Facial asymmetries are usually measured and interpreted as proxies to developmental noise. However, analyses focused on its developmental and genetic architecture are scarce. To advance on this topic, studies based on a comprehensive and simultaneous analysis of modularity, morphological integration and facial asymmetries including both phenotypic and genomic information are needed. Here we explore several modularity hypotheses on a sample of Latin American mestizos, in order to test if modularity and integration patterns differ across several genomic ancestry backgrounds. To do so, 4104 individuals were analyzed using 3D photogrammetry reconstructions and a set of 34 facial landmarks placed on each individual. We found a pattern of modularity and integration that is conserved across sub-samples differing in their genomic ancestry background. Specifically, a signal of modularity based on functional demands and organization of the face is regularly observed across the whole sample. Our results shed more light on previous evidence obtained from Genome Wide Association Studies performed on the same samples, indicating the action of different genomic regions contributing to the expression of the nose and mouth facial phenotypes. Our results also indicate that large samples including phenotypic and genomic metadata enable a better understanding of the developmental and genetic architecture of craniofacial phenotypes. Modularity means that, on a complex structure, some set of traits exhibit greater internal integration in relation to the integration with traits belonging to other modules3. Modules observed in complex structures, such as the human face, consequently exhibit some degree of independence, according to their development, evolutionary, population-specific or experimentally induced conditions4.Modularity is an important principle of organization in biological systems, which is also manifest at the morphological level9. Conversely, other authors have suggested that it is closely linked to changes occurring in the neurocranium during brain growth10. This link has been evidenced in several experiments made on mouse models. Martínez-Abadías et al.11, for instance, analyzed integration of brain and skull in newborn mouse models of Apert syndromes and demonstrated that skull dysmorphology affects not only the coronal suture closure, but also the facial skeleton. The authors proposed that shared phenogenetic processes, such as regional differentiation by dynamic inductive signaling and repetitive patterning by quantitative interactions affecting several tissues during head morphogenesis, underlie not only premature suture closure, but additional cranial anomalies in Apert syndrome, including facial traits. Hallgrímsson et al.14 analyzed perturbations to craniofacial development in mouse models to disentangle the processes that determine covariation structure. These studies have shown that mutations that influence the growth of the chondrocranium produce a common pattern of integrated shape change throughout the mouse skull. Furthermore, Martínez-Abadías et al.16 have shown that this same integrated pattern is also a main component of covariation structure in the human skull. In other words, shape changes observed in the integrated effects of variation in chondrocranial growth in mouse mutants also correspond to a significant axis of covariation in humans.The human face is a bilateral complex phenotype composed of a combination of structures that vary simultaneously in an integrated and modular way. For example, it has been reported that as part of the skull, the face is constrained in some way by variations in the skull base17. More generally, the face can be represented after the Palimpsest Model14, that propose that patterns of covariation observed in the adult phenotype emerge from different processes of variance generation, that gradually overlap, add or integrate sequentially during the individual’s ontogeny18. Such particularities, then, define the face as a complex phenotype. However, the modular nature of structures into the face has been scarcely analyzed, to the exception of some recent approaches to the craniofacial modular patterns using network theory19.Viewed from a different perspective, an analysis of simulated amniote developmental facial morphospaces suggest that epigenetic factors such as organismal geometry and shape impact facial morphogenesis and alter the locus of adaptive selection to variation in later developmental events22. Developmental modules are defined by interactions between precursors that ultimately participate during the development of the adult structure12. This includes a wide range of processes, such as developmental switches leading track bifurcations of development, or signaling between tissues through different molecular mechanisms23. Development processes can influence each other and therefore achieve a coordinated development of tissues, organs and the whole body, promoting the so called developmental stability. Also, the relationship between development stability-instability can mediate the expression of genetic and environmental variation by transmitting their effects through different routes in development. Therefore, developmental modularity contributes to all components of phenotypic covariance between traits24.Modularity can be studied at four different, non-exclusive levels: developmental, genetic, functional and evolutionary1. The typical example in the human face includes the array of muscles and bones that perform mastication, which generates intense strains able to modify the osseous phenotypes by, for instance, bone remodeling25.Functional modularity, by contrast refers to interactions among parts of the organism performing a given function26. This type of modularity needs to be placed in the context of the “genotype-phenotype map”27. Because developmental processes mediate the expression of genetic variation in phenotypic traits, developmental and genetic modularity are related. As stated by Klingenberg1, this relationship need not be a perfect congruence, however, because the expression of genetic variation is not exclusively controlled by developmental interactions1. Moreover, genetic changes can influence developmental modularity by causing alterations in the interactions among the developmental pathways that affect the traits of interest28.Genetic modularity refers to patterns of joint effects between genes on traits that can be represented as a network of relationships between pleiotropic traits29, genetic parcellation contributes substantially to evolutionary modularity. Functional modularity is also an important determinant of evolutionary diversification because it provides a link between the modular structure of morphological traits and selection on performance in organismal functions1.Finally, evolutionary modularity is intended as the coordinated evolutionary divergence in different traits. As genetic variation is a critical determinant for evolutionary change by selection and drift24, identifying the modular components of a configuration of landmarks is an important task of morphometric analyses in evolutionary developmental biology. Since traits within modules are tightly correlated with each other, but relatively independent of traits in other modules, then hypotheses concerning the boundaries of modules in a landmark configuration can be tested by comparing the strength of covariation among alternative partitions of the configuration into subsets of landmarks. If a subdivision coincides with the true boundaries between modules, the correlations among subsets should be minimal24. Furthermore, this variational modularity concept can be applied to both, the symmetric and the asymmetric components of total shape variation, and the signals of modularity in both morphospaces can be potentially informative of modularity acting at specific levels of organization. For instance, since fluctuating asymmetry has been postulated as an indicator of developmental instability (DI), the conservation or disruption of modularity and integration patterns across several genomic backgrounds can be informative of aspects of the developmental and genetic architecture of the human face.As observed, the four levels of modularity  are interrelated in complex ways. In many occasions, a common way to infer modularity at different levels departs from the analysis of phenotypic covariation patterns. As explained by Klingenberg23 is useful for understanding the basis for the development of the covariance on morphological features. A development path denotes the set of processes that generate a trait30. Therefore, it is a term that incorporates molecular and cellular mechanisms underlying development processes, resulting in complex networks of interactions30. To characterize developmental integration/modularity it is important to distinguish the different origins of morphological covariation. A range of different mechanisms of development can produce interactions between developing traits and, therefore, generate covariation between them1. As Klingenberg31 proposed, a way to identify developmental covariation or disruption simultaneously controlling for the effects of genetic and environmental covariation is to analyze patterns of fluctuating asymmetry (FA). Because the left and right sides of an individual share the same underlying genome, the study of FA is an effective way to detect developmental noise and minimize the effects of among-individuals genetic and environmental variation. In other words, covariation in the FA of different traits is due solely to the covariation of direct development pathways32. Therefore, it is possible to evaluate the role of direct developmental interactions that generate covariance by comparing between versus among-individuals FA covariation patterns. Thus, by quantifying FA one can test whether patterns of developmental interactions (observed in the asymmetric component) correspond to the main components of among-individual variation, as would be expected if the integration of development is a significant constraint on evolutionary variation33.The metaphor of paths of development34 we replicated all the analyses on ancestry-based subsamples, to see if the recent microevolutionary history of the studied groups affect the covariation patterns, or else remain stable across the different genetic backgrounds.Here we aim to explore several modularity hypotheses in the human face, comparing their stability on the asymmetric and symmetric components of shape variation, and testing for covariation disruptions across several subsamples of Latin American mestizos differing in their Native American, European or African genomic ancestry. Specifically, we have used a composite sample of admixed Latin Americans including landmark data aimed to recover facial shape, along with genomic estimations of ancestry. Then, we have used a covariational statistical approach to test for modularity patterns in both, the symmetric and asymmetric component of facial shape variation, as a way to identify associations among developmental and among-individual variation. By organizing the original landmark configuration into several arrays, here we evaluate four general facial modularity hypotheses: a) functional modularity hypothesis (FMH), b) midline modularity hypothesis (MLMH), c) facial thirds modularity hypothesis (FTMH), and d) neurocranium-splachnocranium modularity hypothesis (NSMH) see Fig. . Since w35, computed on the phenotype under study. When only a few eigenvalues are large relative to the rest, then the variance of all eigenvalues will be higher than if all eigenvalues explain similar amounts of variation. In the case that the variance is large, it is considered