Cognitive decline in Alzheimer's disease and other dementias typically begins long before clinical impairment. Identifying people experiencing subclinical decline may facilitate earlier intervention. This study developed cognitive trajectory clusters using longitudinally based random slope and change point parameter estimates from a Preclinical Alzheimer's disease Cognitive Composite and examined how baseline and most recently available clinical/health-related characteristics, cognitive statuses and biomarkers for Alzheimer's disease and vascular disease varied across these cognitive clusters. Data were drawn from the Wisconsin Registry for Alzheimer's Prevention, a longitudinal cohort study of adults from late midlife, enriched for a parental history of Alzheimer's disease and without dementia at baseline. Participants who were cognitively unimpaired at the baseline visit with ≥3 cognitive visits were included in trajectory modelling (n = 1068). The following biomarker data were available for subsets: positron emission tomography amyloid (amyloid: n = 367; [11C]Pittsburgh compound B (PiB): global PiB distribution volume ratio); positron emission tomography tau (tau: n = 321; [18F]MK-6240: primary regions of interest meta-temporal composite); MRI neurodegeneration (neurodegeneration: n = 581; hippocampal volume and global brain atrophy); T2 fluid-attenuated inversion recovery MRI white matter ischaemic lesion volumes (vascular: white matter hyperintensities; n = 419); and plasma pTau217 (n = 165). Posterior median estimate person-level change points, slopes' pre- and post-change point and estimated outcome (intercepts) at change point for cognitive composite were extracted from Bayesian Bent-Line Regression modelling and used to characterize cognitive trajectory groups (K-means clustering). A common method was used to identify amyloid/tau/neurodegeneration/vascular biomarker thresholds. We compared demographics, last visit cognitive status, health-related factors and amyloid/tau/neurodegeneration/vascular biomarkers across the cognitive groups using ANOVA, Kruskal-Wallis, χ2, and Fisher's exact tests. Mean (standard deviation) baseline and last cognitive assessment ages were 58.4 (6.4) and 66.6 (6.6) years, respectively. Cluster analysis identified three cognitive trajectory groups representing steep, n = 77 (7.2%); intermediate, n = 446 (41.8%); and minimal, n = 545 (51.0%) cognitive decline. The steep decline group was older, had more females, APOE e4 carriers and mild cognitive impairment/dementia at last visit; it also showed worse self-reported general health-related and vascular risk factors and higher amyloid, tau, neurodegeneration and white matter hyperintensity positive proportions at last visit. Subtle cognitive decline was consistently evident in the steep decline group and was associated with generally worse health. In addition, cognitive trajectory groups differed on aetiology-informative biomarkers and risk factors, suggesting an intimate link between preclinical cognitive patterns and amyloid/tau/neurodegeneration/vascular biomarker differences in late middle-aged adults. The result explains some of the heterogeneity in cognitive performance within cognitively unimpaired late middle-aged adults.

