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Quantitative craniometrical traits have been successfully incorporated into population genetic methods to provide insight into human population structure. However, little is known about the degree of genetic and non-genetic influences on the phenotypic expression of functionally based traits. Many studies have assessed the heritability of craniofacial traits, but complex patterns of correlation among traits have been disregarded. This is a pitfall as the human skull is strongly integrated. Here we reconsider the evolutionary potential of craniometric traits by assessing their heritability values as well as their patterns of genetic and phenotypic correlation using a large pedigree-structured skull series from Hallstatt (Austria). The sample includes 355 complete adult skulls that have been analysed using 3D geometric morphometric techniques. Heritability estimates for 58 cranial linear distances were computed using maximum likelihood methods. These distances were assigned to the main functional and developmental regions of the skull. Results showed that the human skull has substantial amounts of genetic variation, and a t-test showed that there are no statistically significant differences among the heritabilities of facial, neurocranial and basal dimensions. However, skull evolvability is limited by complex patterns of genetic correlation. Phenotypic and genetic patterns of correlation are consistent but do not support traditional hypotheses of integration of the human shape, showing that the classification between brachy- and dolicephalic skulls is not grounded on the genetic level. Here we support previous findings in the mouse cranium and provide empirical evidence that covariation between the maximum widths of the main developmental regions of the skull is the dominant factor of integration in the human skull.
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The human skull is an important source of information for phylogenetic and population-genetic studies (Strait, 2001; González-José et al. 2003; Ackermann & Cheverud, 2004a,b). The complex morphology of the skull is usually decomposed in a series of craniometric measurements and it has been demonstrated that moderate amounts of genetic heritable variation underlie these traits (Sjøvold, 1984; Sparks & Jantz, 2002; Carson, 2006a). To some extent this suggests that skull morphology has substantial potential to evolve and that craniometric characters have the potential to provide consistent phylogenetic signals. Nevertheless, most studies have disregarded the integrated nature of the skull (Lieberman et al. 2000a,b; McCarthy & Lieberman, 2001; Bookstein et al. 2003; González-José et al. 2004; Bastir et al. 2004; Bastir & Rosas, 2004, 2005, 2006). Morphological integration can constrain the evolvability of traits (Merilä & Björklund, 2004) and bias the results of phylogenetic analysis (Strait et al. 2007; Lockwood, 2007; Sherwood et al. 2008).
Although the most appropriate approach to address this issue is to account for genetic and phenotypic covariation patterns of multivariate skull shape (Klingenberg & Leamy, 2001; Klingenberg, 2004, 2005), an alternative approach is to assess both the patterns of genetic variation and the correlation of univariate craniometric measurements. Here we explore the genetic architecture underlying the skull following this latter approach, which is relevant for evolutionary biology because craniometric traits are still in full use. For instance, recent studies have extensively applied population-genetics-based models using classical measurements (Roseman, 2004; Neves & Hubbe, 2005; Schillaci & Stojanowski, 2005; Harvati & Weaver, 2006). Our goal is to reconsider the evolutionary potential of craniometric traits, accounting both for their heritabilities and for the patterns of genetic and phenotypic correlation among them. Furthermore, we will test hypotheses of cranial integration formulated on the basis of these kind of traits (Enlow & Hans, 1996; Hallgrímsson et al. 2007).
Genetic variation in the human skull
The estimation of the genetic and non-genetic components underlying the phenotypic variation of the human skull has long been a major focus of anthropological research (Boas, 1912; Kohn, 1991; Varela & Cocilovo, 1999; Konigsberg, 2000). The first studies addressing this issue date back to the first decades of the 20th century (Dahlberg, 1926) but the interest increased at the end of the century because evolutionary biologists reconsidered the use of skeletal remains to unravel human microevolutionary paths (Relethford & Lees, 1982; Relethford, 1994). This new paradigm was built upon the growing evidence that suggested that human patterns of craniofacial variation reflected the underlying genetic patterns of variation (Cheverud, 1988; Buikstra et al. 1990). Craniometric traits were thus regarded as useful tools to study the structure and history of human populations (Relethford & Lees, 1982) and population-genetic models were adapted to be used after craniometric traits (Relethford & Blangero, 1990; Relethford, 2002, 2004). The heritability of complex metric traits, considered in the narrow sense, expresses the proportion of total phenotypic variance due to additive genetic variance (Falconer & MacKay, 1996). Heritability provides a measure of the proportion of variance in a trait explained by genetic transmission and is therefore a key parameter in models of evolution of quantitative traits (Konigsberg, 2000).
