The control of copy number of IS6110 in Mycobacterium tuberculosis

Insertion sequence (IS) elements are bacterial genes that are able to transpose to different locations in the genome. These elements are often used in molecular epidemiology as genetic markers that track the spread of pathogens. Transposable elements have frequently been described as "selfish DNA" because they facilitate their own transposition, causing damage when they insert into coding regions, while contributing little if anything to the bacterial host. According to this hypothesis, the expansion of copy number of insertion sequences is opposed by negative selection against high copy numbers. From an alternative point of view, we might expect IS elements to intrinsically regulate transposition within cells, thereby limiting damage to their bacterial host. Here, we report evidence that the copy number of IS6110 in Mycobacterium tuberculosis is controlled by selection against the element. We first construct 12 different models of marker change resulting from a combination of possible transposition functions and selective regimes. We then compute the Akaike Information Criterion for each model to identify the models that best explain data consisting of serial isolates of M. tuberculosis genotyped with IS6110. We find that the best performing models all include selection against the accumulation of copies. Specifically, our analysis points to the interaction of separate copies of the element causing lethal effects. We discuss the implications of these findings for genome evolution and molecular epidemiology.     

Isolation of homozygous mutant mouse embryonic stem cells using a dual selection system

Obtaining random homozygous mutants in mammalian cells for forward genetic studies has always been problematic due to the diploid genome. With one mutation per cell, only one allele of an autosomal gene can be disrupted, and the resulting heterozygous mutant is unlikely to display a phenotype. In cells with a genetic background deficient for the Bloom's syndrome helicase, such heterozygous mutants segregate homozygous daughter cells at a low frequency due to an elevated rate of crossover following mitotic recombination between homologous chromosomes. We constructed DNA vectors that are selectable based on their copy number and used these to isolate these rare homozygous mutant cells independent of their phenotype. We use the piggyBac transposon to limit the initial mutagenesis to one copy per cell, and select for cells that have increased the transposon copy number to two or more. This yields homozygous mutants with two allelic mutations, but also cells that have duplicated the mutant chromosome and become aneuploid during culture. On average, 26% of the copy number gain events occur by the mitotic recombination pathway. We obtained homozygous cells from 40% of the heterozygous mutants tested. This method can provide homozygous mammalian loss-of-function mutants for forward genetic applications.     

Comparative contribution of different genetic factors in induction of MGE transpositions under isogenization

Localization patterns of mobile genetic element 412 in polytene chromosomes of larvae from the control (riC) line, the balancer line, the F1 and F2 generations of the isogenization scheme, and 10 final isogenic lines were obtained and compared. The contributions of the recombination transfer of mobile genetic element copies from the balancer line, the outbreeding of control and balancer lines, and the inbreeding of isogenized lines to the rate of transposition were determined and estimated. These constituted < 0.187, < 0.30, and > 0.207 events per initial mobile genetic element copy per isogenized haploid genome per isogenization, respectively. During consecutive steps of isogenization (F1-F2-isogenic lines), the total transposition rate decreased: 2.09, 1.78, and 0.69. This was explained in terms of the existence of large selective and random losses in the variability of mobile genetic elements within the sites of their patterns during isogenization. The existence of a recombination transfer does not change the main conclusions and estimates regarding isogenization-induced transpositions.     

Genotype x environment interaction in QTL analysis of an intervarietal almond cross by means of genetic markers

Besides QTL location and the estimation of gene effects, QTL analysis based on genetic markers could be used to comprehensively investigate quantitative trait-related phenomena such as pleiotropy, gene interactions, heterosis, and genotype-by-environment interaction (G x E). Given that the G x E interaction is of great relevance in tree improvement, the objective of the research presented here was to study the effect of years on QTL detection for 15 quantitative traits by means of isozymatic markers in a large progeny group of an intervarietal cross of almond. At least 17 putative QTLs were detected, 3 of which had alleles with opposite effects to those predicted from the parental genotypes. Only 3 QTLs behaved homogeneously over the years. Three possible causes are discussed in relation to this lack of stability: the power of the test statistic being used, the low contribution of the QTL to the genetic variation of the trait, and a differential gene expression dependent on the year (G x E). Most cases showing lack of stability involved traits whose heritability estimates change drastically from year to year and/or whose correlation coefficients between years are low, suggesting the presence of G x E as the most likely cause. A marker-assisted selection scheme to improve late flowering and short flowering duration is suggested for an early and wide screening of the progeny.     