Cognitive decline in Alzheimer’s disease (AD) and other dementias may accelerate in preclinical phases as brain pathology increases. Here we: 1) developed cognitive trajectory profiles using longitudinally-based random slope and change point (CP) parameter estimates from a cognitive composite; and 2) examined how AD-related biomarkers varied across these cognitive profiles. WRAP participants with > = 3 Preclinical Alzheimer’s Cognitive Composite (PACC3) scores, dementia-free at baseline, were included (n = 1068). Amyloid measures from positron emission tomography (PET) [C-11]Pittsburgh Compound B (PiB; n = 361) scans included Global PiB DVR and proportion PiB+ (Global PiB DVR>1.16). Tau measures included PET MK-6240 regional entorhinal cortex and hippocampal SUVR (n = 321). Neurodegeneration measures included MRI hippocampal volume and global brain atrophy (GBA; n = 581). Plasma measures included pTau217 (n = 166). Posterior median estimate person-level CPs, slopes pre- and post-CP, and intercepts at CP for PACC3 were extracted from Bayesian random CP mixed models (BRCPMM; age = time scale) and used to characterize cognitive trajectory profiles (K-means clustering). We compared demographic, last visit cognitive statuses (cognitively unimpaired-stable (CU-S), CU-declining (CU-D), and MCI/Dementia), amyloid, tau, neurodegenerations and plasma measures across the cognitive trajectory profiles using analysis of variance, chi-square and Fisher’s exact tests. Significant omnibus tests (p<.05) were followed with pairwise comparisons. Mean(sd) last cognitive assessment age was 66.6(6.6) years; PiB, MK, MRI scans and plasma occurred within mean(sd) 1.1(2.7), 1.6(1.4), .6(3) and .09(.7) years of cognitive assessment. Cluster analysis identified 3 groups of performance patterns representing highest to lowest risk of cognitive decline (high: n = 77(7.2%); intermediate: n = 446(41.8%); and low: n = 545(51.0%); Figure 1 & 2). The high risk group was older, had more females, APOE e4 carriers, and MCI/Dementia at last visit (Table 1). The high risk group also had worse PiB-amyloid, MK-tau, pTau217, and MRI-neurogeneration levels than the lower risk groups (Table2; Figure 3). In this initially non-demented sample, differences between cognitive clusters across multiple biomarkers and AD risk factors indicate that within-person PACC3 performance patterns are sensitive to preclinical change. Identifying cognitive trajectory profiles may provide an opportunity for early intervention for high-risk subjects at the right time for treatment and/or enrollment in a clinical trial.

Preclinical Alzheimer's disease (AD), the earliest stage in the AD continuum, can last fifteen to twenty years, with cognitive decline trajectories nonlinear and heterogeneous between subjects. Characterizing cognitive decline in the preclinical phase of AD is critical for the development of early intervention strategies when disease-modifying therapies may be most effective. In the last decade, there has been an increased interest in the application of change point (CP) models to longitudinal cognitive outcomes. Because patients' change points can vary greatly, it is essential to model this variation. In this paper, we introduce a BAyesian Bent-Line Regression model longitudinal data on cognitive function in middle-aged adults with a high risk of AD. We provide an approach for estimating the fixed (group-level) and random (person-level) CPs, slopes pre- and post-CP, and intercepts at CP for cognition. Our model not only estimates the individual cognitive trajectories but also the distributions of the cognitive bent line curves at each age, enabling researchers and clinicians to estimate subjects' quantiles. Simulation studies show that the estimation and inferential procedures perform reasonably well in finite samples. The practical use is illustrated by an application to a longitudinal cognitive composite in the Wisconsin Registry for Alzheimer's Prevention (WRAP).

Previous studies indicate that rates of cognitive decline in Alzheimer’s disease (AD) and other dementias may accelerate significantly as brain pathology increases. The objectives of this study included to: 1) characterize changepoints and related slope parameters across four cognitive composites in a longitudinal cohort study; and 2) examine how these parameters varied across predictors of interest. Dementia-free participants at baseline from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) with > = 3 cognitive assessments were eligible (n≈1071; mean(sd) baseline age = 58.4(6.4)). We used newly-developed Bayesian random change point mixed models (BRCPMM) to estimate fixed (group-level) and random (person-level) change points (CPs), slopes pre- and post-CP, and intercepts at CP for four cognitive composites: three-test preclinical Alzheimer’s cognitive composite (PACC3; primary outcome), immediate learning, delayed learning, and executive function. We used non-parametric statistics to test whether PACC3 estimates differed by: last cognitive status observed (cognitively unimpaired-stable or -declining (CU-S, CU-D), MCI, and dementia); sex/gender, and APOE-e4 carrier status. Posterior median fixed and random effect estimates (corresponding 95% credible intervals from 20000 BRCPMM runs) are summarized in Table 1 by outcome. The PACC3 group-level CP was earliest at ∼75.56 [72.98,78.44] years old with 95% of individuals hitting CP between 59.69 and 91.86. Timing of CP was significantly associated with last observed cognitive status (Table 2); those 37 (3.46%) progressing to MCI/dementia had estimated CP’s before or during study participation while those remaining unimpaired had estimated CP’s after their last visit. All random effects (person-level) PACC3 estimates differed by worst cognitive status (Figure_1) and sex/gender (Figure_2). For example, while the CU-D and MCI groups had similar slopes before CP, CP occurred earlier, estimated PACC3 at CP was lower, and post-CP slope was worse in the MCI group compared to CU-D (Figure_1). APOE-e4 random-effects estimates differed by CP and post-CP slopes (Figure 3). The timing and rates of change in different cognitive composites differ by cognitive status, gender, and APOE-e4 status. Predicting individual cognition trajectories and change points may provide an opportunity for early intervention by diagnosing high-risk subjects at the right time for treatment and/or enrollment in a clinical trial.