that the trait analyzed is strongly integrated, as variation is confined to a limited morphospace in relation to the theoretical total morphospace36. Hallgrimsson et al.14 argued that the increase in the variance does not necessarily imply an increase in integration, and propose that to verify whether integration (as measured using SVE) is accompanied by an increase in the phenotypic variance (measured as the trace of the variance/covariance TVC) then the regression of SVE on TVC should be large and significant when the structure strongly covariates and phenotypic variance is high, as well. Thus, we complete our analyses by exploring putative disruptions in the internal integration of facial traits according to the Hallrimsson et al. proposal.To further explore the modular structure of the face, we investigated the magnitude of integration of different facial structures. Theoretically, integration is stronger if all the variation is concentrated in a single dimension of the morphospace, indicating a perfect correlation of all traits, and is absent if the variation is uniformly distributed across all available dimensions. Accordingly, morphological integration in geometric morphometric data can be measured as the scaled variance of the eigenvalues (SVE) of a principal component analysis37. In the symmetric morphospace, the first PC show facial shape changes associated with eyes placement in relation to other structures, being more inferiorly placed in the positive scores. Also, the positive values exhibit more anteriorly and superiorly placed mouths and chins. The perifrontal region is displaced forwardly in the positive scores, whereas the ears are placed slightly higher and posteriorly in the positive values. The second PC describes shape changes related to the eversion of the ears, along with a reduction of the distance between them. The asymmetric morphospace can be decomposed on a first PC describing changes associated to directional asymmetry towards the right side in the positive scores. The eyes seem to be the more symmetric structures.An exploratory Principal Component Analysis of the symmetric and assymetric morphospaces is presented in Fig. 38. For random sets of variables, the covariation ratio (CR) has an expected value of one. While CR values lower than one will indicate some degree of modularity within the structure, CR values higher than one will indicate greater covariation between regions than within them38. Results corresponding to the Adams’ CR modularity index38 indicated that all the modularity hypothesis are significant with p < 0.006  on the variance of the structure, intended here as the trace of the covariation matrix, indicates a significant relationship only for the symmetric component in the total sample and the European subsamples . Such functional patterning operating at the developmental scale have been reported previously34 the modular pattern seems to remain unaltered across all of them, reinforcing the idea that phenotypic evolution can be canalized throgouth stable patterns of covariation52. In this regard, González-José et al.51 showed that inter-population differences in the matrices of variance/covariance are not associated with matrices of molecular or morphological distances among modern human populations, suggesting that the stability of integration is independent of the history and structure of populations. They also argued that a possible explanation for this result is that patterns of integration could be limited to intraspecific level and, in the skull, some speciation events may involve large rearrangements of integration patterns that would facilitate developmental scenarios on different regions of the skull51. The above assumes that morphological integration of functionally or developmentally related traits will be consistent and they will respond to selective pressures on a coordinated way55.Another remarkable point is that the abovementioned results remain stable across different subsamples diverging in terms of ancestry/heterozigosity. It is interesting to note that, even when we previously reported differences in the asymmetric facial phenotypes of the different ancestry sub-samples used hereTo sum up, our results suggest that a modular organization directed by the functionally-based apportionment of covariation patterns can be seen as a regular pattern maintained across both, the genotype-phenotype map, and across several genomic backgrounds.et al.51), even when individuals with a disease-altered facial development are considered. For instance, Martínez-Abadías et al.56 suggested that FGF/FGFR signaling is a covariance-generating mechanism established early in vertebrate evolution that acts as a global factor modulating coordinated development of various skull components. Thus, this as well as other unknown global genetic and non-genetic factors can be seen as covariance-generating processes that constrain both, normal or disease-altered craniofacial phenotypes Martínez-Abadías et al.57. Recent research has also clarified that the signaling activity of mutations at systems such as the FGF/FGFR are not restricted to skeletal tissues, but also occurs at cellular levels, where signaling may trigger specific patterns of gene expression and/or cell/tissue differentiation Martínez-Abadías et al. (2013)58.That the modularity/integration patterns are stable across sub-samples with different genetic ancestry is not that surprising. Previous research has already reported stability when considering different human populations . Several factors can be listed to explain these differences. One is the obvious discrepancy between genetic and developmental pathways in insects, mice, fish and humans. Phylogenetic analyses at the macro-evolutionary level are then necessary to answer these questions on an evolutionary context. Moreover, Debat et al.56 suggest that natural selection acting on the shape of the wings of insects could dilute or hide the differences between within and among-individuals developmental processes. In the case of the human face, selection processes have also been postulated63, thus part of its expression pattern could be mediated by past evolutionary events.An inspection of the literature on this topic appears as inconclusive regarding the congruence among the individual and the FA covariation matrices. For instance, Debat 64. In addition, the medial migration of the eyes from their initial side locations results on the massive growth of the cerebral hemispheres and enlargement of the head, and enable the normal movement of eyes toward the sagittal line65. The largest migratory eye movements occur between the fifth and ninth week of the development. Thereafter, the angulation of the optical axes (between 71°–68°) is stabilized during postnatal phases65. The genetic basis of regulation of the eye is well known and is associated with PAX664, with intervention of the sonic hedgehog (SHH) to the up regulation of PAX2 and down regulation of PAX6. Moreover, fibroblast growth factors (FGF), and transcription factors MITF, CHX10, SOX2, BMP-4, LMAF, SIX3 and PROX1 determine the course of ocular substructures.A more detailed inspection of the discrepancies among FA and individual covariance matrices Table  enabled 66. These features are associated with variants of EDAR, TBX15 and CART1 genes. In summary, the parallelism between individual and FA covariation patterns seems to be supported by the synchronic timing of developmental events in the growing embryo, and some candidate genes have been detected whose role must be further investigated.Regarding ears’ development, they begin its formation around day 22 and its development extends to week six. Specifically, their tissues derive from ectoderm precursors. The pavilion and the ear meatus move from the base of the neck  to its normal adult location on the side of the head. This process is largely due to mandibular growth. On a recent GWAS analysis, we demonstrated that there are seven genomic regions significantly associated with the size and the attachment of the lobe, folding of antihelix, rotation of the helix, the protrusion of the ear and the antitragus size64. The complex processes described above may explain the discrepancy  of the covariance matrices. In another recent GWAS study, four genomic regions  were significantly associated to features related to the nose morphology: the inclination of the columella, the amplitude of the nasal bridge, and the alar amplitude of the nose . The reported SNPs are associated to DCHS2, RUNX2 and GLI3 genes, respectively, while the region 20p11 overlaps with PAX167. Thus, the hits in different and non-overlapping genetic systems also correlates with different phenotypes: the ear structures nucleated in the pinna66, versus localized nasal traits67. In some way, these combinated results can be seen as a preliminary signal of genetic modularity, detected by different methods as the ones used here.In contrast, the mouth and the nose arise from a rather complex combination of developmental processes, i.e.: they condense the combined action of several mechanisms, tissue origins and differentiation types. Notably, they come from the same layer, the endoderm, forming during the middle of week three and keeping their development until birth. During the half of the sixth week only the oronasal membrane divides the oral and nasal cavities. The mouth results from the interrelation of the maxillary and mandibular prominence, and besides depends on the closure of the palate, the vomeronasal system, the configuration of the nose and in general of all the pharyngeal cavity49.Note that mouth and nose, as already mentioned, are structures where several functional, developmental, and growth processes simultaneously converge. Maybe, this complexity trigger the observed differences in the covariance patterns observed on eyes, ears and neurocranium. A point to note is that all the comparisons made in the middle third, which include three landmarks associated with the neurocranium, nose, and mouth Figs  and 3 we56, the complexity of structures68, and the phenotypic plasticity of the nose and mouth, as a whole, can be a plausible explanation to the observed differences in terms of matrix proportionality. For example, the maxillary region exhibits large craniofacial plasticity69, maybe due to the fact that the masticatory apparatus grows even up to 25 years70. Similarly, in the case of the nose, there is evidence of plasticity associated with the climate and it is possibly a structure that evolved under a regime of natural selection to cold climates73. Considering all the above, it is likely that the mouth and nose do not achieve the integration levels exhibited by other facial modules.Moreover, natural selection acting quite independently upon the modules76 focuses on the symmetric component form. Instead, our approach was twofold, focusing also in the asymmetric dimension of variation. In this regard, a noticeable result is that the eyes, mouth, and ears tend to present larger-than-average morphological integration . In the abovementioned examples, the effects of environmental stress artificially induced the integration of development and variation in the jaws of shrew, and also showed that fluctuating asymmetry and the variation of the stressed population increased and that this increasing was canalized along the same direction of between-species variation78. In this context, we have obtained a recurrent correlation between the pattern of fluctuating asymmetry and morphological integration across all the subsamples in the symmetric component of shape, which is not the case when the asymmetric morphospace is explored for such correlation . Furthermore, these results are of importance to future