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Estimating Genetic Relatedness in Admixed Populations
1Department of Biological Sciences, California State University San Marcos, CA 92096 asethuraman@csusm.edu.
G3 (Bethesda, Md.)
|August 15, 2018
Summary
This study introduces InRelate, a new method for estimating genetic relatedness in admixed populations. It offers more accurate relatedness estimates by accounting for complex population structures and multi-allelic genomic data.
Area of Science:
- Population genetics
- Quantitative genetics
- Genomics
Background:
- Estimating genetic relatedness and inbreeding is crucial for genetics research.
- Traditional methods often rely on assumptions about population structure that may not hold true.
- Unknown population assignments can lead to inaccurate relatedness estimates.
Purpose of the Study:
- To develop a generalized method for estimating genetic relatedness in admixed populations.
- To account for multi-allelic genomic data and all nine Identity By Descent (IBD) states.
- To implement a maximum likelihood-based estimator for pairwise genetic relatedness in structured populations.
Main Methods:
- Developed a generalized method for estimating relatedness in admixed populations.
- Incorporated multi-allelic genomic data and all nine Identity By Descent (IBD) states.
- Implemented a maximum likelihood-based estimator within the InRelate software.
Main Results:
- InRelate demonstrated considerably low bias and error in estimating genetic relatedness for various dyads (full sib, half sib, etc.).
- The method showed superior performance compared to previously developed methods in both simulated and empirical data.
- A bootstrap scheme and Wald Tests were proposed for assigning relatedness categories.
Conclusions:
- The InRelate software provides a robust and accurate method for estimating genetic relatedness in admixed and structured populations.
- This advancement is significant for quantitative genetics, conservation, GWAS, and population genetics.
- The proposed statistical tests aid in categorizing relatedness between individuals.
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