Ecologists rely on field surveys to monitor long‐term ecological change but finite sampling and the prevalence of rare species mean that surveys inevitably miss some species present at a given location. These ‘phantom species’ produce pseudo‐turnover by inflating observed rates of local colonization and extinction in resurvey studies, especially among rare species. In this paper, we quantify the probability that pseudo‐turnover occurs due to imprecise plot relocation and/or shifts in where individuals are located. Using sampling models derived from the binomial distribution, we estimate probabilities of missing species as a function of local abundance and sampling intensity. Spatially explicit simulations confirm that our binomial model is robust to non‐random sampling schemes and clumped species distributions. False absences decline predictably as species abundance and sampling intensity increase allowing us to statistically adjust naïve estimates of colonization and extinction for expected rates of pseudo‐turnover. To illustrate the model's real‐world utility, we analyze apparent colonizations and extinctions for 331 species distributed over 83 sites in southern Wisconsin forests surveyed in both the 1950s and 2000s. Limited sampling in the 1950s means expected rates of pseudo‐colonization are appreciable. Accounting for pseudo‐turnover thus reduces estimated community‐wide colonization rates by 51% compared to naïve (observed) rates. More complete sampling in the 2000s limited overestimates of local extinction to 14%. We distinguish three zones of inference based on sampling intensity and species abundance where: 1) naïve estimates approximate true values of turnover, 2) adjustments are important, and 3) species are too rare relative to sampling to reliably infer turnover. Accounting for phantom species substantially improves our ability to accurately estimate local colonization and extinction rates, enhancing our ability to infer community dynamics and monitor long‐term ecological change.

The techniques discussed in 2.1-2.6 (exploratory data analysis) are useful for describing a data set, or a sample of data. It is often of interest to generalize findings from a sample to a larger group that statisticians call a population. This generalization is called statistical inference. Statistical inference is often concerned with using statistics, characteristics that can be calculated from sample data, to estimate parameters, characteristics of populations. Our goal is to infer from the sample information about the population. To do this we need the sample to be representative of the population. Key ideas very well stated in the book. It is important to read Sections 2.8 and 2.9 (very likely you'll be tested on this concept). This is one of the big picture issues that I hope you remember 20 years from now, reading a newspaper (the internet?). These issues should be in the back of your mind for the rest of the course. Is the sample truly a random sample? Is it representative of the population? In practice, samples are almost never truly random (hopefully they are close). But we have to proceed with what is available. Example Fertility declines in women as they age until ending at menopause. Younger women may become pregnant relatively easier than older pre-menopausal women. A hypothesis rooted in evolution and psychology states that as women age, they may experience increases in sexual motivation and seek sex more frequently to overcome decreasing fertility.

