DRD4). Such a link implies that the within-individual consistency in the same behavior has a genetic basis. Behavioral consistency is also prevalent in the form of between-individual correlation of functionally different behaviors; thus, the relationship between DRD4 polymorphism and exploration may also be manifested for other behaviors. Here, in a Hungarian population of the collared flycatcher, Ficedula albicollis, we investigate how males with distinct DRD4 genotypes differ in the consistent elements of their behavioral displays during the courtship period. In completely natural conditions, we assayed novelty avoidance, aggression and risk-taking, traits that were previously shown repeatable over time and correlate with each other, suggesting that they could have a common mechanistic basis. We identified two single-nucleotide polymorphisms (SNP554 and SNP764) in the exon 3 of the DRD4 gene by sequencing a subsample, then we screened 202 individuals of both sexes for these SNPs. Focusing on the genotypic variation in courting males, we found that “AC” heterozygote individuals at the SNP764 take lower risk than the most common “AA” homozygotes . We also found a considerable effect size for the relationship between SNP554 polymorphism and novelty avoidance. Therefore, in addition to exploration, DRD4 polymorphisms may also be associated with the regulation of behaviors that may incur fear or stress. Moreover, polymorphisms at the two SNPs were not independent indicating a potential role for genetic constraints or another functional link, which may partially explain behavioral correlations.There is increasing evidence that the genetic architecture of exploration behavior includes the dopamine receptor D4 gene ( Unraveling the extent by which behavior is determined by genetic makeup could be vital to understanding the evolutionary ecology of behavioral phenotypes within the normal range of variation in different taxa  gene. The gene product is an important component of the dopaminergic system, and its function is linked to many neurological and psychiatric disorders but also contributes to the guidance of normal behaviors such as novelty seeking in humans, although some evidence is at conflict with this general suggestion  is a small hole-nesting European passerine that has been intensively studied for consistent behaviors of males during the courtship period. Based on the repeated measures of the same individuals in different days in their natural environment, it seems evident that displaying birds demonstrates repeatable singing, territorial and risk-taking behavior over time . Once these males were localized at a known nest box, we initiated our behavioral trials (see below) at the same locality allowing measurements on free individuals. We captured males only after these assays with a conventional nest-box trap, which was followed by the measurements of their morphological traits . We also assigned males into a two-scale age category (yearlings/older), based on the typical plumage coloration . We finally took a blood sample from the brachial vein for the subsequent molecular analyses (see below). After the measurements and blood sampling, birds were released. Most of the released birds  were observed continuing their courtship activity and realizing successful breeding , where about 800 artificial nest boxes had been positioned in the eighties to allow field studies of hole-nesting passerines , we relied on the entire dataset. Although, we aimed at measuring all behavioral traits in all males, due to various constraints, information on particular behaviors may not be available in few cases causing slight variation in sample size among analyses . Individuals assayed in the behavioral tests and screened for DRD4 were not used in other experiments.Although we hold records for these behaviors for 7 years, for the present study, we focused on 3 years from the overall dataset, for which we also have genetic data . For some analyses that did not require information on DRD4 screening for more than two hundred individuals  and females (N = 86) from the courtship as well as from the chick-feeding period in order to obtain an unbiased picture about the frequency distribution of genotypes. However, when exploring the potential relationship between behavior and polymorphism at the DRD4, we focused on a subsample of males from the courtship period with information on the three aforementioned behavioral traits.Altogether, over the 3 years, we collected blood samples for DRD4 (604 bp) in ten randomly selected flycatcher individuals using the primers DRD4_I2F (CACCACACCAGGACTGACT) and DRD4_I3R (GTGKGACAAGSTGGCACATTT). We identified three loci within this region, one of which turned out to be little variable  until the end of the field season. Then, in the laboratory, we amplified and directly sequenced the complete exon 3 of the r et al. .DRD4-exploration associations in the great tit, Parus major  10-transformation to obtain more or less symmetrical distribution  that avoids having more influential values in one end of the distribution than in the other end. The response variables  were either log10-transformed (risk-taking), or left untransformed , but models were also run based on their rank-transformed values as well as based on bivariate categories (see details below). Each individual was represented with a single measurement , we applied logoup Fig. .We used chi-square test to compare the frequency distribution of genotypes with expectations derived from the Hardy–Weinberg theorem and also to compare genotype frequencies between sexes as well as for the association between the two SNPs.DRD4 polymorphism, we created general linear mixed models with the following structure. In our starting models, the measured behavioral variables were treated as response variables, and the error structure and link function of the models were defined according to their distribution . To circumvent the problem posed by potential deviations from the chosen distributions and the effect of censored variables , we also performed alternative analyses based on the rank-transformed values of the behavioral variables. To deal with the effect of the arbitrary ordering of males with the same aggression score, we repeated the ranking procedure multiple times (1000) and run the same models. Moreover, we also created bivariate categories based on the distribution of the variables . These exercises targeting the distribution of the response variables gave qualitatively identical results to those of the starting models; thus, subsequently we focus on these.To analyze the relationship between behavioral phenotypes and DRD4 genotypes. Year effects were handled by entering this variable as a random factor in the model, while we also used the identity of the decoy used in the behavioral test to control for the potential effect of similar response elicited by the same stimulus . Initially, we considered both random intercepts and slopes to capture potential between-year differences in the focal association, but our model comparison exercises (see strategies below) revealed that the inclusion of random slopes into the model did not generally offer higher fit to the data . Hence, we proceeded with the simpler models without random slope structures. We did not include additional variables in the model in order to avoid too many predictors relative to sample size. However, using an extended dataset , we tested whether the response variables and the DRD4 genotypes were related to male size, male condition, and the size of the two sexually selected plumage traits (wing patch size and forehead patch size). These analyses showed that none of these variables describing male quality affected strongly our focal variables  reflects territory quality, as males likely occupy territories in order of quality along their arrival to the breeding grounds Kokko . We alsoBefore interpreting the model outcomes, we performed numerous model diagnostics statistics to avoid misleading results based on statistical artifacts. We first checked assumptions about the distribution of residuals, that is, whether they were normally and homogeneously distributed. The visual inspection of the corresponding diagnostics plots  indicated no obvious deviations from these assumptions. Second, we examined issues about multicollinearity that might potentially lead to instable results and unreliable parameter estimates . Merely for illustration purposes, we provide such an estimation of statistical significance in the form of P value, but for biological interpretations, we focus on effect sizes (sensu Nakagawa and Cuthill χ2/N), where χ2 is from the above likelihood ratio test and N is the total sample size . This index (but only at df = 1) is equivalent to the correlation coefficient r, thus the same guidelines can be applied for making inferences about the magnitude of the effect  = 0.14 and 1−f(T) = f(C) = 0.86. The population did not significantly deviate from Hardy–Weinberg equilibrium that can be predicted from these allele frequencies . Similar calculations for SNP764 revealed allele frequencies of f(C) = 0.12 and f(A) = 0.88 .For SNP554, “TT” homozygotes were extremely rare . Tests for independence of the genetic variation at the two SNPs revealed that they are correlating . This pattern showed that the occurrence of the genotype combinations of SNP554CC × SNP764AA and SNP554CT × SNP764CA was more common than would be expected by chance, indicating that there is some linkage disequilibrium between the C allele of SNP554 and the A allele of SNP764 .Sexes and age categories showed no significant differences in their genotypes .In the whole sample spanning over 7 years, there was an evidence for a weak to medium, and consistently positive correlation between behavioral traits .We compared behavioral scores of males monitored during the courtship period across cts Fig. . A consicts Fig. A and 3. DRD4 orthologue in a wild collared flycatcher population. The genotype frequencies were comparable with the unequal proportion of genotypes detected in other bird species for other DRD4 orthologues .The most striking result of our study was that genetic polymorphism at the SNP764 was related to the phenotypic variation in risk-taking behavior, as estimated by flight initiation distance. In our statistical framework, such a relationship could be best described as an effect with medium size. However, given the limitations of our data, which define a given confidence around this approximation, we remain uncertain about the exact whereabouts of the true effect along the continuum from small to strong effects. In any case, such an effect has a biological importance for at least two reasons. First, behaviors are known to be under the control of multiple genes leaving very small effects for each particular gene  with a confidence interval that highly overlaps with some of the effect sizes we detected in this study  have a higher potential to be associated with DRD4. This contextual overlap is also in line with the fact that novelty seeking is the most important behavioral correlate of the genetic region  than the homozygotes for the common alleles  have been suggested as potential mechanisms that may mediate a functional relationship between the DRD4 SNPs and behavior (Duan et al. In any case, inferences about the underlying genetic model for the impact of DRD4 polymorphism (Table If the detected associations truly reflect the importance of the genetic control of behaviors, they could have important implications for the evolution of behavioral syndromes. The constraint hypothesis posits that the common physiological background can cause the governed behavioral traits to covary Bell . For exasm Table , may imp