Occupancy in dynamic systems: accounting for multiple scales and false positives using environmental DNA to inform monitoring

Occupancy is an important metric to understand current and future trends in populations that have declined globally. In addition, occupancy can be an efficient tool for conducting landscape-scale and long-term monitoring. A challenge for occupancy monitoring programs is to determine the appropriate spatial scale of analysis and to obtain precise occupancy estimates for elusive species. We used a multi-scale occupancy model to assess occupancy of Columbia spotted frogs in the Great Basin, USA, based on environmental DNA (eDNA) detections. We collected three replicate eDNA samples at 220 sites across the Great Basin. We estimated and modeled ecological factors that described watershed and site occupancy at multiple spatial scales simultaneously while accounting for imperfect detection. Additionally, we conducted visual and dipnet surveys at all sites and used our paired detections to estimate the probability of a false positive detection for our eDNA sampling. We applied the estimated false positive rate to our multi-scale occupancy dataset and assessed changes in model selection. We had higher naïve occupancy estimates for eDNA (0.37) than for traditional survey methods (0.20). We estimated our false positive detection rate per qPCR replicate at 0.023 (95% CI: 0.016–0.033). When the false positive rate was applied to the multi-scale dataset, we did not observe substantial changes in model selection or parameter estimates. Conservation and resource managers have an increasing need to understand species occupancy in highly variable landscapes where the spatial distribution of habitat changes significantly over time due to climate change and human impact. A multi-scale occupancy approach can be used to obtain regional occupancy estimates that can account for spatially dynamic differences in availability over time, especially when assessing potential declines. Additionally, this study demonstrates how eDNA can be used as an effective tool for improved occupancy estimates across broad geographic scales for long-term monitoring.     

Genetic variability and gene flow in the globally, critically-endangered Taita thrush

Analysis of 155 individuals with seven polymorphicmicrosatellite DNA markers showed significant genetic differentiationbetween the only three remaining subpopulations of the globally,critically-endangered Taita thrush. Small, recently-disturbedsubpopulations such as studied here may violate the assumptions ofmutation-drift and gene flow-drift equilibrium inherent to mostpopulation genetic tools that estimate gene flow. We thereforeidentified putative dispersers using two recently-developed assignmenttests based on individual genotypes. Previous-generation and currentmigration rates between any two subpopulations were estimated at one andzero individuals per generation, respectively. Strong congruence withnon-genetic estimates of between-fragment dispersal provided indirectevidence for the accuracy of the assignment test. From a conservationperspective, the available demographic and genetic data suggest asubstantial threat to the long-term survival of at least the smallestsubpopulation.     

Lower-order effects adjustment in quantitative traits model-based multifactor dimensionality reduction

Identifying gene-gene interactions or gene-environment interactions in studies of human complex diseases remains a big challenge in genetic epidemiology. An additional challenge, often forgotten, is to account for important lower-order genetic effects. These may hamper the identification of genuine epistasis. If lower-order genetic effects contribute to the genetic variance of a trait, identified statistical interactions may simply be due to a signal boost of these effects. In this study, we restrict attention to quantitative traits and bi-allelic SNPs as genetic markers. Moreover, our interaction study focuses on 2-way SNP-SNP interactions. Via simulations, we assess the performance of different corrective measures for lower-order genetic effects in Model-Based Multifactor Dimensionality Reduction epistasis detection, using additive and co-dominant coding schemes. Performance is evaluated in terms of power and familywise error rate. Our simulations indicate that empirical power estimates are reduced with correction of lower-order effects, likewise familywise error rates. Easy-to-use automatic SNP selection procedures, SNP selection based on "top" findings, or SNP selection based on p-value criterion for interesting main effects result in reduced power but also almost zero false positive rates. Always accounting for main effects in the SNP-SNP pair under investigation during Model-Based Multifactor Dimensionality Reduction analysis adequately controls false positive epistasis findings. This is particularly true when adopting a co-dominant corrective coding scheme. In conclusion, automatic search procedures to identify lower-order effects to correct for during epistasis screening should be avoided. The same is true for procedures that adjust for lower-order effects prior to Model-Based Multifactor Dimensionality Reduction and involve using residuals as the new trait. We advocate using "on-the-fly" lower-order effects adjusting when screening for SNP-SNP interactions using Model-Based Multifactor Dimensionality Reduction analysis.     

