Estimating relatedness and inbreeding using molecular markers and pedigrees: the effect of demographic history

Estimates of inbreeding and relatedness are commonly calculated using molecular markers, although the accuracy of such estimates has been questioned. As a further complication, in many situations, such estimates are required in populations with reduced genetic diversity, which is likely to affect their accuracy. We investigated the correlation between microsatellite‐ and pedigree‐based coefficients of inbreeding and relatedness in laboratory populations of Drosophila melanogaster that had passed through bottlenecks to manipulate their genetic diversity. We also used simulations to predict expected correlations between marker‐ and pedigree‐based estimates and to investigate the influence of linkage between loci and null alleles. Our empirical data showed lower correlations between marker‐ and pedigree‐based estimates in our control (nonbottleneck) population than were predicted by our simulations or those found in similar studies. Correlations were weaker in bottleneck populations, confirming that extreme reductions in diversity can compromise the ability of molecular estimates to detect recent inbreeding events. However, this result was highly dependent on the strength of the bottleneck and we did not observe or predict any reduction in correlations in our population that went through a relatively severe bottleneck of N = 10 for one generation. Our results are therefore encouraging, as molecular estimates appeared robust to quite severe reductions in genetic diversity. It should also be remembered that pedigree‐based estimates may not capture realized identity‐by‐decent and that marker‐based estimates may actually be more useful in certain situations.     

Marker‐based estimates of relatedness and inbreeding coefficients: an assessment of current methods

Inbreeding (F) of and relatedness (r) between individuals are now routinely calculated from marker data in studies in the fields of quantitative genetics, conservation genetics, forensics, evolution and ecology. Although definable in terms of either correlation coefficient or probability of identity by descent (IBD) relative to a reference, they are better interpreted as correlations in marker‐based analyses because the reference in practice is frequently the current sample or population whose F and r are being estimated. In such situations, negative estimates have a biological meaning, a substantial proportion of the estimates are expected to be negative, and the average estimates are close to zero for r and equivalent to F_IS for F. I show that although current r estimators were developed from the IBD‐based concept of relatedness, some of them conform to the correlation‐based concept of relatedness and some do not. The latter estimators can be modified, however, so that they estimate r as a correlation coefficient. I also show that F and r estimates can be misleading and become biased and marker dependent when a sample containing a high proportion of highly inbred and/or closely related individuals is used as reference. In analyses depending on the comparison between r (or F) estimates and a priori values expected under ideal conditions (e.g. for identifying genealogical relationship), the estimators should be used with caution.     

The comparison of RFLP,RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis

The utility of RFLP (restriction fragment length polymorphism), RAPD (random-amplified polymorphic DNA), AFLP (amplified fragment length polymorphism) and SSR (simple sequence repeat, microsatellite) markers in soybean germplasm analysis was determined by evaluating information content (expected heterozygosity), number of loci simultaneously analyzed per experiment (multiplex ratio) and effectiveness in assessing relationships between accessions. SSR markers have the highest expected heterozygosity (0.60), while AFLP markers have the highest effective multiplex ratio (19). A single parameter, defined as the marker index, which is the product of expected heterozygosity and multiplex ratio, may be used to evaluate overall utility of a marker system. A comparison of genetic similarity matrices revealed that, if the comparison involved both cultivated (Glycine max) and wild soybean (Glycine soja) accessions, estimates based on RFLPs, AFLPs and SSRs are highly correlated, indicating congruence between these assays. However, correlations of RAPD marker data with those obtained using other marker systems were lower. This is because RAPDs produce higher estimates of interspecific similarities. If the comparisons involvedG. max only, then overall correlations between marker systems are significantly lower. WithinG. max, RAPD and AFLP similarity estimates are more closely correlated than those involving other marker systems.Abbreviations RFLP restriction fragment length plymorphism - RAPD random-amplified polymorphic DNA - AFLP amplified fragment length polymorphism - SSR simple sequence repeat - PCR polymerase chain reaction - TBE Tris-borate-EDTA buffer - MI marker index - SENA sum of effective numbers of alleles     

