Espaço e diversificação: uma perspectiva teórica

Rossine, Fernando Welker Sapojkin

doi:10.11606/d.41.2014.tde-22092014-112838

Cited by 1 publication

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“…This also prevents the population clumping seen by Felsenstein (1975) (Supplemental Material, Figure S1). Such models have been used extensively in ecological modeling (Durrett and Levin 1994;Bolker and Pacala 1997;Law et al 2003;Fournier and Méléard 2004;Champer et al 2019 preprint), but rarely in population genetics where, to our knowledge, implementations of continuous space models before their availability through SLiM have focused on a small number of genetic loci (e.g., Slatkin and Barton 1989;Barton et al 2002;Robledo-Arnuncio and Rousset 2010;Jackson and Fahrig 2014;Rossine 2014), which limits the ability to investigate the impacts of continuous space on genome-wide genetic variation as is now routinely sampled from real organisms. By simulating chromosome-scale sequence alignments and complete population histories, we are able to treat our simulations as real populations and replicate the sampling designs and analyses commonly conducted on real genomic data.…”

Section: Modeling Evolution In Continuous Spacementioning

confidence: 99%

Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data

2020

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Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result, many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here, we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies (GWAS). We find that most common summary statistics have distributions that differ substantially from those seen in well-mixed populations, especially when Wright’s neighborhood size is < 100 and sampling is spatially clustered. “Stepping-stone” models reproduce some of these effects, but discretizing the landscape introduces artifacts that in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations revealed surprisingly little systematic bias. We also show that the combination of spatially autocorrelated environments and limited dispersal causes GWAS to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms.

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