DOI: 10.5061/dryad.8654d
title: Data from: Using genomic tools to maintain diversity and fitness in conservation programmes

description: Conservation programmes aim at maximising the survival probability of
 populations, by minimising the loss of genetic diversity, which allows
 populations to adapt to changes, and controlling inbreeding increases. The
 best known strategy to achieve these goals is optimising the contributions
 of the parents, to minimise global coancestry in their offspring. Results
 on neutral scenarios showed that management based on molecular coancestry
 could maintain more diversity than management based on genealogical
 coancestry when a large number of markers is available. However, if the
 population has deleterious mutations, managing using optimal contributions
 can lead to a decrease in fitness, especially using molecular coancestry,
 because both beneficial and harmful alleles are maintained, compromising
 the long-term viability of the population. We introduce here two
 strategies to avoid this problem: The first one uses molecular coancestry
 calculated removing markers with low minor allele frequencies, as they
 could be linked to selected loci. The second one uses a coancestry based
 on segments of identity by descent, which measures the proportion of
 genome segments shared by two individuals because of a common ancestor. We
 compare these strategies under two contrasting mutational models of
 fitness effects, one assuming many mutations of small effect and another
 with few mutations of large effect. Using markers at intermediate
 frequencies maintains a larger fitness than using all markers, but leads
 to maintaining less diversity. Using the segment-based coancestry provides
 a compromise solution between maintaining diversity and fitness,
 especially when the population has some inbreeding load.
description: Codes and data for
 "Using genomic tools to maintain diversity and fitness in
 conservation programmes"Files included in file
 DryaData.tar.gz : BigPopMSD.f90, genotiposCGD.dat, OCgen_ranmat.f90,
 OCroh_ranmat.f90, DistFreqsSeg_overReps.f90, genotiposMukai.dat,
 OCmol_ranmat.f90. Genotype data: genotiposCGD.dat and genotiposMukai.dat
 are obtained after 10000 generations of mutation-selection-drift with
 BigPopMSD.f90 for the CGD and the Mukai scenarios, respectively.
 Population size is kept constant at 1000 diploid individuals (500 females
 and 500 males), and the genome is assumed to have 20 chromosomes of 1
 Morgan each. Every chromosome includes 2000 neutral loci, 1000 selected
 loci and 1000 marker loci, all of them biallelic. For the CGD scenario,
 the parameters used are lambda is 0.03, beta is 2.3, mean s is 0.264, and
 mean h is 0.2. For the Mukai scenario, the parameters used are lambda
 equals 0.5, beta 1, mean s is 0.05, and mean h equals 0.3. BigPopMSD.f90
 generates the base population. OCgen_ranmat.f90 performs 10 generations of
 optimal contributions for population management with random matings
 between the individuals who contribute. Contributions are calculated to
 optimise genealogical coancestry, which is calculated assuming the founder
 individuals are unrelated. The number of replicates is 100, but can be
 varied, as well as the population size during management. OCmol_ranmat.f90
 performs 10 generations of optimal contributions for population management
 with random matings between the individuals who contribute. Contributions
 are calculated to optimise molecular coancestry, calculated as identity by
 state (see Toro et al 2002, Conservation Genetics). The number of
 replicates is 100, but can be varied, as well as the population size
 during management. OCroh_ranmat.f90 performs 10 generations of optimal
 contributions for population management with random matings between the
 individuals who contribute. Contributions are calculated to optimise
 IBD-based coancestry, calculated as the proportion of the genome that is
 identical by descent between individuals. It is calculated by looking only
 at marker loci, and it requires a minimum length of a segment for it to be
 considered identical by descent. The number of replicates is 100, but can
 be varied, as well as the population size during management.
 DistFreqsSeg_overReps.f90 analyses replicates from the base population to
 calculate the average number of markers segregating and the average number
 of runs of homozygosity in the base population. Note that the codes
 sometimes assume reading from a file not in the same directory. OC...f90
 all read from file ../genotipos_1.dat but that can be easily changed to
 whichever genotypes you want to read. Should you have any issues, contact
 angeles.decara AT gmailDryadData.tar.gz