Demographic inference

Nina Marchi; Flávia Schlichta; Laurent Excoffier

doi:10.1016/j.cub.2021.01.053

Demographic inference

Curr Biol. 2021 Mar 22;31(6):R276-R279. doi: 10.1016/j.cub.2021.01.053.

Authors

Nina Marchi¹, Flávia Schlichta¹, Laurent Excoffier²

Affiliations

¹ CMPG, Institute of Ecology and Evolution, University of Berne, Berne, Switzerland; Swiss Institute of Ecology and Evolution, 1015 Lausanne, Switzerland.
² CMPG, Institute of Ecology and Evolution, University of Berne, Berne, Switzerland; Swiss Institute of Ecology and Evolution, 1015 Lausanne, Switzerland. Electronic address: laurent.excoffier@iee.unibe.ch.

PMID: 33756135
DOI: 10.1016/j.cub.2021.01.053

Abstract

In the last ten years, the next generation sequencing revolution has multiplied the amount of genetic data for many organisms by orders of magnitude. This has not only led to evolutionary biologists having more data available but also to new and different types of data: from a handful of allozyme markers in the 70s, we got dozens of restriction fragment length polymorphisms (RFLPs) in the 80s, hundreds of microsatellites in the 90s, thousands to hundreds of thousands of single nucleotide polymorphisms (SNPs) in the 2000s, a few full genomes in the 2010s, and thousands of full genomes in the 2020s. These data have provided information not only on the genetic diversity and evolution of the organisms studied but also on genome-wide patterns of selection, linkage disequilibrium, as well as recombination and mutation processes. Below, we will describe how these new genomic data can be used to infer the past demographic history of populations.

MeSH terms

Animals
Demography*
Genome / genetics*
Genomics*
High-Throughput Nucleotide Sequencing
Humans
Linkage Disequilibrium
Models, Genetic*