Time dependency of molecular rate estimates and systematic overestimation of recent divergence times

Studies of molecular evolutionary rates have yielded a wide range of rate estimates for various genes and taxa. Recent studies based on population-level and pedigree data have produced remarkably high estimates of mutation rate, which strongly contrast with substitution rates inferred in phylogenetic (species-level) studies. Using Bayesian analysis with a relaxed-clock model, we estimated rates for three groups of mitochondrial data: avian protein-coding genes, primate protein-coding genes, and primate d-loop sequences. In all three cases, we found a measurable transition between the high, short-term (< 1-2 Myr) mutation rate and the low, long-term substitution rate. The relationship between the age of the calibration and the rate of change can be described by a vertically translated exponential decay curve, which may be used for correcting molecular date estimates. The phylogenetic substitution rates in mitochondria are approximately 0.5% per million years for avian protein-coding sequences and 1.5% per million years for primate protein-coding and d-loop sequences. Further analyses showed that purifying selection offers the most convincing explanation for the observed relationship between the estimated rate and the depth of the calibration. We rule out the possibility that it is a spurious result arising from sequence errors, and find it unlikely that the apparent decline in rates over time is caused by mutational saturation. Using a rate curve estimated from the d-loop data, several dates for last common ancestors were calculated: modern humans and Neandertals (354 ka; 222-705 ka), Neandertals (108 ka; 70-156 ka), and modern humans (76 ka; 47-110 ka). If the rate curve for a particular taxonomic group can be accurately estimated, it can be a useful tool for correcting divergence date estimates by taking the rate decay into account. Our results show that it is invalid to extrapolate molecular rates of change across different evolutionary timescales, which has important consequences for studies of populations, domestication, conservation genetics, and human evolution.     

Estimates of missing heritability for complex traits in Brown Swiss cattle

Background

Genomic selection estimates genetic merit based on dense SNP (single nucleotide polymorphism) genotypes and phenotypes. This requires that SNPs explain a large fraction of the genetic variance. The objectives of this work were: (1) to estimate the fraction of genetic variance explained by dense genome-wide markers using 54 K SNP chip genotyping, and (2) to evaluate the effect of alternative marker-based relationship matrices and corrections for the base population on the fraction of the genetic variance explained by markers.

Methods

Two alternative marker-based relationship matrices were estimated using 35 706 SNPs on 1086 dairy bulls. Both pedigree- and marker-based relationship matrices were fitted simultaneously or separately in an animal model to estimate the fraction of variance not explained by the markers, i.e. the fraction explained by the pedigree. The phenotypes considered in the analysis were the deregressed estimated breeding values (dEBV) for milk, fat and protein yield and for somatic cell score (SCS).

Results

When dEBV were not sufficiently accurate (50 or 70%), the estimated fraction of the genetic variance explained by the markers was around 65% for yield traits and 45% for SCS. Scaling marker genotypes with locus-specific frequencies of heterozygotes slightly increased the variance explained by markers, compared with scaling with the average frequency of heterozygotes across loci. The estimated fraction of the genetic variance explained by the markers using separately both relationships matrices followed the same trends but the results were underestimated. With less accurate dEBV estimates, the fraction of the genetic variance explained by markers was underestimated, which is probably an artifact due to the dEBV being estimated by a pedigree-based animal model.

Conclusions

When using only highly accurate dEBV, the proportion of the genetic variance explained by the Illumina 54 K SNP chip was approximately 80% for Brown Swiss cattle. These results depend on the SNP chip used and the family structure of the population, i.e. more dense SNPs and closer family relationships are expected to result in a higher fraction of the variance explained by the SNPs.     

