DOI: 10.5061/dryad.zs7h44jdv
title: Detecting frequency-dependent selection through the effects of genotype similarity on fitness components

description: Frequency-dependent selection (FDS) is an evolutionary regime that can
 maintain or reduce polymorphisms. Despite the increasing availability of
 polymorphism data, few effective methods are available for estimating the
 gradient of FDS from the observed fitness components. We modeled the
 effects of genotype similarity on individual fitness to develop a
 selection gradient analysis of FDS. This modeling enabled us to estimate
 FDS by regressing fitness components on the genotype similarity among
 individuals. We detected known negative FDS on the visible polymorphism in
 a wild Arabidopsis and damselfly by applying this analysis to single-locus
 data. Further, we simulated genome-wide polymorphisms and fitness
 components to modify the single-locus analysis as a genome-wide
 association study (GWAS). The simulation showed that negative or positive
 FDS could be distinguished through the estimated effects of genotype
 similarity on simulated fitness. Moreover, we conducted the GWAS of the
 reproductive branch number in Arabidopsis thaliana and found that negative
 FDS was enriched among the top-associated polymorphisms of FDS. These
 results showed the potential applicability of the proposed method for FDS
 on both visible polymorphism and genome-wide polymorphisms. Overall, our
 study provides an effective method for selection gradient analysis to
 understand the maintenance or loss of polymorphism.
description: # Regression model of frequency-dependent selection Source codes and data
 of Sato et al. "Detecting frequency-dependent selection through the
 effects of genotype similarity on fitness components". A
 vignette-style instruction is available in Rmarkdown and its output HTML
 as `index.Rmd` and `index.html`, respectively (see also
 for viewing). A developer
 version is available at .
 ## application This directory includes source codes and/or input data for
 empirical analyses on *Arabidopsis halleri*, *Ischnura elegans*, and
 *Arabidopsis thaliana*, proceeding as follows. ### GLMM analysis of
 *Arabidopsis halleri* * Ahal_FieldSurvey.R\ Poisson GLMM of the flower
 number in wild *Arabidopsis halleri* in a natural population. Data are
 available via Dryad repository
 (). ### GLMM analysis of
 *Ischnura elegans* * damselfly_cage_data.R\ Poisson GLMM of the egg number
 in *Ischnura elegans* in semi-field cages. The original data are generated
 by Takahashi et al. (2014) Nature Communications 5:4468. ### Branch number
 GWAS in *Arabidopsis thaliana* * pheno_branchNum.csv\ Accession list and
 phenotype data as an input file for "BranchNo2019GWAS.R",
 including the following columns. * IndivID: Plant ID * Name: Name of the
 accession * Source: CS number of ABRC seed stock * gwasID: GWAS genotype
 ID assigned to the accession * Block: Experimental block ID * position_X:
 cell-tray position along x-axis within a block * position_Y: cell-tray
 position along y-axis within a block * edge: Edge of the block (1) or not
 (0). * InitLeafLen: The length of largest leaf at the start of field
 experiment * Bolting: Presence (1) or absence (0) of inflorescence 2 wk
 after the start of experiment * BranchNo: No. of reproductive branches.
 Dead plants are considered zero numbers. * subsetSNP.py\ Python script to
 extract genotype data from .hdf available at AraGWAS Catalog
 (). * reshapeSNP.R\ R script to
 prepare genotype data for for "BranchNo2019GWAS.R" after
 subsetSNP.py. * BranchNo2019GWAS.R\ Field GWAS of the branch number using
 199 accessions of *A. thaliana*. * BranchNo2019GWASfigure.R\ Manhattan and
 QQ plots for the output from "BranchNo2019GWAS.R". ## simulation
 This directory includes source codes for simulations, numerical examples,
 and their figure presentation, proceeding as follows. ### Ising model
 simulation * IsingGenoWeighted.R\ R script for the forward Ising
 simulation when transition probabilities follow Mendelian inheritance. ###
 Fitness function * AsymFDSfitnessFunctions.R\ Numerical analysis and
 figure presentation for fitness functions in response to allele frequency.
 ### GWAS simulation * SLiM codes (xxxx.slim) for forward genetic
 simulations of negative frequency-dependent selection (NFDS); positive
 frequency-dependent selection (PFDS); overdominance (OD); and
 spatiotemporally varying selection (STVS). * outputSLiM_and_GWAS.R GWAS
 simulation and power analysis using the SLiM output. * outputFigures.R\
 Figure presentations for the GWAS simulation. * /output\ A folder to save
 SLiM and R outputs, including the following input data for the vignette
 (index.Rmd) * toy1.rds: Single-locus example data with split
 subpopulations * toy2.rds: Single-locus example data with continuous space
 * toy3.rds: GWAS example with with split subpopulations * toy4.rds: GWAS
 example with with continuous space ### Vignette * toydata.R\ R script to
 create input toy data for index.Rmd in the top directory