The idea that we can reconstruct a human face from a DNA sample has great appeal: DNA from a crime scene could be used to create a facial image of a suspect; the faces of prehistoric peoples could be reconstructed from their remains; the face of a child could be predicted in utero from amniocentesis. This is the promise implicit in the study of Claes et al. “…our methods provide the means of identifying the genes that affect facial shape and for modeling the effects of these genes to generate a predicted face.” (pg. 10)Unfortunately, this promise greatly overreaches the data and analyses represented by the study, and it misrepresents our current understanding of the genetics of complex morphological traits. Worse, this claim, and the fairly sensational media reports that have stemmed from it, detract significantly from what is otherwise an important paper that highlights the potential of an interesting new technique to investigate the genetic basis for variation in the shape of the human face.Claes et al. Shh plays crucial roles in forming the face But why is the claim that facial shape can be predicted from DNA so troubling? The first reason is that it isn't actually supported by the work done in this study. Claes et al. The second, and deeper, issue, though, is whether genomic prediction of complex morphologies is even feasible. Obviously, variation in genes causes variation at the phenotypic level. This does not mean, though, that a complex phenotype can be accurately predicted from genetic data. For a trait such as coronary heart disease (CHD), prediction of risk is highly problematic, even though quite a lot is known about the underlying genetics Complex patterns of interactions among developmental processes can also generate unexpected patterns of heritability. Although genetic variance may be predominantly additive for complex traits such as oil content in maize or body mass in mice The overselling of these results is unfortunate and unnecessary because it detracts from what is otherwise a very interesting study. The bootstrapped response-based imputation modeling (BRIM) technique, for example, is an intriguing extension of the shape analysis toolkit. Like other geometric morphometric methods, it is based on Procrustes superimposition and multivariate data reduction of variation in landmark position. The method allows for estimation of a single quantitative axis of variation that would correspond to a multidimensional factor such as ancestry, or a discrete variable such as sex. The method relies on machine learning algorithms to define shape axes that best correspond to such variables. As such, the method has great potential for furthering quantitative analyses of the genetics of complex morphologies. The use of dense landmark representation and machine learning algorithms similarly has potential in the analysis of complex morphologies, and this study points the way towards future applications of such techniques. This aspect of the paper would be stronger had they validated BRIM by comparison to existing methods. Estimation of the effects of single genes, ancestry, sex or any other factor of interest on total shape variation and local shape variation can already be done using the current GM toolkit Developing a mechanistic understanding of the genotype–phenotype map is undoubtedly one of the greatest challenges of modern biology. Claes et al. offer us a new and valuable tool to apply to this grand challenge. We should not be fooled, however, into thinking that we are anywhere close to understanding developmental genetics at the level where prediction of complex morphological traits is feasible. Overselling and overpromising in science is dangerous because it creates unreasonable expectations both at the public and policymaker levels. Ultimately, this runs the risk of diverting valuable scientific resources away from the important task of understanding how variation in genes plays through developmental processes to produce the amazing diversity of organismal form.


Together with the avoidance of any negative impact of inbreeding, preservation of genetic variability for life-history traits that could undergo future selective pressure is a major issue in endangered species management programmes. However, most of these programmes ignore that, apart from the direct action of genes on such traits, parents, as contributors of offspring environment, can influence offspring performance through indirect parental effects . Using quantitative genetic models, we estimated the additive genetic variance for juvenile survival in a population of the endangered Cuvier's gazelle kept in captivity since 1975. The dataset analyzed included performance recording for 700 calves and a total pedigree of 740 individuals. Results indicated that in this population juvenile survival harbors significant additive genetic variance. The estimates of heritability obtained were in general moderate (0.115–0.457) and not affected by the inclusion of inbreeding in the models. Maternal genetic contribution to juvenile survival seems to be of major importance in this gazelle's population as well. Indirect genetic and indirect environmental effects assigned to mothers  roughly explain a quarter of the total variance estimated for the trait analyzed. These findings have major evolutionary consequences for the species as show that offspring phenotypes can evolve strictly through changes in the environment provided by mothers. They are also relevant for the captive breeding programme of the species. To take into account, the contribution that mothers have on offspring phenotype through indirect genetic effects when designing pairing strategies might serve to identify those females with better ability to recruit, and, additionally, to predict reliable responses to selection in the captive population. Juvenile survival is a critical component of population dynamics. In endangered species managed through captive breeding programmes, the survival of juveniles is crucial for population viability. These conservation programmes focus mainly on the preservation of genetic variability to avoid any negative impact of inbreeding. The genetic effect of inbreeding is the inbreeding depression: the decrease of the individual fitness through reduced fecundity, offspring viability, and individual survivorship (Charlesworth and Charlesworth h2), defined as the proportion of total phenotypic variance that can be ascribed to additive genetic variance , representing a wealth of invaluable untapped data for quantitative genetic approaches, as they contain detailed pedigree information rarely available for wild populations  i Ogilby . In thisCuvier's gazelle Fig.  is a medFi), defined as the probability that an individual has two identical alleles by descent , including traumatisms and injuries due to intraspecific agonistic behavior with adults in the herd. The final dataset analyzed consisted of 700 Cuvier's gazelle calf studbook records , that is, the variation of a quantitative trait explained by the genotype of the individual on which performance is recorded. Here, the direct genetic effect is referred to calf. The ratio of the variance explained by the direct genetic effect to the total phenotypic variance will be referred as ‘heritability’ (h2).Direct genetic effects (m) defined as any phenotypic influence from a dam on her offspring (excluding the effects of directly transmitted genes) that affect offspring performance (Willham m2).Maternal genetic effects ( Willham . Biologic), that is, those effects on offspring phenotype shared by offspring of the same mother, independent of additive genetic effects. These are a particular case of environmental effects shared by groups of individuals, for instance, effects shared by groups of relatives or individuals belonging to the same cohort. The ratio of the estimates of this effect to the total phenotypic variance will be termed as c2.Permanent maternal environmental effects  is a flat, uniform function, while the a priori function for random effects is Gaussian.Juvenile survival is a discrete, dichotomous trait. The estimates of genetic parameters in dichotomous traits may depend on the population mean for the trait and, theoretically, threshold models would better account for the probabilistic structure of categorical data than linear models do (Gianola and Foulley Continuous (C) model assuming that the analyzed trait was a continuous variable with normal distribution.Threshold (T) model, also called probit, Gianola  that theWe first analyzed juvenile survival running a complete reference model (equation 1) where offspring survival is treated as a trait of the calf as well as of the mother and of the father; that it, we run a model including all the possible random effects. This model is, however, irresolvable because relationship coefficients involved are less than the number of parameters to be estimated , with d as their vector; P is the incidence matrix of paternal genetic effects (s), with s as their vector; W is the incidence matrix of maternal permanent environmental effects (c), with p as their vector; e is a vector of residuals effects; m, s, σud the covariance between the direct (additive) and the additive genes underlying m, σus the covariance between the direct (additive) and the additive genes underlying s, σds is the covariance between the additive genes underlying m and s, c), I is an identity matrix, and A is the numerator relationship matrix. Due to the dichotomous nature of the analyzed trait, in threshold models, a restriction was set so that residual variance was set to 1 and threshold was set to 0.where b: year of calving , mother parity (2 levels: primiparous or multiparous), age of the dam at calving in days, as linear and quadratic covariate, and litter composition . As fitted, this litter composition accounts for the different probability of survival in a male or female twin whether or not the cotwin is the same sex.The model includes the following systematic effects in In mammals (livestock and wild), the magnitude of maternal effects is generally larger than the magnitude of the paternal effects Cheverud . Thus coCalf model: Offspring survival is treated as a trait of calves. In this model, only direct additive genetic effect of the calf is fitted as random effects besides the residual.Calf-dam model: Offspring survival is treated as a trait determined by calves and maternal genetic effects.Calf-permanent model: Offspring survival is treated as a trait determined by calves and maternal permanent environmental effects.Calf-dam-permanent model: Offspring survival is treated as a trait determined by calves, maternal genetic effects, and maternal permanent environmental effects.These models included 700 calves producing data and a relationship matrix of 740 individuals Table .Gazella cuvieri genetic background. Then, models described above were also classified according to the assessment made regarding the influence of inbreeding on the trait as:In the studied