DOI: 10.5061/dryad.170k4
title: Data from: A universal mechanism generating clusters of differentiated loci during divergence-with-migration

description: Genome-wide patterns of genetic divergence reveal mechanisms of adaptation
 under gene flow. Empirical data show that divergence is mostly
 concentrated in narrow genomic regions. This pattern may arise because
 differentiated loci protect nearby mutations from gene flow, but recent
 theory suggests this mechanism is insufficient to explain the emergence of
 concentrated differentiation during biologically realistic timescales.
 Critically, earlier theory neglects an inevitable consequence of genetic
 drift: stochastic loss of local genomic divergence. Here, we demonstrate
 that the rate of stochastic loss of weak local differentiation increases
 with recombination distance to a strongly diverged locus and, above a
 critical recombination distance, local loss is faster than local “gain” of
 new differentiation. Under high migration and weak selection, this
 critical recombination distance is much smaller than the total
 recombination distance of the genomic region under selection.
 Consequently, divergence between populations increases by net gain of new
 differentiation within the critical recombination distance, resulting in
 tightly linked clusters of divergence. The mechanism responsible is the
 balance between stochastic loss and gain of weak local differentiation, a
 mechanism acting universally throughout the genome. Our results will help
 to explain empirical observations and lead to novel predictions regarding
 changes in genomic architectures during adaptive divergence.
description: ReadMeFirstInstructions and explanations for all files uploaded in connection to this publication.Divergence_With_MigrationSimulation of multilocus model of divergence-with-migration.EstablishmentModel_DifferentiatedLocusSimulation of two-locus establishment model. The code estimates the establishment probability at the differentiated locus.EstablishmentModel_UndifferentiatedLocusSimulation of the two-locus establishment model. The code estimates the establishment probability at an undifferentiated locus.GainModel_MoreStronglyDifferentiatedLocusSimulation of the two-locus gain model. The code estimates the establishment probability at the more strongly differentiated locus.GainModel_WeaklyDifferentiatedLocusSimulation of the two-locus gain model. The code estimates the establishment probability at the weakly differentiated locus.Data_Fig2A_3_gainData used to generate Fig. 2A (circles), and Fig. 3. For further details, please refer to ReadMeFirst file (available separately).Data_Fig2A_LargeLocus_gainData used to generate Fig. 2A (squares). For further details, please refer to ReadMeFirst file (available separately).Data_Fig2B_Fig4AB_gainData used to generate Fig. 2B (circles), and Fig. 4. For further details, please refer to ReadMeFirst file (available separately).Data_Fig2B_LargeLocus_gainData used to generate Fig. 2B (squares). For further details, please refer to ReadMeFirst file (available separately).Data_Fig2C_3_lossData used to generate Fig. 2C, and Fig. 3. For further details, please refer to ReadMeFirst file (available separately).Data_Fig2D_4_lossData used to generate Fig. 2D, and Fig. 4. For further details, please refer to ReadMeFirst file (available separately).Data_Fig1_AB_S1.txtData used to generate Fig. 1A, B, and Fig. S1. For further details and explanations, please refer to the ReadMeFirst file (available separately).Data_Fig1_CD.txtData used to generate Fig. 1C,D. For further details and explanations, please refer to ReadMeFirst file (available separately).LossModelSimulation of the two-locus loss model. For further details and explanations, please refer to ReadMeFirst file (available separately).