Genome Wide Association Studies based on CANDELA (and other) datasets, since they provide a first identification of integration and modularity at the phenotypic scale, that can be used to submit more refined traits to the association analyses, as a preferred approach instead of heuristic searches based on analyzing non-supervised, raw characters.Most, if not all evidence provided so far by previous studieson Table . Previouon Table . In thison Table . This su37). Volunteers with antecedents of craniofacial dysmorphologies, orthodontics treatments or severe facial trauma were not considered in this study. Further sample details are provided in Supplementary Table 86 and conforms the main database of the CANDELA consortium87. All methods and procedures used here were performed in accordance with relevant guidelines and regulations (see below). Validation dataset are available to download from https://laofunam.com/data.As part of the CANDELA initiative, we recruited 4,104 volunteers from six Latin-American cities: Mexico City (Mexico), Medellin (Colombia), Lima (Perú), Arica (Chile), Porto Alegre, and Jequié (Brazil). The CANDELA consortium aims to evaluate the genetic basis of nonpathological phenotypes differentiated between European, American, and African populations through the analysis of admixed populations  placed a set of 34 standard facial landmarks using Photomodeler . As described elsewhere86, this platform corrects for any lens distortion automatically, and we have followed the standard recommendations for quality and accuracy provided by the software. A scale factor was assessed using the nasion-gnathion distance measured directly on the individuals using a standard anthropometric caliper.Facial shape was captured following scientific photographic protocols described in detail in references37). The SNPs were pruned to remove Linkage Disequilibrium, and after removing correlated SNPs, 90,000 SNPs were left for analysis. Ancestry estimation was performed with this SNP data. Genome-wide average heterozygosity was estimated from this data using PLiNK89, which provides a measure of excess heterozygosity compared to the overall sample. It is calculated as 1—excess homozygosity, while excess homozygosity is estimated using the inbreeding coefficient as the average excess of homozygous alleles across all SNPs for an individual as compared to the overall sample. To enable among-subsample comparisons, individuals were allotted to American, European, or Admixed groups if their population-specific ancestry markers were above 80% in the first two cases, and below that percentages in the third group (admixed).On each individual, blood samples were collected and DNA extraction was performed following standard laboratory procedures. Genomic data involving 730,525 marker SNPs was obtained from these samples  determined by the soft tissues and cranial cavities, and limited by their bony surroundings. In this regard, the soft tissue components would guide the development of skeletal units. It should be noted that the functional components are based on assumptions around form and function links rather than the results of quantitative analyses or empirical evidences19.The FMH Fig.  is based92. Recently, it has been suggested that the expression of FA not only depends on developmental stability, but also on the cost of growth of the trait, defined as the amount of structural components necessary to form a unit of length of a given character68. In accordance with this argument, a trait with more structural components per unit of length should show lower asymmetry than a simpler one. In other words, this hypothesis seeks to verify whether those landmarks that are located closer to the sagittal plane versus those placed on the sides follow a modular pattern.MLMH Fig.  is aimed95. It is important to check how much these regions respond to a modularity pattern with empirical support.The FTMH Fig.  is aimed41 experience are enough to generate a modularity pattern observed in the soft structures studied here.Finally, the NSMH Fig.  is an atAll the above-mentioned hypotheses will be tested in the whole sample as well as in sub-samples organized according to their genomic ancestry. Also, the hypotheses are evaluated in both the symmetric and the asymmetric components of shape variation.38. For random sets of variables, the CR has an expected value of one. Whereas CR values lower than indicate some degree of modularity within the structure, CR values higher than one depict greater covariation between regions than within them38. Thus, we calculated the CR values on the several modularity hypotheses tested here, and across the different ancestry subsamples. The significance values were obtained by comparing the observed CR values with permutational distributions of 999 CR values obtained by assigning the landmarks randomly to modules. The proportion of permuted values lower than the observed CR value was used as the significance of the test38.The CR coefficient is a ratio of the covariation between modules over the covariation within modules, and consequently it ranges between zero and positive values39. Similarity of the covariance matrices resulting from the Procrustes ANOVA was tested by computing pairwise matrix correlations, as a way to compare FA versus individual covariance patterns. Matrix correlation is a measure of the overall similarity of covariance matrices and its use is ubiquitous in geometric morphometrics97. Statistical significances were determined through matrix permutation tests, with 10,000 iterations, against the null hypothesis of complete dissimilarity between the covariance matrices concerned39.Similarity between FA and individuals covariance matrices was tested following Klingenberg and McIntyre35, made on the structure under study. When only a few eigenvalues are large relative to the rest, then the variance of all eigenvalues will be higher than if all eigenvalues explain similar amounts of variation. In the case that the variance is large, it is considered that the trait analyzed is strongly integrated, as variation is confined to a limited morphospace in relation to the theoretical total morphospace36. Hallgrimsson et al.14 argued that the increase in the variance does not necessarily imply an increase in integration, and propose that to verify whether integration (as measured using SVE) is accompanied by an increase in the phenotypic variance (measured as the trace of the variance/covariance TVC) then the regression of SVE on TVC should be large and significant when the structure strongly covariates and phenotypic variance is high, as well. Analyses were performed on both, the symmetric and asymmetric component39.Theoretically, integration is stronger if all the variation is concentrated in a single dimension, indicating a perfect correlation of all traits, and is absent if the variation is uniformly distributed across all available dimensions. Accordingly, morphological integration in geometric morphometric data can be measured as the scaled variance of the eigenvalues (SVE) of a principal component analysisSupplementary information


GALR2 > BDNF > P2RX7 > HTR1A > SLC6A4 > CB1 > HTR2A, with strong relevance for the first four. Robust gene-gene-environment interaction was found between BDNF and HTR1A. Gene-environment interaction effect was confirmed, namely no main effect of genes, but a significant modulatory effect on environment-induced development of depression were found. Our data support the strong causative role of the environment modified by genetic factors, similar to animal models. Gene-environment interactions point to epigenetic factors associated with risk SNPs. Galanin-2 receptor, BDNF and X-type purin-7 receptor could be drug targets for new antidepressants.Depression is a polygenic and multifactorial disorder where environmental effects exert a significant impact, yet most genetic studies do not consider the effect of stressors which may be one reason for the lack of replicable results in candidate gene studies, GWAS and between human studies and animal models. Relevance of functional polymorphisms in seven candidate genes previously implicated in animal and human studies on a depression-related phenotype given various recent stress exposure levels was assessed with Bayesian relevance analysis in 1682 subjects. This Bayesian analysis indicated a gene-environment interaction whose significance was also tested with a traditional multivariate analysis using general linear models. The investigated genetic factors were only relevant in the moderate and/or high stress exposure groups. Rank order of genes was  After such exposures behavioural phenotypes related to depression  and anxiety  develop in rodents. These animal models are widely used to study biological pathways involved in depression, to discover new target proteins for new antidepressants, to test potential new antidepressant compounds and to find candidate genes for depression. In turn, candidate genes were usually selected from data of stressed animals4. GWAS are the state-of-the-art approach to test genetic main effects, but not interaction effects. In GWAS candidate genes generally do not show evidence for association with depression5 which led several researchers to question the candidate gene approach in general7. Furthermore, statistical power to detect interactions is typically less than for main effects8. In this study we selected seven candidate genes and used two completely different statistical approaches to test whether candidate gene approach has or has not relevance in a population with well-documented data on time and type of recent negative life events.This approach sharply contrasts genome-wide association studies (GWAS), where several thousands of patients (cases) and controls are included without any knowledge of presence or type of previous stressors5. Heritability of depression depends on severity, estimated at approximately 37% in general population9, and 48–75% in hospital samples, the latter heritability of 75% shown in recurrent depression10. Specific gene variants have been difficult to identify11; positive findings in candidate gene studies have not been consistently replicated and very few statistically robust risk loci have emerged from genome-wide association studies (GWAS)12, although one recent study using the extremely large cohort with less intensive phenotyping approach identified 15 loci in major depression . In contrast, environmental factors such as recent stressful life events and childhood trauma13 are associated with considerably greater risk of depression than specific genetic variants. Indeed, positive genetic studies of depression have emerged almost exclusively in interaction with adverse psychosocial exposures14.Family, twin, epidemiologic and molecular studies indicate that depression is a multifactorial illness showing a highly complex genetic architecture with a large number of loci, each contributing a very small effect size to the phenotype15, ii) using risk groups, e.g. by separately analyzing those with marked exposure to psychosocial adversity16, and iii) using pathways, e.g. by using enrichment methods for aggregating the effect of variants toward gene and pathway levels17. We have previously demonstrated these by showing that functionally related gene variants within a pathway from genomically diverse regions show similar GxE interactions with respect to a complex phenotype18. The applied Bayesian multivariate methods allow these 3 approaches to be combined; they blur the distinction between dependent and independent variables and instead evaluate the strength of all, or a predefined set of possible relationships across variables. Using these methods, we have reported convergence