Nonequilibrium demography impacts coalescent genealogies leaving detectable, well-studied signatures of variation. However, similar genomic footprints are also expected under models of large reproductive skew, posing a serious problem when trying to make inference. Furthermore, current approaches consider only one of the two processes at a time, neglecting any genomic signal that could arise from their simultaneous effects, preventing the possibility of jointly inferring parameters relating to both offspring distribution and population history. Here, we develop an extended Moran model with exponential population growth, and demonstrate that the underlying ancestral process converges to a time-inhomogeneous psi-coalescent. However, by applying a nonlinear change of time scale-analogous to the Kingman coalescent-we find that the ancestral process can be rescaled to its time-homogeneous analog, allowing the process to be simulated quickly and efficiently. Furthermore, we derive analytical expressions for the expected site-frequency spectrum under the time-inhomogeneous psi-coalescent, and develop an approximate-likelihood framework for the joint estimation of the coalescent and growth parameters. By means of extensive simulation, we demonstrate that both can be estimated accurately from whole-genome data. In addition, not accounting for demography can lead to serious biases in the inferred coalescent model, with broad implications for genomic studies ranging from ecology to conservation biology. Finally, we use our method to analyze sequence data from Japanese sardine populations, and find evidence of high variation in individual reproductive success, but few signs of a recent demographic expansion.

The characterization of the distribution of mutational effects is a key goal in evolutionary biology. Recently developed deepsequencing approaches allow for accurate and simultaneous estimation of the fitness effects of hundreds of engineered mutations by monitoring their relative abundance across time points in a single bulk competition. Naturally, the achievable resolution of the estimated fitness effects depends on the specific experimental setup, the organism and type of mutations studied, and the sequencing technology utilized, among other factors. By means of analytical approximations and simulations, we provide guidelines for optimizing time-sampled deep-sequencing bulk competition experiments, focusing on the number of mutants, the sequencing depth, and the number of sampled time points. Our analytical results show that sampling more time points together with extending the duration of the experiment improves the achievable precision disproportionately compared with increasing the sequencing depth or reducing the number of competing mutants. Even if the duration of the experiment is fixed, sampling more time points and clustering these at the beginning and the end of the experiment increase experimental power and allow for efficient and precise assessment of the entire range of selection coefficients. Finally, we provide a formula for calculating the 95%-confidence interval for the measurement error estimate, which we implement as an interactive web tool. This allows for quantification of the maximum expected a priori precision of the experimental setup, as well as for a statistical threshold for determining deviations from neutrality for specific selection coefficient estimates.KEYWORDS experimental design; experimental evolution; distribution of fitness effects; mutation; population genetics M UTATIONS provide the fuel for evolutionary change, and their fitness effects critically influence the course and dynamics of evolution. The distribution of fitness effects (DFE) lies at the heart of many evolutionary concepts, such as the genetic basis of complex traits (Eyre-Walker 2010) and diseases (Keightley and Eyre-Walker 2010), the rate of adaptation to a new environment (Gerrish and Lenski 1998;Orr 1998Orr , 2005b, the maintenance of genetic variation (Charlesworth et al. 1995), and the relative importance of selection and drift in molecular evolution (Ohta 1977(Ohta , 1992Kimura 1979). Unsurprisingly, considerable effort has been devoted, both empirically (e.g., Sawyer et al. 2003;Sousa et al. 2012;Gordo and Campos 2013;Bernet and Elena 2015) and theoretically (e.g., Gillespie 1983;Orr 2005a;Martin and Lenormand 2006b;Connallon and Clark 2015;Rice et al. 2015), to assess the fraction of all possible mutations that are beneficial, neutral, or deleterious. Until recently, the two main approaches for assessing the DFE have been based either on the analysis of polymorphism and divergence data (Jensen et al. 2008;Keightley and Eyre-Walker 2010;Schneider et al. 2011) or on laboratory evolution studies ...

In clinical ion beam therapy, protons as well as heavier ions such as carbon are used for treatment. For protons, β(+)-emitters are only induced by fragmentation reactions in the target (target fragmentation), whereas for heavy ions, they are additionally induced by fragmentations of the projectile (further referred to as autoactivation). An approach utilizing these processes for treatment verification, by comparing measured Positron Emission Tomography (PET) data to predictions from Monte Carlo simulations, has already been clinically implemented. For an accurate simulation, it is important to consider the biological washout of β(+)-emitters due to vital functions. To date, mathematical expressions for washout have mainly been determined by using radioactive beams of (10)C- and (11)C-ions, both β(+)-emitters, to enhance the counting statistics in the irradiated area. Still, the question of how the choice of projectile (autoactivating or non-autoactivating) influences the washout coefficients, has not been addressed. In this context, an experiment was carried out at the Heidelberg Ion Beam Therapy Center with the purpose of directly comparing irradiation-induced biological washout coefficients in mice for protons and (12)C-ions. To this aim, mice were irradiated in the brain region with protons and (12)C-ions and measured after irradiation with a PET/CT scanner (Siemens Biograph mCT). After an appropriate waiting time, the mice were sacrificed, then irradiated and measured again under similar conditions. The resulting data were processed and fitted numerically to deduce the main washout parameters. Despite the very low PET counting statistics, a consistent difference could be identified between (12)C-ion and proton irradiated mice, with the (12)C data being described best by a two component fit with a combined medium and slow washout fraction of 0.50 ± 0.05 and the proton mice data being described best by a one component fit with only one (slow) washout fraction of 0.73 ± 0.06.