TY - JOUR AB - Pedigree and sibship reconstruction are important methods in quantifying relationships and fitness of individuals in natural populations. Current methods employ a Markov chain-based algorithm to explore plausible possible pedigrees iteratively. This provides accurate results, but is time-consuming. Here, we develop a method to infer sibship and paternity relationships from half-sibling arrays of known maternity using hierarchical clustering. Given 50 or more unlinked SNP markers and empirically derived error rates, the method performs as well as the widely used package Colony, but is faster by two orders of magnitude. Using simulations, we show that the method performs well across contrasting mating scenarios, even when samples are large. We then apply the method to open-pollinated arrays of the snapdragon Antirrhinum majus and find evidence for a high degree of multiple mating. Although we focus on diploid SNP data, the method does not depend on marker type and as such has broad applications in nonmodel systems. AU - Ellis, Thomas AU - Field, David AU - Barton, Nicholas H ID - 286 IS - 5 JF - Molecular Ecology Resources TI - Efficient inference of paternity and sibship inference given known maternity via hierarchical clustering VL - 18 ER - TY - DATA AB - Data and scripts are provided in support of the manuscript "Efficient inference of paternity and sibship inference given known maternity via hierarchical clustering", and the associated Python package FAPS, available from www.github.com/ellisztamas/faps. Simulation scripts cover: 1. Performance under different mating scenarios. 2. Comparison with Colony2. 3. Effect of changing the number of Monte Carlo draws The final script covers the analysis of half-sib arrays from wild-pollinated seed in an Antirrhinum majus hybrid zone. AU - Ellis, Thomas ID - 5583 TI - Data and Python scripts supporting Python package FAPS ER - TY - DATA AB - File S1. Variant Calling Format file of the ingroup: 197 haploid sequences of D. melanogaster from Zambia (Africa) aligned to the D. melanogaster 5.57 reference genome. File S2. Variant Calling Format file of the outgroup: 1 haploid sequence of D. simulans aligned to the D. melanogaster 5.57 reference genome. File S3. Annotations of each transcript in coding regions with SNPeff: Ps (# of synonymous polymorphic sites); Pn (# of non-synonymous polymorphic sites); Ds (# of synonymous divergent sites); Dn (# of non-synonymous divergent sites); DoS; ⍺ MK . All variants were included. File S4. Annotations of each transcript in non-coding regions with SNPeff: Ps (# of synonymous polymorphic sites); Pu (# of UTR polymorphic sites); Ds (# of synonymous divergent sites); Du (# of UTR divergent sites); DoS; ⍺ MK . All variants were included. File S5. Annotations of each transcript in coding regions with SNPGenie: Ps (# of synonymous polymorphic sites); πs (synonymous diversity); Ss_p (total # of synonymous sites in the polymorphism data); Pn (# of non-synonymous polymorphic sites); πn (non-synonymous diversity); Sn_p (total # of non-synonymous sites in the polymorphism data); Ds (# of synonymous divergent sites); ks (synonymous evolutionary rate); Ss_d (total # of synonymous sites in the divergence data); Dn (# of non-synonymous divergent sites); kn (non-synonymous evolutionary rate); Sn_d (total # of non- synonymous sites in the divergence data); DoS; ⍺ MK . All variants were included. File S6. Gene expression values (RPKM summed over all transcripts) for each sample. Values were quantile-normalized across all samples. File S7. Final dataset with all covariates, ⍺ MK , ωA MK and DoS for coding sites, excluding variants below 5% frequency. File S8. Final dataset with all covariates, ⍺ MK , ωA MK and DoS for non-coding sites, excluding variants below 5% frequency. File S9. Final dataset with all covariates, ⍺ EWK , ωA EWK and deleterious SFS for coding sites obtained with the Eyre-Walker and Keightley method on binned data and using all variants. AU - Fraisse, Christelle ID - 5757 KW - (mal)adaptation KW - pleiotropy KW - selective constraint KW - evo-devo KW - gene expression KW - Drosophila melanogaster TI - Supplementary Files for "Pleiotropy modulates the efficacy of selection in Drosophila melanogaster" ER - TY - CONF AB - There has been renewed interest in modelling the behaviour of evolutionary algorithms by more traditional mathematical objects, such as ordinary differential equations or Markov chains. The advantage is that the analysis becomes greatly facilitated due to the existence of well established methods. However, this typically comes at the cost of disregarding information about the process. Here, we introduce the use of stochastic differential equations (SDEs) for the study of EAs. SDEs can produce simple analytical results for the dynamics of stochastic processes, unlike Markov chains which can produce rigorous but unwieldy expressions about the dynamics. On the other hand, unlike ordinary differential equations (ODEs), they do not discard information about the stochasticity of the process. We show that these are especially suitable for the analysis of fixed budget scenarios and present analogs of the additive and multiplicative drift theorems for SDEs. We exemplify the use of these methods for two model algorithms ((1+1) EA and RLS) on two canonical problems(OneMax and LeadingOnes). AU - Paixao, Tiago AU - Pérez Heredia, Jorge ID - 1112 SN - 978-145034651-1 T2 - Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms TI - An application of stochastic differential equations to evolutionary algorithms ER - TY - JOUR AB - Variation in genotypes may be responsible for differences in dispersal rates, directional biases, and growth rates of individuals. These traits may favor certain genotypes and enhance their spatiotemporal spreading into areas occupied by the less advantageous genotypes. We study how these factors influence the speed of spreading in the case of two competing genotypes under the assumption that spatial variation of the total population is small compared to the spatial variation of the frequencies of the genotypes in the population. In that case, the dynamics