Genetic variation, which is generated by mutation, recombination and gene flow, can reduce the mean fitness of a population, both now and in the future. This ‘genetic load’ has been estimated in a wide range of animal taxa using various approaches. Advances in genome sequencing and computational techniques now enable us to estimate the genetic load in populations and individuals without direct fitness estimates. Here, we review the classic and contemporary literature of genetic load. We describe approaches to quantify the genetic load in whole-genome sequence data based on evolutionary conservation and annotations. We show that splitting the load into its two components — the realized load (or expressed load) and the masked load (or inbreeding load) — can improve our understanding of the population genetics of deleterious mutations.

Genetic load: genomic estimates and applications in non-model animals / Bertorelle, G.; Raffini, F.; Bosse, M.; Bortoluzzi, C.; Iannucci, A.; Trucchi, E.; Morales, H. E.; van Oosterhout, C.. - In: NATURE REVIEWS GENETICS. - ISSN 1471-0056. - ELETTRONICO. - 23:8(2022), pp. 492-503. [10.1038/s41576-022-00448-x]

Genetic load: genomic estimates and applications in non-model animals

Trucchi E.
Writing – Original Draft Preparation
;
2022-01-01

Abstract

Genetic variation, which is generated by mutation, recombination and gene flow, can reduce the mean fitness of a population, both now and in the future. This ‘genetic load’ has been estimated in a wide range of animal taxa using various approaches. Advances in genome sequencing and computational techniques now enable us to estimate the genetic load in populations and individuals without direct fitness estimates. Here, we review the classic and contemporary literature of genetic load. We describe approaches to quantify the genetic load in whole-genome sequence data based on evolutionary conservation and annotations. We show that splitting the load into its two components — the realized load (or expressed load) and the masked load (or inbreeding load) — can improve our understanding of the population genetics of deleterious mutations.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/297184
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