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Published on: August 12, 2019
Variation in Genetic Relatedness Is Determined by the Aggregate Recombination Process
Carl Veller1, Nathaniel B Edelman2, Pavitra Muralidhar2
1Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts 02138 carl.veller@gmail.com.
Genetic relatedness variance depends on the genome proportion shared, not just genetic map length. Factors like chromosome number and recombination influence this variance, impacting genetic studies.
Area of Science:
- Genetics
- Population Genetics
- Bioinformatics
Background:
- Genetic relatedness, the proportion of the genome shared identically by descent, can vary between relatives due to recombination and segregation.
- Previous variance calculations relied on genetic map length and overlooked crossover interference and sex-specific recombination.
Purpose of the Study:
- To calculate the variance of genetic relatedness based on physical genome proportion (bp) for general pedigrees.
- To explore how recombination processes influence genetic relatedness variance.
Main Methods:
- Developed a model to calculate genetic relatedness variance using physical genome size.
- Analyzed the impact of recombination metrics analogous to the proportion of recombining loci on variance.
- Investigated the role of chromosome number, size heterogeneity, crossover distribution, and sex differences in recombination.
Main Results:
- For grandparent-grandoffspring and sibling relationships, variance is a function of the average proportion of recombining loci.
- For general pedigrees, variance depends on analogous metrics reflecting recombination patterns.
- Crossover interference was shown to reduce genetic relatedness variance by increasing the proportion of recombining loci.
Conclusions:
- The physical proportion of the genome shared identically by descent is a more accurate measure for genetic relatedness variance.
- Understanding recombination processes is crucial for accurately estimating genetic relatedness variance.
- The developed methods enable precise statistical inference for selection in F2 hybrid crosses.
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