population, there is no clear evidence for the influence of inbreeding on performance across different life-history traits as some studies have found support for this influence  producing data in the model both as a linear and a quadratic covariate. This model account for the well-known nonlinear relationship between inbreeding coefficients and inbreeding depression  or the influence of the father  should also be explored. Therefore, a number of complementary models were fitted as well to find out the likely influence of the mother, the father, or of both parents in this phenotypic trait of their offspring. A full description of the complementary models fitted, and their results are given in the Supplementary Material and in Tables S1 and S2.b were assigned as bounded uniform prior distribution, and the variance components v = 2 and S = 0). A total Gibbs chain length of 1,000,000 samples for each analysis were defined, with a burn-in period of 100,000 and a thinning interval of 100.All estimations were carried out in a Bayesian frame using the TM program Legarra . MarginaModels were tested and examined to choose the one that best predicted performance instead of goodness of fit, as models with the best fit are not always those that provide the best prediction. At present, cross-validation , the genetic parameters reestimated running the models solved without them, and the removed records estimated according to the obtained solutions. The solutions obtained for the records removed were compared to the real performance data via classical correlation to assess the predictive ability of the model. Then, the correlation (r) between the real removed record and the continuous solution (not rounded estimated record in the continuous models and the underlying variable in the threshold models) was computed. To avoid sampling bias, each model was rerun for 20 random samples and the correlation averaged. Once the best model was chosen, additive genetic values were averaged within year of birth to explore signs of genetic trend of the trait.When the best model had been selected by cross-validation, inferences about systematic effects were carried out in a Bayesian context. Therefore, as marginal posterior distributions are available, inferences can be performed in terms of probability of the parameter being located between arbitrary values. In this case, inferences were provided in terms of probability of some desired parameters being higher than 0.−3; 87% of probability being positive) for the linear adjustment and negative  for the quadratic adjustment which means that offspring born to young and to old mothers are less likely to survive than those born to middle-aged mothers  than their threshold counterparts (Table h2 = 0.457 ± 0.173 vs. h2 = 0.245 ± 0.0.085). These estimates decreased with inclusion of maternally related random effects in the models fitted (Table m and c) were even higher than estimates of direct additive genetic effects. In continuous models, however, such maternal effects are always lower than direct genetic effects (Table r = 0.008 for the continuous and r = 0.015 for the threshold model). From all these models, the best prediction ability was shown by the Calf-dam continuous model, with r = 0.103  by year of birth of the individuals. A positive phenotypic trend for juvenile survival over time was found. The genetic ability for juvenile survival has increased over years. The probability of the genetic response to be higher than zero increased across years, increasing from 81% to 89% for the calves and from 71% to 82% for the mothers since 2000). The increase in both calf and mother's genetic ability for the trait was noticeably congruent. As genetic trends were assessed in a Bayesian context, they are not affected by correlated prediction error among cohorts and genetic drift, as they were if we had used the best linear unbiased prediction (BLUP) to predict breeding values  versus indirect  effects underlying this fundamental life-history trait is essential to predict the strength and direction of the evolution of this captive population. In this extremely bottlenecked population, the heritability of juvenile survival is 0.36 (with 98% of probability of being higher than 0.05), which suggests that a non-negligible phenotypic variation observed in this fitness trait is ascribed to additive genetic variance. There are also indirect parental  effects in this trait which may produce phenotypic resemblance between relatives equivalent to or even greater than that due to the additive genetic variance. Thus, genes influencing juvenile survival are not only those expressed in the individual , but also those of an interacting phenotype, its mother. This means that a calf's phenotype may also evolve through changes in the environment provided by its mother.Juvenile survival in Cuvier's gazelle is highly influenced by both mother parity and mother age Fig. , which iLitter composition (a factor that captures sex and litter size) influences infant survival as well in Cuvier's gazelles. The highest mortality was found for single male offspring (M) and for offspring with a male cotwin [F(M); M(M); see also Ibáñez et al. Maternal permanent environmental effect also explains a proportion of the variance of juvenile survival. The data fit for the Calf-dam-permanent models were slightly lower than for the Calf-dam models. The small size of the available dataset led to poorer performance of the models fitted as the number of effects included increased. Although these maternal effects do not contribute directly to the evolutionary response to selection  of juvenile survival in the Cuvier's gazelle was moderate  were higher when the trait was considered only as a calf trait than with the inclusion of maternally related random effects in the models fitted, suggesting that the additive genetic variances were overestimated due to previously unaccounted for genetic and environmental maternal effects. In our analyses, the maternal variance components indicated that mothers vary in their influence on the survival of their offspring. The models fitted allowed us to separate maternal variance from offspring additive variance. As maternal effects were consistent across models, we infer that indirect maternal effects operate on juvenile survival through maternal selection. When maternal genetic effects are not negligible, response to selection depends not only on direct, but also on the additive genes underlying the maternal genetic effect (m), which can result in accelerated, or dampened response to selection  the Cuvier's Gazelle captive breeding program is effective in achieving genetic improvement in this fitness trait despite increased inbreeding since it began in 1975  did not affect the estimates of heritability, suggesting the maintenance of genetic variability in our population. Although a potential change in variance components dependant on inbreeding has not been modelled, if such relationship exists, residual variance would have decreased and heritability would have increased. Even when inbreeding increased, there was no depression, as juvenile survival progressively increased over the 35-year study period. The low impact of inbreeding depression observed in our study  represent the only way to rear and maintain the sustained populations that ensure their survival (Magin et al.


Material and Methods. We recall the definition of epistasis and extend it for metagenomic biomarkers and then we describe the overview of our method metaBOOST and provide detailed information about each step of metaBOOST. Results. We describe the data sources for both simulation studies and real metagenomic datasets. Then, we describe the procedure of simulation studies and provide results for it. After that, we conduct real datasets studies and report the results. Conclusions and Discussion. Finally, we conclude our method and discuss some possible improvements for the future. The importance of microbial communities to human health has been attracting more and more attention, with examples including the reveal of associations between intestinal microbiome and Crohn's disease , the stuParticularly, the application of metagenomics to the study of human complex diseases has yielded the so-called metagenome-wide association (MGWA) studies . For exaAlthough a MGWA study has supplied us with a powerful way to search metagenomic markers associated with a disease under investigation, this approach may encounter similar problems as the traditional genome-wide association (GWA) studies. For example, it is believed that a complex disease is typically caused by multiple genetic factors, their interactions, or their interactive effects with environmental factors . ParticuWith these understandings, we proposed in this paper the first study of epistatic interactions in MGWA studies. More specifically, we designed a method called metaBOOST to detect such epistatic interactions for metagenome-wide case-control data. Our method consists of three main steps: (1) inference of metagenomic abundance level, (2) detection of possible epistasis using statistical methods, and (3) validation and visualization of epistatic interactions patterns. We validated our method by using both simulated experiments and real datasets studies. Results not only demonstrate the effectiveness of our approach but also provide biological insights for the pathogenic mechanisms of microbial communities to human complex diseases.X1, X2 at the right hand represent the independent effect caused by two different loci, and the multiplicative item (X1X2) represents the epistatic interaction. With this model, the epistatic interaction can be inferred by hypothesis testing whether the regression coefficient γ is equal to zero, which can be conducted by a likelihood ratio test. Furthermore, by enumerating all possible combinations of loci pairs and performing statistical tests, we are able to detect all epistatic interactions. However, because of the large number of loci in a genome-wide association studies, such an exhaustive search can hardly be practical, and a variable selection strategy should then be applied to reduce the search space.Studies in medical genetics have shown that epistasis, or epistatic interactions between two or more genes, widely exists in such human complex disease as diabetes , asthma On the other hand, in a metagenome-wide association study with the case-control design, one typically sequences the microbial community of a number of patients and normal individuals, obtains gene scaffold by assembling the sequencing data, mapping sequence reads to the scaffolds to obtain abundance levels of the genes, and applies statistical approaches to test whether the abundance level of such a gene is significantly different between the case and the control. In such a study, a microbial gene is used as a marker, analogous to a locus in a traditional genome-wide association study. However, there also exists significant difference between metagenome-wide and genome-wide association studies. For example, the number of markers in a metagenome-wide study can be as large as 4 million, while that in a genome-wide study is typically less than 1 million. Another more important difference is the property of markers. In a genome-wide study, markers are factors, while in a metagenome-wide study, markers are continuous variables. Obviously, the