University of Manchester Faculty of Life Sciences

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The genetic basis of shape variation has been investigated for some time by combining the methods of geometric morphometrics with the multivariate theory of quantitative genetics. This combined approach has been applied extensively in studies of animals (e.g., Klingenberg and Leamy 2001; Mezey and Houle 2005; Willmore et al. 2005; Klingenberg et al. 2010), plants (Gómez et al. 2009), and humans (Martínez-Abadías et al. 2009b; Martínez-Abadías et al. 2012). The key requirement for quantitative genetic studies of shape is a dataset containing morphometric data and information on the genealogical relationships among the individuals. From this information, biologically relevant parameters such as the genetic covariance matrix can be estimated using individual-based statistical models (e.g., Wilson et al. 2010). The data for our study were from the population of Hallstatt (Austria), where identified skulls and records of genealogical relationships are available. This population has been used for quantitative genetic studies of traditional morphometric traits (Carson 2006; Martínez-Abadías et al. 2009a) and also with geometric morphometrics (Martínez-Abadías et al. 2009b; Martínez-Abadías et al. 2012). Alternative data sources are large-scale medical surveys, which also have been used in analyses of traditional morphometric traits (Sherwood et al. 2008). Estimates of selection on shape can be obtained by regression of fitness measures on shape. Such studies have been done in plants (e.g., Herrera 1993; Gómez et al. 2006), but not yet in animals or humans. Here, we use the estimate of the genetic covariance matrix and estimated selection on skull shape to predict the expected response to selection, and we compare this prediction to the observed secular change over the approximately 150-year period covered by the data.

Quantitative genetics of human shape

Estimating selection on skull shape Selection was estimated using four different fitness measures: (1) a composite measure of fitness, lambda, taking into account the number of children and the parental age at birth of the children, (2) fertility, the total number of children, (3) lifetime reproductive success, the number of children surviving to adulthood, and (4) longevity. For each fitness measure, linear and nonlinear selection was estimated (Blows and Brooks 2003) and statistical significance was tested with a permutation approach.

Predicted responses to selection were similar for all four fitness measures and featured relative shifts of landmarks that correspond to those in the selection gradients.

Quantitative genetic analyses of shape variation in humans hold considerable promise for research in evolutionary anthropology. They can supply crucial information in various contexts, such as inferences of past selection or neutral variation, adaptation, and population structure. Using the complete information from geometric morphometric analyses is feasible with currently available methods, as published studies have shown (Martínez-Abadías et al. 2009b; Martínez-Abadías et al. 2012). Analyses of selection are feasible whenever data on shape and a measure of fitness are available. Such analyses can provide valuable extra information that supplement indirect inferences of past selection or genetic drift or genomic analyses of selection. Fully multivariate methods for mapping quantitative trait loci (QTL) of shape are also available (Klingenberg et al. 2001; Workman et al. 2002; Klingenberg et al. 2004), but are yet to be applied to human populations. In humans, QTL analyses of craniofacial traits have been conducted based on traditional morphometrics (Sherwood et al. 2011), but studies using geometric morphometrics are yet to be done.

Nonlinear selection was not statistically significant for any of the fitness measures, indicating that there is no stabilizing selection. Significant linear selection was found and the shape features favoured by selection (selection gradients) were similar for all four fitness measures.

For each skull, 29 anatomical landmarks were digitized in three dimensions with a MicroScribe digitizer (full details in Martínez-Abadías et al. 2012). Shape information was extracted with a Procrustes superimposition. All geometric morphometric analyses were carried out in MorphoJ (Klingenberg 2011). The genetic covariance matrix was estimated for the first 32 principal components of the shape variation in the sample using the Wombat software (Meyer 2007). The resulting matrix was rotated back into the coordinate system of the landmark coordinates (full details in Martínez-Abadías et al. 2012).

Conclusions and outlook

Quantitative genetic variation and selection on skull shape in humans

1Department de Biologia Animal, Secció d’Antropologia, Universitat de Barcelona, Barcelona, Spain. 2EMBL-Center for Genomic Regulation (CRG) Systems Biology Research Unit, CRG, Barcelona, Spain.

3Osteologiska Enheten, Stockholms Universitet, Stockholm, Sweden. 4Faculty of Life Sciences, University of Manchester, Manchester, UK.

Neus Martínez-Abadías1,2, Mireia Esparza1, Torstein Sjøvold3, Miquel Hernández1 and Christian Peter Klingenberg4

The skulls from Hallstatt (Austria) provide a unique opportunity for studying the genetics of cranial variation. Our sample includes 390 individuals buried on the Catholic churchyard of the town. As a tradition, the skeletal remains were exhumed and various decorations were painted on the skulls. Most decorations include the name of the deceased, so that the skulls can be linked to church records.

The data

The response to selection was predicted with the multivariate breeders’ equation (Lande 1979), using the genetic covariance matrix and selection gradients obtained for the Hallstatt population.

Predicted response to selection

The pattern of secular change was estimated by a regression of shape on birth year (Drake and Klingenberg 2008; Weisensee and Jantz 2011) This pattern is quite different from the patterns of predicted response to selection. There can be several possible reasons for this discrepancy. First, it is likely that selection not only affects skull shape, but also other variables that are not included in our estimate of selection. Second, environmental factors such as nutrition, care for children and medical provision may have changed during the period covered by our data. Third, processes such as population turnover also might produce changes that are different from those induced by selection.

Secular change of cranial shape

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References