of functionally related genes within the signaling pathway of the neuropeptide galanin on a multivariate phenotype, including quantitative measure of anxiety and depression, which operated only in those exposed to environmental stress. The use of the broader multivariate phenotype including symptoms of depression and also anxiety after stressful events also parallels animal models like chronic unpredictable stress.As demonstrated by numerous studies the power to detect genetic influences may be increased by i) using “deep” or multiple related phenotypes, e.g. by considering a broader ‘multivariate’ phenotype than one categorical diagnosisa priori based on: i) evidence of role in depression in animal models and also in our previous human studies, ii) having functional polymorphisms if possible, and iii) directly or indirectly influencing the main putative pathways of depression, including serotonin, HPA, neuropeptide, neurotrophin, endocannabinoid, and neuroinflammatory mechanisms. The following gene polymorphism were selected: rs6265 in BDNF18, rs8836 in GALR214, rs7766029 in CB119, rs6311 in HTR2A20, rs7958311 in P2RX721, rs6295 in HTR1A22 and 5-HTTLPR in SLCA416.In the present study we investigated whether candidate genes from both animal models and human studies across different pathways implicated in depression also converge to show a strong main effect and/or influence risk in the context of environmental stress. We quantified and compared the Bayesian relevance of these genes in populations differently exposed to stressful recent negative life events and also sought gene-gene interaction effects. Thus we selected candidate genes The population was in Hardy-Weinberg equilibrium . Description of the population is shown in Table ). In case of the low RLE exposure group all genetic factors were non-relevant with low posterior probabilities (pr <0.1). In this analysis relevance increases in parallel with posterior probability, and a strong relevance is associated with pr >0.5. Moderate and/or high RLE exposure levels entailed an increased relevance of all SNPs compared to the low exposure case. For the investigated SNPs some were more relevant in the moderate, some others in the high RLE exposure groups. On one hand, SNPs rs6265 (BDNF) and rs8836 (GALR2) were strongly relevant with high posterior probabilities (pr = 0.74 and pr = 0.84 respectively) after moderate exposure. On the other hand, rs7958311 (P2RX7), rs6295 (HTR1A) and 5-HTTLPR (SLC6A4) appeared more relevant in the high RLE exposure group. Among those the rs7958311 (P2RX7) SNP was the most relevant (pr = 0.62) followed by rs6295 (HTR1A) (pr = 0.59) and rs6265 (BDNF) (pr = 0.57). Comparatively, 5-HTTLPR (SLC6A4), rs7766029 (CB1) and rs6311 (HTR2A) had relatively low posteriors even after moderate or high exposure, and can be considered less relevant in this multivariate model.Bayesian relevance analysis revealed that the investigated genetic factors were only relevant to the multiple depression-related phenotype in case of moderate or high RLE exposure Fig. . In caseBDNF) and rs6295 (HTR1A) was revealed by both Bayesian relevance analysis and the GLM analysis of the models (Table 5-HTTLPR (SLC6A4)-rs8836 (GALR2) was also suggested by Bayesian relevance analysis, but was not confirmed by the GLM analysis. It is noteworthy that the P2RX7 polymorphism clearly stands alone, its effect is completely apart from any other SNPs included in the analysis, as shown on Fig. Two gene-gene interactions were found in the data Fig. . A stron−5). Model-2 containing only main effects of RLE and genetic factors is not significantly different  from Model-1 containing only RLE . However, Model-3 including also the interaction between genetic factors and RLE is significantly different compared to Model-1 . In addition, an analysis of explained variance of the depression phenotype was also performed. Model-1 .Finally, possible biases resulting from population differences and RLE exposure categories were investigated. A Bayesian analysis involving a population descriptor variable was carried out on the whole dataset as well on all RLE subpopulations. The analysis involving the whole dataset indicated no direct dependencies with respect to the population descriptor (pr <0.07), suggesting that there is no population stratification bias in this sample regarding the investigated SNPs. In case of the RLE subpopulations, inclusion of the population descriptor caused only minor differences (<0.15) in the relevance posteriors of genetic variables supporting stable genetic effects . This approach may have power advantage over diagnostic categories in epidemiological, population genetic samples32, which was quantified in each person.Another important novel aspect of our study is considering several functional polymorphisms previously described in human and animal studies from distinct pathways including the serotonergic and endocannabinoid systems, neuropeptides, neuroinflammation and neuroplasticity in one multivariate model. In our study we targeted depression as a complex disease and a product of multiple networks, where the activity of the pathways may be shaped by environmental experience9, influence of environmental effects is estimated at 63% in depression28. Etiologically relevant distal and proximal stressors are relatively common, and while frequency of severe life events is estimated to be one in every 3–4 years, depression is triggered in only about one fifth of those with acute stress exposure33. There is a pronounced individual variation in the sensitivity towards environmental exposure at least in part due to genetic factors, and several genes are not directly involved in the development of depression but via influencing vulnerability towards effects of environmental stressors12.Compared to heritability which accounts for 37–42% in the variance in general population samples36. The exact nature of the interaction between genes and environment (GxE) is controversial. While the diathesis-stress model37 proposes that vulnerability towards environmental triggers depends on the biological background, including the genotype, which buffers or exacerbates the effect of stressors, the differential susceptibility model38 incorporates a possible positive environmental aspect5, postulating that the biological context modulates the sensitivity to both positive and negative environmental influences, making individuals not vulnerable, but ‘plastic’, in their response to the environment39.The relationship between environmental and genetic influences is manifold. The effect of genetic and environmental factors may be additive, or genetic variants may increase the likelihood of subjects seeking risk environments or life events causing gene-environment correlation as shown by twin and adoption studies40. The only GWAS where additional genome-wide by environment interaction study (GWEIS) was also performed included population data from African American and Hispanic/Latina women25. Despite the fact that the population was markedly different from ours, including ethnicity, gender, age, prevalence of negative life events and stress exposure, there are important conclusions from that study. No genome-wide significant main effect was found in the GWAS, but a significant GWEIS hit was described in the African American sample. Furthermore, depressive symptoms and stressful life events were strongly genetically correlated25. Interaction of polygenic risk scores with environmental effects has been investigated in a similarly small number of studies, with negative results for interaction with recent life events13.Lack of consideration of such environmental effects may be one prominent reason for the failure of replication in candidate gene studies and GWAS into the genetic background of depression12. This may be even more evident in our candidate gene selection, as animal models of depression usually depend on stress-induced depression-like behaviour. The larger increase in depression risk due to an interaction between genetic effects and life events than expected by combining their individual effects  was not controlled for timing of recent life events. Furthermore, this is a hypothesis driven candidate gene study which assessed a limited number of genetic variants  on a moderately large sample. Therefore, results can be potentially inflated, and an independent study is required in the future to validate these results preferably using imputed GWAS data. That would allow the investigation of the contributions of all genes in the genome in the context of environmental stressors. The application of Bayesian systems-based methods would be advantageous in such a scenario as well, as they are better powered to uncover modest interaction effectsthan non-Bayesian methods using correction against multiple hypothesis testing. However, the computational scaling of systems-based methods to a genome-wide level still requires further research.With these limitations in mind, our results suggest a potential role of the candidate gene-environment interaction. They indicate a complex interaction pattern between candidate genetic variants in different neurobiological pathways and severity of exposure to life events in the emergence of depression. Neither the separate, nor the combined effect of the investigated polymorphisms was detectable on depression in the absence of stress exposure. In contrast, several genes modulated the effects of recent stressors, and their effects converged to a significant modulation on the development of depression. Furthermore, different levels of stress exposure were modulated differently by distinct genes reflecting multiple mechanisms underlying the effects of different severity of life events in the development of depression. Given the near complete lack of effect of our studied polymorphisms in the absence of at least moderate exposure to recent stressors, it is obvious that effects of important genetic variants are overlooked in samples, if the presence of stressors has not been considered in depression. Also, it is possible that different pathways play a role in the development of depression depending on whether life events are present or not; and different types or even severity of life events may be modulated by different neural mechanisms and pathways. These data emphasize the need for incorporating environmental variables in GWAS.The study was part of the EU-funded NewMood study  approved by local Ethics Committees , and carried out in accordance with the Declaration of Helsinki and all relevant guidelines and regulations. Before participating, all subjects provided written informed consent.19. In this current study we investigated how genetic variants of candidate genes interact with life-stressors on a multiple depression-related phenotype in a European population cohort of 1682 subjects which is a subset of the NewMood study population consisting of subjects that were successfully genotyped with respect to all seven investigated genetic factors and had available data for all parameters included in this analysis. Recent negative life events (RLE), lifetime depression, Brief Symptom Inventory (BSI) depression and anxiety scores were determined in each subject. Relevant population data are provided in Table Description of the population and questionnaires have been published previously24. Reported lifetime depression (DEP) was determined using the background questionnaire. Subsequently, the Structured Clinical Interview for DSM-IV (SCID) was used to validate findings18. Current depressive symptoms were measured based on the depression items plus the additional items (BSI-DEP), and anxiety using the anxiety items (BSI-ANX) of the BSI41. A weighted score was computed separately for