A wide range of studies have estimated the heritability of craniofacial traits (Vandenberg, 1962; Hiernaux, 1963; Nakata et al. 1974; Susanne, 1975, 1977; Sjøvold, 1984; Devor et al. 1986; Sharma, 1987; Sharma & Susanne, 1991; Konigsberg & Ousley, 1995; Nikolova, 1996; Sharma, 1998; Sparks & Jantz, 2002; Arya et al. 2002; Johannsdottir et al. 2005; Carson, 2006a). The general conclusion of these studies is that human craniofacial traits have moderate to high degrees of genetic variation. However, the comparison of results from different studies is controversial as they have been computed on very different kinds of samples (living humans or skeletal remains) from different geographical regions, accounting for different familiar relationships (twins, nuclear or extended families) and using different statistical methods (regression, anova, path analysis or maximum likelihood analysis (ML). ML methods are considered the most efficient methods to estimate genetic parameters in natural populations (Konigsberg, 2000). However, they have not been used until recently because they are computationally highly demanding (Roff, 1997).
Moreover, one of the main problems concerning the heritability estimation of cranial measurements in humans is that suitable, large and pedigree-structured skull series are almost non-existent. Such a collection of skulls with genealogical-associated data exists in Hallstatt (Austria), and has been previously studied to measure the heritability of metric and non-metric cranial traits (Sjøvold, 1984; Carson, 2006a,b). The work by Sjøvold (1984) was one of the first surveys of heritability on a human skull pedigreed series and the heritabilities of cranial traits were estimated using regression analysis. Sjøvold (1984) concluded that most of Howell's measurements were significantly hereditable and suggested that the structures showing the highest heritabilities were those connected to the size of the brain, the orbits, the nose and the masticatory apparatus. In a recent study, Carson (2006a) used an ML method to provide alternative estimates of the heritability of Howell's measurements. The main conclusion of this study was in agreement with Sjøvold's study and reported that craniometric traits show low to moderate narrow sense heritabilities. However, Carson (2006a) pointed out some differences and concluded that facial dimensions and cranial breadth measures are the less heritable characters of the skull. According to Carson (2006a) these differences stem from the different statistical techniques used for the heritability estimation.
Morphological integration in the human skull
Integration is expressed through covariation between traits and it plays a key role in the evolution of complex morphological structures such as the human skull, as it can enhance or constrain the evolution of its morphology towards certain directions of shape change (Klingenberg, 2004, 2005). Morphological integration assumes that functionally and/or developmentally related traits will be coinherited and will produce coordinate responses to evolution (Olson & Miller, 1958; Cheverud, 1982, 1984, 1995, 1996a).
The human skull comprises three regions with different developmental origins and functional requirements (Carlson, 1999): the cranial base, the cranial vault and the face. The cranial base is formed from endochondral bone that arises from a cartilaginous precursor originated from mesoderm (Mooney et al. 2002). The base supports the inferior parts of the brain as well as the pons, the medulla oblongata and the brain stem (Richtsmeier, 2002). The cranial vault is formed from membranous bone of paraxial mesodermal and neural crest origin and it gives room and protects the cerebral hemispheres and the cerebellum (Sperber, 2001). The facial skeleton ossifies intramembranously from neural crest precursors (Sperber, 2002) and it surrounds the pharynx as well as the oral, respiratory and orbital cavities, supporting the functions of feeding, breathing and vision. The cranial base is the most ancient structure and has been highly preserved through phylogeny (Carlson, 1999). Therefore, it is considered that the cranial base is under stronger genetic control than the cranial vault and the face (Schilling & Thorogood, 2000; Sperber, 2001). Moreover, it is assumed that the face is the most sensitive skull region to non-genetic factors because it plays a key role in foraging and adaptation to environment and because facial growth is more extended into the postnatal period (Siebert & Swindler, 2002).
