an additional 562 markers compared to the discrete dosage approach.
  • Most newly mapped markers were assigned to their expected chromosomal positions.
  • The method demonstrated effectiveness in handling genotype uncertainty inherent in sequencing data.

Conclusions:

  • Probabilistic marker dosages offer a significant improvement for polyploid linkage map construction.
  • This approach enhances marker density and mapping accuracy, particularly for sequencing-based data.
  • The method is highly relevant for genetic studies in polyploid species, including crop improvement.