A Beta-mixture model for assessing genetic population structure

Summary An important fraction of recently generated molecular data is dominant markers. They contain substantial information about genetic variation but dominance makes it impossible to apply standard techniques to calculate measures of genetic differentiation, such as F‐statistics. In this article, we propose a new Bayesian beta‐mixture model that more accurately describes the genetic structure from dominant markers and estimates multiple F_ST s from the sample. The model also has important application for codominant markers and single‐nucleotide polymorphism (SNP) data. The number of F_ST is assumed unknown beforehand and follows a random distribution. The reversible jump algorithm is used to estimate the unknown number of multiple F_ST s. We evaluate the performance of three split proposals and the overall performance of the proposed model based on simulated dominant marker data. The model could reliably identify and estimate a spectrum of degrees of genetic differentiation present in multiple loci. The estimates of F_ST s also incorporate uncertainty about the magnitude of within‐population inbreeding coefficient. We illustrate the method with two examples, one using dominant marker data from a rare orchid and the other using codominant marker data from human populations.     

The scale‐of‐choice effect and how estimates of assortative mating in the wild can be biased due to heterogeneous samples

The mode in which sexual organisms choose mates is a key evolutionary process, as it can have a profound impact on fitness and speciation. One way to study mate choice in the wild is by measuring trait correlation between mates. Positive assortative mating is inferred when individuals of a mating pair display traits that are more similar than those expected under random mating while negative assortative mating is the opposite. A recent review of 1134 trait correlations found that positive estimates of assortative mating were more frequent and larger in magnitude than negative estimates. Here, we describe the scale‐of‐choice effect (SCE), which occurs when mate choice exists at a smaller scale than that of the investigator's sampling, while simultaneously the trait is heterogeneously distributed at the true scale‐of‐choice. We demonstrate the SCE by Monte Carlo simulations and estimate it in two organisms showing positive (Littorina saxatilis) and negative (L. fabalis) assortative mating. Our results show that both positive and negative estimates are biased by the SCE by different magnitudes, typically toward positive values. Therefore, the low frequency of negative assortative mating observed in the literature may be due to the SCE's impact on correlation estimates, which demands new experimental evaluation.     

A model‐based solution for observational errors in laboratory studies

Molecular techniques for detecting microorganisms, macroorganisms and infectious agents are susceptible to false‐negative and false‐positive errors. If left unaddressed, these observational errors may yield misleading inference concerning occurrence, prevalence, sensitivity, specificity and covariate relationships. Occupancy models are widely used to account for false‐negative errors and more recently have even been used to address false‐positive errors, too. Current modelling options assume false‐positive errors only occur in truly negative samples, an assumption that yields biased inference concerning detection because a positive sample could be classified as such not because the target agent was successfully detected, but rather due to a false‐positive test result. We present an extension to the occupancy modelling framework that allows false‐positive errors in both negative and positive samples, thereby providing unbiased inference concerning occurrence and detection, as well as reliable conclusions about the efficacy of sampling designs, handling protocols and diagnostic tests. We apply the model to simulated data, showing that it recovers known parameters and outperforms other approaches that are commonly used when confronted with observation errors. We then apply the model to an experimental data set on Batrachochytrium dendrobatidis, a pathogenic fungus that is implicated in the global decline or extinction of hundreds of amphibian species. The model‐based approach we present is not only useful for obtaining reliable inference when data are contaminated with observational errors, but also eliminates the need for establishing arbitrary thresholds or decision rules that have hidden and unintended consequences.     

Adaptive enrichment designs with a continuous biomarker

A popular design for clinical trials assessing targeted therapies is the two-stage adaptive enrichment design with recruitment in stage 2 limited to a biomarker-defined subgroup chosen based on data from stage 1. The data-dependent selection leads to statistical challenges if data from both stages are used to draw inference on treatment effects in the selected subgroup. If subgroups considered are nested, as when defined by a continuous biomarker, treatment effect estimates in different subgroups follow the same distribution as estimates in a group-sequential trial. This result is used to obtain tests controlling the familywise type I error rate (FWER) for six simple subgroup selection rules, one of which also controls the FWER for any selection rule. Two approaches are proposed: one based on multivariate normal distributions suitable if the number of possible subgroups, k, is small, and one based on Brownian motion approximations suitable for large k. The methods, applicable in the wide range of settings with asymptotically normal test statistics, are illustrated using survival data from a breast cancer trial.     

