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Dihybrid Crosses

Dihybrid Crosses

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...

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...

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.

Law of Independent Assortment

Law of Independent Assortment

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.

Multiple Allele Traits

Multiple Allele Traits

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EXPLoRA-web: linkage analysis of quantitative trait loci using bulk segregant analysis.

Sergio Pulido-Tamayo¹, Jorge Duitama², Kathleen Marchal³

¹Department of Information Technology, iGent Toren, Technologiepark 15, 9052 Gent, Belgium Department of Plant Biotechnology and Bioinformatics, UGent, Technologiepark 927, 9052 Gent, Belgium Bioinformatics Institute Ghent, Technologiepark 927, 9052 Gent, Belgium Department of Microbial and Molecular Systems, KU Leuven, Kasteelpark Arenberg 20, B-3001 Leuven, Belgium.

Nucleic Acids Research

|April 24, 2016

Summary

EXPLoRA-web simplifies genomic region identification for traits using bulk segregant analysis (BSA). This web service enhances quantitative trait loci (QTL) mapping accuracy and accessibility for researchers.

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Area of Science:

Genomics and Bioinformatics
Plant and Animal Breeding
Quantitative Genetics

Background:

Identifying genomic regions linked to specific traits is crucial for biological research and industrial applications.
Bulk segregant analysis (BSA) coupled with high-throughput sequencing is a powerful method for trait-associated genomic region discovery.
Existing statistical models for BSA analysis are often complex and difficult for non-expert users to implement.

Purpose of the Study:

To develop a user-friendly web service, EXPLoRA-web, for analyzing BSA data.
To improve the accuracy and power of quantitative trait loci (QTL) identification in BSA.
To facilitate easier exploration and analysis of BSA-generated data for a broader range of researchers.

Main Methods:

Developed EXPLoRA-web as a web service utilizing the previously established EXPLoRA algorithm.
The algorithm leverages linkage disequilibrium to enhance QTL identification.
The web service offers a user-friendly interface for data upload, parallel processing, and graphical/file output (BED, text).

Main Results:

EXPLoRA-web provides a simplified platform for BSA data analysis.
It enables efficient exploration of data and parallel testing of various parameter settings.
Results are presented in accessible formats, including graphical visualizations and standard file types.

Conclusions:

EXPLoRA-web significantly lowers the barrier to entry for complex BSA data analysis.
The tool enhances the accuracy and efficiency of identifying genomic regions associated with traits.
It empowers researchers, including non-experts, to conduct advanced genetic analyses.