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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

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Published on: July 1, 2020

How Machine Learning Methods Helped Find Putative Rye Wax Genes Among GBS Data.

Magdalena Góralska¹, Jan Bińkowski¹, Natalia Lenarczyk¹

¹Department of Plant Genetics, Breeding and Biotechnology, West-Pomeranian University of Technology, Szczecin, ul. Słowackiego 17, 71-434 Szczecin, Poland.

International Journal of Molecular Sciences

|October 15, 2020

Summary

Researchers mapped a rye gene for epicuticular wax formation using machine learning. This gene, located on chromosome 2R, is linked to the ABCG11 transporter and controls plant waxiness.

Keywords:

Keywords: ATP-binding cassette (ABC) transporters Secale cereale L.fatty acid desaturase (FAD), genetic map glaucousness large-scale sequence-based markers

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Published on: July 16, 2019

Area of Science:

Plant genetics
Molecular biology
Bioinformatics

Background:

Epicuticular wax is crucial for plant survival, protecting against abiotic stress.
Genetic mapping of wax formation genes aids in crop improvement.
Machine learning offers novel approaches for genetic analysis.

Purpose of the Study:

To map the rye gene responsible for epicuticular wax formation (glaucous phenotype) using advanced genetic and machine learning methods.
To identify candidate genes and DNA markers associated with wax layer development in rye.
To report the chromosomal localization of the wax formation gene for the first time.

Main Methods:

Genotyping of over 180 F2 rye plants using DArTseq technology.
Application of Maximum Likelihood (MLH) and machine learning (ML) algorithms including logistic regression, random forest, and XGBoost for marker selection.
Genetic mapping using JoinMap 5.0 software.

Main Results:

The gene conditioning the nonglaucous phenotype was successfully mapped to chromosome 2R in rye.
Machine learning methods identified DArT-Silico 3585843 as a DNA marker closely linked to wax segregation.
The identified gene is a strong candidate for encoding the ABCG11 transporter, a key component in wax biosynthesis.

Conclusions:

The study successfully localized a major gene for epicuticular wax formation in rye to chromosome 2R.
The integration of machine learning with genetic mapping provides a powerful tool for dissecting complex plant traits.
The ABCG11 transporter is implicated in regulating wax layer formation in rye, offering insights for breeding programs.