Genotype‐free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data

Restriction‐site associated DNA sequencing (RADSeq) facilitates rapid generation of thousands of genetic markers at relatively low cost; however, several sources of error specific to RADSeq methods often lead to biased estimates of allele frequencies and thereby to erroneous population genetic inference. Estimating the distribution of sample allele frequencies without calling genotypes was shown to improve population inference from whole genome sequencing data, but the ability of this approach to account for RADSeq‐specific biases remains unexplored. Here we assess in how far genotype‐free methods of allele frequency estimation affect demographic inference from empirical RADSeq data. Using the well‐studied pied flycatcher (Ficedula hypoleuca) as a study system, we compare allele frequency estimation and demographic inference from whole genome sequencing data with that from RADSeq data matched for samples using both genotype‐based and genotype free methods. The demographic history of pied flycatchers as inferred from RADSeq data was highly congruent with that inferred from whole genome resequencing (WGS) data when allele frequencies were estimated directly from the read data. In contrast, when allele frequencies were derived from called genotypes, RADSeq‐based estimates of most model parameters fell outside the 95% confidence interval of estimates derived from WGS data. Notably, more stringent filtering of the genotype calls tended to increase the discrepancy between parameter estimates from WGS and RADSeq data, respectively. The results from this study demonstrate the ability of genotype‐free methods to improve allele frequency spectrum‐ (AFS‐) based demographic inference from empirical RADSeq data and highlight the need to account for uncertainty in NGS data regardless of sequencing method.     

A new multivariate zero‐adjusted Poisson model with applications to biomedicine

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log‐normal model (Aitchison and Ho, 1989) cannot be used to fit multivariate count data with excess zero‐vectors; (ii) The multivariate zero‐inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero‐truncated/deflated count data and it is difficult to apply to high‐dimensional cases; (iii) The Type I multivariate zero‐adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods.     

Brood size moderates associations between relative size,telomere length,and immune development in European starling nestlings

For young birds in a nest, body size may have implications for other aspects of development such as telomere length and immune function. However, it is possible to predict associations in either direction. On the one hand, there may be trade‐offs between growth and telomere maintenance, and growth and investment in immune function, suggesting there will be negative correlations. On the other hand, relatively larger individuals might be advantaged in competition with their nest‐mates, allowing them to garner more resources overall, leading to positive correlations. We studied development over the nestling period in 34 nests of wild European starlings, Sturnus vulgaris. Intrabrood competition is typically more intense in larger broods. Hence, we predicted that body size should become an increasingly positive predictor of telomere length and immune functioning as brood size increases. In partial support of our prediction, there were significant interactions between brood size and body size in predicting both erythrocyte telomere length change and plasma levels of the cytokine interleukin‐6. The associations between body size and these outcomes went from negative in the smallest broods to positive in the largest. A further immune marker, high‐sensitivity C‐reactive protein, showed no systematic patterning with body size or brood size. Our results confirm that the size to which a nestling grows is important for telomere dynamics and the development of the immune system, but the phenotypic associations are moderated by the competitive context.     

In situ marker‐based assessment of leaf trait evolutionary potential in a marginal European beech population

Evolutionary processes are expected to be crucial for the adaptation of natural populations to environmental changes. In particular, the capacity of rear edge populations to evolve in response to the species limiting conditions remains a major issue that requires to address their evolutionary potential. In situ quantitative genetic studies based on molecular markers offer the possibility to estimate evolutionary potentials manipulating neither the environment nor the individuals on which phenotypes are measured. The goal of this study was to estimate heritability and genetic correlations of a suite of leaf functional traits involved in climate adaptation for a natural population of the tree Fagus sylvatica, growing at the rear edge of the species range. Using two marker‐based quantitative genetics approaches, we obtained consistent and significant estimates of heritability for leaf phenological (phenology of leaf flush), morphological (mass, area, ratio mass/area) and physiological (δ¹³C, nitrogen content) traits. Moreover, we found only one significant positive genetic correlation between leaf area and leaf mass, which likely reflected mechanical constraints. We conclude first that the studied population has considerable genetic diversity for important ecophysiological traits regarding drought adaptation and, second, that genetic correlations are not likely to impose strong genetic constraints to future population evolution. Our results bring important insights into the question of the capacity of rear edge populations to evolve.     