Similar estimates of population genetic composition and sex ratio derived from carcasses and faeces of Eurasian otter Lutra lutra

Collecting faeces is viewed as a potentially efficient way to sample elusive animals. Nonetheless, any biases in estimates of population composition associated with such sampling remain uncharacterized. The goal of this study was to compare estimates of genetic composition and sex ratio derived from Eurasian otter Lutra lutra spraints (faeces) with estimates derived from carcasses. Twenty per cent of 426 wild-collected spraints from SW England yielded composite genotypes for 7-9 microsatellites and the SRY gene. The expected number of incorrect spraint genotypes was negligible, given the proportions of allele dropout and false allele detection estimated using paired blood and spraint samples of three captive otters. Fifty-two different spraint genotypes were detected and compared with genotypes of 70 otter carcasses from the same area. Carcass and spraint genotypes did not differ significantly in mean number of alleles, mean unbiased heterozygosity or sex ratio, although statistical power to detect all but large differences in sex ratio was low. The genetic compositions of carcass and spraint genotypes were very similar according to confidence intervals of theta and two methods for assigning composite genotypes to groups. A distinct group of approximately 11 carcass and spraint genotypes was detected using the latter methods. The results suggest that spraints can yield unbiased estimates of population genetic composition and sex ratio.     

Bayesian Coalescent Analysis Reveals a High Rate of Molecular Evolution in GB Virus C

GB virus C/hepatitis G (GBV-C) is an RNA virus of the family Flaviviridae. Despite replicating with an RNA-dependent RNA polymerase, some previous estimates of rates of evolutionary change in GBV-C suggest that it fixes mutations at the anomalously low rate of ∼10⁻⁷ nucleotide substitution per site, per year. However, these estimates were largely based on the assumption that GBV-C and its close relative GBV-A (New World monkey GB viruses) codiverged with their primate hosts over millions of years. Herein, we estimated the substitution rate of GBV-C using the largest set of dated GBV-C isolates compiled to date and a Bayesian coalescent approach that utilizes the year of sampling and so is independent of the assumption of codivergence. This revealed a rate of evolutionary change approximately four orders of magnitude higher than that estimated previously, in the range of 10⁻² to 10⁻³ sub/site/year, and hence in line with those previously determined for RNA viruses in general and the Flaviviridae in particular. In addition, we tested the assumption of host-virus codivergence in GBV-A by performing a reconciliation analysis of host and virus phylogenies. Strikingly, we found no statistical evidence for host-virus codivergence in GBV-A, indicating that substitution rates in the GB viruses should not be estimated from host divergence times.     

Development of a SNP genotyping panel for genetic monitoring of the laboratory mouse

We have developed a genotyping system for detecting genetic contamination in the laboratory mouse based on assaying single-nucleotide polymorphism (SNP) markers positioned on all autosomes and the X chromosome. This system provides a fast, reliable, and cost-effective way for genetic monitoring, while maintaining a very high degree of confidence. We describe the allelic distribution of 235 SNPs in 48 mouse strains, thereby creating a database of polymorphisms useful for genotyping purposes. The SNP markers used in this study were chosen from publicly available SNP databases. Four genotyping methods were evaluated, and dynamic two-tube allele-specific PCR assays were developed for each marker and tested on a set of 48 inbred mouse strains. The minimal number of assays sufficient to distinguish groups consisting of different numbers of mouse strains was estimated, and a panel of 28 SNPs sufficient to distinguish virtually all of the inbred strains tested was selected. Amplifluor SNP detection assays were developed for these markers and tested on an extended list of 96 strains. This panel was used as a genetic quality control approach to monitor the genotypes of nearly 300 inbred, wild-derived, congenic, consomic, and recombinant inbred strains maintained at The Jackson Laboratory. We have concluded that this marker panel is sufficient for genetic contamination monitoring in colonies containing a large number of genetically diverse mouse strains and that reduced versions of the panel could be implemented in facilities housing a lower number of strains.     

Application of EST-derived microsatellite markers for analysis of genetic variation in tall fescue and its comparison with morphological markers

Analysis of genetic diversity in germplasm collections is an important component of crop improvement programs. This study was conducted to analyze genetic variation and to classify tall fescue genotypes based on phenotypic evaluation and EST-SSR molecular markers. Twenty-five genotypes were assessed based on phenotypic and 42 EST-SSR molecular markers according to a completely randomized block design with three replications during eight years (2007–2014). Results indicated that the effect of year, genotype and their interaction were significant for all of the measured traits. Both morphological and molecular assessments showed considerable genetic variation among genotypes. The estimates of broad-sense heritability (h²_b) were moderate to high (h²_b = 42.1–78.4) for the traits studied. Based on EST-SRR analysis, a total number of 229 alleles were detected with an average of 4.58 alleles per marker. Average PIC value was 0.49 with a range of 0.014 for NFA140 to 0.95 for NFA047. Phenotypic evaluations and EST-SSR molecular marker classified genotypes into 3 and 7 clusters, respectively which mainly supported geographical origins. The general correspondence was observed between morphological and molecular classification. Therefore, combining the molecular markers with morphological responses could be more beneficial to describe genetic variation and distinguish superior genotypes for future breeding programs.     

