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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...

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...

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.

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...

Gene Conversion

Gene Conversion

Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...

Exon Recombination

Exon Recombination

The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes.
Exon shuffling follows “splice frame rules.” Each exon...

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

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Published on: June 21, 2018

An Accidental Genetic Epidemiologist.

Robert C Elston¹

¹Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio 44106, USA;

Annual Review of Genomics and Human Genetics

|January 15, 2020

Summary

This work details the discovery of the Elston-Stewart algorithm, crucial for genetic analysis, and the development of the Statistical Analysis for Genetic Epidemiology (SAGE) software. It also clarifies statistical versus biological interactions and disease heritability models.

Keywords:

S.A.G.E.autobiography familial correlations and heritability interaction linkage and association paternity testing segregation analysis

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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Area of Science:

Genetic Epidemiology
Statistical Genetics
Bioinformatics

Background:

Personal journey into genetic epidemiology.
Historical context of genetic analysis algorithms.

Discussion:

Clarification of statistical versus biological interaction concepts.
Development and legacy of the Statistical Analysis for Genetic Epidemiology (S.A.G.E.) software.
Exploration of methods for familial correlation analysis.

Key Insights:

The Elston-Stewart algorithm advanced segregation, linkage, and association analyses.
Genotyping labs facilitated linkage discoveries and paternity testing.
The liability model for heritability estimation may be sensitive to assumed liability distributions.

Outlook:

Continued development and application of genetic epidemiology tools.
Further research into complex disease heritability and familial correlations.