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Crossing Over

Crossing Over

Crossing over is the exchange of genetic information between homologous chromosomes during prophase I of meiosis I. Genetic recombination gives rise to allelic diversity in the newly formed daughter cells. In humans, crossing over produces genetically distinct haploid egg and sperm cells that undergo fertilization to produce unique offspring. Before cell division starts, the germ cell’s chromosome(s) undergo duplication in the S phase of the cell cycle. As the cells enter prophase I, duplicated...

Crossing Over

Crossing Over

Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
The homologous pairs of sister chromosomes—one from the maternal and one from the paternal genome—then begin to align alongside each other lengthwise, matching corresponding DNA positions in a process called synapsis.
In order to...

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).Mechanisms of Genetic VariationThe original sources of genetic variation are mutations,...

Gene Conversion

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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...

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Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

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Genome Reshuffling for Advanced Intercross Permutation (GRAIP): simulation and permutation for advanced intercross

Jeremy L Peirce¹, Karl W Broman, Lu Lu

¹Center for Neuroscience, Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America. jpeirce@utmem.edu

|April 24, 2008

Summary

Advanced intercross lines (AIL) require specialized analysis due to family structure. Genome Reshuffling for Advanced Intercross Permutation (GRAIP) correctly analyzes AIL data, improving quantitative trait loci (QTL) mapping accuracy and identifying new genetic loci.

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Area of Science:

Genetics
Bioinformatics
Animal Models

Background:

Advanced intercross lines (AIL) are crucial for fine mapping quantitative trait loci (QTL) in complex genetic studies.
Traditional QTL mapping methods fail to account for AIL family structure, leading to inaccurate significance thresholds.
Individual animals in AIL populations are not exchangeable units, complicating standard analysis.

Purpose of the Study:

To address the limitations of traditional QTL mapping in AIL populations.
To introduce a novel method, Genome Reshuffling for Advanced Intercross Permutation (GRAIP), for accurate AIL data analysis.
To optimize the design and utility of AIL populations for genetic research.

Main Methods:

Developed GRAIP, a method that accounts for AIL family structure by permuting parental genomes.
Simulated AIL population regeneration based on exchanged parental identities.
Applied GRAIP to a large, densely genotyped mouse AIL population.

Main Results:

GRAIP establishes appropriate genome-wide significance thresholds and locus-specific P-values for AILs.
Naïve permutation analysis of a mouse AIL population resulted in high false-positive rates for coat color QTL.
GRAIP analysis corrected significance levels and identified both a known hippocampus weight locus and a novel locus (Hipp9a).

Conclusions:

GRAIP provides accurate statistical thresholds for AIL and similar family-structured populations.
Understanding family structure is critical for optimal AIL design and effective QTL fine mapping.
GRAIP enhances the utility of AIL populations for genetic discovery.