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Genetic load: genomic estimates and applications in non-model animals
Giorgio Bertorelle1, Francesca Raffini2, Mirte Bosse3,4
1Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy. ggb@unife.it.
Nature Reviews. Genetics
|February 9, 2022
Summary
Genetic load, caused by genetic variation, can decrease population fitness. New genomic methods allow estimation of this load without direct fitness measurements, improving our understanding of deleterious mutations.
Area of Science:
- Population genetics
- Genomics
- Evolutionary biology
Background:
- Genetic variation arises from mutation, recombination, and gene flow.
- Genetic variation can negatively impact population fitness, a phenomenon known as genetic load.
- Estimating genetic load has traditionally relied on direct fitness measurements across various taxa.
Purpose of the Study:
- To review classic and contemporary literature on genetic load.
- To describe novel approaches for quantifying genetic load using whole-genome sequence data.
- To enhance understanding of deleterious mutations by differentiating between realized and masked genetic load.
Main Methods:
- Review of existing literature on genetic load.
- Application of genome sequencing and computational techniques.
- Analysis of whole-genome sequence data utilizing evolutionary conservation and annotations.
Main Results:
- Advances in genomics and computation enable genetic load estimation without direct fitness data.
- Methods for quantifying genetic load from whole-genome sequences are described.
- Distinguishing between realized (expressed) and masked (inbreeding) load offers improved insights.
Conclusions:
- Genomic approaches provide powerful tools for estimating genetic load in populations and individuals.
- The distinction between realized and masked genetic load is crucial for understanding the population genetics of deleterious mutations.
- This framework advances the study of genetic load and its impact on evolutionary trajectories.
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