The level of integration between these skull regions is a matter of current research. Most studies of morphological integration in the skull of mammals (Hallgrímsson et al. 2004, 2006; Goswami, 2006, 2007), non-human primates (Cheverud, 1982, 1995; Marroig & Cheverud, 2001; Hallgrímsson et al. 2004; Ackermann & Cheverud, 2004b) and humans (Lieberman et al. 2000a,b; McCarthy & Lieberman, 2001; Bookstein et al. 2003; González-José et al. 2004; Bastir et al. 2004; Bastir & Rosas, 2004, 2005, 2006) have considered integration at the phenotypic level. However, researchers have not identified yet which phenotypic units reflect morphogenetic units (Lieberman et al. 2004) and little is known about genetic integration and constraint in the functional and developmental regions of the skull.
The first studies of cranial integration in primates were developed by Cheverud (1982, 1995) and evidenced that functionally and developmentally related traits were in fact integrated. These findings provided support to the functional matrix hypothesis (Moss & Young, 1960), which predicts that covariation within functional units is stronger than covariation within individual bones or osseous subdivisions with different developmental/tissue origins. Afterwards, Hallgrímsson et al. (2004) reported that this functional/developmental pattern of craniofacial integration was consistent in rhesus macaques but not in mice. More recent studies of modularity in mammals (Goswami, 2006, 2007) and primates (Ackermann & Cheverud, 2004b) have identified six phenotypic cranial modules, corresponding to four functional regions of the face (namely the oro-nasal, the molar, the orbital and the zygomatic-pterygoid regions), one neurocranial region (the vault) and one basicranial region (the basicranium). The patterns of covariation within and among regions indicated that the face (the oro-nasal and the molar regions) and the cranial base were the highest integrated structures of the skull, whereas the cranial vault showed differing levels of integration across taxa. According to Ackermann & Cheverud (2004b), the zygomatic region is one of the main sources of facial integration in African apes and humans. Furthermore, they report that the loose integration of the cranial vault provided the skull with more capability to evolve in response to encephalization.
Other studies (Lieberman et al. 2000a,b; Bastir & Rosas, 2004) support the existence of two modules in the human skull, namely the face and the braincase. Lieberman et al. (2000a,b) consider that the basicranium and the neurocranium form a highly integrated morphological unit, the neuro-basicranial complex, which is partially independent from the face. However, Bastir et al. (2006) highlighted that the cranial base can not be interpreted as an integrated unit, at least at the ontogenetic level, as midline and lateral basicranial structures show different growth patterns. Further differences in growth may also explain the lack of integration between the braincase and the face: whereas the basicranium and the neurocranium grow jointly following a rapid neural trajectory (Bastir et al. 2006), facial growth extends more into the postnatal period and is more influenced by environmental factors (especially mechanical loadings). According to this, the face would be more prone to plastic responses (Kohn, 1991; Strand Vidarsdóttir et al. 2002; Bastir & Rosas, 2004), and it has been suggested that from the phylogenetic point of view, facial traits would not be as informative as neuro- and basicranial traits, which are more conservative and would reflect more reliably the underlying genetic patterns (Collard & Wood, 2000; Collard & O’Higgins, 2001).
