Assessing intervention effects in a randomized trial within a social network

Studies of social networks provide unique opportunities to assess the causal effects of interventions that may impact more of the population than just those intervened on directly. Such effects are sometimes called peer or spillover effects, and may exist in the presence of interference, that is, when one individual's treatment affects another individual's outcome. Randomization-based inference (RI) methods provide a theoretical basis for causal inference in randomized studies, even in the presence of interference. In this article, we consider RI of the intervention effect in the eX-FLU trial, a randomized study designed to assess the effect of a social distancing intervention on influenza-like-illness transmission in a connected network of college students. The approach considered enables inference about the effect of the social distancing intervention on the per-contact probability of influenza-like-illness transmission in the observed network. The methods allow for interference between connected individuals and for heterogeneous treatment effects. The proposed methods are evaluated empirically via simulation studies, and then applied to data from the eX-FLU trial.     

Discriminating between allele- and genotype-specific transmission ratio distortion

Transmission ratio distortion (TRD) is defined as the observed deviation from the expected Mendelian inheritance of alleles from heterozygous parents. This phenomenon is attributed to various biological mechanisms acting on germ cells, embryos or fetuses, or even in early postnatal life. Current statistical approaches typically use two independent parametrizations assuming that TRD relies on allele- or genotype-related mechanisms, although they have never been tested and compared. This study compared allele- and genotype-related TRD models on simulated datasets with 1000 genotyped offspring and real data from 168 sire–dam–offspring beef cattle trios. The analysis of simulated datasets favored the true model of analysis in most cases (>93%), and a low percentage of missidentification occurred under (almost) null dominance (genotype-related model) or similar and moderate-to-low sire- and dam-specific TRD parameters (allele-related model). Moreover, the correlation between simulated and predicted distortion parameters was high (>0.97) under the true model. The comparison of allele- and genotype-related TRD models is an appealing tool to infer the biological source of TRD (i.e. haploid vs. diploid cells) when screening the whole genome. The analysis of beef cattle data corroborated a TRD region previously reported in chromosome 4, although discarding allele-related mechanisms and favoring the genotype-related model as the more reliable one. The results of this study highlight the relevance of implementing and comparing different parametrizations to capture all kinds of TRD, and to compare them using appropriate statistical methods.     

Random Partition Models and Exchangeability for Bayesian Identification of Population Structure

We introduce a Bayesian theoretical formulation of the statistical learning problem concerning the genetic structure of populations. The two key concepts in our derivation are exchangeability in its various forms and random allocation models. Implications of our results to empirical investigation of the population structure are discussed.     

Amplified fragment length polymorphisms and sequence data in the phylogenetic analysis of polyploids: multiple origins of Veronica cymbalaria (Plantaginaceae)

The origin of polyploid Veronica cymbalaria (Plantaginaceae) was investigated using DNA sequence data and amplified fragment length polymorphism (AFLP) fingerprints to reveal the parentage of this taxon. The use of AFLP fingerprints in phylogenetic analysis is problematic and various methods have therefore been compared. DNA sequence data (for the internal transcribed spacer (ITS) region and the plastid trnL-F region (trnL intron, 3'exon, and trnL-F spacer)) and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis of the ITS region suggested a reliable hypothesis for the evolution of the V. cymbalaria complex. This hypothesis allowed evaluation of the effect of different distance measures (Jaccard and Nei-Li) in phenetic, character-state weighted parsimony, and Bayesian analyses of AFLP markers. The study establishes that tetraploid V. cymbalaria originated at least twice in the eastern Mediterranean, with one parent differing in the two separate origins. Hexaploid V. cymbalaria originated even more often. The results illustrate that even subtle differences in the analyses of AFLP markers can lead to drastically different conclusions. The study reveals multiple origins of a Mediterranean polyploid species. Furthermore, it demonstrates that the analysis of a complex marker system such as AFLP fingerprints using only one type of analysis can easily be misleading.     

A Markov chain Monte Carlo strategy for sampling from the joint posterior distribution of pedigrees and population parameters under a Fisher-Wright model with partial selfing

A simple population genetic model is presented for a hermaphrodite annual species, allowing both selfing and outcrossing. Those male gametes (pollen) responsible for outcrossing are assumed to disperse much further than seeds. Under this model, the pedigree of a sample from a single locality is loop-free. A novel Markov chain Monte Carlo strategy is presented for sampling from the joint posterior distribution of the pedigree of such a sample and the parameters of the population genetic model (including the selfing rate) given the genotypes of the sampled individuals at unlinked marker loci. The computational costs of this Markov chain Monte Carlo strategy scale well with the number of individuals in the sample, and the number of marker loci, but increase exponentially with the age (time since colonisation from the source population) of the local population. Consequently, this strategy is particularly suited to situations where the sample has been collected from a population which is the result of a recent colonisation process.     

Application of the secondary structure model of rRNA for phylogeny: D2-D3 expansion segments of the LSU gene of plant-parasitic nematodes from the family Hoplolaimidae Filipjev, 1934

Knowledge of rRNA structure is increasingly important to assist phylogenetic analysis through reconstructing optimal alignment, utilizing molecule features as an additional source of data and refining appropriate models of evolution of the molecule. We describe a procedure of optimization for alignment and a new coding method for nucleotide sequence data using secondary structure models of the D2 and D3 expansion fragments of the LSU-rRNA gene reconstructed for fifteen nematode species of the agriculturally important and diverse family Hoplolaimidae, order Tylenchida. Using secondary structure information we converted the original sequence data into twenty-eight symbol codes and submitted the transformed data to maximum parsimony analysis. We also applied the original sequence data set for Bayesian inference. This used the doublet model with sixteen states of nucleotide doublets for the stem region and the standard model of DNA substitution with four nucleotide states for loops and bulges. By this approach, we demonstrate that using structural information for phylogenetic analyses led to trees with lower resolved relationships between clades and likely eliminated some artefactual support for misinterpreted relationships, such as paraphyly of Helicotylenchus or Rotylenchus. This study as well as future phylogenetic analyses is herein supported by the development of an on-line database, NEMrRNA, for rRNA molecules in a structural format for nematodes. We also have developed a new computer program, RNAstat, for calculation of nucleotide statistics designed and proposed for phylogenetic studies.     

Performance of a new invariants method on homogeneous and nonhomogeneous quartet trees

An attempt to use phylogenetic invariants for tree reconstruction was made at the end of the 80s and the beginning of the 90s by several researchers (the initial idea due to Lake [1987] and Cavender and Felsenstein [1987]). However, the efficiency of methods based on invariants is still in doubt (Huelsenbeck 1995; Jin and Nei 1990). Probably because these methods only used few generators of the set of phylogenetic invariants. The method studied in this paper was first introduced in Casanellas et al. (2005) and it is the first method based on invariants that uses the "whole" set of generators for DNA data. The simulation studies performed in this paper prove that it is a very competitive and highly efficient phylogenetic reconstruction method, especially for nonhomogeneous models on phylogenetic trees.