BSI-DEP and BSI-ANX variables by summing the corresponding item scores divided by the number of items completed and categorized into low (0 − < 1), moderate (1 − < 2), and severe (2–4) categories.In the present study, we analyzed reported lifetime depression, current depression and anxiety jointly as a multiple depression-related phenotype described previously42 which queried problems related to illnesses/injuries, financial difficulties, problems related to intimate relationships, and social network occurring in the last year. Based on corresponding items the number of RLEs was counted for each subject, and categorized  as used in our previous studies19. The resulting low, moderate and high RLE exposure groups were used in the statistical analysis.In order to identify recent negative life events (RLE) we used the List of Threatening Experiences questionnaire5-HTTLPR) based on evidence from previous studies on involvement in depression. Buccal mucosa cells were collected for genotyping using a cytology brush  and 2.0 mL collection buffer in 15-mL plastic tubes. Genomic DNA was extracted according to a previously published protocol43. Laboratory work was carried out according to the ISO 9001:2000 quality management requirements and was blinded with regard to phenotype.We genotyped seven a priori selected polymorphisms , which is referred to as Bayesian relevance analysis in the paper. This method utilizes special hierarchical structural properties of Bayesian networks representing essential concepts in relevance analysis such as Markov blanket memberships, Markov blanket sets, and Markov blanket graphs. Bayesian relevance analysis applies a Markov Chain Monte Carlo sampling method to perform a random walk in the space of directed acyclic graphs44. This MCMC process results in samples of DAG structures which are used to estimate the consistent posteriors for structural properties representing relevance, e.g. posteriors for Markov blanket memberships representing strong relevance45. Thus this method quantifies the relevance of variables with respect to one or more selected target variables as probability scores (posterior probability of strong relevance) relying on Bayesian model averaging46.In order to efficiently explore relevant factors and interactions for complex phenotypes represented by multiple variables, we applied the Bayesian network based Bayesian multi-level analysis of relevance BN-BMLA  and possible statistical interactions with recent negative life events. The Markov blanket graphs representing these relations are the hypotheses in our approach, which takes into account the potential interdependencies of the target variables. Because the Bayesian approach induces a posterior distribution over this hypothesis space, it provides an efficient handling of multiple hypothesis testing.In this study, all genetic factors were analyzed jointly in a multivariate model47 with 106 burn-in and 5 × 106 normal steps. In addition, the maximum number of parents (i.e. number of incoming edges) was limited to 5. The parameter prior was non-informative with respect to the data set, and it was selected based on our previous experiments with parameter priors48. Analyses were carried out for each RLE exposure group separately. Age and gender was included in each multivariate model. Note that the multivariate model provided by Bayesian relevance analysis allows the investigation of interactions on a system level, which is in line with recent recommendations regarding GxE analyses7.The Bayesian relevance analysis was configured and executed to estimate Markov blanket membership posterior probabilities using a uniform structure prior, a Cooper-Herskovits parameter prior) for an illustration of included dependency relationships): (1) Model-1 containing only main effect of the environmental factor (relationship ‘a’), and (2) Model-2 containing only main effects of the environmental and genetic factors (‘a’ + ‘b’), and (3) Model-3 containing main effects of the environmental and genetic factors (see Model-2) plus interactions of environmental and genetic factors (‘a’ + ‘b’ + ‘c’). Age and gender were included as cofactors in all models. The residual variance of the models was compared and tested using an F-statistic. GLM analyses were performed with SPSS21.0 for Windows  using a significance threshold 0.05.In addition, a frequentist multivariate analysis using general linear models (GLM) was applied to test the significance of the interaction of the investigated genetic factors and recent negative life events versus the effect of recent negative life events on the multiple depression-related phenotype. Three GLM models were constructed Fig. , Table 22https://figshare.com/s/737f45fe41522e601d0a.The datasets generated and analysed during the current study are available in the Figshare repository, Supplementary Information


Zosterops borbonicus white-eye of Reunion (Mascarene archipelago), a colour polymorphic songbird in which all highland populations contain individuals belonging to either a brown or a grey plumage morph. Using extensive phenotypic and genomic data, we demonstrate that this melanin-based colour polymorphism is controlled by a single locus on chromosome 1 with two large-effect alleles, which was not previously described as affecting hair or feather colour. Differences between colour morphs appear to rely upon complex cis-regulatory variation that either prevents the synthesis of pheomelanin in grey feathers, or increases its production in brown ones. We used coalescent analyses to show that, from a ‘brown’ ancestral population, the dominant ‘grey’ allele spread quickly once it arose from a new mutation. Since colour morphs are always found in mixture, this implies that the selected allele does not go to fixation, but instead reaches an intermediate frequency, as would be expected under balancing selection.Understanding the mechanisms responsible for phenotypic diversification within and among species ultimately rests with linking naturally occurring mutations to functionally and ecologically significant traits. Colour polymorphisms are of great interest in this context because discrete colour patterns within a population are often controlled by just a few genes in a common environment. We investigated how and why phenotypic diversity arose and persists in the  In thisMC1R) gene, currently one of the most studied ‘colour genes’. This gene displays relatively low pleiotropic effect  and has [The recent development of next-generation sequencing has allowed research to transcend candidate-gene approaches in wild non-model species, leading to the identification of genomic regions associated with fitness-related traits, including colour traits ,34. Herein birds . This spirds [2) . One of irds [2) . Field oirds [2) .Figure MC1R, Agouti, Corin, Pro-opiomelanocortin and Tyrosinase-related protein 1 [Previously, we used a candidate-gene approach to search for associations between plumage colour and genetic variants in the Reunion grey white-eye, and did not detect any for a series of genes that play a critical role in melanin-based pigmentation patterns in other species, including rotein 1 ,38. In trotein 1  on poolsrotein 1 , we perfrotein 1  and whol2.2.1.Birds were captured in the field using mist nets between 2007 and 2012 on Reunion , were weighed and marked with a uniquely numbered aluminium ring, and approximately 10 µl of blood was collected from each bird. Blood was conserved in Queen's lysis buffer  and stor2.2.Ninety-six individuals from the 137 used for pooled RAD-sequencing were used for characterizing phenotypes by spectrophotometry analyses. We used reflectance spectrophotometry to characterize variation in plumage colour, because it provides an objective quantification of colour ,46. We s2.3.ca 7 mg) from the back of 12 brown and 12 grey individuals were homogenized with a Ten-Broeck homogenizer at a concentration of 10 mg ml−1 and 100 µl aliquots were subjected to Soluene-350 solubilization [We used microanalytical methods to quantify the eumelanin and pheomelanin content of the feathers from brown and grey morphs . The genotyping error rates for each locus, the number of unsampled sires and the number of unsampled females were all estimated jointly from the pedigree. Markov chains were run for 1.1 million iterations, with a burn-in of 100 000 iterations and a thinning interval of 1000. Parents (or father or mother) were assigned to an offspring with a 95% joint posterior probability threshold.Performing controlled matings in wild populations is challenging, and hence to study inheritance patterns, we inferred pedigrees in three natural populations monitored over multiple years at three localities. Given the reduced dispersal in rbonicus ,54, samp v. 2.50  and geno v. 2.50 . A total2.5.Z. lateralis genome [Z. lateralis on the zebra finch genome  using LASTZ [To identify loci associated with colour, we used a paired-end RAD-sequencing protocol, using a dataset described previously in which six pools of 18–25 individuals representing the two colour morphs in each of three separate populations  were sequenced . We tooks genome  to map rs genome , insteads genome . We aligng LASTZ . We remong LASTZ . SNPs weng LASTZ .2.6.Z. lateralis genome using BWA. SNPs were called using freebayes (v0.9.15-1) and filtered with VCFTOOLS (0.1.12b) using the following criteria: (i) a mean sequencing depth between 6 and 20×; (ii) a minimal genotype quality of 20; (iii) a minor allele count of 3, which resulted in around 25 000 unambiguous SNPs.We further validated the results obtained by the pooled RAD-seq approach by performing a genotyping by sequencing analysis  on 42 in2.7.rlequin v. 3.5 [To validate the lack of genetic structure according to morphs, we performed a PCA , using cn v. 3.5  on the 1p-value (eBPis) and Bayes factors (BF) expressed in deciban units (dB) to determine the level of association of each SNP with the grey/brown trait. Decibans are a commonly used statistic that describes here the probability that data are produced under a given model. They are often used as a unit for BF. Here, it describes the odds of association to morphs versus the null hypothesis of non-association. BAYPASS was run using default parameters under the core model. Empirical Bayesian p-values and BF were then computed for a pseudo-observed dataset containing 1 000 000 SNPs simulated from the actual data. We then compared the observed values to this distribution in order to calibrate the statistics.To detect loci displaying a significant association with coloration, we performed an association analysis on the pooled RAD-seq data using the software BAYPASS (v. 2.1) . We compFor GBS data, we performed a single analysis in PLINK  by corre2.8.To validate the hereditary transmission of plumage colour and confirm the validity of pedigrees, 12 individuals from three distinct families were selected for 150 bp paired-end Illumina sequencing. Individuals included 4 brown and 8 grey birds . Three types of matings were considered: two grey parents with only grey offspring, two grey parents with both brown and grey offspring, and one parent of each morph with both types of offspring. DNA sequencing libraries were prepared according to Illumina's protocols using the Illumina TruSeq Nano DNA LT Library Prep Kit. Briefly, DNA was fragmented by sonication (Covaris M220) and adaptators were ligated to be sequenced. Eight cycles of PCR were then applied to amplify the libraries. Library quality was assessed using an Agilent Bioanalyzer and libraries were quantified by QPCR using the Kapa Library Quantification Kit. Sequencing was performed in paired-end (2 × 125 pb) on an Illumina HiSeq2500 sequencer at the GeT-PlaGe core facility . The mean sequencing