Linkage between loci of quantitative traits and marker loci: multi-trait analysis with a single marker

An efficient approach to increase the resolution power of linkage analysis between a quantitative trait locus (QTL) and a marker is described in this paper. It is based on a counting of the correlations between the QTs of interest. Such correlations may be caused by the segregation of other genes, environmental effects and physiological limitations. Let a QT locus A/a affect two correlated traits, x and y. Then, within the framework of mixture models, the accuracy of the parameter estimates may be seriously increased, if bivariate densities f _aa(x, y), f _Aa(x, y) and f _AA(x, y) rather than the marginals are considered as the basis for mixture decomposition. The efficiency of the proposed method was demonstrated employing Monte-Carlo simulations. Several types of progeny were considered, including backcross, F₂ and recombinant inbred lines. It was shown that provided the correlation between the traits involved was high enough, a good resolution to the problem is possible even if the QTL groups are strongly overlapping for their marginal densities.     

Genomic data detect corresponding signatures of population size change on an ecological time scale in two salamander species

Understanding the demography of species over recent history (e.g. <100 years) is critical in studies of ecology and evolution, but records of population history are rarely available. Surveying genetic variation is a potential alternative to census‐based estimates of population size, and can yield insight into the demography of a population. However, to assess the performance of genetic methods, it is important to compare their estimates of population history to known demography. Here, we leveraged the exceptional resources from a wetland with 37 years of amphibian mark–recapture data to study the utility of genetically based demographic inference on salamander species with documented population declines (Ambystoma talpoideum) and expansions (A. opacum), patterns that have been shown to be correlated with changes in wetland hydroperiod. We generated ddRAD data from two temporally sampled populations of A. opacum (1993, 2013) and A. talpoideum (1984, 2011) and used coalescent‐based demographic inference to compare alternate evolutionary models. For both species, demographic model inference supported population size changes that corroborated mark–recapture data. Parameter estimation in A. talpoideum was robust to our variations in analytical approach, while estimates for A. opacum were highly inconsistent, tempering our confidence in detecting a demographic trend in this species. Overall, our robust results in A. talpoideum suggest that genome‐based demographic inference has utility on an ecological scale, but researchers should also be cognizant that these methods may not work in all systems and evolutionary scenarios. Demographic inference may be an important tool for population monitoring and conservation management planning.     

A New Overall-Subgroup Simultaneous Test for Optimal Inference in Biomarker-Targeted Confirmatory Trials

We propose a joint hypothesis test for simultaneous confirmatory inference in the overall population and a pre-defined marker-positive subgroup under the assumption that the treatment effect in the marker-positive subgroup is larger than that in the overall population. The proposed confirmatory overall-subgroup simultaneous test (COSST) is based on partitioning the sample space of the test statistics in the marker-positive and marker-negative subgroups. We define two rejection regions in the joint sample space of the two test statistics: (1) efficacy in the marker-positive subgroup only; (2) efficacy in the overall population. COSST achieves higher statistical power to detect the overall and subgroup efficacy than most sequential procedures while controlling the family-wise type I error rate. COSST also takes into account the potentially harmful effect in the subgroups in the decision. The optimal rejection regions depend on the specific alternative hypothesis and the sample size. COSST can be useful for Phase III clinical trials with tailoring objectives.     