ABSTRACT Hydrologic exchange plays a critical role in biogeochemical cycling within the hyporheic zone (the interface between river water and groundwater) of riverine ecosystems. Such exchange may set limits on the rates of microbial metabolism and impose deterministic selection on microbial communities that adapt to dynamically changing dissolved organic carbon (DOC) sources. This study examined the response of attached microbial communities ( in situ colonized sand packs) from groundwater, hyporheic, and riverbed habitats within the Columbia River hyporheic corridor to “cross-feeding” with either groundwater, river water, or DOC-free artificial fluids. Our working hypothesis was that deterministic selection during in situ colonization would dictate the response to cross-feeding, with communities displaying maximal biomass and respiration when supplied with their native fluid source. In contrast to expectations, the major observation was that the riverbed colonized sand had much higher biomass and respiratory activity, as well as a distinct community structure, compared with those of the hyporheic and groundwater colonized sands. 16S rRNA gene amplicon sequencing revealed a much higher proportion of certain heterotrophic taxa as well as significant numbers of eukaryotic algal chloroplasts in the riverbed colonized sand. Significant quantities of DOC were released from riverbed sediment and colonized sand, and separate experiments showed that the released DOC stimulated respiration in the groundwater and piezometer colonized sand. These results suggest that the accumulation and degradation of labile particulate organic carbon (POC) within the riverbed are likely to release DOC, which may enter the hyporheic corridor during hydrologic exchange, thereby stimulating microbial activity and imposing deterministic selective pressure on the microbial community composition. IMPORTANCE The influence of river water-groundwater mixing on hyporheic zone microbial community structure and function is an important but poorly understood component of riverine biogeochemistry. This study employed an experimental approach to gain insight into how such mixing might be expected to influence the biomass, respiration, and composition of hyporheic zone microbial communities. Colonized sands from three different habitats (groundwater, river water, and hyporheic) were “cross-fed” with either groundwater, river water, or DOC-free artificial fluids. We expected that the colonization history would dictate the response to cross-feeding, with communities displaying maximal biomass and respiration when supplied with their native fluid source. By contrast, the major observation was that the riverbed communities had much higher biomass and respiration, as well as a distinct community structure compared with those of the hyporheic and groundwater colonized sands. These results highlight the importance of riverbed microbial metabolism in organic carbon processing in hyporheic corridors.

Traditionally, phylogeny and sequence alignment are estimated separately: first estimate a multiple sequence alignment and then infer a phylogeny based on the sequence alignment estimated in the previous step. However, uncertainty in the alignment is ignored, resulting, possibly, in overstated certainty in phylogeny estimates. We develop a joint model for co-estimating phylogeny and sequence alignment which improves estimates from the traditional approach by accounting for uncertainty in the alignment in phylogenetic inferences. Our insertion and deletion (indel) model allows arbitrary-length overlapping indel events and a general distribution for indel fragment size. We employ a Bayesian approach using MCMC to estimate the joint posterior distribution of a phylogenetic tree and a multiple sequence alignment. Our approach has a tree and a complete history of indel events mapped onto the tree as the state space of the Markov Chain while alternative previous approaches have a tree and an alignment. A large state space containing a complete history of indel events makes our MCMC approach more challenging, but it enables us to infer more information about the indel process. The performances of this joint method and traditional sequential methods are compared using simulated data as well as real data. Software named BayesCAT (Bayesian Co-estimation of Alignment and Tree) is available at https://github.com/heejungshim/BayesCAT.

Objective and designThe goal of the study was to determine whether the mutational profile of early colorectal polyps correlated with growth behaviour. The growth of small polyps (6–9 mm) that were first identified during routine screening of patients was monitored over time by interval imaging with CT colonography. Mutations in these lesions with known growth rates were identified by targeted next-generation sequencing. The timing of mutational events was estimated using computer modelling and statistical inference considering several parameters including allele frequency and fitness.ResultsThe mutational landscape of small polyps is varied both within individual polyps and among the group as a whole but no single alteration was correlated with growth behaviour. Polyps carried 0–3 pathogenic mutations with the most frequent being inAPC,KRAS/NRAS,BRAF,FBXW7andTP53. In polyps with two or more pathogenic mutations, allele frequencies were often variable, indicating the presence of multiple populations within a single tumour. Based on computer modelling, detectable mutations occurred at a mean polyp size of 30±35 crypts, well before the tumour is of a clinically detectable size.ConclusionsThese data indicate that small colon polyps can have multiple pathogenic mutations in crucial driver genes that arise early in the existence of a tumour. Understanding the molecular pathway of tumourigenesis and clonal evolution in polyps that are at risk for progressing to invasive cancers will allow us to begin to better predict which polyps are more likely to progress into adenocarcinomas and which patients are at greater risk of developing advanced disease.