Population genetics of the yellow fever mosquito in Trinidad: comparisons of amplified fragment length polymorphism (AFLP) and restriction fragment length polymorphism (RFLP) markers

Recent development of DNA markers provides powerful tools for population genetic analyses. Amplified fragment length polymorphism (AFLP) markers result from a polymerase chain reaction (PCR)-based DNA fingerprinting technique that can detect multiple restriction fragments in a single polyacrylamide gel, and thus are potentially useful for population genetic studies. Because AFLP markers have to be analysed as dominant loci in order to estimate population genetic diversity and genetic structure parameters, one must assume that dominant (amplified) alleles are identical in state, recessive (unamplified) alleles are identical in state, AFLP fragments segregate according to Mendelian expectations and that the genotypes of an AFLP locus are in Hardy-Weinberg equilibrium (HWE). The HWE assumption is untestable for natural populations using dominant markers. Restriction fragment length polymorphism (RFLP) markers segregate as codominant alleles, and can therefore be used to test the HWE assumption that is critical for analysing AFLP data. This study examined whether the dominant AFLP markers could provide accurate estimates of genetic variability for the Aedes aegypti mosquito populations of Trinidad, West Indies, by comparing genetic structure parameters using AFLP and RFLP markers. For AFLP markers, we tested a total of five primer combinations and scored 137 putative loci. For RFLP, we examined a total of eight mapped markers that provide a broad coverage of mosquito genome. The estimated average heterozygosity with AFLP markers was similar among the populations (0.39), and the observed average heterozygosity with RFLP markers varied from 0.44 to 0.58. The average FST (standardized among-population genetic variance) estimates were 0.033 for AFLP and 0.063 for RFLP markers. The genotypes at several RFLP loci were not in HWE, suggesting that the assumption critical for analysing AFLP data was invalid for some loci of the mosquito populations in Trinidad. Therefore, the results suggest that, compared with dominant molecular markers, codominant DNA markers provide better estimates of population genetic variability, and offer more statistical power for detecting population genetic structure.     

Sex-specific genetic structure in Schistosoma mansoni: evolutionary and epidemiological implications

We studied the population genetic structure of 360 and 1247 adult Schistosoma mansoni using seven microsatellite and seven random amplified polymorphic DNA (RAPD) markers, respectively. Parasites were collected from their natural definitive host Rattus rattus in Guadeloupe (West Indies). We found a sex-specific genetic structure, a pattern never before reported in a parasitic organism. Male genotypes were more randomly distributed among rats than female genotypes. This interpretation was consistent with a lower differentiation between hosts for males relative to females, the higher genetic similarity between females in the same host and the observed local (i.e. within-individual-host) differences in allele frequencies between the two sexes. We discuss our results using ecological and immunological perspectives on host-parasite relationships. These results change our view on the epidemiology of schistosomiasis, a serious disease affecting humans in African and American intertropical zones.     

High mitochondrial mutation rates estimated from deep-rooting Costa Rican pedigrees

Estimates of mutation rates for the noncoding hypervariable Region I (HVR-I) of mitochondrial DNA vary widely, depending on whether they are inferred from phylogenies (assuming that molecular evolution is clock-like) or directly from pedigrees. All pedigree-based studies so far were conducted on populations of European origin. In this article, we analyzed 19 deep-rooting pedigrees in a population of mixed origin in Costa Rica. We calculated two estimates of the HVR-I mutation rate, one considering all apparent mutations, and one disregarding changes at sites known to be mutational hot spots and eliminating genealogy branches which might be suspected to include errors, or unrecognized adoptions along the female lines. At the end of this procedure, we still observed a mutation rate equal to 1.24 × 10(-6) , per site per year, i.e., at least threefold as high as estimates derived from phylogenies. Our results confirm that mutation rates observed in pedigrees are much higher than estimated assuming a neutral model of long-term HVRI evolution. We argue that until the cause of these discrepancies will be fully understood, both lower estimates (i.e., those derived from phylogenetic comparisons) and higher, direct estimates such as those obtained in this study, should be considered when modeling evolutionary and demographic processes.     