A comparison of the efficacy of different disinfection methods in eliminating Salmonella contamination from turkey houses

Aims: This study aimed to compare the efficacy of different disinfection methods in eliminating Salmonella contamination from turkey houses. Methods and Results: Fifty depopulated turkey houses which had all housed Salmonella‐positive flocks were visited after cleaning and disinfection. A minimum of 45 swab samples from different surfaces were taken per house and analysed for the presence of Salmonella. The sampled surfaces included intact floor surfaces, floor cracks, walls, feeders, drinkers, anteroom, nestboxes and miscellaneous items. Houses were grouped according to the disinfectant which had been used and the efficacy of the different groups of disinfectants was compared. Sixty‐eight % of houses tested positive for Salmonella after C&D. Out of 4440 samples, 207 tested positive for Salmonella, giving an overall sample prevalence of 4·7%. There was no significant difference in the level of residual contamination between breeding, rearing and finishing houses. Products containing a mixture of formaldehyde, glutaraldehyde and quaternary ammonium compounds (QAC) performed significantly better than products containing hydrogen peroxide and peracetic acid. Cleaning and disinfection was least effective in nestboxes and anterooms. Conclusions: Thorough cleaning and the choice of a suitable disinfectant are crucial if Salmonella contamination of turkey houses is to be eliminated. Significance and impact of the study: This study shows that disinfectants containing a mixture of formaldehyde, glutaraldehyde and QAC perform significantly better under field conditions than oxidising products and should therefore be the first choice for disinfection of turkey premises where Salmonella is present.     

Life‐history constraints on maximum population growth for loggerhead turtles in the northwest Atlantic

Conservation planning for protected species often relies on estimates of life‐history parameters. A commonly used parameter is the instantaneous maximum population growth rate (r_max) that can be used to limit removals and design recovery targets. Estimation of r_max can be challenging because of limited availability of species‐ and population‐specific data and life‐history information. We applied a method proposed by Neil and Lebreton, originally developed for birds, to loggerhead turtles. The method uses age‐at‐first‐reproduction and adult survival to estimate r_max. We used a variety of datasets and matrix population models to confirm an allometric assumption required by the method, and to generate estimates of age‐at‐first‐reproduction and adult survival. A meta‐analysis was applied to parameters from reported growth curves, which were then combined with the size distribution of neophyte nesters to derive estimates of age‐at‐first‐reproduction. Adult survival rates were obtained from an existing matrix population model. Monte Carlo simulation was then used to combine the estimates of the allometric coefficients, age‐at‐first‐reproduction, and adult survival to obtain a probability distribution of approximate r_max values. Estimated annual maximum population growth rates averaged 0.024, with a mode of 0.017 and a 95% highest density interval of 0.006–0.047. These estimates were similar to values reported by others using different methods and captured the variability in positive, annual change estimates across nesting beach sites for the northwest Atlantic loggerhead population. The use of life‐history parameters has a long history in wildlife and fisheries management and conservation planning. Our estimates of r_max, while having some biases and uncertainty, encompassed values presently used in recovery planning for loggerhead turtles and offer additional information for the management of endangered and threatened species.     

Models for Estimating Abundance from Repeated Counts of an Open Metapopulation

Summary Using only spatially and temporally replicated point counts, Royle (2004b, Biometrics 60, 108–115) developed an N ‐mixture model to estimate the abundance of an animal population when individual animal detection probability is unknown. One assumption inherent in this model is that the animal populations at each sampled location are closed with respect to migration, births, and deaths throughout the study. In the past this has been verified solely by biological arguments related to the study design as no statistical verification was available. In this article, we propose a generalization of the N ‐mixture model that can be used to formally test the closure assumption. Additionally, when applied to an open metapopulation, the generalized model provides estimates of population dynamics parameters and yields abundance estimates that account for imperfect detection probability and do not require the closure assumption. A simulation study shows these abundance estimates are less biased than the abundance estimate obtained from the original N ‐mixture model. The proposed model is then applied to two data sets of avian point counts. The first example demonstrates the closure test on a single‐season study of Mallards (Anas platyrhynchos), and the second uses the proposed model to estimate the population dynamics parameters and yearly abundance of American robins (Turdus migratorius) from a multi‐year study.     