Complex vocal signals, such as birdsong, contain acoustic elements that differ in both order and duration. These elements may convey socially relevant meaning, both independently and through their interactions, yet statistical methods that combine order and duration data to extract meaning have not, to our knowledge, been fully developed. Here we design novel semi-Markov methods, Bayesian estimation and classification trees to extract order and duration information from behavioural sequences and apply these methods to songs produced by male European starlings, Sturnus vulgaris, in two social contexts in which the function of song differs: a spring (breeding) and autumn (nonbreeding) context. Additionally, previous data indicate that damage to the medial preoptic nucleus (POM), a brain area known to regulate male sexually motivated behaviour, affects structural aspects of starling song such that males in a sexually relevant context (i.e. spring) sing shorter songs than appropriate for this context. We further test the utility of our statistical approach by comparing attributes of song structure in POM-lesioned males to song produced by control spring and autumn males. Spring and autumn songs were statistically separable based on the duration and order of phrase types. Males produced more structurally complex aspects of song in spring than in autumn. Spring song was also longer and more stereotyped than autumn song, both attributes used by females to select mates. Songs produced by POM-lesioned males in some cases fell between measures of spring and autumn songs but differed most from songs produced by autumn males. Overall, these statistical methods can effectively extract biologically meaningful information contained in many behavioural sequences given sufficient sample sizes and replication numbers.

While there is no doubt among evolutionary biologists that all living species, or merely all living species within a particular group (e.g., animals), share descent from a common ancestor, formal statistical methods for evaluating common ancestry from aligned DNA sequence data have received criticism. One primary criticism is that prior methods take sequence similarity as evidence for common ancestry while ignoring other potential biological causes of similarity, such as functional constraints. We present a new statistical framework to test separate ancestry versus common ancestry that avoids this pitfall. We illustrate the efficacy of our approach using a recently published large molecular alignment to examine common ancestry of all primates (including humans).We find overwhelming evidence against separate ancestry and in favor of common ancestry for orders and families of primates. We also find overwhelming evidence that humans share a common ancestor with other primate species.The novel statistical methods presented here provide formal means to test separate ancestry versus common ancestry from aligned DNA sequence data while accounting for functional constraints that limit nucleotide base usage on a site-by-site basis.

Since Darwin, biologists have come to recognize that the theory of descent from common ancestry (CA) is very well supported by diverse lines of evidence. However, while the qualitative evidence is overwhelming, we also need formal methods for quantifying the evidential support for CA over the alternative hypothesis of separate ancestry (SA). In this article, we explore a diversity of statistical methods using data from the primates. We focus on two alternatives to CA, species SA (the separate origin of each named species) and family SA (the separate origin of each family). We implemented statistical tests based on morphological, molecular, and biogeographic data and developed two new methods: one that tests for phylogenetic autocorrelation while correcting for variation due to confounding ecological traits and a method for examining whether fossil taxa have fewer derived differences than living taxa. We overwhelmingly rejected both species and family SA with infinitesimal P values. We compare these results with those from two companion papers, which also found tremendously strong support for the CA of all primates, and discuss future directions and general philosophical issues that pertain to statistical testing of historical hypotheses such as CA.

1. The common ancestry of life is supported by an enormous body of evidence and is universally accepted within the scientific community. However, some potential sources of data that can be used to test the thesis of common ancestry have not yet been formally analyzed.2. We developed a new test of common ancestry based on nucleotide sequences at amino acid invariant sites in aligned homologous protein coding genes. We reasoned that since nucleotide variation at amino acid invariant sites is selectively neutral and, thus, unlikely to be due to convergent evolution, the observation that an amino acid is consistently encoded by the same codon sequence in different species could provide strong evidence of their common ancestry. Our method uses the observed variation in codon sequences at amino acid invariant sites as a test statistic, and compares such variation to that which is expected under three different models of codon frequency under the alternative hypothesis of separate ancestry. We also examine hierarchical structure in the nucleotide sequences at amino acid invariant sites and quantified agreement between trees generated from amino acid sequence and those inferred from the nucleotide sequences at amino acid invariant sites.3. When these tests are applied to the primate families as a test case, we find that observed nucleotide variation at amino acid invariant sites is considerably lower than nucleotide variation predicted by any model of codon frequency under separate ancestry. Phylogenetic trees generated from amino-acid invariant site nucleotide data agree with those generated from protein-coding data, and there is far more hierarchical structure in amino-acid invariant site data than would be expected under separate ancestry.4. We definitively reject the separate ancestry of the primate families, and demonstrate that our tests can be applied to any group of interest to test common ancestry.