former difference requires more efficient approaches in a metagenome-wide study, while the latter difference suggests either the customization of methods in genome-wide study to facilitate the manipulation of continuous variables or the development of novel statistical methods that take the continuous nature of microbial genes into consideration.With the above analysis, we proposed in this paper the analysis of epistatic interactions between microbial genes. More specifically, we proposed a bioinformatics approach called metaBOOST that is designed based on a highly efficient epistasis detection algorithm named BOOST  and inclThe objective of this step is to obtain abundance levels of microbial genus or KEGG orthologous groups. In detail, given sequencing reads generated by a deep sequencing technology , we mapped the reads to known microbial genes and summarized the number of mapped reads to obtain abundance levels of a microbial gene. The abundance levels are further normalized to illuminate the possible influence of different sequencing depths for different samples. Then, we made use of mapping between genes and genus to obtain genus abundance by summing up all of the abundance of genes corresponding to the same genus, in which gene length is considered and serves as divider since longer genes generate more reads. Similarly, we obtained KEGG orthologous (KO) abundance.The abundance is continuous, ranging from 0 to 1 and in nature different from GWAS data. In order to utilize the methods developed for GWAS, we discretized the abundance data first into two or three levels, that is, {0,1} or {0,1, 2}. Particularly, in the two-level case, 0 represents nonexistence and 1 existence. In the three-level case, 0 represents nonexistence, 1 low-level existence, and 2 high-level existence. In our method, the discretization is performed automatically by fitting abundance levels of both case and control populations to a Gaussian mixture model and estimates the parameters using an Expectation-Maximization algorithm. And there exists two strategies for determining the number of levels for abundance data. First, we can use some objective criteria such as Bayesian information criterion (BIC). Second, we can use some empirical and heuristic strategies such as discarding a level that contains mainly zeros. In our paper, we adopt the latter for the purpose of seeking for simplicity. without interaction, with interaction:The most popular method to detect epistasis is likelihood ratio test or logistic regression, in which two models are considered, that is, model with interaction term and the model without interaction term, such as the following:P value can then be computed and serves as a measurement of the strength of the epistatic interaction. Considering that the theoretical P value derived from chi-squared test may not reflect the true null distribution, we further use permutation test as described briefly in P value and derive q-value to control false discovery rate. Here, we adopted BOOST for the calculation of P value since this method was validated for its power and speed recently [After fitting the above two models, we calculated the likelihood ratio or log-likelihood difference and this score is object to chi-squared distribution. A recently .q-values. We then adopted the strategy used in multifactor dimension reduction (MDR) [After the above two steps, we obtained a list containing potential epistasis with correspond on (MDR)  to supplIn general, there exist two strategies for validation. First, we can validate detected epistatic interactions by using independent computational methods. Second, we can perform validation through an experimental way, such as using extra samples to check the statistical significance of our findings. As for the former, systematic comparisons between different computational methods have been carried out intensively in literature , 25, andIn simulation studies, we relied on existing benchmark datasets for epistatic detection to generate artificial data for evaluating the effectiveness of our method. Briefly, Velez et al. explored 70 models with different penetrance functions and generated a total of 42,000 simulated datasets . From thFor real metagenome-wide association studies, we selected two real datasets published recently , 27. In Existing metagenomics simulator such as MetaSim  can simuxi denotes genotype data generated by simulator and yi corresponding abundance levels.We generated epistatic data in continuous case based on discrete case, as formulated below:After the generation of simulated data, we applied BOOST to identify embedded epistatic interactions in the original case-control data and metaBOOST to identify epistatic interactions in the simulated metagenomic case-control data, and we present results in With the power of metaBOOST being verified by the simulation studies, we applied this method to a real metagenome-wide case-control dataset of gut microbe of 368 Chinese individuals , includiP value for each pair and derive a q-value to characterize the statistical significance. Finally, we list top 10 interactions of the smallest q-values in We first fitted the distribution of abundance levels of genus of both the case and the control populations using a Gaussian mixture model and find that a two-component model fits the data very well  except f Bifidobacterium and Actinobacillus is reported with a very significant q-value (<106). By reviewing the literature, we find that this potential epistatic interaction has been supported by several existing studies. For example, in literature [ Bifidobacterium was reported to be associated with type 2 diabetes. The increase in abundance level of this genus will improve high-fat-diet-induced diabetes in mice, and such phenomenon shows significant difference between type 2 diabetes and healthy people. Besides, in literature [ Actinobacillus was suggested to be associated with type II diabetes. Therefore, it is reasonable to assume that the interaction between Actinobacillus and Bifidobacterium may further increase the risk of carrying type 2 diabetes.In the list, the interaction betweenq-value <6. By revterature , 32, Bifterature , Actinob Megamonas and Selenomonas are low or high and otherwise the disease risk is high. Similar interactions pattern can also be found in other subfigures.We then plot four possible interaction patterns generated by MDR in P value and q-value for each pair of genuses, and we list top 10 interactions with the smallest q-values in We also applied metaBOOST to another real metagenome-wide case-control dataset of gut microbe of 124 European individuals , includi Lautropia and Thauera is reported to have the smallest P value (4e − 6) and the smallest q-value (0.127). By reviewing literature, we find that both of Lautropia and Thauera belong to Proteobacteria, which is reported to be associated with IBD in [ Lautropia and Thauera may play an important role in the process of dysbiosis and IBD.In the list, the interaction betweenh IBD in  and the  Slackia and Cellulosilyticum are high or low, and the disease risk increases significantly otherwise. And as another example at the right bottom subfigure, the disease risk is high when both of the abundance levels of Mobiluncus and Acinetobacter are low or high, and the disease risk decreases otherwise.We then plot four potential interaction patterns generated by MDR in Besides genus, functional annotation such as KEGG orthologues groups is also important for understanding the functions of human gut microbiome . After hP value. The P value can measure the effect of one KO on disease and we only select those 265 KOs with P value < 0.1. Then we enumerate all pairwise interactions of the selected KO and fit a logistic regression model for each pair. We further perform the permutation test 100,000 times to estimate a P value for each pair and derive a q-value to characterize the statistical significance. Finally, we list top 10 interactions of the smallest q-values in We then applied metaBOOST only on KEGG orthologue groups or KO since more annotation information can be retrieved. Similarly, we fitted the abundance of KO using Gaussian mixture model with EM algorithm, and P values (≤2e − 5) and small q-values (≤0.06). But to the best of our knowledge, we find no literature to associate KOs with type 2 diabetes. Then, we map KOs into pathways using KEGG database and find 5 of 10 epistatic interactions that can share the same pathways as In the list, all interactions are reported to have small We then plot four potential interaction patterns generated by MDR in P value for each pair and derive a q-value to characterize the statistical significance. Finally, we list top 10 interactions of the smallest q-values in We then applied the same procedure as detailed in the above sections to analyze the IBD data. We mapped genes in the gene catalogue of the 124 European samples into the KEGG database to obtain the abundance of KEGG orthologue groups. We also fitted the distribution of abundance using Gaussian mixture model and we can see that the two-component model is also a good choice from q-value (<0.05). After mapping into pathways, we cannot find common pathways in those KOs. We then plot four potential interaction patterns generated by MDR in In the list, only the first two pairs have significant small In this paper, we proposed a method called metaBOOST to detect epistatic interactions in metagenome-wide association studies. We first resorted to a Gaussian mixture model to automatically discretize abundance levels of microbial genus and microbial genes to categorical values and then relied on a logistic regression model to detect epistatic interactions at the genus and KO level. Results not only show the effectiveness of our approach in simulation studies but also suggest the existence of several potential epistatic interactions between microbial biomarkers in two real datasets of human gut microbial communities. The merit feature of our approach is the automatic discretization of abundance levels of microbial genus and genes. As one of the main differences between metagenome-wide and genome-wide association studies, the continuous form of microbial biomarkers brings the main difficulty for detecting epistatic interactions between such markers and makes the discretization step the prerequisite. Resorting to the Gaussian mixture model, as we have done in this paper, is certainly an effective way.Certainly, our method can further be extended from the following aspects. First, since the understanding of epistatic interaction is not unique, the exploration of epistatic interactions between microbial biomarkers under different definitions will be necessary. Second, although our method of discretizing continuous abundance levels of microbial biomarkers has been demonstrated to be effective, it is still worth pursuing to directly build a statistical or machine learning method that is capable of detecting epistatic interactions between numeric-valued markers. The main difficulty is that the huge number of markers in a metagenome-wide association study prevents the exhaustive search of combinations of microbial markers, and the continuous form of such markers prevents the application of highly efficient computational tricks such as bit-wise operations that have been adopted in existing methods in genome-wide studies.


