Phenotypic and genotypic data, as well as pedigree-based relatedness, of experimental plants exposed to naturally occurring pollinators

Data obtained in the greenhouse, experimental garden and laboratory by direct measurement. Relatedness inferred from pedigree data. Format: Excel file. Variable definition is provided in the accompanying Read Me file

Supplementary Informations from Genetic and linguistic histories in Central Asia inferred using approximate Bayesian computations

Linguistic and genetic data have been widely compared, but the histories underlying these descriptions are rarely jointly inferred. We developed a unique methodological framework for analysing jointly language diversity and genetic polymorphism data, to infer the past history of separation, exchange and admixture events among human populations. This method relies on approximate Bayesian computations that allow to identify the most probable historical scenario underlying each type of data, and to infer the parameters of these scenarios. For this purpose, we developed a new computer program <i>PopLingSim</i> that simulates the evolution of linguistic diversity, which we coupled with an existing coalescent-based genetic simulation program, to simulate both linguistic and genetic data within a set of populations. Applying this new program to a wide linguistic and genetic dataset of Central Asia, we found several differences between linguistic and genetic histories. In particular, we showed how genetic and linguistic exchanges differed in the past in this area: some cultural exchanges were maintained without genetic exchanges. The methodological framework and the linguistic simulation tool here developed can be successfully used in future work for disentangling complex linguistic and genetic evolutions underlying human biological and cultural histories

Accuracy of ABC estimation and relative importance of the summary statistics.

<p>Prediction error for the estimated population size in each time window (left) and standard deviation of this error (right), evaluated from 2,000 random population size histories. Summary statistics considered in the ABC analysis included different combinations of (i) the AFS (possibly without the overall proportion of SNPs) and (ii) the average zygotic LD for several distance bins. These statistics were computed from <i>n</i> = 25 diploid individuals, using all SNPs for AFS statistics and only those with a MAF above 20% for LD statistics. The posterior distribution of each parameter was obtained by neural network regression [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005877#pgen.1005877.ref032" target="_blank">32</a>], with a tolerance rate of 0.005. Population size point estimates correspond to the median of the posterior distribution. The prediction errors were scaled in order that point estimates obtained from the prior distribution would result in a prediction error of 1.</p

Estimation of population size history in four cattle breeds using ABC.

<p>Angus (<i>n</i> = 25 animals), Fleckvieh (<i>n</i> = 25), Holstein (<i>n</i> = 25) and Jersey (<i>n</i> = 15). Estimations were obtained independently in each breed, based on whole genome NGS data from sampled animals. Summary statistics considered in the ABC analysis were (i) the AFS and (ii) the average zygotic LD for several distance bins. These statistics were computed using SNPs with a MAF above 20%. Other parameter settings are the same as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005877#pgen.1005877.g005" target="_blank">Fig 5</a>.</p

Estimation of population size history using MSMC with two haplotypes in five different simulated scenarios.

<p>For each scenario, the five PODs considered for MSMC estimation were the same as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005877#pgen.1005877.g003" target="_blank">Fig 3</a>. The expected TMRCA shown here is also the same as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005877#pgen.1005877.g003" target="_blank">Fig 3</a>, it corresponds to samples of 50 haploid sequences.</p

Influence of phasing and sequencing errors on ABC estimation.

<p>Estimation of population size history in the Holstein cattle breed using ABC, based on whole genome NGS data from <i>n</i> = 25 animals. Summary statistics considered in the ABC analysis were (i) the AFS and (ii) the average LD for several distance bins. LD statistics were computed either from haplotypes or from genotypes, using SNPs with a MAF above 20%. AFS statistics were computed using either all SNPs or SNPs with a MAF above 10 or 20%. The posterior distribution of each parameter was obtained by neural network regression [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005877#pgen.1005877.ref032" target="_blank">32</a>], with a tolerance rate of 0.005. Population size point estimates were obtained from the median of the posterior distribution. Generation time was assumed to be five years.</p