In the primate skull, the cranial base appears to have a key integrative role (Lieberman et al. 2000a,b; Bookstein et al. 2003; Zollikofer & Ponce de León, 2004). Anatomically, it is a hinge-structure between the face and the cranial vault and developmental and growth studies support this view. Enlow & Hans (1996) suggested that the craniofacial architecture is based on a system of hierarchical modules organized into several craniofacial levels, in which the basicranium responds to modifications of the brain and translates them epigenetically into changes of facial proportions along a cerebro-mandibular gradient. Therefore, the base is the structural foundation that sets out the spatial development of the face and to some extent regulates the overall cranial development via integration with the brain and the cranial vault. Regarding human craniofacial variation, Enlow & Hans (1996) considered that there are two extreme headform types along a continuous spectrum: the dolicocephalic type, which is characterized by a long and narrow skull associated with a flat base and a supero-inferiorly longer face; and the brachycephalic type, in which a short and broad skull is associated with a more flexed cranial base and the face reveals a decreased anterior height and increased breadths. However, this traditional hypothesis of integration is not supported by developmental models of craniofacial biology (Lieberman et al. 2000a; Bastir & Rosas, 2004).
Recent experimental research using mice as animal models (Hallgrímsson et al. 2007) suggests that integration in the mammalian skull is highly structured following a hierarchical scheme that is dominated by strong covariation between the widths of the neurocranium and the basicranium and also with that of the face, but to a lesser extent. This study has further emphasized the stronger integration of the neurocranium and the basicranium with respect to the face, which is more independent but still covaries with the braincase (Hallgrímsson et al. 2007). After analysing the influence of epigenetic factors in craniofacial variation, the authors conclude that phenotypic variation arises from a few key developmental processes (such as brain growth) that channel the underlying genetic variation towards certain phenotypic expressions that maintain an integrated functional skull.
In the present study we reanalyse the pedigreed skull collection from Hallstatt (Austria) to explore the genetic patterns of variation determining the phenotypic expression of the skull and to assess the levels of correlation in craniometric characters. This will allow us to account for both the heritable and the integration patterns of the human skull. Here we test several hypotheses regarding these issues.
Hypothesis 1 (H1) examines the heritability patterns of facial, neurocranial and basicranial dimensions and tests whether there are differences in the amounts of genetic variation underlying these regions. The null hypothesis states that there are no significant differences among the heritability of each region, whereas rejection of the null hypothesis indicates differential genetic contribution to the phenotype of each region, suggesting that they are subject to different evolvabilities and levels of plasticity.
Hypothesis 2 (H2) explores genetic and phenotypic patterns of correlation of specific suites of craniofacial traits within and among major and minor developmental/ functional regions of the skull. The null hypothesis implies no correlation between the genetic (G) and phenotypic (P) matrices; that is, the patterns of phenotypic correlation do not reflect the genetic ones and show different strengths of morphological integration. The null hypothesis is rejected if the correlation of G and P is high and significant, which would suggest that genetic and environmental effects on development produce similar patterns of phenotypic variation. Thus, in those cases where G is not available, P could be used as a good proxy to G in population quantitative genetic models (Cheverud, 1988).
Hypothesis 3 (H3) tests the traditional hypothesis of integration of the human skull (Enlow & Hans, 1996). Under this hypothesis, maximum cranial breadth should be positively correlated with facial breadth and negatively correlated with facial height, neurocranial length and neurocranial height. The null hypothesis is rejected if the observed patterns of correlation between these pairs of distances do not fit the expected patterns of integration.
Hypothesis 4 (H4) tests whether the overall pattern of genetic integration in the human skull is dominated by the covariation between the maximum widths of the major developmental regions, namely, the face, the neurocranium and the basicranium. This hypothesis was put forward by Hallgrímsson et al. (2007), who investigated the influence of epigenetic factors in the patterns of morphological integration of mice skull. The null hypothesis predicts that the genetic correlations between facial, neurocranial and basicranial width are high and significant.