Lepeophtheirus salmonis) resistance (LR), measured as (log) density on the skin and fillet color (FC), with respective estimated heritabilities of 0.14 and 0.43. All genomic models outperformed the classical pedigree-based model, for both traits and at all marker densities. However, the relative improvement differed considerably between traits, models and marker densities. For the highly heritable FC, the IBD-GS had similar reliability as GBLUP at high marker densities (>22 k). In contrast, for the lowly heritable LR, IBD-GS was clearly inferior to GBLUP, irrespective of marker density. Hence, GBLUP was robust to marker density for the lowly heritable LR, but sensitive to marker density for the highly heritable FC. We hypothesize that this phenomenon may be explained by historical admixture of different founder populations, expected to reduce short-range lice density (LD) and induce long-range LD. The relative importance of LD/relationship information is expected to decrease/increase with increasing heritability of the trait. Still, using the ordinary GBLUP, the typical long-range LD of an admixed population may be effectively captured by sparse markers, while efficient utilization of relationship information may require denser markers .Reliability of genomic selection (GS) models was tested in an admixed population of Atlantic salmon, originating from crossing of several wild subpopulations. The models included ordinary genomic BLUP models (GBLUP), using genome-wide SNP markers of varying densities (1–220 k), a genomic identity-by-descent model (IBD-GS), using linkage analysis of sparse genome-wide markers, as well as a classical pedigree-based model. Reliabilities of the models were compared through 5-fold cross-validation. The traits studied were salmon lice ( Aquaculture populations are characterized by high male and female fecundity, typically resulting in large full-sib families. For invasive traits, traditional aquaculture selection programs involve sib-testing, which has limited reliability under classical selection schemes, as selection candidates are evaluated based on mid-parent means. Furthermore, this also leads to increased co-selection among close relatives, and enforcing restrictions on inbreeding will therefore hamper selection on such traits more than selection for individually evaluated traits. For individually evaluated traits, the sizeable family groups of aquaculture species give a substantial potential for within-family selection.Marker-assisted selection can be used to select directly for favorable QTL alleles, a method that allows individual selection of genotyped animals even in absence of phenotyping. This requires that the QTL effects are known and that carriers of the favorable alleles can be identified through the markers. For Atlantic salmon, the method has been utilized with great success in selection for reduced incidence of infectious pancreatic necrosis (IPN), for which a single QTL explains (nearly) all genetic variation ;Population-wide linkage disequilibrium (LD).The original idea behind GS was that the genome-wide markers would capture linkage disequilibrium between marker loci and QTL  is indeed reflected through inheritance of marker loci and is thus implicitly included in the dense marker information, although pedigree is not used directly. Co-segregation is the deviation from independent segregation of alleles as a result of linkage , while LD is the statistical dependency between alleles at different loci in the base generation . Information on (1) and (2) can thus explain the non-zero reliability of GS even in absence of LD. Furthermore, in populations of strong relationship structure  LD may not even be the most important of these factors under GS; Wientjes et al.  showed tThere are currently numerous available GS methodologies. The most widely used methods are GS models using identity-by-state (IBS) information on dense genome-wide SNP markers, including the so-called genomic BLUP (GBLUP) and Bayesian methods  (q + λ) + DI in population I, where (p + κ) and (q + λ) are the frequencies of the alleles M1 and N1, expressed as deviations from the across population frequencies (p and q), with frequency deviations κ and λ, and DI is the LD in population I. Similarly, (p − κ)(q − λ) + DII is the haplotype frequency in population II. The haplotype frequency in their crossbred-offspring (F1) is thus: (p + q + D), where D is the average of DI and DII. The LD in the F1 cross is (κλ + D), which comprises a ALD term κλ due to the crossbreeding (depends on frequency differences and is independent of distances between the loci), and the average of the original population-specific LD coefficients between the loci. D is on average smaller than either DI or DII since they may have opposite signs in the two populations, resulting in a reduced short-range LD in the admixed population.Admixture between genetically distinct populations increases LD between all loci (linked and unlinked) that have different allele frequencies in the founding populations  can be effectively captured even by sparse markers, but may explain a limited fraction of the genetic variance, depending on the degree of differentiation between the founding populations.Hence, effectiveness of GS in admixed populations depends on several layers of information: remaining LD from the founder populations, long-range ALD, and the relationship structure within the existing population. Furthermore, the relative importance of these factors likely depends on genetic architecture, marker density, heritability and the GS methodology used.The aim of the study was to quantify the importance of marker density on the reliability of ordinary GBLUP models and to compare these estimates with IBD-based models completely ignoring LD, i.e., classical pedigree-based models and IBD-GS models. To this end, two traits measured on Atlantic salmon [fillet color (FC) and salmon lice resistance], with high and low heritability, using alternative GS models and marker densities were studied. So far, no QTL of large effect has yet been found for lice resistance, but major QTL have been found for FC, still these do not explain all genetic variance  challenge testing, and 30–40 fish from each of these families were transferred to Nofima at Averøy, Norway and put into sea net-cages in October 2011. Two separate lice tests were conducted the following year, with all families being represented in both tests. Test 1 was conducted in the period July 16–18, 2012 and Test 2 in the period October 17–19, 2012. The total number of challenge-tested fish was 5198, with 2850 and 2348 fish in Test 1 and 2, respectively. Lice challenge testing of the fish was approved by the Norwegian Animal Research Authority (S-2012/148773).The fish used in the material were from the AquaGen population year-class first-fed in 2011. In total, 157 full-sib families (offspring of 99 dams and 97 sires) were sampled for salmon lice  of the fish. Hence, Gjerde et al.  developewhere BW is body weight (g) at time of recording, and  Figures , 2, indiUsing the latter trait definition increased the estimated heritability  increased substantially compared with a linear model applied to untransformed LiceD (results not shown).FC was recorded in a subsequent slaughter test in April 2013, where fish originating from both lice challenge tests were jointly recorded for FC (majority of the recorded fish originated from Test 1). The trait FC was defined as the pigmentation (redness) of the fillet and was automatically measured using image analysis with PhotoFish equipment and software . The recorded FC was found to be approximately normally distributed , but a smaller fraction in Test 2 . The genotyping strategy was supposed to serve two purposes: (1) Application of GS; and (2) a genome-wide association study, potentially followed by marker-assisted selection (MAS) for the most significant SNP(s). The aim was to establish two experimental selection lines for, respectively, high and low sea lice resistance, using a combination of pedigree-based selection, GS and MAS. Hence, genotyping was not completely random, but particularly focused on the most extreme families (in both directions) with respect to lice resistance. Of the 1963 genotyped animals, 1869 had phenotype for FC.A preliminary analysis showed that there was no significant genetic correlation between logLD and FC, and the two traits were therefore analyzed separately in subsequent analyses.For logLD, an initial quantitative genetic analysis was run, treating phenotypes of the two lice tests as two correlated genetic traits. A bivariate linear animal model was used for analysis of the data:1y and 1y are vectors of LogLD phenotypes from Test 1 and 2, respectively, 1β and 2β are vector of fixed effects , genetic effects are given in 1Z and 1Z are appropriate incidence matrices , I is an identity matrix of appropriate size, A is the pedigree−based numerator relationship matrix, and 0G is the additive genetic (co)variance matrix for the two genetic traits. As the two tests were performed on different individuals, the residual covariance between the two traits was assumed to be zero.where As the genetic correlation between logLD of the two tests was lower than unity (results shown below) and the majority of the genotyped animals came from lice challenge test 1, predictive ability of the genomic models for lice resistance was performed using phenotypes of the first test only. The FC was recorded on a later stage with fish originating both lice challenge tests, recorded at same age within the same slaughter test. FC of all fish was therefore analyzed jointly as a single genetic trait. Hence, predictive abilities of the different classical and genomic models for logLD (test 1) and FC were assessed using univariate animal models, with the following general characteristics:2g)a~ N , and e~ N  is a vector of random residuals. The fixed effects (b) included person (responsible for counting) by day for logLD, and gender of fish for FC. Common environmental effects of family were also tested, but these effects were small and not significantly different from zero (P > 0.20) for both traits, and were thus dropped in the final model.Where y is a vector of phenotypes (logLD or FC), G:G = A (numerator relationship matrix).PED: Classical pedigree-based analysis, i.e., G.IBD-GS: Identity-by-descent GS, using a linkage-based IBD relationship matrix for the genotyped animals. The matrix was calculated from a sparse marker set containing 5590 mapped genome-wide SNP markers, using the LDMIP software  1100 (1 K), (b) 2200 (2 k), (c) 4400 (4 k), (d) 22 000 (22 k), (e) 55 000 (55 k), or (f) all 220 000 (220 k) SNP markers, respectively. For (a) to (d) at total of 10 non-overlapping replicates (sub-sets of marker genotypes) were generated, while (e) was replicated 4 times. Results were