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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.

Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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IntroUNET: Identifying introgressed alleles via semantic segmentation.

Dylan D Ray¹, Lex Flagel^2,3, Daniel R Schrider¹

¹Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.

|February 20, 2024

Summary

This study introduces a deep learning method to precisely identify introgressed alleles in genomes. The new approach accurately pinpoints which individuals carry introgressed genetic material and where it is located, advancing evolutionary inference.

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Area of Science:

Genomics
Population Genetics
Bioinformatics

Background:

Gene flow and introgression are common between species.
Identifying introgressed genomic regions is crucial for understanding adaptation and speciation.
Existing methods often lack the precision to pinpoint individual introgressed alleles.

Purpose of the Study:

To develop a deep learning method for precise identification of introgressed alleles within individuals.
To infer the location and extent of introgression across a genome.
To apply this method to real genomic data for evolutionary insights.

Main Methods:

Adapted a deep learning semantic segmentation algorithm to population genetic data.
Trained a neural network to classify alleles as introgressed or native.
Validated the method using simulated data, including unsampled ('ghost') populations.

Main Results:

The deep learning method accurately identifies introgressed alleles at the individual and genomic position level.
The approach performs comparably to specialized methods for detecting introgression from unsampled populations.
Analysis of Drosophila data revealed introgressed alleles at lower frequencies in genic regions, suggesting selection, and higher frequencies in a known adaptive introgression region.

Conclusions:

Deep learning semantic segmentation offers a powerful tool for detailed inference of introgression.
The method enhances our ability to study the evolutionary impact of gene flow.
This approach facilitates richer evolutionary inferences from genomic data, particularly in complex scenarios.