Musashi 1‐positive cells derived from mouse embryonic stem cells can differentiate into neural and intestinal epithelial‐like cells in vivo

Msi1 (Musashi 1) is regarded as a marker for neural and intestinal epithelial stem cells. However, it is still unclear whether Msi1‐positive cells derived from mouse embryonic stem cells have the ability to differentiate into neural or intestinal epithelial cells. A pMsi1–GFP (green fluorescent protein) reporter plasmid was constructed in order to sort Msi1‐positive cells out of the differentiated cell population. The GFP‐positive cells (i.e. Msi1‐positive cells) were sorted by FACS and were hypodermically engrafted into the backs of NOD/SCID (non‐obese diabetic/severe combined immunodeficient) mice. The presence of neural and intestinal epithelial cells in the grafts was detected. Msi1 was highly expressed in the GFP‐positive cells, but not in the GFP‐negative cells. The markers for neural cells (Nestin and Tubulin β III) and intestinal epithelial cells [FABP2 (fatty acid binding protein 2), Lyz (lysozyme) and ChA (chromogranin A)] were more highly expressed in the grafts from Msi1‐positive cells than those from Msi1‐negative cells (P<0.05). The grafts from the Msi1‐negative cells contained more mesodermal‐like tissues than those from the Msi1‐positive cells. The pMsi1–GFP vector can be used to sort Msi1‐positive cells from a cell population derived from mouse embryonic stem cells. The Msi1‐positive cells can differentiate into neural and intestinal epithelial‐like cells in vivo.     

Density‐dependence in common tree species in a tropical dry forest in Mudumalai,southern India

Abstract. Density‐dependence in tree population dynamics has seldom been examined in dry tropical forests. Using long‐term data from a large permanent plot, this study examined 16 common species in a dry tropical forest in southern India for density‐dependence. Employing quadrat‐based analyses, correlations of mortality, recruitment and population change with tree densities were examined. Mortality in 1–10 cm diameter trees was largely negatively correlated with conspecific density, whereas mortality in > 10 cm diameter trees was positively correlated. Mortality was, however, largely unaffected by the basal area and abundance of heterospecific trees. Recruitment was poor in most species, but in Lagerstroemia microcarpa (Lythraceae), Tectona grandis (Verbenaceae) and Cassia fistula (Fabaceae), species that recruited well, strong negative correlations of recruitment with conspecific basal area and abundance were found. In a few other species that could be tested, recruitment was again negatively correlated with conspecific density. In Lagerstroemia, recruitment was positively correlated with the basal area and abundance of heterospecific trees, but these correlations were non‐significant in other species. Similarly, although the rates of population change were negatively correlated with conspecific density they were positive when dry‐season ground fires occurred in the plot. Thus, the observed positive density‐dependence in large‐tree mortality and the negative density‐dependence in recruitment in many species were such that could potentially regulate tree populations. However, repeated fires influenced density‐dependence in the rates of population change in a way that could promote a few common species in the tree community.     

Evaluating food availability and nest predation risk as sources of bias in aural bird surveys

ABSTRACT The use of aural surveys to estimate population parameters is widespread in avian studies. Despite efforts to increase the efficacy of this method, the potential for ecological context to bias population estimates remains largely unexplored. For example, food availability and nest predation risk can influence singing activity independent of density and, therefore, may bias aural estimates where these ecological factors vary systematically among habitats or other categories of ecological interest. We used a natural fire event in a mixed‐conifer forest that experienced variation in fire severity (low, intermediate, and high) to determine if aural surveys produce accurate density estimates of Dark‐eyed Juncos ( Junco hyemalis) independent of ecological context. During the first 2‐yr postfire, we censused junco populations in each burn type with intensive spot‐mapping and nest searching, locating 168 nests. Simultaneously, we conducted fixed‐radius point‐count surveys and estimated food availability and nest predation risk in each burn type to test whether ecological context may influence aural detection probability independent of actual density. We found no difference in nesting densities among patches burned at different severity. Arthropod food availability was inversely related to fire severity during the first postfire breeding season, but increased to higher levels across all severities during the second. In both years, aural detections were significantly greater in intermediate severity patches that consistently represented the habitat with the lowest nest predation risk. These results suggest that nest predation risk may significantly bias aural estimates of avian populations. Although traditional aural survey methods such as the Breeding Bird Survey measure habitat attributes, our findings highlight the difficulty in assessing relevant covariates in estimates of avian population. Future research must consider the potential for nest predation and other ecological factors to drive interannual or interhabitat variation in avian population estimates independent of true changes in population size.