We develop a model-based methodology for integrating gene-set information with an experimentally-derived gene list. The methodology uses a previously reported samplingmodel, but takes advantage of natural constraints in the high-dimensional discrete parameter space in order to work from a more structured prior distribution than is currently available. We show how the natural constraints are expressed in terms of linear inequality constraints within a set of binary latent variables. Further, the currently available prior gives low probability to these constraints in complex systems, such as Gene Ontology (GO), thus reducing the efficiency of statistical inference. We develop two computational advances to enable posterior inference within the constrained parameter space: one using integer linear programming for optimization and one using a penalized Markov chain sampler. Numerical experiments demonstrate the utility of the new methodology for a multivariate integration of genomic data with GO or related information systems. Compared to available methods, the proposed multi-functional analyzer covers more reported genes without mis-covering nonreported genes, as demonstrated on genome-wide data from association studies of type 2 diabetes and from RNA interference studies of influenza.

ABSTRACT Microbial symbionts provide benefits that contribute to the ecology and fitness of host plants and animals. Therefore, the evolutionary success of plants and animals fundamentally depends on long-term maintenance of beneficial associations. Most work investigating coevolution and symbiotic maintenance has focused on species-level associations, and studies are lacking that assess the impact of bacterial strain diversity on symbiotic associations within a coevolutionary framework. Here, we demonstrate that fitness in mutualism varies depending on bacterial strain identity, and this is consistent with variation shaping phylogenetic patterns and maintenance through fitness benefits. Through genome sequencing of nine bacterial symbiont strains and cophylogenetic analysis, we demonstrate diversity among Xenorhabdus bovienii bacteria. Further, we identified cocladogenesis between Steinernema feltiae nematode hosts and their corresponding X. bovienii symbiont strains, indicating potential specificity within the association. To test the specificity, we performed laboratory crosses of nematode hosts with native and nonnative symbiont strains, which revealed that combinations with the native bacterial symbiont and closely related strains performed significantly better than those with more divergent symbionts. Through genomic analyses we also defined potential factors contributing to specificity between nematode hosts and bacterial symbionts. These results suggest that strain-level diversity (e.g., subspecies-level differences) in microbial symbionts can drive variation in the success of host-microbe associations, and this suggests that these differences in symbiotic success could contribute to maintenance of the symbiosis over an evolutionary time scale. IMPORTANCE Beneficial symbioses between microbes and plant or animal hosts are ubiquitous, and in these associations, microbial symbionts provide key benefits to their hosts. As such, host success is fundamentally dependent on long-term maintenance of beneficial associations. Prolonged association between partners in evolutionary time is expected to result in interactions in which only specific partners can fully support symbiosis. The contribution of bacterial strain diversity on specificity and coevolution in a beneficial symbiosis remains unclear. In this study, we demonstrate that strain-level differences in fitness benefits occur in beneficial host-microbe interactions, and this variation likely shapes phylogenetic patterns and symbiotic maintenance. This highlights that symbiont contributions to host biology can vary significantly based on very-fine-scale differences among members of a microbial species. Further, this work emphasizes the need for greater phylogenetic resolution when considering the causes and consequences of host-microbe interactions.

Genome sequence data contain abundant information about genealogical history, but methods for extracting and interpreting this information are not yet fully developed. We analyzed genome sequences for multiple accessions of the selfing plant, Arabidopsis thaliana, with the goal of better understanding its genealogical history. As expected from accessions of the same species, we found much discordance between nuclear gene trees. Nonetheless, we inferred the optimal population tree under the assumption that all discordance is due to incomplete lineage sorting. To cope with the size of the data (many genes and many taxa), our pipeline is based on parallel computing and divides the problem into four-taxon trees. However, just because a population tree can be estimated does not mean that the assumptions of the multispecies coalescent model hold. Therefore, we implemented a new, nonparametric test to evaluate whether a population tree adequately explains the observed quartet frequencies (the frequencies of gene trees with each resolution of each four-taxon set). This test also considers other models: panmixia and a partially resolved population tree, that is, a tree in which some nodes are collapsed into local panmixia. We found that a partially resolved population tree provides the best fit to the data, providing evidence for tree-like structure within A. thaliana, qualitatively similar to what might be expected between different, closely related species. Further, we show that the pattern of deviation from expectations can be used to identify instances of introgression and detect one clear case of reticulation among ecotypes that have come into contact in the United Kingdom. Our study illustrates how we can use genome sequence data to evaluate whether phylogenetic relationships are strictly tree-like or reticulating.