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This study explored the levels of genetic variation and correlation of craniometric traits through a developmental/functional approach to assess the evolutionary potential of the human skull. The above results confirm that the human skull has substantial amounts of genetic variation, which confers to the skull a high ability to evolve (Tables 2–5). However, evolvability is compromised by complex patterns of genetic integration that may constrain the potential evolution of the skull towards certain directions of change (Appendix 1). That is, free evolution of the skull is unlikely because of morphological integration, and this suggests that the developmental system plays an important role, channelling the paths through which genetic and environmental components of phenotypic variation can be expressed (Cheverud, 1988, Lieberman et al. 2004).
It has been suggested that the different cranial regions could be subject to different levels of evolvability and/or plasticity (Kohn, 1991; Strand Vidarsdóttir et al. 2002; Bastir & Rosas, 2004). We tested this assumption in hypothesis H1 and we did not find significant differences between the amounts of genetic variation underlying the three major developmental regions of the skull. Craniometric traits from the face, the cranial vault and the base show similar percentages of significant heritability estimations and low to moderate levels of genetic components of variation. This result confirms previous evidence indicating that within the primate skull basicranial, neurocranial and facial dimensions show similar levels of heritability (Cheverud & Buikstra, 1982; Sjøvold, 1984; Cheverud, 1996b). Moreover, there is no evidence suggesting that the face is the most plastic region of the skull. For instance, our results showed that some facial dimensions associated with functional regions (such as the nasal, the orbital and the zygomatic regions) have some of the highest heritabilities of the skull (Fig. 2). Characters with no heritability, with all their variation due to environmental effects, are not limited to the face but are widespread through the whole skull and can also be found at the neurocranium and the basicranium (Tables 2–5).
Our results support the hypothesis that the cranial base is more conservative and may be under slightly stronger genetic control, as most distances within the basicranium show moderate and significant heritabilities, and phenotypic and genetic correlations between the width and length of the cranial base are strong (Appendix 1). Also, we corroborate the hypothesis that the cranial base acts as the ‘skull's central integrator’ (Lieberman et al. 2000a,b, 2002). In fact, the cranial base strongly influences the overall cranial shape, constraining facial breadth, height and length, as well as neurocranial breadth and length. This mechanism would contribute to preventing the different regions from evolving independently and would preserve the functional and architectural requirements of the skull.
Craniofacial traits have substantial amounts of genetic variation, but are significantly affected by other non-genetic factors such as sex and year of birth, as revealed by the genetic analyses. This may be reflecting the influences of sexual dimorphism and secular trends in the Hallstatt population. Sexual dimorphism is one of the main sources of intraspecific variation in skull morphology, which is probably the result of allometric factors and differences in body composition between males and females (O’Higgins & Dryden, 1993; Rosas & Bastir, 2002). Therefore, it is not an unexpected result that sex was a significant covariate affecting most of the measurements; especially because linear distances were not corrected for size.
Secular changes are also a well-known source of morphological variation involving both genetic and environmental components (Jantz & Meadows Jantz, 2000). Secular trends may have been driven by random genetic changes by gene flow (Lahr, 1996) and admixture, as well as by specific adaptations due to a release in selective pressures due to masticatory, dietary, and technological changes (Larsen, 1997). Secular trends have been reported in American (Jantz & Meadows Jantz, 2000) and European (Rösing & Schwidetzky 1979; 1984) populations, including Hallstatt (Sjøvold 1990, 1995; Carson, 2006a). These studies showed that the secular trends detected in Hallstatt follow the general trend of gracilization of European modern populations (Henneberg et al. 1978; Rösing & Schwidetzky, 1979, 1984). In Hallstatt, at least from the transition between the 18th and the 19th centuries, there was a reduction of maximum cranial breadth, accompanied by an increase of neurocranial height (Sjøvold 1990, 1995; Carson, 2006a). Here, we have not assessed the temporal gradient of cranial measurements, but we have found evidence that the minor functional regions (i.e. the zygomatic and nasal regions) of the skull are the most affected (Table 2), which could be reflecting dietary and climatic changes (Jantz & Meadows Jantz, 2000). Neurocranial and basicranial dimensions are also affected, especially the maximum cranial breath (Table 3), and these changes might be caused both by genetic and non-genetic factors.
