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Dihybrid Crosses

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Chi-square Analysis

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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.
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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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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...

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Related Experiment Video

Updated: Sep 20, 2025

Lateral Root Inducible System in Arabidopsis and Maize

Lateral Root Inducible System in Arabidopsis and Maize

Published on: January 14, 2016

Covariance between nonrelatives in maize.

¹Department of Agronomy and Plant Genetics, University of Minnesota, 411 Borlaug Hall, 1991 Buford Circle, Saint Paul, MN, 55108, USA. bernardo@umn.edu.

|June 8, 2022

Summary

This summary is machine-generated.

Covariance between non-related maize lines was investigated. Some traits showed significant covariance, suggesting a unified framework for genetic resemblance is needed.

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Area of Science:

Quantitative genetics
Plant breeding
Genomics

Background:

Covariance between relatives is a fundamental concept in quantitative genetics.
The covariance between non-relatives in crops, specifically maize (Zea mays L.), has not been extensively studied.
Understanding genetic resemblance in non-related individuals is crucial for breeding programs.

Purpose of the Study:

To determine if a covariance exists between non-related maize lines.
To assess the presence and nature of covariance for specific phenotypic traits in unrelated maize.
To propose a unified framework for expressing genetic resemblance.

Main Methods:

Utilized 272 maize lines genotyped with 28,626 single nucleotide polymorphism (SNP) markers.
Identified pairs of unrelated lines based on membership probabilities in five subpopulations.
Assessed covariance between non-relatives using regression of phenotypic similarity on SNP similarity.

Main Results:

Seven out of 77 regressions were significant at a 5% false discovery rate for traits including flowering time, plant and ear height, oil, starch, and protein content.
A negative covariance was observed for protein between specific unrelated lines (B73 and Mo17).
This negative covariance was attributed to differing linkage phases between markers and causal variants in subpopulations.

Conclusions:

Covariance between non-related individuals in maize is not universal but occurs for specific traits and certain groups.
A negative covariance can arise due to distinct subpopulation genetic architectures.
Proposed a generalized covariance framework combining related and non-related individual resemblance for a unified approach.