International Statistical ReviewVolume 83, Issue 1 p. 160-161 Book Review Bayesian Phylogenetics: Methods, Algorithms and Applications Bret Larget, Bret Larget [email protected] Departments of Statistics and of Botany 1300 University Avenue, University of Wisconsin, Madison, WI 53706 USASearch for more papers by this author Bret Larget, Bret Larget [email protected] Departments of Statistics and of Botany 1300 University Avenue, University of Wisconsin, Madison, WI 53706 USASearch for more papers by this author First published: 13 April 2015 https://doi.org/10.1111/insr.12095_1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References Felsenstein J. (2004). Inferring Phylogenies. Sunderland, MA, USA: Sinauer Associates, Inc.. Google Scholar Volume83, Issue1April 2015Pages 160-161 ReferencesRelatedInformation

Phylogeography investigates the historical process that is responsible for the contemporary geographic distributions of populations in a species. The inference is made on the basis of molecular sequence data sampled from modern-day populations. The estimates, however, may fluctuate depending on the relevant genomic regions, because the evolution mechanism of each genome is unique, even within the same individual. In this article, we propose a genome-differentiated population tree model that allows the existence of separate population trees for each homologous genome. In each population tree, the unique evolutionary characteristics account for each genome, along with their homologous relationship; therefore, the approach can distinguish the evolutionary history of one genome from that of another. In addition to the separate divergence times, the new model can estimate separate effective population sizes, gene-genealogies and other mutation parameters. For Bayesian inference, we developed a Markov chain Monte Carlo (MCMC) methodology with a novel MCMC algorithm which can mix over a complicated state space. The stability of the new estimator is demonstrated through comparison with the Monte Carlo samples and other methods, as well as MCMC convergence diagnostics. The analysis of African gorilla data from two homologous loci reveals discordant divergence times between loci, and this discrepancy is explained by male-mediated gene flows until the end of the last ice age.

Maintenance of genetic variation at loci under selection has profound implications for adaptation under environmental change. In temporally and spatially varying habitats, non-neutral polymorphism could be maintained by heterozygote advantage across environments (marginal overdominance), which could be greatly increased by beneficial reversal of dominance across conditions. We tested for reversal of dominance and marginal overdominance in salinity tolerance in the saltwater-to-freshwater invading copepod Eurytemora affinis. We compared survival of F1 offspring generated by crossing saline and freshwater inbred lines (between-salinity F1 crosses) relative to within-salinity F1 crosses, across three salinities. We found evidence for both beneficial reversal of dominance and marginal overdominance in salinity tolerance. In support of reversal of dominance, survival of between-salinity F1 crosses was not different from that of freshwater F1 crosses under freshwater conditions and saltwater F1 crosses under saltwater conditions. In support of marginal overdominance, between-salinity F1 crosses exhibited significantly higher survival across salinities relative to both freshwater and saltwater F1 crosses. Our study provides a rare empirical example of complete beneficial reversal of dominance associated with environmental change. This mechanism might be crucial for maintaining genetic variation in salinity tolerance in E. affinis populations, allowing rapid adaptation to salinity changes during habitat invasions.

In this article I introduce the idea of conditional independence of separated subtrees as a principle by which to estimate the posterior probability of trees using conditional clade probability distributions rather than simple sample relative frequencies. I describe an algorithm for these calculations and software which implements these ideas. I show that these alternative calculations are very similar to simple sample relative frequencies for high probability trees but are substantially more accurate for relatively low probability trees. The method allows the posterior probability of unsampled trees to be calculated when these trees contain only clades that are in other sampled trees. Furthermore, the method can be used to estimate the total probability of the set of sampled trees which provides a measure of the thoroughness of a posterior sample.