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Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

Incomplete Dominance

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...

Genetic Variation

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...

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Related Experiment Video

Updated: Jul 17, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Finding the best ridge regression subset by genetic algorithms: applications to multilocus quantitative trait

Bin Zhang¹, Steve Horvath

¹Dept. of Human Genetics, California Univ., Los Angeles, CA, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 3, 2007

Summary

Genetic algorithms optimize ridge regression for complex genomic data, effectively handling many correlated features and preventing overfitting. This approach yields interpretable results for quantitative trait analysis in mouse models.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Computational Biology
Statistical Genetics
Machine Learning

Background:

Large-scale genomic datasets often present challenges in multivariable linear regression due to a high number of features relative to observations.
Ridge regression is a statistical method used to address multicollinearity and reduce the impact of small sample sizes in linear regression.
Optimization of fitness functions within genetic algorithms (GAs) can enhance the performance of statistical modeling techniques.

Purpose of the Study:

To apply genetic algorithms (GAs) for optimizing fitness functions in ridge regression.
To demonstrate the utility of GA-optimized ridge regression for analyzing complex genomic data.
To provide a method that handles overfitting and collinearity in high-dimensional datasets.

Main Methods:

Utilized genetic algorithms (GAs) to optimize fitness functions associated with ridge regression.
Applied the GA-optimized ridge regression model to a mouse cross dataset (69 F2 mice).
Modeled the relationship between a quantitative trait and genetic markers.

Main Results:

The GA-optimized ridge regression successfully modeled the relationship between a quantitative trait and genetic markers.
The method demonstrated effectiveness in avoiding overfitting and handling collinearity.
Results were easily interpretable, facilitating biological insights.

Conclusions:

Genetic algorithms offer a powerful approach to optimize ridge regression for high-dimensional genomic data analysis.
This method is particularly useful when the number of features significantly exceeds the number of observations.
The GA-enhanced ridge regression provides a robust and interpretable tool for genetic association studies.