depth ranged from 6.9× to 11.6× after removing duplicates . After calling with freebayes, SNPs were filtered with VCFTOOLS using the following criteria: (i) a mean sequencing depth between 6 and 20×; (ii) a minimal Phred score of 20 and a genotype quality above 20; (iii) at least eight individuals genotyped.Putative pedigrees obtained from microsatellite data were confirmed by checking the coefficients of relatedness between individuals in KING (v. 1.4)  using 102.9.To determine when and on which specific allele selection occurred, we used the coalescent framework implemented in ARGWeaver . ARGWeavWe ran the analyses using whole-genome sequences from the three parental pairs, as these individuals displayed low levels of relatedness in the whole-genome association analysis and were predicted to display an equal number of brown and grey alleles. To reduce computational burden, we performed the analysis on the scaffold covering the candidate region (scaffold 40) and one on chromosome 2 (scaffold 30) with no SNPs associated with colour. The algorithm was run for 1000 iterations. We then extracted trees and half-time TMRCAs from the output.3.3.1.Brown and grey birds were different in coloration when examined in an avian-appropriate colour space, clustering into two distinct groups along the first component axis in a PCA . Pheomelanin was mostly concentrated in feather barbs and rachis , consistent with the fact that most of the variation in reflectance spectra between morphs is related to variation in pheomelanin content.We analysed the melanic content of feathers by determining eumelanin and pheomelanin concentrations . This lack of structure was also found for individual GBS data (figure 2b) and was further confirmed by an AMOVA  which revealed a significant effect of population structure in shaping neutral genetic diversity , yet no effect of colour morph .Previous field observations suggested a lack of assortative mating relative to plumage colour in white-eye populations, which should lead to a lack of genetic structure according to morph . Pooled figure 2To study the pattern of inheritance of the two colour morphs, we generated pedigrees for 36 putative parent–offspring triads based on the genotyping of 11 microsatellite markers in a total of 260 individuals. Matings involving two brown parents systematically led to brown offspring, while grey offspring always had at least one grey parent , suggest3.3.Z. lateralis genome (b). Thirty-three SNPs had an empirical Bayesian p-value (eBPis) higher than 1.48 (at the 0.001% threshold). Among these SNPs, 28 were found on scaffold 40 on the reference genome for Z. lateralis which covers approximately positions 15–21 Mb on chromosome 1 of the better assembled zebra finch reference genome (c). Density of strongly associated SNPs (with an eBPis above the 0.01% threshold) was particularly high around 3 Mb of this scaffold, which corresponds approximately to position 18 Mb on the zebra finch chromosome 1. This pattern was further confirmed by individual GBS data, with which seven variants were found to be associated to coloration with an uncorrected p-value between 9.1 × 10−5 and 8.6 × 10−7 on scaffold 40, while only two other variants were found to be associated with similar levels of significance on scaffold 172, which is directly upstream of scaffold 40 according to the zebra finch reference genome.To identify the genomic regions responsible for the observed colour polymorphism, we performed a genome-wide association analysis on a total of 627 795 SNPs obtained from the pooled RAD-sequencing data, and mapped it onto the s genome b. Thirtye genome c. DensitAP1S2, GPM6B, RAB9A and TRAPPC2—lie within regions where associated SNP density is highest . Only one non-synonymous substitution at a level of significance less than 1 × 10−3 was found in ACE2, an angiotensin (a). The induced change led to the substitution of methionin to isoleucin, two hydrophobic amino acids which are extremely similar. Therefore, there is no non-synonymous change that would make a suitable candidate for the observed phenotypic variation. This suggests a role for regulatory mutations in determining the observed phenotype.Having pinpointed the locus responsible for plumage colour variation, we searched for functional polymorphism at non-synonymous sites in the candidate region by obtaining whole-genome sequences from 12 individuals sampled across three distinct families from different populations . Coefficients of kinship estimated from whole-genome data for these individuals were consistent with the pedigrees estimated from microsatellite data . The highest density of associated SNPs was found between 17.7 and 18.7 Mb on the zebra finch chromosome 1 a. Only oiotensin a. The in3.5.b). An examination of the local trees for regions containing SNPs associated with phenotype clearly showed that the most recent haplotypes were found in grey individuals only (c). These haplotypes also harboured the allele associated with grey coloration in the association analysis. Median half-TMRCA was estimated at 1733 generations in the region, significantly shorter than the estimate for the remaining regions included in the analysis . This indicates that grey coloration has been positively selected and that brown and grey phenotypes represent ancestral and derived conditions, respectively.To assess whether selection had a role in the emergence of the observed polymorphism and to identify which of the two allele categories (brown or grey) is ancestral, we sampled ancestral recombination graphs for the entire candidate scaffold. Here, we took advantage of the fact that the individuals used in this test displayed six ‘brown’ and six ‘grey’ alleles at the most strongly associated markers. We found that the candidate region displayed a clear signature of a recent selective sweep, with an extremely short half-TMRCA between positions 2 700 000 and 3 100 000 on scaffold 40 b. An exaals only c. These 4.Z. lateralis reference genome [cis-regulatory mutations that either prevent the synthesis of pheomelanin in grey feathers, or increase its production in brown ones. The first mechanism seems more likely in the present case since the grey phenotype seems to represent the derived condition. The fact that eumelanin is found in both brown and grey feathers, while pheomelanin is only present in brown feathers suggests that the underlying genes may be involved in melanogenesis and melanosome transport.We confirmed that highland populations of the Reunion grey white-eye present a genetic colour polymorphism that results from the differential deposition of pheomelanin on feathers, leading to predominantly grey or brown plumage, and is controlled by a simple genetic mechanism with a recessive ‘brown’ allele and a dominant ‘grey’ allele. A genome-wide association analysis designed to search for loci involved in the polymorphism led to the identification of a single genomic region on chromosome 1 characterized by SNPs strongly associated with colour phenotype. A coalescent analysis of selection pinpoints the same region, and suggests recent selection on the grey allele. Annotation of these SNPs using the e genome  did not RS1, APXL and OA1, are known to affect melanogenesis and pigmentation in retinal pigment cells. In addition to these genes, the colour locus includes genes  known to be involved in intracellular trafficking and in shaping melanosomes in melanocytes , where multiple independent mutations were selected for cryptic coloration after the colonization of a novel selective environment [Our work reveals a new genomic region not previously associated with melanic coloration in vertebrates, and underscores the importance of genome-based research on non-model species to understand the genetic basis of ecologically significant traits and their role in phenotypic diversification . Our resironment ,32. In wironment . Disting


CRTAP, GLB1, and TMPPE) demonstrating a significant association in gene-based tests. Meta-analyzed MDD, recurrent MDD and female MDD yielded equivalent heritability estimates, showed no detectable difference in association with polygenic scores, and were each genetically correlated with six health-correlated traits . Whilst stratified GWAS analysis revealed a genome-wide significant locus for male MDD, the lack of independent replication, and the consistent pattern of results in other MDD classifications suggests that phenotypic stratification using recurrence or sex in currently available sample sizes is currently weakly justified. Based upon existing studies and our findings, the strategy of maximizing sample sizes is likely to provide the greater gain.Few replicable genetic associations for Major Depressive Disorder (MDD) have been identified. Recent studies of MDD have identified common risk variants by using a broader phenotype definition in very large samples, or by reducing phenotypic and ancestral heterogeneity. We sought to ascertain whether it is more informative to maximize the sample size using data from all available cases and controls, or to use a sex or recurrent stratified subset of affected individuals. To test this, we compared heritability estimates, genetic correlation with other traits, variance explained by MDD polygenic score, and variants identified by genome-wide meta-analysis for broad and narrow MDD classifications in two large British cohorts - Generation Scotland and UK Biobank. Genome-wide meta-analysis of MDD in males yielded one genome-wide significant locus on 3p22.3, with three genes in this region ( Until recently, the largest international mega-analysis of clinically diagnosed MDD (9240 MDD cases and 9519 controls) yielded no genome-wide significant findings2. Given the success of similarly sized studies for other adult psychiatric disorders4, this study suggested that MDD is an extensively heterogeneous phenotype. This heterogeneity, in addition to the relatively high prevalence and low heritability of MDD, impacts substantially on the statistical power to detect genetic effects5. Possible means of improving statistical power include stratifying the phenotype into potentially more homogeneous subtypes, or considerably increasing the sample size whilst accepting a broader phenotype. Both of these approaches have since identified associations between genetic variants and MDD9.Major Depressive Disorder (MDD) is a frequently disabling, chronic disorder for which there is substantial evidence of a genetic contribution to its liability6. A subsequent study by the Psychiatric Genomics Consortium (PGC) stratified the PGC MDD mega-analysis sample2 by age of onset and identified a risk conferring locus at chromosome 3q27.2 in individuals with onset after 27 years of age9.A genome-wide association study (GWAS) from the CONVERGE consortium identified two genome-wide significant loci (chromosome 10q21.3 and 10q26.13) using a severe depressive phenotype (5053 cases and 5337 controls) in female Han Chinese individuals, treated in a hospital setting7. More recently, Hyde et al8 conducted a GWAS using 23andMe data of self-reported depression in 45,773 cases and 106,354 controls, revealing 15 genome-wide significant loci. The genetic correlation (rG) between the 23andMe depression phenotype and the clinical phenotype reported by the PGC was rG(SE) = 0.73(0.09), suggesting a strong association between the additive genetic components of each trait. Previous work comparing self-reported depression and clinically defined MDD in Generation Scotland: Scottish Family Health Study (GS:SFHS) also provides evidence that these traits have substantially overlapping common genetic architectures10.In contrast to the above study designs, two studies have utilized larger sample sizes with less detailed structured clinical