Evolutionary quantitative genomics of Populus trichocarpa

Evolutionary quantitative genomics of Populus trichocarpa

Ilga Porth, Jaroslav Klapste, Athena D McKown, Jonathan La Mantia, Robert D Guy, Paer K Ingvarsson, Richard Hamelin, Shawn D Mansfield, Juergen Ehlting, Carl J Douglas, Yousry A El-Kassaby

Genetic evidence challenges the native status of a threatened freshwater fish (Carassius carassius) in England

Daniel L Jeffries, Gordon H Copp, Lori-Jayne Lawson Handley, Carl D Sayer, Bernd Hänfling

Comparing RADseq and microsatellites to infer complex phylogeographic patterns, a real data informed perspective in the Crucian carp, Carassius carassius, L.

Daniel L Jeffries, Gordon H Copp, Lori-Jayne Lawson Handley, Håkan Olsén, Carl D Sayer, Bernd Hänfling

Application of a dense genetic map for assessment of genomic responses to selection and inbreeding in Heliothis virescens.

Application of a dense genetic map for assessment of genomic responses to selection and inbreeding in Heliothis virescens.

Megan Fritz, Sandra Paa, Jennifer Baltzegar, Fred Gould

Extreme Mitogenomic Variation Without Cryptic Speciation in Chaetognaths

Extreme Mitogenomic Variation Without Cryptic Speciation in Chaetognaths

Ferdinand Marletaz, Yannick Le Parco, Shenglin Liu, Katja Peijnenburg

Using Genetic Distance to Infer the Accuracy of Genomic Prediction

Using Genetic Distance to Infer the Accuracy of Genomic Prediction
Marco Scutari, Ian Mackay, David Balding

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on.
In this paper we investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either $\F$ or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics.

Fundamental Properties of the Evolution of Mutational Robustness

Fundamental Properties of the Evolution of Mutational Robustness
Lee Altenberg

Evolution on neutral networks of genotypes has been found in models to concentrate on genotypes with high mutational robustness, to a degree determined by the topology of the network. Here analysis is generalized beyond neutral networks to arbitrary selection and parent-offspring transmission. In this larger realm, geometric features determine mutational robustness: the alignment of fitness with the orthogonalized eigenvectors of the mutation matrix weighted by their eigenvalues. “House of cards” mutation is found to preclude the evolution of mutational robustness. Genetic load is shown to increase with increasing mutation in arbitrary single and multiple locus fitness landscapes. The rate of decrease in population fitness can never grow as mutation rates get higher, showing that “error catastrophes” for genotype frequencies never cause precipitous losses of population fitness. The “inclusive inheritance” approach taken here naturally extends these results to a new concept of dispersal robustness.

An in-host model of HIV incorporating latent infection and viral mutation

An in-host model of HIV incorporating latent infection and viral mutation
Stephen Pankavich, Deborah Shutt

We construct a seven-component model of the in-host dynamics of the Human Immunodeficiency Virus Type-1 (i.e, HIV) that accounts for latent infection and the propensity of viral mutation. A dynamical analysis is conducted and a theorem is presented which characterizes the long time behavior of the model. Finally, we study the effects of an antiretroviral drug and treatment implications.

Estimating Reproducibility in Genome-Wide Association Studies

Estimating Reproducibility in Genome-Wide Association Studies
Wei Jiang, Jing-Hao Xue, Weichuan Yu

Genome-wide association studies (GWAS) are widely used to discover genetic variants associated with diseases. To control false positives, all findings from GWAS need to be verified with additional evidences, even for associations discovered from a high power study. Replication study is a common verification method by using independent samples. An association is regarded as true positive with a high confidence when it can be identified in both primary study and replication study. Currently, there is no systematic study on the behavior of positives in the replication study when the positive results of primary study are considered as the prior information.
In this paper, two probabilistic measures named Reproducibility Rate (RR) and False Irreproducibility Rate (FIR) are proposed to quantitatively describe the behavior of primary positive associations (i.e. positive associations identified in the primary study) in the replication study. RR is a conditional probability measuring how likely a primary positive association will also be positive in the replication study. This can be used to guide the design of replication study, and to check the consistency between the results of primary study and those of replication study. FIR, on the contrary, measures how likely a primary positive association may still be a true positive even when it is negative in the replication study. This can be used to generate a list of potentially true associations in the irreproducible findings for further scrutiny. The estimation methods of these two measures are given. Simulation results and real experiments show that our estimation methods have high accuracy and good prediction performance.

There are no caterpillars in a wicked forest

There are no caterpillars in a wicked forest
James H. Degnan, John A. Rhodes

Species trees represent the historical divergences of populations or species, while gene trees trace the ancestry of individual gene copies sampled within those populations. In cases involving rapid speciation, gene trees with topologies that differ from that of the species tree can be most probable under the standard multispecies coalescent model, making species tree inference more difficult. Such anomalous gene trees are not well understood except for some small cases. In this work, we establish one constraint that applies to trees of any size: gene trees with “caterpillar” topologies cannot be anomalous. The proof of this involves a new combinatorial object, called a population history, which keeps track of the number of coalescent events in each ancestral population.