Hypothesis 2 (H2) tested the similarity between the genetic and the phenotypic correlation matrices, and the Mantel test revealed that they are significantly similar. This is important because many studies are using phenotypic data in population genetic models without any knowledge of the genetic architecture of the skull (Steadman, 2001; González-José et al. 2003, 2005, 2007; Ackermann & Cheverud, 2004a; Roseman, 2004; Schillaci & Stojanowski, 2005; Stojanowski, 2005; Martínez-Abadías et al. 2006; Stojanowski & Schillaci, 2006). This is done assuming that the G and P matrices are similar and proportional, a conclusion drawn from Cheverud's (1988) work. This study compared genetic and phenotypic correlation matrices obtained from 23 published studies, which included a wide range of animals (from human to amphipods) and of kinds of traits (from morphological to cognitive). Here we provide empirical data exclusively for human craniometric traits and support the view that G and P display consistent patterns of morphological variation (Cheverud, 1988).
The proportionality of G and P, however, is not a straightforward consequence of the similarity between these correlation matrices. We could not directly assess the proportionality of G and P variance-covariance matrices because variances and covariances are not available after the inverse normalization. However, in another study (Martínez-Abadías, 2007) we tested this assumption using a set of multivariate landmark data representing the shape of the cranium and applying geometric morphometric methods. Our data strongly contradicted this expectation (Martínez-Abadías, 2007). This result, along with earlier findings from mice (Klingenberg & Leamy, 2001) and humans (Sherwood et al. 2008), supports theoretical arguments (Willis et al. 1991) and suggests that phenotypic data may introduce a potential bias in population and quantitative genetic studies unless the sample size is sufficiently large or familial information is available (Sherwood et al. 2008). In conclusion, our analyses suggest that the genetic and the phenotypic covariation matrices are similar but not identical or proportional (Martínez-Abadías, 2007). Genetic covariation matrices show more complex and structured patterns of morphological integration than the phenotypic covariation matrices. This should be taken into account in studies using P as a proxy of G variance-covariance matrix.
Regarding the pattern of genetic correlations between facial and neurocranial dimensions considered in H3, our results show that these patterns do not follow Enlow's expected pattern of craniofacial variation and headform in humans (Enlow & Hans, 1996). Under this hypothesis, maximum cranial breadth should be positively correlated with facial breadth and negatively correlated with facial height, neurocranial length and neurocranial height. However, we only found a significant correlation between neurocranial and facial breadth, as has been previously hypothesized (Weidenreich, 1941) and supported by studies of artificial cranial deformation (Antón, 1989). Therefore, we conclude that the traditional classification between dolico- and brachycephalic skulls does not reflect the genetic architecture of the human skull or provide any valuable hypothesis of morphological-genetic integration. This is relevant because many bio-anthropological issues are still being synthesized in terms of dolico- vs. brachycephalic forms. For instance, the classical study of Boas on European immigrants to USA (Boas, 1912; Gravlee et al. 2003; Relethford, 2004), studies of morphological variation among ancient and modern Native Americans (Gonzalez et al. 2003; Fiedel, 2004) and studies analysing the relationship among head shape and climate (Beals, 1972; Goodman, 1995, 1997) still use this terminology to describe human craniofacial variation.
The clearest integrated module is formed by breadth dimensions covering the neurocranium, the basicranium and the face: the overall pattern of integration in the human skull is dominated by the covariation between the maximum widths of the major developmental regions. This pattern was first reported in the mice cranium (Hallgrímsson et al. 2007) and here we extend it to humans. Evolutionary developmental studies use model organisms such as mice to identify candidate genes that are involved in the phenotypic expression of skull morphology (Lieberman et al. 2004; Hallgrímsson et al. 2004, 2006, 2007). To extrapolate the results obtained from such organisms to humans it is important to compare them with other primate species. Hallgrímsson et al. (2004) compared phenotypic and genetic correlations in macaques and two strains of mice and did not find a consistent pattern of modularity in these groups. Therefore, it is relevant to find the same predicted pattern of integration in humans and mice. This suggests that covariation between cranial widths is an integrated feature that has been conserved across the evolution of the mammalian craniofacial form.