assessments. The first of these studies came from the CHARGE consortium and employed a quantitative assessment of depressive symptoms using the Center for Epidemiological Studies Depression Scale. In a combined dataset of 51 258 individuals, a genome-wide significant locus was identified at chromosome 5q21.2The findings from these four studies support both phenotypic stratification and increased sample size as strategies which may help reveal the underlying architecture of MDD. The international collaborative efforts by groups such as the PGC and the development of large-scale biobanks with genetic and extensive phenotypic information will ensure ever increasing sample numbers. It is therefore timely to investigate the contrasting strategies that may be employed in the analysis of these emerging datasets.12 and UK Biobank (UKB)14. To maximize the sample size, an unstratified analysis was initially conducted. This used MDD diagnostic information obtained at structured clinical interview15 in GS:SFHS , and a probable MDD phenotype obtained from a touchscreen questionnaire16, previously validated by Smith et al17, in UKB . Subsequent analyses stratified the phenotype on the basis of recurrence or sex. Each approach was evaluated using several metrics: the successful identification of variants reaching genome-wide significance in GWAS meta-analysis, an increased SNP-based heritability estimate, identification of significant genetic correlations with other traits using LD score regression, and increased variance explained by polygenic profile scores for MDD derived from three independent cohorts. These metrics aim to test whether basic stratification of the MDD phenotype improves etiological insight.In the current study, we sought to compare these strategies by conducting a suite of genetic analyses for depression and stratified subtypes in two UK-based cohorts: GS:SFHShttp://www.generationscotland.co.uk) and UKB, (data available on request: http://www.ukbiobank.ac.uk). GS:SFHS received ethical approval from the NHS Tayside Committee on Medical Research Ethics (REC Reference Number: 05/S1401/89). UK Biobank received ethical approval from the Research Ethics Committee (REC Reference Number: 11/NW/0382). The present analyses were conducted under UK Biobank data application number 4844. All participants provided informed consent.This study analyzed data from Generation Scotland: The Scottish Family Health Study (GS:SFHS),  was made using the structured clinical interview for DSM-IV disorders (SCID)15. Participants who answered yes to either of the two screening questions were invited to continue the interview, which provided information on the presence or absence of a lifetime history of MDD, age of onset and number of depressive episodes. Participants who answered no to both screening questions or who completed the SCID but did not meet the criteria for depression were assigned control status. Case definition was further refined through NHS data linkage. Controls with a history of antidepressants or who had been referred to a secondary psychiatric care centre (n = 1 072) were excluded, as were cases who had received a previous diagnosis of schizophrenia or bipolar disorder (n = 47). This resulted in 2603 depression cases (of which 1289 were recurrent) and 16,122 controls. Stratification by sex resulted in 1859 female cases, 9159 female controls, 770 male cases and 6958 male controls.GS:SFHS is a family and population-based study consisting of 23,690 participants recruited via general medical practices across Scotland. The recruitment protocol and sample characteristics are described in detail elsewhere13 is a population-based health research resource consisting of approximately 500,000 people, aged between 40 and 69 years, who were recruited between the years 2006 and 2010 from across the UK14. Of these, 152,729 individuals were included in the first genotype data release. In the current study we restricted the sample to individuals of white British ancestry. Participants who were also in GS:SFHS, their relatives and relatives of remaining UKB participants (relatives: up to and including third degree) were identified by a kinship coefficient ≥ 0.0442, using the KING toolset18, and subsequently excluded (n = 7 698). Depression case/control status was assessed in 172,751 of the 500,000 individuals using a self-diagnosed touchscreen questionnaire. Case status was defined as either “probable single lifetime episode of major depression” or “probable recurrent major depression (moderate and severe)”. Control status was defined as “no mood disorder”, as described by Smith et al17. 149,847 individuals had sufficient data to allow an assessment of case/control status. Individuals with probable bipolar disorder (n = 1 615) or mild depressive/manic symptoms (n = 26 847) were excluded. After exclusions outlined above, this resulted in 8248 depression cases (of which 6056 were recurrent) and 16,089 controls. Stratification by sex resulted in 5138 female cases, 7013 female controls, 3110 male cases and 9076 male controls. Further information on sample collection, genotyping and assessment of the depression phenotype in GS:SFHS and UKB are provided in the UKBn = 18 725). Genotypes were imputed using the Haplotype Reference Consortium reference panel (HRC.r1-1)19 via the Sanger Imputation Server pipeline (https://imputation.sanger.ac.uk). Prior to imputation, individuals with missingness ≥ 3% were excluded, as were SNPs with a call rate of ≤98%, Hardy Weinberg Equilibrium (HWE) P-value ≤ 1 × 10−6, and a minor allele frequency (MAF) ≤ 1%. Phasing of genotype data was performed using the SHAPEIT2 algorithm20 utilizing the duoHMM option, which refines phasing by utilizing pedigree information. Imputation was performed using PBWT software21. Multi-allelic variants, monomorphic variants and SNPs with an imputation INFO score < 0.8 were removed22. Population outliers ) were identified and removed from the sample23, as were one from each of 52 monozygotic twin pairs, identified by IBD , and 7 individuals who matched samples from the Psychiatric Genomics Consortium, identified using genotype checksums24. After imputation, individuals with missingness ≥ 2%, and genotype with a call rate of ≤98%, MAF ≤ 0.5% and HWE P-value ≤ 1E-05 were excluded using PLINK version 1.9.26. Strand ambiguous SNPs with 40% ≤ MAF ≤ 50% were also excluded.Autosomal genotype data were available for all GS:SFHS individuals in the present study (n = 24 337). Pre-imputation QC, phasing and imputation are described elsewhere27. In brief, prior to phasing, multiallelic SNPs or those with MAF ≤ 1% were removed. Phasing of genotype data was performed using a modified version of the SHAPEIT2 algorithm28. Imputation to a reference set combining the UK10K haplotype and 1000 Genomes Phase 3 reference panels29 was performed using IMPUTE2 algorithms31. A further QC protocol was then applied at the Wellcome Trust Centre for Human Genetics before the data was released, as described elsewhere32. The analyses presented here were restricted to autosomal variants with an imputation INFO score ≥ 0.9 and MAF ≥ 0.5%.Autosomal genotypes were available for all UKB individuals in the present study , all controls and recurrent cases (rMDD), female controls and cases (fMDD), and male controls and cases (mMDD). The total sample size for each depression subgroup was 33, implemented in GCTA (v1.25.)34. To account for population structure, two genomic relationship matrices (GRMs) were used, as this method allows the inclusion of closely and distantly related individuals in genetic analyses35. The first GRM included pairwise relationship coefficients for all individuals. The second GRM had off-diagonal elements < 0.05 set to 0. GRMs were created using the mixed linear model with candidate marker excluded (MLMe) approach, where GRMs are calculated excluding SNPs located on the chromosome under analysis33. No fixed effects covariates were fitted in this analysis as sex was being assessed as a stratifier, and the two GRMs adequately accounted for population stratification (tested using univariate LD Score Regression36). MLMA employs restricted maximum likelihood methods on the linear scale. As such, test statistics (betas and their corresponding standard errors) were transformed to Odds Ratios and their corresponding 95% Confidence Intervals on the liability scale using a Taylor transformation expansion series38. Further details of GWAS can be found in the GWAS of MDD, rMDD, fMDD and mMDD in GS:SFHS were conducted using mixed linear model based association (MLMA) analysis25. Assessment centre, genotype array and batch were fitted as fixed effects. The first 8 PCs (out of 15) supplied by UKB were also fitted, as visual inspection indicated that these PCs resulted in multiple clusters, indicating structure in the data.GWAS of MDD, rMDD, fMDD, and mMDD in UKB were conducted using logistic regression, implemented in PLINK v1.939. SNPs with a meta-analysis P-value of P ≤ 1E-05 were subjected to clump-based linkage disequilibrium pruning using PLINK25 using an LD r2 cut off of 0.1 and a 500 kb sliding window to create SNP sets of approximately independent “lead SNPs”. All SNPs which surpassed genome-wide significance were entered into the NHGRI-EBI catalog of published GWAS41 (www.ebi.ac.uk/gwas/) to observe whether these SNPs had been previously observed in association analysis.The meta-analysis of GS:SFHS and UKB was conducted using the classical inverse-variance approach, which weights effect sizes by sampling distribution, implemented in the METAL package42. The gene-based statistics were derived using the summary statistics from each meta-analysis. Genetic variants were assigned to genes based on their position according to the NCBI 37.3 build, with a gene boundary defined by an extended region between 20 kb upstream of transcript start site and 20 kb downstream of transcript end site for each of the genes. This resulted in a total of 18 111 genes for MDD, fMDD, and mMDD, and 17,225 genes for rMDD being analyzed. The European panel of the 1000 Genomes data  was used as a reference panel to account for linkage disequilibrium43. A genome-wide significance threshold for gene-based associations was calculated using the Bonferroni method .Gene-based analysis was performed for MDD, rMDD, fMDD, and mMDD using MAGMA44, reference to RegulomeDB45 (http://www.regulomedb.org/) and to the Genotype-Tissue Expression Portal  for independent SNPs with P < 1.0 × 10−5 and all genome-wide significant SNPs . Further information on pathway and functional genomic analysis can be found in the Pathway and functional genomic analyses were performed using the GWAS results for each of the MDD meta-analyses. These included DEPICT analyses46 analyses were used to estimate the proportion of variance explained by all common (MAF > 1%) SNPs for each of the depression phenotypes. A relatedness cutoff of 0.05 was used in the generation of the genetic relationship matrix, as including close relatives inflates heritability estimates47. This did not alter the sample size in UKB due to previous sample filtering, however in GS:SFHS this reduced the sample size by 38.5–58.4%  correction was applied across the 800 tests to correct for multiple testing53.Univariate GCTA-GREMLon MAF > % SNPs fon = 50 455 cases, 105,411 controls; minus UKB, n = 43 204 cases, 95,680 controls)54, UKB and GS:SFHS (from the current study) were used to provide weights for polygenic profile scores (PGS).To test the association of MDD-associated alleles with each subtype of MDD in GS:SFHS and UKB, summary statistics for major depressive disorder from the Psychiatric Genomics Consortium  using PRSice55. Genotyped SNPs (with MAF > 1%) were subjected to clump-based linkage