Wood plays a major role in land ecosystems and in human activity. Better understanding the genetic basis and evolutionary implication of wood variability are thus key issues with both ecological and economical implica...Wood plays a major role in land ecosystems and in human activity. Better understanding the genetic basis and evolutionary implication of wood variability are thus key issues with both ecological and economical implications. The present paper addresses the question of the extending and the nature of natural selection on wood related genes in Eucalyptus urophylla, a tropical tree species with key economical importance. We conducted a genetic study on an E. urophylla population from Timor Island using a set of 17 SSR characterized on a main sample of 43 individuals and six candidate genes sequenced on a subset of 18 individuals. The candidate genes include three cellulose synthase genes (EuCesA1, EuCesA2 and EuCesA3), and three genes involved in lignin synthesis (EuCAD2, EuC4H1 and EuC4H2). Based on SSR data, the investigated population appeared to have no structure and have undergone past population expansion. Accounting for this demographic history, we were able to draw neutral expectation for polymorphism distribution on candidate genes and to determine their potential selective status. We hence identified two gene portions exhibiting unexpected polymorphism pattern, consistent with natural selection imprint.展开更多
Approximate Bayesian Computation (ABC) is a popular sampling method in applications involving intractable likelihood functions. Instead of evaluating the likelihood function, ABC approximates the posterior distributio...Approximate Bayesian Computation (ABC) is a popular sampling method in applications involving intractable likelihood functions. Instead of evaluating the likelihood function, ABC approximates the posterior distribution by a set of accepted samples which are simulated from a generating model. Simulated samples are accepted if the distances between the samples and the observation are smaller than some threshold. The distance is calculated in terms of summary statistics. This paper proposes Local Gradient Kernel Dimension Reduction (LGKDR) to construct low dimensional summary statistics for ABC. The proposed method identifies a sufficient subspace of the original summary statistics by implicitly considering all non-linear transforms therein, and a weighting kernel is used for the concentration of the projections. No strong assumptions are made on the marginal distributions, nor the regression models, permitting usage in a wide range of applications. Experiments are done with simple rejection ABC and sequential Monte Carlo ABC methods. Results are reported as competitive in the former and substantially better in the latter cases in which Monte Carlo errors are compressed as much as possible.展开更多
文摘Wood plays a major role in land ecosystems and in human activity. Better understanding the genetic basis and evolutionary implication of wood variability are thus key issues with both ecological and economical implications. The present paper addresses the question of the extending and the nature of natural selection on wood related genes in Eucalyptus urophylla, a tropical tree species with key economical importance. We conducted a genetic study on an E. urophylla population from Timor Island using a set of 17 SSR characterized on a main sample of 43 individuals and six candidate genes sequenced on a subset of 18 individuals. The candidate genes include three cellulose synthase genes (EuCesA1, EuCesA2 and EuCesA3), and three genes involved in lignin synthesis (EuCAD2, EuC4H1 and EuC4H2). Based on SSR data, the investigated population appeared to have no structure and have undergone past population expansion. Accounting for this demographic history, we were able to draw neutral expectation for polymorphism distribution on candidate genes and to determine their potential selective status. We hence identified two gene portions exhibiting unexpected polymorphism pattern, consistent with natural selection imprint.
文摘Approximate Bayesian Computation (ABC) is a popular sampling method in applications involving intractable likelihood functions. Instead of evaluating the likelihood function, ABC approximates the posterior distribution by a set of accepted samples which are simulated from a generating model. Simulated samples are accepted if the distances between the samples and the observation are smaller than some threshold. The distance is calculated in terms of summary statistics. This paper proposes Local Gradient Kernel Dimension Reduction (LGKDR) to construct low dimensional summary statistics for ABC. The proposed method identifies a sufficient subspace of the original summary statistics by implicitly considering all non-linear transforms therein, and a weighting kernel is used for the concentration of the projections. No strong assumptions are made on the marginal distributions, nor the regression models, permitting usage in a wide range of applications. Experiments are done with simple rejection ABC and sequential Monte Carlo ABC methods. Results are reported as competitive in the former and substantially better in the latter cases in which Monte Carlo errors are compressed as much as possible.
