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Runs of homozygosity (ROHs) are indicative of elevated homozygosity and inbreeding due to mating of closely related individuals. Self-fertilization can be a major source of inbreeding which elevates genome-wide homozygosity and thus should also create long ROHs. While ROHs are frequently used to understand inbreeding in the context of conservation and selective breeding, as well as for consanguinity of populations and their demographic history, it remains unclear how ROH characteristics are altered by selfing and if this confounds expected signatures of inbreeding due to demographic change. Using simulations, we study the impact of the mode of reproduction and demographic history on ROHs. We apply random forests to identify unique characteristics of ROHs, indicative of different sources of inbreeding. We pinpoint distinct features of ROHs that can be used to better characterize the type of inbreeding the population was subjected to and to predict outcrossing rates and complex demographic histories. Using additional simulations and four empirical datasets, two from highly selfing species and two from mixed-maters, we predict the selfing rate and validate our estimations. We find that self-fertilization rates are successfully identified even with complex demography. Population genetic summary statistics improve algorithm accuracy particularly in the presence of additional inbreeding, e.g. from population bottlenecks. Our findings highlight the importance of ROHs in disentangling confounding factors related to various sources of inbreeding and demonstrate situations where such sources cannot be differentiated. Additionally, our random forest models provide a novel tool to the community for inferring selfing rates using genomic data. [ABSTRACT FROM AUTHOR]

Policies ensuring that research data are available on public archives are increasingly being implemented at the government [1], funding agency [2-4], and journal [5, 6] level. These policies are predicated on the idea that authors are poor stewards of their data, particularly over the long term [7], and indeed many studies have found that authors are often unable or unwilling to share their data [8-11]. However, there are no systematic estimates of how the availability of research data changes with time since publication. We therefore requested data sets from a relatively homogenous set of 516 articles published between 2 and 22 years ago, and found that availability of the data was strongly affected by article age. For papers where the authors gave the status of their data, the odds of a data set being extant fell by 17% per year. In addition, the odds that we could find a working e-mail address for the first, last, or corresponding author fell by 7% per year. Our results reinforce the notion that, in the long term, research data cannot be reliably preserved by individual researchers, and further demonstrate the urgent need for policies mandating data sharing via public archives.

The distribution of fitness effects (DFE) for new mutations is one of the most theoretically important but difficult to estimate properties in population genetics. A crucial challenge to inferring the DFE from natural genetic variation is the sensitivity of the site frequency spectrum to factors like population size change, population substructure, genome structure, and nonrandom mating. Although inference methods aim to control for population size changes, the influence of nonrandom mating remains incompletely understood, despite being a common feature of many species. We report the DFE estimated from 326 genomes of Caenorhabditis elegans, a nematode roundworm with a high rate of self-fertilization. We evaluate the robustness of DFE inferences using simulated data that mimics the genomic structure and reproductive life history of C. elegans. Our observations demonstrate how the combined influence of self-fertilization, genome structure, and natural selection on linked sites can conspire to compromise estimates of the DFE from extant polymorphisms with existing methods. These factors together tend to bias inferences toward weakly deleterious mutations, making it challenging to have full confidence in the inferred DFE of new mutations as deduced from standing genetic variation in species like C. elegans. Improved methods for inferring the DFE are needed to appropriately handle strong linked selection and selfing. These results highlight the importance of understanding the combined effects of processes that can bias our interpretations of evolution in natural populations. [ABSTRACT FROM AUTHOR]

QST, a measure of quantitative genetic differentiation among populations, is an index that can suggest local adaptation if QST for a trait is sufficiently larger than the mean FST of neutral genetic markers. A previous method by Whitlock and Guillaume derived a simulation resampling approach to statistically test for a difference between QST and FST, but that method is limited to balanced data sets with offspring related as half-sibs through shared fathers. We extend this approach (i) to allow for a model more suitable for some plant populations or breeding designs in which offspring are related through mothers (assuming independent fathers for each offspring; half-sibs by dam); and (ii) by explicitly allowing for unbalanced data sets. The resulting approach is made available through the R package QstFstComp. [ABSTRACT FROM AUTHOR]

Species invading new ranges are subject to a series of demographic events that can strongly shape genetic diversity. Describing this demographic history is important for understanding where invasive species come from and how they spread, and is critical to testing hypotheses of postinvasion adaptation. Here, we analyse nuclear and chloroplast genetic diversity to study the invasion history of the widespread colonizing weed, Silene latifolia ( Caryophyllaceae). Bayesian clustering and PCA revealed strong population structure in the native range of Europe, and although genotypes from multiple native sources were present in the introduced range of North America, the spatial distribution of genetic variance was dramatically reorganized. Using approximate Bayesian computation ( ABC), we compared support for different invasion scenarios, including the number and size of independent introduction events and the amount of admixture occurring between sources of introduced genotypes. Our results supported independent introductions into eastern and western North America, with the latter forming a bridgehead for a secondary invasion into the Great Lakes region of central North America. Despite small estimated founder population sizes, the duration of the demographic bottleneck after the initial introduction appeared extremely short-lived. This pattern of repeated colonization and rapid expansion has effectively eroded the strong population structure and cytonuclear associations present in Europe, but has retained overall high genetic diversity since invasion. Our results highlight the flexibility of the ABC approach for constructing a narrative of the demographic history of species invasions and provide baseline for future studies of evolutionary changes in introduced S. latifolia populations. [ABSTRACT FROM AUTHOR]

The program structure has been used extensively to understand and visualize population genetic structure. It is one of the most commonly used clustering algorithms, cited over 11 500 times in Web of Science since its introduction in 2000. The method estimates ancestry proportions to assign individuals to clusters, and post hoc analyses of results may indicate the most likely number of clusters, or populations, on the landscape. However, as has been shown in this issue of Molecular Ecology Resources by Puechmaille (), when sampling is uneven across populations or across hierarchical levels of population structure, these post hoc analyses can be inaccurate and identify an incorrect number of population clusters. To solve this problem, Puechmaille () presents strategies for subsampling and new analysis methods that are robust to uneven sampling to improve inferences of the number of population clusters. [ABSTRACT FROM AUTHOR]

Under some circumstances, selection and drift can be disentangled with comparisons of the QST of a quantitative trait and the FST of putatively neutral loci. Most previous comparisons of FST and QST are carried out at a single spatial scale. We derive a hierarchical QST appropriate for study across varying levels of spatial structure. [ABSTRACT FROM AUTHOR]

QST , a measure of quantitative genetic differentiation among populations, is an index that can suggest local adaptation if QST for a trait is sufficiently larger than the mean FST of neutral genetic markers. A previous method by Whitlock and Guillaume derived a simulation resampling approach to statistically test for a difference between QST and FST , but that method is limited to balanced data sets with offspring related as half-sibs through shared fathers. We extend this approach (i) to allow for a model more suitable for some plant populations or breeding designs in which offspring are related through mothers (assuming independent fathers for each offspring; half-sibs by dam); and (ii) by explicitly allowing for unbalanced data sets. The resulting approach is made available through the R package QstFstComp., (© 2014 John Wiley & Sons Ltd.)

Under some circumstances, selection and drift can be disentangled with comparisons of the Q(ST) of a quantitative trait and the F(ST) of putatively neutral loci. Most previous comparisons of F(ST) and Q(ST) are carried out at a single spatial scale. We derive a hierarchical Q(ST) appropriate for study across varying levels of spatial structure., (© 2012 Blackwell Publishing Ltd.)