disequilibrium (LD) pruning, using an LD r2 cut off of 0.25 and 200 kb sliding window to create SNP sets in approximate linkage equilibrium. PGS were then standardized to have a mean of zero and a unit standard deviation.PGS for GS:SFHS and UKB individuals were derived at 5 GWAS P-value thresholds (χ2) + 0.5(0) distribution56. An additive genetic component was fitted as a random effect to account for the increased relatedness within GS:SFHS. To ensure that common environment was adequately modeled, models incorporating shared parent-offspring, sibling, and spousal environmental components as additional random effects were tested using a stepwise LRT approach, however no environmental component improved model fit. Further details of mixed linear model selection are provided in the F-statistics, degrees of freedom, effect sizes, Z-scores and P-values were derived using the Wald Conditional F-test57, in ASReml-R58.In GS:SFHS, the associations between PGS and MDD, rMDD, fMDD, and mMDD were tested using a mixed linear model, covarying for the first 20 PCs to account for population stratification. Prior to this analysis, the requisite number of PCs was established using a stepwise linear regression approach, adding one PC at a time, and using a likelihood-ratio test (LRT), the output of which was assessed against a mixed 0.5)/var(y), where x was the standardized PGS, β was the corresponding regression coefficient and y was the phenotype59.FDR correction was applied across the 80 tests to correct for multiple testingP = 2.29 × 10−8). None of the SNPs achieving genome-wide significance (nSNPs = 6) associated with any phenotype in currently published GWAS available via the NHGRI-EBI catalog. One variant (rs7613051) within the local genomic region (defined 3:33000000–33200000) has previously shown an association with Atopic dermatitis60, however this SNP is not in LD with the genome-wide significant SNPs (r2 < 0.1). Meta-analysis of MDD, rMDD, and fMDD did not yield any genome-wide significant findings. Manhattan plots are shown for each trait in Fig. P ≤ 1 × 10−6 are shown in Table P = 8.05 × 10−4 in MDD, β(SE) = 0.10(0.05)). Full details of all independent loci used in downstream analyses (P ≤ 1 × 10−5) are shown in Supplementary Tables GC ranged from 1.02–1.06, comparable to the value (1.056) observed in the PGC mega-analysis of MDD2. Univariate LDSR analyses estimated that meta-analyzed MDD subtypes had mean chi-squared statistic (μχ2) values ranging from 1.018 (mMDD) to 1.062 (MDD) with a Ratio, defined as (Intercept-1)/(μχ2-1), ≤ 0.35 across subtypes, indicating that any inflation in μχ2 can be attributed to polygenicity rather than residual population stratification36.One genomic region on chromosome 3p22.3 achieved genome-wide significance in the males only case/control (mMDD) analysis  = 0.29(0.05), CRTAP, GLB1, and TMPPE) were significantly associated with mMDD after Bonferroni correction  receptor subtype that binds endogenous 5-HT62. To assess whether significant association of these 3 genes was due to LD in the region, the meta-analysis of MDD in males was re-run conditional on SNPs with an R2 > 0.9 with the top ranking SNP, rs4478037. This analysis, implemented in GCTA (v1.25)34, indicated that the signal was being driven by LD across the region , 25 multi-tissue cis-eQTL associations were identified for 16 independent lead SNPs with meta-analysis P < 1 × 10−5  for 6 of the lead SNPs with meta-analysis P ≤ 1 × 10−5 . Of the six SNPs which achieved genome-wide significance in the meta-analysis of mMDD, 2 SNPs (rs11558338 and rs6809511) showed evidence of transcription factor binding, position weight matrix, histone modification, DNase hypersensitivity, and FAIRE regulatory elements. Evidence of regulatory evidence for all independent SNPs with meta-analysis P ≤ 1 × 10−5 are shown in Supplementary Table Using the GTEx database  estimates in UKB were consistent and significant across MDD subtypes, with h2SNP(SE) estimates of MDD = 0.20(0.04); rMDD = 0.20(0.03); fMDD = 0.22(0.06) and mMDD = 0.18(0.06). Due to the unrelated subset of individuals in GS:SFHS being markedly smaller than the full sample (nmax = 7 795), the heritability estimates were non-significant across all MDD definitions. Results from GCTA-GREML are shown for MDD subtypes in Supplementary Table h2SNP estimates than GCTA-GREML methods (Supplementary Table Using GCTA-GREML methodsP < 0.05) genetic correlations (rG) between meta-analyzed MDD and 28 of the 200 health-related traits assessed. Of these, 8 traits survived multiple testing correction: neuroticism (rG(SE) = 0.67(0.07); P = 7.06 × 10−21), depressive symptoms (rG(SE) = 0.81(0.09); P = 1.72 × 10−19), subjective wellbeing (rG(SE) = −0.56(0.08); P = 9.12 × 10−13), age at first birth (rG(SE) = −0.35(0.05); P = 1.92 × 10−10), major depressive disorder (rG(SE) = 0.67(0.12); P = 4.57 × 10−8), PGC cross-disorder analysis (rG(SE) = 0.46(0.09); P = 8.60 × 10−8), bipolar disorder (rG(SE) = 0.32(0.08); P = 4.35 × 10−5) and systemic lupus erythematosus (rG(SE) = 0.28(0.08); P = 8.00 × 10−4). These findings are consistent with previously reported, well-established relationships between MDD and neuroticism65, bipolar disorder66, PGC cross-disorder66, depressive symptoms67 and subjective well-being68. Relationships between MDD and age at first birth, and SLE have been previously reported although these have been based on phenotypic correlations71. The majority of these traits (with the exception of age at first birth and systemic lupus erythematosus) demonstrated rG of a similar magnitude, direction and significance with recurrent and female MDD. In contrast to these results, bivariate genetic correlations between mMDD and health-related traits were all non-significant after adjustment for multiple testing . In addition, the univariate LDSR h2SNP(SE) = 0.05(0.03) for mMDD. The LDSR rG is calculated as rG = ρg/ρg is the genetic covariance between traits, 36. Near-zero h2 estimates can therefore cause the rG estimate to become out of bounds (rG > 1), as observed in three out of five nominally significant traits, with large standard errors72, as observed in all nominally associated traits. Fig. Bivariate LDSR showed nominally significant . Results from PT ≤ 0.05 are shown in Fig. PT explained the most variance in the target datasets. Results from all PT are shown in Supplementary Tables PT ≤ 0.01 in GS:SFHS). PGS derived using information from the UKB MDD GWAS yielded significant associations with MDD, rMDD, and mMDD phenotypes in GS:SFHS at all thresholds, however fMDD did not survive multiple testing correction at any PT. PGS derived using information from the GS:SFHS MDD GWAS yielded significant associations with MDD, rMDD, and fMDD phenotypes in UKB at all thresholds, except PT ≤ 0.01—presumably due to the low number of SNPs contributing to the score. However, mMDD did not survive multiple testing correction at any PT. Across all associations, the largest proportion of variance explained in GS:SFHS was 0.66% for fMDD using the MDD polygenic score derived using SNPs at PT ≤ 0.5 using weights from the PGC MDD29 GWAS. The largest proportion of variance explained in UKB was 0.72% for fMDD, again using SNPs at PT ≤ 0.5 using weights from the PGC MDD29 GWAS.The GWAS results from the MDD phenotype in 3 discovery samples  were used to build polygenic profile scores (PGS) in GS:SFHS and UKB, incorporating SNPs with a discovery sample association 8 or by refining the phenotype by illness course9, recurrence and sex6. In this study we used techniques designed to interrogate complex traits to ascertain whether maximizing the sample size  or phenotypic stratification by recurrence or sex was more advantageous for investigating the genetic architecture of MDD, using data from two large UK cohorts. Each MDD definition was evaluated using several metrics: the successful identification of variants reaching genome-wide significance in GWAS meta-analysis, an increased SNP-based heritability estimate, identification of significant genetic correlations with other traits using LD score regression, and increased variance explained by polygenic profile scores for MDD derived from three independent cohorts.For many years, the depressed phenotype has been refractory to genetic inquiry due to issues regarding statistical power. Recently, studies have successfully identified loci associated with depression by either substantially increasing the sample size9).For all analyses, MDD, recurrent MDD and MDD in females returned similar results: overlapping SNP-based heritability estimates; genetic correlations with consistent magnitude of effect, direction, and significance with six health-related traits; low trait variance explained (<1%) and overlapping effect size estimates in polygenic profiling analysis, and no genome-wide significant findings from GWAS meta-analysis . Ever-increasing sample sizes from international consortia will provide much-needed larger replication datasets for corroborating or dispelling this finding. The lack of replication also highlights the importance of moving towards linking results at the functional level.With the exception of polygenic profiling analysis, MDD in males generally did not conform to the pattern of results demonstrated by other MDD definitions. Some of these differences, such near-zero SNP-based heritability estimate and subsequently no genetic correlations with other traits surviving multiple testing correction, can be attributed to reduced statistical power in this MDD definition. Interestingly, genome-wide meta-analysis yielded a single genome-wide significant locus in depression in males. The locus at chromosome 3p22.3 includes n = 43,062 and n = 39,556, respectively), therefore perhaps rMDD is not the best stratifier in this sample. The sample size of all depressed cases could have been increased by including individuals with mild depressive/manic symptoms , however as case classification was based on very few items (two symptoms and help-seeking behavior), it wasn’t possible to determine whether mild symptoms should be classified as cases or controls17, therefore including these individuals could introduce further phenotypic heterogeneity. Whilst the differential model selection in GS:SFHS and UKB used to adequately account for differential family and population structure introduces analytical heterogeneity, the genetic correlation of MDD GWAS summary statistics from the two samples was rG(SE) = 0.997(0.26). Lastly, the higher prevalence of females in GS:SFHS caused a gender imbalance in the sample sizes, resulting in lower statistical power for the male only analysis.There are several limitations to this study. Firstly, the sample size for MDD and rMDD groups are very similar  in polygenic profiling supports increasing the sample size over phenotypic refinement. Similarly, the lack of discernible difference between h9 and use of polygenic risk scores derived from health-related traits73 have been shown to result in subsets of depression with improved heritability. Our study suggests that, until a better understanding of the determinants of genetic heterogeneity in depression exist—increasing sample number remains the optimal strategy.Phenotypic stratification still has plenty of scope for aiding the tractability of genetic analysis in depression. There are many traits which could be used to create subgroups, including treatment response and physical comorbidities, and perhaps these will be more successful than sex and recurrence. However, it is worth noting that due to the limitations of statistical power with current sample sizes, the performance of the stratified phenotypes presented here is a lower bound of the stratification strategy. Recently, age-at onsetSupplementary materials