averaged over replicates.GBLUP: Identity-by-state GS (ordinary GBLUP), calculating the Vanraden . AlternaThe different models differed solely with respect to their specification of the relationship matrix H. Furthermore, the G matrices were adjusted to the same average rate of inbreeding and relationship as the numerator relationship matrix, using the ADJUST option in DMU  used one-step estimation of EBVs  and the recorded phenotype (y), while h2 is the estimated heritability of the trait.where P < 0.001).Heritability of lice resistance (logLD) was estimated for the two tests using a bivariate PED model, as described in the above section. The estimated heritabilities for the two tests were low to moderate , and the estimated genetic correlation between lice resistance in the two tests was high (0.72 ± 0.12). The estimated heritability of FC, based on an univariate PED model, was high (0.43 ± 0.06). Based on likelihood ratio tests, genetic effects were highly significant for both traits  than for lice resistance 0.34), the relative increases in reliabilities for the different GS models (compared with PED) are presented in Figures , the relUsing GBLUP, higher marker densities were always favorable, but the relative advantage was considerably more expressed in FC than in lice resistance. For example, the relative increase in reliability of GBLUP for FC was 39% when going from 4 to 220 k, while the corresponding increase for lice resistance was only 11%. Nevertheless, GBLUP was superior to PED for both traits, even at the lowest marker densities (1 k). For both traits, going from 22 to 220 k SNPs increased reliability by only ~1%. Hence, increasing SNP density beyond 22 k would have little practical effect on selection.Another striking result was the enormous difference between the traits with respect to the relative reliability of the IBD-GS model. For the lowly heritable lice resistance the relative improvement compared with a classical pedigree-based model was considerably lower for IBD-GS than (220 k) GBLUP . In contrast, for the more highly heritable FC, increases in reliability for the two models were similar .The estimated heritabilities (0.14 ± 0.03 and 0.13 ± 0.03) for lice resistance (measured as log of LD) obtained in the current study was lower than recent estimates (0.26 ± 0.05) obtained for similar trait definitions (untransformed LD) and testing methods in a previous study on lice resistance in a different salmon population , despite the considerable difference in heritability of the two traits . Despite this, the relative improvement of the reliability through GS was substantial for LR (up to 52%). For FC, selective genotyping with respect to LR had likely little impact, due to the low correlation between the traits.Ne , as well as the heritability of the traits involved. In general, it is expected that for a lowly heritable trait, genetic effects estimated over larger groups of individuals, such as LD-associated effects  and mid-parent means would be more robust and thus relatively more important for the reliability, while linkage-analysis based deviations from pedigree relationships  would be relatively more important at higher heritabilities. Thus, the relative advantage of the GBLUP model may be largest at low heritability , while IBD-GS would be expected to perform relatively better at higher heritability , which is consistent with results of this study. Another contributing factor may be the genetic architecture of the two traits; A major QTL has been published for FC , linkage analysis  as well as LD. However, its ability to utilize the different sources of information depends on marker density. IBD-GS utilizes pedigree and linkage analysis and is robust to marker density, while PED, by definition, utilizes the pedigree relationships only. For the GBLUP model, high marker density would be needed to capture both short-range LD and (tiny) variations in co-segregation among relatives. In contrast, the IBD-GS model will utilize linkage analysis information accurately, even at very low marker densities. Furthermore, the relative importance of the different types of information depends on several factors such as structure of the dataset , historical Still, the factors discussed above do not explain the favorable performance of GBLUP for lice resistance at extremely low marker densities , for which limited LD is usually expected (in homogenous populations), and linkage-based deviations from the expected relationships are unlikely to be accurately captured by IBS information. The explanation may thus lie in the selection history of farmed Atlantic salmon. As described in the introduction, admixture from several distinct wild strains is expected to introduce long-range LD, and simultaneously reduce the short-range LD in the population. This will likely reduce the relative advantage of dense SNP data, as a relatively larger fraction of the available LD may be captured even by sparse marker panels, potentially explaining the good performance of GBLUP for lice resistance even at extremely low marker densities. Current terrestrial livestock populations may also have been formed by admixtures of old populations, but these admixture events occurred longer ago and may have been less extreme than in Atlantic salmon. Still, some admixture effects on the LD structure may also be seen in terrestrial livestock species, and thus contribute to the rather small increases observed in accuracy of GS as marker density increases (Vanraden et al., The number of genotyped animals was rather limited in the current study. Genotyping larger fractions of the population would be expected to increase the reliability of GS models even further.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The genome-wide association study (GWAS) techniques that have been used for genetic mapping in other organisms have not been successfully applied to mosquitoes, which have genetic characteristics of high nucleotide diversity, low linkage disequilibrium, and complex population stratification that render population-based GWAS essentially unfeasible at realistic sample size and marker density.Plasmodium falciparum, mosquitoes were phenotyped for parasite load, and DNA pools for phenotypically similar mosquitoes were Illumina sequenced. Phenotype-genotype mapping was carried out in two stages, coarse and fine.We designed a novel mapping strategy for the mosquito system that combines the power of linkage mapping with the resolution afforded by genetic association. We established founder colonies from West Africa, controlled for diversity, linkage disequilibrium and population stratification. Colonies were challenged by feeding on the infectious stage of the human malaria parasite, TOLL 11 protects mosquitoes against P. falciparum infection.In the first mapping stage, pooled sequences were analysed genome-wide for intervals displaying relativereduction in diversity between phenotype pools, and candidate genomic loci were identified for influence upon parasite infection levels. In the second mapping stage, focused genotyping of SNPs from the first mapping stage was carried out in unpooled individual mosquitoes and replicates. The second stage confirmed significant SNPs in a locus encoding two Toll-family proteins. RNAi-mediated gene silencing and infection challenge revealed that Anopheles founder colonies, and demonstrate its applicability for mapping in a complex non-model genome. This approach is a practical and preferred alternative to population-based GWAS for first-pass mapping of phenotypes in Anopheles. This design should facilitate mapping of other traits involved in physiology, epidemiology, and behaviour.We present an efficient and cost-effective method for genetic mapping using natural variation segregating in defined recent The online version of this article (doi:10.1186/s12864-015-2009-z) contains supplementary material, which is available to authorized users. Anopheles gambiae is the principal vector of human malaria in sub-Saharan Africa, particularly of the most deadly malaria species, Plasmodium falciparum. Mosquito susceptibility to P. falciparum has a strong genetic component, indicated by population-based mapping of quantitative trait loci (QTLs) [Plasmodium genes [The mosquito i (QTLs) –3, and li (QTLs) . Howeveri (QTLs) , 6 and Pum genes , have noA. gambiae pedigrees identified a cluster of loci that form the Plasmodium resistance island (PRI) [P. falciparum. QTL mapping has high power to detect loci, but the extended linkage blocks reduce resolving power, and the 15 Mb PRI contains over 900 genes. Association mapping in a complex population in principle can resolve loci to many fewer genes. Population-based association testing of a small panel of candidate immune genes was applied to the mosquito in two studies [A. gambiae.Previous QTL mapping in wild nd (PRI) , a regio studies , 9, whicr2 = 1), the frequency difference between alleles is near 0, and the variation in phenotype can be explained by a single allele of strong effect. However, A. gambiae offers conditions far from optimal, with negligible levels of LD  is a particularly important determinant of the power to detect a locus, while high levels of genetic diversity decrease the power of detection at a feasible sample size. Optimum statistical power is achieved when LD is highest , anhin 1 kb .Drosophila Synthetic Population Resource [Anopheles is similar, yet the context very different. In model organisms, the interest is to map a wide range of phenotypes within a series of highly controlled lines that still display sufficient phenotypic diversity. In comparison, vector biologists are more interested in particular phenotypes related to disease transmission such as pathogen interaction, behaviour and insecticide resistance, where it is desirable to capture natural variation. As well as clarifying the basic biology of mosquito-pathogen interactions and immunity, sampling natural alleles identifies natural factors that influence the epidemiology of malaria transmission. Any method used for diversity-controlled GWAS mapping should, therefore, provide a valid model for natural population structure of the species. That is, it should also represent a consistent panmictic subset of that population structure, as well as providing the same advantages as other controlled-diversity mapping approaches.Modified experimental approaches combining the strengths of linkage mapping with the resolution of association mapping have been used in some model systems. Controlled-diversity colonies enable fine-resolution mapping under tractable genetic conditions in, for example, the mouse Diversity Outbred lines and the Resource –16. BothA. gambiae founder colonies for use in genetic mapping and functional studies. The founder colonies are more favourable than wild mosquitoes for mapping because they display