Empirical evaluation of genetic clustering methods using multilocus genotypes from 20 chicken breeds

We tested the utility of genetic cluster analysis in ascertaining population structure of a large data set for which population structure was previously known. Each of 600 individuals representing 20 distinct chicken breeds was genotyped for 27 microsatellite loci, and individual multilocus genotypes were used to infer genetic clusters. Individuals from each breed were inferred to belong mostly to the same cluster. The clustering success rate, measuring the fraction of individuals that were properly inferred to belong to their correct breeds, was consistently approximately 98%. When markers of highest expected heterozygosity were used, genotypes that included at least 8-10 highly variable markers from among the 27 markers genotyped also achieved >95% clustering success. When 12-15 highly variable markers and only 15-20 of the 30 individuals per breed were used, clustering success was at least 90%. We suggest that in species for which population structure is of interest, databases of multilocus genotypes at highly variable markers should be compiled. These genotypes could then be used as training samples for genetic cluster analysis and to facilitate assignments of individuals of unknown origin to populations. The clustering algorithm has potential applications in defining the within-species genetic units that are useful in problems of conservation.     

The risks of learning: confounding detection and demographic trend when using count‐based indices for population monitoring

Theory recognizes that a treatment of the detection process is required to avoid producing biased estimates of population rate of change. Still, one of three monitoring programmes on animal or plant populations is focused on simply counting individuals or other fixed visible structures, such as natal dens, nests, tree cavities. This type of monitoring design poses concerns about the possibility to respect the assumption of constant detection, as the information acquired in a given year about the spatial distribution of reproductive sites can provide a higher chance to detect the species in subsequent years. We developed an individual‐based simulation model, which evaluates how the accumulation of knowledge about the spatial distribution of a population process can affect the accuracy of population growth rate estimates, when using simple count‐based indices. Then, we assessed the relative importance of each parameter in affecting monitoring performance. We also present the case of wolverines (Gulo gulo) in southern Scandinavia as an example of a monitoring system with an intrinsic tendency to accumulate knowledge and increase detectability. When the occupation of a nest or den is temporally autocorrelated, the monitoring system is prone to increase its knowledge with time. This happens also when there is no intensification in monitoring effort and no change in the monitoring conditions. Such accumulated knowledge is likely to increase detection probability with time and can produce severe bias in the estimation of the rate and direction of population change over time. We recommend that a systematic sampling of the population process under study and an explicit treatment of the underlying detection process should be implemented whenever economic and logistical constraints permit, as failure to include detection probability in the estimation of population growth rate can lead to serious bias and severe consequences for management and conservation.     

A model-based Bayesian estimation of the rate of evolution of VNTR loci in Mycobacterium tuberculosis

Variable numbers of tandem repeats (VNTR) typing is widely used for studying the bacterial cause of tuberculosis. Knowledge of the rate of mutation of VNTR loci facilitates the study of the evolution and epidemiology of Mycobacterium tuberculosis. Previous studies have applied population genetic models to estimate the mutation rate, leading to estimates varying widely from around to per locus per year. Resolving this issue using more detailed models and statistical methods would lead to improved inference in the molecular epidemiology of tuberculosis. Here, we use a model-based approach that incorporates two alternative forms of a stepwise mutation process for VNTR evolution within an epidemiological model of disease transmission. Using this model in a Bayesian framework we estimate the mutation rate of VNTR in M. tuberculosis from four published data sets of VNTR profiles from Albania, Iran, Morocco and Venezuela. In the first variant, the mutation rate increases linearly with respect to repeat numbers (linear model); in the second, the mutation rate is constant across repeat numbers (constant model). We find that under the constant model, the mean mutation rate per locus is (95% CI: ,)and under the linear model, the mean mutation rate per locus per repeat unit is (95% CI: ,). These new estimates represent a high rate of mutation at VNTR loci compared to previous estimates. To compare the two models we use posterior predictive checks to ascertain which of the two models is better able to reproduce the observed data. From this procedure we find that the linear model performs better than the constant model. The general framework we use allows the possibility of extending the analysis to more complex models in the future.