of the frequency of one of the genotypes is approximately described by a generalized Fisher–Kolmogorov–Petrovskii–Piskunov (F–KPP) equation. This generalized F–KPP equation with (nonlinear) frequency-dependent diffusion and advection terms admits traveling wave solutions that characterize the invasion of the dominant genotype. Our existence results generalize the classical theory for traveling waves for the F–KPP with constant coefficients. Moreover, in the particular case of the quadratic (monostable) nonlinear growth–decay rate in the generalized F–KPP we study in detail the influence of the variance in diffusion and mean displacement rates of the two genotypes on the minimal wave propagation speed. AU - Kollár, Richard AU - Novak, Sebastian ID - 1191 IS - 3 JF - Bulletin of Mathematical Biology TI - Existence of traveling waves for the generalized F–KPP equation VL - 79 ER - TY - JOUR AB - Most phenotypes are determined by molecular systems composed of specifically interacting molecules. However, unlike for individual components, little is known about the distributions of mutational effects of molecular systems as a whole. We ask how the distribution of mutational effects of a transcriptional regulatory system differs from the distributions of its components, by first independently, and then simultaneously, mutating a transcription factor and the associated promoter it represses. We find that the system distribution exhibits increased phenotypic variation compared to individual component distributions - an effect arising from intermolecular epistasis between the transcription factor and its DNA-binding site. In large part, this epistasis can be qualitatively attributed to the structure of the transcriptional regulatory system and could therefore be a common feature in prokaryotes. Counter-intuitively, intermolecular epistasis can alleviate the constraints of individual components, thereby increasing phenotypic variation that selection could act on and facilitating adaptive evolution. AU - Lagator, Mato AU - Sarikas, Srdjan AU - Acar, Hande AU - Bollback, Jonathan P AU - Guet, Calin C ID - 570 JF - eLife SN - 2050084X TI - Regulatory network structure determines patterns of intermolecular epistasis VL - 6 ER - TY - JOUR AB - Small RNAs (sRNAs) regulate genes in plants and animals. Here, we show that population-wide differences in color patterns in snapdragon flowers are caused by an inverted duplication that generates sRNAs. The complexity and size of the transcripts indicate that the duplication represents an intermediate on the pathway to microRNA evolution. The sRNAs repress a pigment biosynthesis gene, creating a yellow highlight at the site of pollinator entry. The inverted duplication exhibits steep clines in allele frequency in a natural hybrid zone, showing that the allele is under selection. Thus, regulatory interactions of evolutionarily recent sRNAs can be acted upon by selection and contribute to the evolution of phenotypic diversity. AU - Bradley, Desmond AU - Xu, Ping AU - Mohorianu, Irina AU - Whibley, Annabel AU - Field, David AU - Tavares, Hugo AU - Couchman, Matthew AU - Copsey, Lucy AU - Carpenter, Rosemary AU - Li, Miaomiao AU - Li, Qun AU - Xue, Yongbiao AU - Dalmay, Tamas AU - Coen, Enrico ID - 611 IS - 6365 JF - Science SN - 00368075 TI - Evolution of flower color pattern through selection on regulatory small RNAs VL - 358 ER - TY - JOUR AB - Our focus here is on the infinitesimal model. In this model, one or several quantitative traits are described as the sum of a genetic and a non-genetic component, the first being distributed within families as a normal random variable centred at the average of the parental genetic components, and with a variance independent of the parental traits. Thus, the variance that segregates within families is not perturbed by selection, and can be predicted from the variance components. This does not necessarily imply that the trait distribution across the whole population should be Gaussian, and indeed selection or population structure may have a substantial effect on the overall trait distribution. One of our main aims is to identify some general conditions on the allelic effects for the infinitesimal model to be accurate. We first review the long history of the infinitesimal model in quantitative genetics. Then we formulate the model at the phenotypic level in terms of individual trait values and relationships between individuals, but including different evolutionary processes: genetic drift, recombination, selection, mutation, population structure, …. We give a range of examples of its application to evolutionary questions related to stabilising selection, assortative mating, effective population size and response to selection, habitat preference and speciation. We provide a mathematical justification of the model as the limit as the number M of underlying loci tends to infinity of a model with Mendelian inheritance, mutation and environmental noise, when the genetic component of the trait is purely additive. We also show how the model generalises to include epistatic effects. We prove in particular that, within each family, the genetic components of the individual trait values in the current generation are indeed normally distributed with a variance independent of ancestral traits, up to an error of order 1∕M. Simulations suggest that in some cases the convergence may be as fast as 1∕M. AU - Barton, Nicholas H AU - Etheridge, Alison AU - Véber, Amandine ID - 626 JF - Theoretical Population Biology SN - 00405809 TI - The infinitesimal model: Definition derivation and implications VL - 118 ER - TY - GEN AB - This text provides additional information about the model, a derivation of the analytic results in Eq (4), and details about simulations of an additional parameter set. AU - Lukacisinova, Marta AU - Novak, Sebastian AU - Paixao, Tiago ID - 9849 TI - Modelling and simulation details ER - TY - GEN AB - In this text, we discuss how a cost of resistance and the possibility of lethal mutations impact our model. AU - Lukacisinova, Marta AU - Novak, Sebastian AU - Paixao, Tiago ID - 9850 TI - Extensions of the model ER -