higher levels of informative LD, which facilitates detection of associations, they carry lower levels of genetic variation, which also augments the power of detection, and population subdivision is controlled to avoid the risk of spurious associations due to stratification. Each founder colony was initiated with the eggs of ~10 wild-caught females from West Africa, which were genotyped for markers of population structure before combining into the defined founder colonies. Using the founder colonies, we carried out a GWAS study of Anopheles susceptibility to infection with P. falciparum. The first stage used Illumina sequence of infection phenotype pools as genotyping, in order to rapidly highlight candidate loci. In the second stage, mosquitoes were individually genotyped with a panel of informative SNPs filtered from the candidate loci, for technical validation, replication, and fine mapping of the association locus. Finally, candidate genes were identified and functionally tested. An important advantage of the founder colony strategy as compared to GWAS in wild populations is that functional follow-up studies can be done in the same genetic background before attempting to type candidate variants in the wild population.To this end, we developed a set of diversity-controlled A. gambiae M-form mosquitoes from the West African population  as a tool to reduce genetic complexity and enhance informativeness for mapping by pooled sequencing. The approach relies on unsupervised mating, each colony initiated with the progeny of ~10 wild-captured gravid female mosquitoes. The founder mosquitoes assortatively mated in nature, thus ensuring that the progeny reproduce the genetic subdivisions of the natural population.We initiated founder colonies of The genetic composition of founder colonies was determined by microsatellite typing. First, we compared genotypes for a highly polymorphic microsatellite, H603, located at 42 Mb on the left of the chromosome 2. Each of three founder colonies tested segregate six marker alleles, as compared to 22 alleles at this locus in the wild source population Fig. . OverallAnalysis of genetic variation and sharing between colonies using five microsatellite markers further indicates that the artificial population bottleneck creates the advantages of founder populations, which have been extensively discussed in human genetics , 18. TheA. gambiae [−2 = 0.7 cM. This is a genomic average estimate of resolution, which will vary empirically according to the recombinational properties of different regions of the genome. In addition, loss of information due to incomplete penetrance of a trait would also extend the resolved mapping interval.We derived an estimate of the genetic mapping resolution of the founder colonies. The crossovers from a given point  are exponentially distributed with rate Νρ, where Ν is the number of crossovers and ρ is the crossover rate , about o gambiae , 20. TheP. falciparum gametocytes, and individual mosquito infection phenotypes were determined by dissection and counts of midgut oocysts. DNA from mosquitoes with similar phenotypes were combined and Illumina-sequenced as pools. A quantitative description of the pools is given in the methods. Sequences of pools were compared across the genome to detect regions displaying reduction in haplotype diversity, in order to detect candidate intervals carrying variants that underlie the phenotype. By definition, these decreased heterozygosity candidate intervals will be enriched for haplotypes carrying the causative haplotype, and other non-causative haplotypes will be simultaneously depleted from the same phenotype pool. Regions of the genome not associated with the phenotype should display random segregation of haplotypes across pools.Mosquitoes from two founder colonies, Fd03 and Fd09, were challenged by feeding on cultured HpR) was calculated as the proportion of heterozygosity in a phenotype pool relative to total heterozygosity within the whole founder colony after normalising for overall read-depth in each pool. Standard deviation of HpR values (SHpR) was used to identify regions with over-represented haplotypes in a given phenotype pool by comparison to the same positions in the whole founder colony. The measurement of heterozygosity within a pool was done in comparison to the same positions in the whole founder colony, and thus was normalized for local variation of heterozygosity across the genome. The analysis yielded three candidate loci in two different founder colonies, located at chromosome:coordinates, 3 L:17409-19071 kb (colony Fd03), 2R:17385-26524 kb (colony Fd09), 2R:47490-60531 kb (colony Fd09). These candidate loci were named 3.1, 9.1, and 9.2, respectively. Plots of candidate locus 3.1 are shown in Fig. The first mapping stage was comprised of genome-wide ascertainment of candidate loci. Pooled heterozygosity (Hp) was calculated across sliding windows for each of the phenotype pools individually, as well as total heterozygosity for the whole founder colony combined. Relative diversity .In order to estimate significance values for mapped loci, a permutation analysis was carried out on the P = 7.8e-6) for monoxygenase function in candidate locus 9.1, and the presence of multiple peroxidases in candidate locus 9.2, consistent with either a detoxification or ROS-based immune response. Analysis of genes with significantly enriched GO terms in candidate locus 9.1, however, indicated that most of these genes belong to a single cytochrome P450 cluster between 17.4 and 21.1 Mb.Coding sequences within candidate loci were analysed for enrichment of Gene Ontology (GO) predicted functional categories. The two candidate loci from Fd09 contain 609 genes for candidate locus 9.1 and 708 genes for candidate locus 9.2. While the large number of genes in the two Fd09 loci might reduce the probability of detecting significant enrichments, both Fd09 loci demonstrated enrichment for genes with potential immune functions, with highly significant enrichment .Due to the coincidence of candidate locus 9.1 with the 2Rb chromosomal inversion, molecular karyotyping was carried out on all Fd09 samples. There was only one 2Rb/b homozygote in the high pool, with heterozygotes occurring randomly between all three pools . Thus, there was low power to test, but also no evident support for association of the frequency of 2Rb inversion genotypes or alleles with membership in phenotypic pools. For all mapped loci, we have tested for chromosomal inversions, and the The second mapping stage comprised candidate locus confirmation and prioritization of candidate genes. SNPs within the candidate loci displaying the greatest difference in minor allele frequencies between any two phenotype pools were selected for genotyping in individual mosquitoes. SNPs were selected based on sequence from individual founder colonies, and were tested only within the same founder colony. A total of 44 SNPs were chosen from Fd09 across candidate loci 9.1 and 9.2, and 23 SNPs from Fd03 for candidate locus 3.1.For each founder colony, fine-mapping was performed by genotyping all of the unpooled individual mosquitoes from the original infections from which the initial phenotype pools were constructed. For Fd03, an independent replicate infection, which did not contribute to the sequenced phenotype pools, was also genotyped. These two replicate infections were assessed by logistic regression separately, and where the odds ratio indicated the same effect, also together as one experiment . The variant at 3 L:18559884 is a C:A mutation in the intergenic region between TOLL 11 (AGAP011186) and TOLL 10 (AGAP011187) with an odds ratio of 7.79 for oocyst infection intensity . The variant at 3 L:18552220 is a T:C mutation located immediately downstream of TOLL 10, with an odds ratio of 3.15 for oocyst infection prevalence . Individual genotyping did not confirm association with infection phenotype for either of the chromosome 2R candidate loci, 9.1 and 9.2, and they were not analysed further. The size of the locus 3.1 interval, ~2 Mb, is broadly consistent with the above theoretical prediction of genetic resolution in the founder colonies.Candidate locus 3.1 contained two SNPs with significant association after permutation, influencing both oocyst infection prevalence and intensity Fig. . The intTOLL 10 and TOLL 11. A functional test of TOLL 11 effect by RNAi-mediated gene silencing followed by challenge with P. falciparum reveals that TOLL 11 mediates significant protection against oocyst infection . The consistent increase in infection prevalence across three replicates was highly significant . There was no effect of TOLL 11 silencing upon a distinct phenotype, oocyst intensity  GWAS in field populations, and ii) linkage mapping with isogenic lines.A. gambiae is a technically difficult subject for genomic association mapping. Low levels of LD impair the power of locus detection, while high levels of diversity and the number of statistical tests necessary to query all diversity weaken the ability to detect a significant signal. The mosquito founder colony design used in this study alleviate both of these problems. The founder effect generated by initiating colonies from limited numbers of wild chromosomes increases informative LD, which augments power of locus detection, while simultaneously reducing the level of genetic diversity to a tractable level. At the time of colony initiation, the minor allele and minor haplotype frequencies were ~0.05, indicating that the founder bottleneck effectively created a mapping population with a large number of SNPs that perfectly type their haplotypes. The continued propagation of these strata-controlled colonies will aid subsequent follow-up studies, because functional assessment of candidate genes can be carried out in the same genetic background.The presented mapping design has a number of significant advantages over previous approaches and provides a basis for future association studies in mosquitoes. As compared to association mapping at the level of individual mosquitoes, our method using founder colonies and pooled sequencing requires only moderate expenditure for both sequencing and genotyping, although the benefits are not limited just to cost savings. Pooled sequencing is in a cost trade-off with individual sequencing, which would be maximally informative but is currently cost-prohibitive for routine use. The main weakness of pooled sequencing is that it is difficult to measure allele frequencies, which can decrease efficiency of ascertainment of informative SNPs for fine mapping. However, the practical consequence is not severe, because a reduced conversion rate from predicted assay to typable SNP is overcome merely by testing more SNPs. An area of improvement in the future could be to analyze enrichment and depletion of particular alleles from extreme