DOI: 10.5061/dryad.3p5n8bj
title: Data from: Measuring viability selection from prospective cohort mortality studies: a case study in Maritime pine

description: By changing the genetic background available for selection at subsequent
 life stages, stage-specific selection can define adaptive potential across
 the life cycle. We propose and evaluate here a neutrality test and a
 Bayesian method to infer stage-specific viability selection coefficients
 using sequential random genotypic samples drawn from a longitudinal cohort
 mortality study, within a generation. The approach is suitable for
 investigating selective mortality in large natural or experimental cohorts
 of any organism in which individual tagging and tracking are unfeasible.
 Numerical simulation results indicate that the method can discriminate
 loci under strong viability selection and, provided samples are large,
 yield accurate estimates of the corresponding selection coefficients.
 Genotypic frequency changes are largely driven by sampling noise under
 weak selection, however, compromising inference in that case. We apply the
 proposed methods to analyze viability selection operating at early
 recruitment stages in a natural maritime pine (Pinus pinaster Ait.)
 population. We measured temporal genotypic frequency changes at 384
 candidate-gene SNP loci among seedlings sampled from the time of emergence
 in autumn until the summer of the following year, a period with high
 elimination rates. We detected five loci undergoing allele frequency
 changes larger than expected from stochastic mortality and sampling, with
 putative functions that could influence survival at early seedling stages.
 Our results illustrate how new statistical and sampling schemes can be
 used to conduct genomic scans of contemporary selection on specific life
 stages.
description: SNP genotypes of
 naturally established seedlings in Pinus pinaster at Fuencaliente (Ciudad
 Real, Spain)One column per
 individual genotype, comprising 356 diploid SNP loci. The two alleles at
 each locus are listed together in one column. Missing data coded with
 hyphens. Seedling samples collected in the field where found over the
 entire population at three sequential times: "R1" (November
 2010), "R2" (March 2011) and "R3" (July 2011). DNA
 extracted with Invisorb DNA Plant HTS 96 Kit. Genotyped with an oligo pool
 assay including a selection of 384 SNPs using the Illumina VeraCode
 platform (see Table S2 in Budde et al. 2014 New Phytologist 201:230-241
 for details about the assay and the candidate genes
 involved).Ppinaster_seedlings_Fuencaliente_SNPs.csvC++ code of the SGCS computer program to infer viability selection coefficients using sequential random genotypic samples drawn from a genetic cohort mortality studyThe SGCS program implements a quasi-exact neutrality test and a Bayesian method to infer stage-specific viability selection coefficients using sequential random genotypic samples drawn from a longitudinal cohort mortality study, within a generation. An executable file for Windows OS, along with the user manual and test data sets will be available at https://sites.google.com/site/jjrobledo2/softwareSGCS_0.7.7.cppC++ code of the sim4SGCS program used to simulated temporal genotypic samples under viability selectionProgram used for the simulation analysis of the performance of the SGCS inference method, as described in "Measuring viability selection from prospective cohort mortality studies: a case study in Maritime pine" by Robledo-Arnuncio JJ & Unger GM.sim4SGCS.cpp