The present study presents similarities but also some differences to previous analyses carried out with the Hallstatt skull collection (Sjøvold, 1984, Carson, 2006a). Although they are all grounded on the same population, results are not totally coincident. However, this is not an unexpected output as each study took its point of departure from different familiar data, accounted for different sources of covariation and did not use exactly the same crania. As sample size is limited, standard errors are substantially large (Falconer & MacKay, 1996) and slight differences in sample composition, model definition and data treatment can alter the results. Therefore, general trends are more reliable quantitative parameters than the exact value of the heritability estimations. In common, all studies have shown that craniometric traits are low to moderate hereditary characteristics. However, we do not confirm previous evidence suggesting that breadth and facial dimensions are the less heritable characters of the human skull (Carson, 2006a). This study reports low to moderate heritability estimates for breadth measures (Tables 2–5) and has tested statistically that there are no significant differences in the amount of genetic variation underlying the main developmental regions of the skull. Although we used the same statistical method to estimate heritability (ML), inconsistencies between studies might also arise due to other methodological issues regarding the number of skulls included in the analyses and the complexity of the pedigree structure. In this study, we extended and revised the pedigrees constructed by Sjøvold (1984), checked the identifications made by the gravedigger by sex confirmation, and thanks to Sjøvold's photographic records from the mid seventies we could identify the original names of the individuals (Fig. 1). In comparison with previous studies, our analysis included a larger skull sample, did not contain missing values and used larger and more complex genealogies since the whole population was reconstructed.
Understanding the patterns of morphological integration among skull regions will improve our ability to make evolutionary and phylogenetic inferences about human evolution. The use of craniodental characters in phylogenetic analyses of primate and hominid evolution is widespread (Strait et al. 1997; Strait & Grine, 1999; Strait et al. 2007; Lockwood, 2007) and they are essential because cranial remains are one of the main sources of information on extant and fossil species (Ackermann & Cheverud, 2004b; Lockwood, 2007). Although skull morphology is affected to some extent by environmental factors and is under lower genetic control than molecular traits, it is accepted that craniometric traits are phylogenetically informative (Collard & Wood, 2007; Lockwood, 2007). However, as there is strong evidence that morphological integration plays an important role in evolutionary biology and can bias the results of such cladistic analyses (Strait et al. 2007; Lockwood, 2007), further understanding about how and why morphological complexes arise in the skull is needed.
Our analysis reports that the human skull has substantial amounts of genetic variation that are constrained by integration. Furthermore, it demonstrates that craniometric traits from the face, the neurocranium and the basicranium do not differ in their heritability patterns. We also provide empirical evidence that genetic and phenotypic correlation patterns in the human skull are consistent and show similar morphological variation patterns. Regarding integration, results suggest that traditional integration hypotheses (Enlow & Hans, 1996) do not have a genetic basis, but confirm recent modularity patterns found in mice, emphasizing strong covariation between relative widths of the neurocranium, the basicranium and the face as the most dominant integration pattern in the mammal skull (Hallgrímsson et al. 2007).
Our results concerning the heritability and correlation patterns of craniometric traits shed light into the genetic architecture of the human skull. Also, they are especially useful to provide an evolutionary context based on quantitative genetics for classic morphometric studies and databases using univariate measurements. For a greater comprehension of modularity and integration patterns in the skull, future analyses should account for the multivariate nature of shape (Klingenberg, 2004). This could be achieved by combining quantitative genetic methods with geometric morphometric tools, as suggested by Klingenberg & Leamy (2001). It would then be possible to discuss in greater detail the genetic and modular basis of complex phenotypes.