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Law of Segregation

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.

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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).
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Law of Independent Assortment

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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.

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Related Experiment Video

Updated: Jul 2, 2026

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

Fluorescence-microscopy Screening and Next-generation Sequencing: Useful Tools for the Identification of Genes Involved in Organelle Integrity

Published on: April 13, 2012

Methodologies for segregation analysis and QTL mapping in plants.

Yuan-Ming Zhang¹, Junyi Gai

¹Section on Statistical Genomics, State Key Laboratory of Crop Genetics and Germplasm Enhancement and National Center for Soybean Improvement, College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China. soyzhang@njau.edu.cn

|August 30, 2008

Summary

This summary is machine-generated.

Researchers developed advanced methods for analyzing quantitative traits, focusing on major-gene plus polygenes inheritance and quantitative trait locus (QTL) discovery. These approaches improve genetic architecture understanding in crops.

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Area of Science:

Quantitative genetics
Genomic analysis
Crop breeding

Background:

Quantitative traits significantly impact biological and economic importance.
Understanding the genetic basis of quantitative traits is crucial for crop improvement.
Two primary approaches, mixed major-gene plus polygenes inheritance and quantitative trait locus (QTL) discovery, are popular for this research.

Purpose of the Study:

To review and present advancements in analytical models for mixed major-gene plus polygenes inheritance.
To detail progress in quantitative trait locus (QTL) discovery methodologies.
To introduce novel methods for mining genes in crop inbred lines.

Main Methods:

Proposed joint segregation analysis for multiple generations of mixed major-gene plus polygenes inheritance.
Extended multilocus methods to handle distorted, dominant, and missing markers, including linked segregation distortion loci.
Developed Bayesian shrinkage estimation (BSE) for QTL mapping, including significance testing and penalized maximum likelihood for efficiency.
Introduced methods for gene mining in crop inbred lines: two-stage variance component analysis, multi-QTL Haseman-Elston regression, and multiple loci in silico mapping.

Main Results:

Developed a generalized multilocus method accommodating complex marker data.
Introduced the Bayesian shrinkage estimation (BSE) method for robust QTL effect estimation and significance testing.
Proposed efficient computational methods, including penalized maximum likelihood, to reduce analysis time.
Provided specific gene mining strategies tailored to crop inbred lines based on available genealogical data.

Conclusions:

The reviewed methodologies offer powerful tools for dissecting the genetic architecture of quantitative traits.
Advancements in QTL mapping and gene discovery accelerate crop breeding programs.
The developed approaches enhance the accuracy and efficiency of genetic analysis in quantitative trait research.