phenotype pools, which would not require exact measurements of allele frequencies within pools, but rather a comparison of allele frequencies across pools.P. falciparum oocyst infection prevalence for TOLL 11. The Toll family possess a single transmembrane domain, a leucine-rich repeat region of variable size, highly divergent extracellular N-terminal and a conserved C-terminal intracellular TOLL-ILK1 domain [TOLL 10 and TOLL 11 genes identified in this locus together represent a species-specific expansion in A. gambiae, without orthologs in Drosophila.Genetic mapping identified a small locus containing two Toll-family proteins, neither of which have a previously described phenotype. Functional tests revealed a protective phenotype against 1 domain . The TOLTOLL 1, has been extensively characterized in Drosophila. In addition to a role in embryonic pattern formation, the Toll pathway is activated in response to pathogens, including fungi and gram positive bacteria [Drosophila TOLL 5 [TOLL 7 [Aedes aegypti also indicate an antiviral role for the Toll pathway [The canonical Toll-family member, bacteria . Only a a TOLL 5  and TOLL [TOLL 7 , respect pathway . The preAnopheles Toll family genes are thought to play immune roles, with TOLL 1 and TOLL 5 activity against gram positive bacteria [TOLL 5 [TOLL 10 and TOLL 11 display differential transcriptional regulation during embryonic development [TOLL 11 (though not TOLL 10) has further been shown to be differentially regulated in response to infection with P. berghei and falciparum [TOLL 10 and TOLL 11 with oocyst number generated negative results [Two of the 11 bacteria , and an  [TOLL 5 . TOLL 10elopment  and bothelopment . TOLL 11lciparum . The exp results , 9. The Follow-up studies of the Pfin7 locus in the natural population would be necessary to query the phenotypic penetrance of the locus, including sensitivity to epistasis with host genetic background and parasite genetic variation. Another reason to study the population is to determine the natural allele frequency of the locus. For that purpose, a high-density marker map of the Pfin7 locus could be used to type wild collections, in order to identify a core haplotype and measure its frequency.Anopheles genetic association and mapping studies in nature remain a challenge. Wild samples must be carefully typed for population structure and likely other confounding factors such as polymorphic inversions, which means that a large excess of samples need to be collected in order to assemble sufficient numbers of the targeted panmictic group. Another challenge is phenotyping of the wild samples. For malaria infection, wild-infected samples could be used, but this introduces variables such as mosquito age, host preference, and survival that, at best, demand larger sample size to overcome the noise, and at worst could introduce spurious results. Alternatively, wild samples can be phenotyped by experimental infection with blood from gametocyte carriers as we have done previously [Plasmodium resistance and other phenotypes in Anopheles, with possible follow-up in the natural population as appropriate.eviously , 32, butA. gambiae s.l. females originated either from Burkina Faso (Goundry region) or from Mali (Bancoumana region). Gravid females were captured by aspiration indoors, ensuring that at the time of capture they had already mated assortatively under natural conditions and bloodfed. They were then placed individually in oviposition tubes with wet filter paper. Females that laid eggs were collected and stored in ethanol before genomic DNA extraction. Eggs from individual oviposition were placed in a pan of water with Tetramin fish food. Emerged adults were reared under standard conditions at 26 °C and 80 % humidity, 12 h light/dark cycle with access to cotton soaked in 10 % sucrose solution.Wild caught A. coluzzii (M form) with the karyotype 2La/a, were used to initiate colonies. No hybrid families resulting from MxS form crosses were seen. Founder colony 03 (Fd03) was started with the F1 offspring from six mated females originating from Mali and founder colonies 5 (Fd05) and 9 (Fd09) were created with the offspring of 10 and 11, respectively, females from Burkina Faso. Colonies are routinely monitored for species and 2La inversion karyotype.Females that laid eggs were typed for species, molecular form and the molecular karyotype of the 2La chromosomal inversion . MaternaIndividual mosquitoes from founder colonies Fd03, Fd05, and Fd09 were genotyped for five microsatellite markers  using described primers and methods . EstimatP. falciparum isolate NF54 was cultured using an automated tipper-table system [e system  as implee system . For infInfection phenotypes were oocyst infection prevalence and intensity. Oocyst prevalence is the fraction of mosquitoes carrying at least one oocyst, while intensity is the number of oocysts per mosquito determined only in the subset of mosquitoes with ≥1 oocyst. Midguts of bloodfed females were dissected 7–8 days post-infection, stained in 1xPBS buffer with 0.4 % mercury dibromofluorescein (Sigma) and the number of oocysts per midgut was determined by light microscopy. Carcasses of the dissected mosquitoes were stored at −20 °C until DNA extraction. Genomic DNA was extracted from individual female mosquitoes by homogenization in 100ul DNAZol  using a disposable pestle, following the manufacturer’s protocol.P. falciparum oocysts, mosquitoes were assigned to one of three phenotype categories, and phenotype pools were constituted from ≥14 mosquitoes each for i) the “Zero” pool of bloodfed mosquitoes carrying zero oocysts, ii) the “Low” pool with 1–6 oocysts, and iii) the “High” pool with ≥10 oocysts. Thresholds for phenotype pools are determined empirically. Specifically for Fd03, the zero pool was comprised of 20 mosquitoes, the low pool (carrying 1–5 oocysts) included 17 mosquitoes and the high pool (>10 oocysts) included 14 mosquitoes. The entire infection comprised 93 individuals and the pools included 51, or 55 %. For Fd09, each pool was comprised of 20 individuals, with the low pool defined as 1–6 oocysts and the high pool as >29 oocysts. The complete infection had 102 individuals and thus the pools comprised 59 % of the entire infection. DNA concentrations were determined by the picogreen method [Based on the observed number of a n method , and DNAHp metric proposed by Qanbari et al. [HpR) was calculated as the proportion of heterozygosity in a phenotype pool relative to total heterozygosity within the whole founder colony after normalising for overall read-depth in each pool. Standard deviation of HpR values (SHpR) was used to identify regions with over-represented haplotypes as compared to the whole founder colony. High-SHpR regions within ≤5 Mb were combined to constitute a single locus.Illumina sequences were aligned to the AgamP3 genome  using Boi et al. . RelativHpR values were then segmented using the fastseg Bioconductor [−4 probability according to the permutation analysis were removed. Three regions were selected for subsequent fine mapping: two from Fd09 and one from Fd03.To establish significance thresholds, random resampling was performed for 1000 permutations for each window. Sonductor  package Loci identified from pooled sequence during the genome-wide mapping phase were filtered on the basis of differences in the proportion of reads showing the alternate allele . SNPs with the greatest differences in read-counts between phenotype pools were used to design SNP plexes for genotyping using the Sequenom MassARRAY platform. A single plex  was designed for each locus. Individual DNAs from the same experimental infection that was pool-sequenced, including individuals used to generate the pools and additional samples that did not contribute to the phenotype pools, were typed with SNPs specific to that founder colony. For both Fd03 and Fd09 there were 42 individuals that were SNP genotyped, but had not been part of the original extreme pools. These individuals either had zero oocysts or phenotypes intermediate between the low and high pools. In addition, a second, completely independent experimental infection of the same founder colony that had not been subjected to pooled sequencing was genotyped in the same way. This independent infection of Fd03 had 41 individual mosquitoes whose infection levels varied from 0 to 23 oocysts. Correlation between allele frequencies derived from pooled sequencing and individual genotyping via Sequenom is presented in Additional file Individual mosquitos were categorized into binary phenotypes with respect to infection prevalence (uninfected/infected) and infection intensity (low infected/high infected) using the same oocyst cutoffs employed for pooling. Logistic regression was used to test for significant association with phenotype using PLINK  and all dN:dS ratios were assessed by locus counting using custom R scripts. Due to the lack of available codon substitution data for this species, multiple substitutions or codon bias could not be analysed in dN:dS results. Molecular karyotyping of the 2Rb inversion for Fd09 was carried out by a published method [. Molecular karyotyping results were confirmed against a panel of individuals previously karyotyped by polytene chromosome analysis (not shown).Putative variants were filtered for sequencing quality, and consequences of variants were called for both colonies using the Ensembl Variant Effect Predictor (v2.3)  against d method . MoleculA. gambiae females using a nanoinjector . The efficiency of gene silencing was monitored 4 d after dsRNA injection as follows. cDNA synthesis was performed using the M-MLV reverse transcriptase with random hexamers (Invitrogen). In each case, 1 μg of total RNA was used in triplicate reactions. The triplicates were pooled and the mixture was used as template for PCR analysis. Primers used in PCR for gene silencing verification are listed in Additional file Double-stranded RNAs were synthesized from PCR amplicons using the T7 Megascript Kit (Life Technologies) as described previously . Primersp ≤ 0.05, a third replicate was done. The p-values from independent tests of significance were combined using the meta-analytical approach of Fisher [p = 0.01.Midgut oocysts were counted as described above, and were analysed for the same two phenotypes, infection prevalence and oocyst intensity. Oocyst infection values for gene silencing experiments were calculated from replicates of ≥30 dissected mosquitoes. All replicates per condition were analysed for oocyst infection prevalence. In contrast, for analysis of oocyst intensity, only the mosquitoes carrying ≥1 oocyst are considered. Therefore, for analysis of differences in oocyst intensity, a threshold of ≥30 % oocyst infection prevalence per replicate was imposed , 32. Forf Fisher , and thiAll supporting data are included as additional files.