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Abstract The genetic dissection of reproductive barriers between diverging lineages provides enticing clues into the origin of species. One strategy uses linkage analysis in experimental crosses to identify genomic locations involved in phenotypes that mediate reproductive isolation. A second framework searches for genomic regions that show reduced rates of exchange across natural hybrid zones. It is often assumed that these approaches will point to the same loci, but this assumption is rarely tested. In this perspective, we discuss the factors that determine whether loci connected to postzygotic reproductive barriers in the laboratory are inferred to reduce gene flow in nature. We synthesize data on the genetics of postzygotic isolation in house mice, one of the most intensively studied systems in speciation genetics. In a rare empirical comparison, we measure the correspondence of loci tied to postzygotic barriers via genetic mapping in the laboratory and loci at which gene flow is inhibited across a natural hybrid zone. We find no evidence that the two sets of loci overlap beyond what is expected by chance. In light of these results, we recommend avenues for empirical and theoretical research to resolve the potential incongruence between the two predominant strategies for understanding the genetics of speciation. 

The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties. 

ThUe:fiPelledaosefcpoonpfiurlmattihoantagllehneoamdinicgslehvaeslsagrreorwepnrreaspenidtelydcinorrreescptloyn: se to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.