Dysmorphic facial features play a prominent role in the diagnosis of a variety of classical neurodevelopmental disorders. For example, Down syndrome1, Prader-Willi syndrome2 and Foetal Alcohol Syndrome3 are all associated with characteristic facial features that are used as part of the diagnostic criteria. In recent years, the development of 3D geometric morphometrics (the statistical analysis of face shape) allowed researchers to identify more subtle facial dysmorphologies4. Hennessy et al5 found subtle differences in the 3D facial shape of schizophrenia patients compared to controls, such as a lengthened lower mid-facial height in schizophrenia patients. Hennessy et al6 also found that bipolar patients exhibited several facial dysmorphologies relative to controls and schizophrenia patients. The development of the face and the brain is closely connected7. Facial dysmorphologies therefore provide insight into the developmental origins of neurodevelopmental disorders, since the developmental biology of facial features is better understood than that of the brain5.

Craniofacial morphology is highly heritable8-11. Yet, fairly little is known about the genetic variants that influence normal craniofacial development in the general population. 3D geometric morphometric techniques can be used to identify these genetic variants, which could provide a valuable resource for future studies on craniofacial development and a variety of genetically determined neurodevelopmental disorders. For example, Paternoster et al12 found a significant association between a variant of the PAX3 gene and nasion position (the point in the skull where the nasal and frontal bones unite) in the Avon Longitudinal Study of Parents and their Children (ALSPAC) data set. Mutations in the PAX3 gene has previously been associated with Waardenburg syndrome13.

Evolutionary approaches to the study of facial traits provide three general facial traits that could be useful in the identification of genetic variants associated with craniofacial development: symmetry, averageness and sexual dimorphism. Symmetry indicates an individual's ability to resist environmental and genetic stresses during development14-16. It follows that fluctuating asymmetry (a deviation from bilateral symmetry in normally bilaterally symmetrical traits) is associated with a variety of genetic disorders and some indicators of poor health during development16-20. For example, Hammond et al19 found significantly higher facial asymmetry in young boys with autism spectrum disorders, compared to age/sex/ethnicity matched controls.

Facial averageness is defined as the proximity of a face to the sex-specific average face for that population21. In other words, how closely the face resembles the majority of same sex faces in the population. Symons22 hypothesised that average features could be functionally optimal as a result of the stabilising effect of natural selection. Theoretically, averageness might also denote genetic heterozygosity23. Few studies have investigated the relationship between facial averageness, genetic disorders and more general health. Nevertheless, genetic diseases are often associated with multiple facial dysmorphologies1,2,5,6, which do make these faces fairly distinctive compared to the population mean. Rhodes et al24 also found a weak positive association between facial averageness and general health scores.

Sexual dimorphism is defined as the phenotypic difference between males and females of the same species. In other words, how masculine or feminine a person is. Male and female faces diverge at puberty due to the action of sex hormones16,25. The age of onset, rate and extent to which faces become sexually dimorphic differ between individuals and are partly determined by genes26. Masculinity in male faces is generally assumed to serve as an indicator of immunocompetence21,27,28, although recent work has called the relationship into question28-30. Femininity in female faces does not seem to be associated with immunocompetence31,32, but is significantly associated with late follicular oestrogen levels33. Abnormal sexual dimorphism is also a characteristic of certain genetic diseases, such as Klinefelter syndrome34.

The primary aim of this study is to identify common genetic variants, and ultimately putative genes, that are associated with facial symmetry, averageness and sexual dimorphism using Genome-Wide Association (GWA) methodologies. We specifically chose the ALSPAC dataset because it is, to our knowledge, the largest dataset with both facial images and GWA data. To accomplish this aim, the 3D facial images will be manually delineated by defining 38 feature points in the software program MorphAnalyser (developed by Dr Tiddeman, Aberystwyth University, UK); this method is identical to the method used in Coetzee et al35. The delineated 3D images will then be used to calculate indexes for symmetry, averageness and sexual dimorphism in MorphAnalyser. The ALSPAC team have cleaned and imputed a GWA study dataset consisting of 8365 individuals with genotype calls for ~2.5 million common variants spread across the genome. We will use this resource to conduct a 2 stage (discovery and replication) genome-wide association study. Initially the discovery phase will include analysing ~ 5000 individuals that have both genotypic data and facial images. Power analysis (PowerGwas/QT version 1.0)36 indicate a sample size of 5000 is adequate to provide 80% statistical power to detect single nucleotide polymorphisms (SNPs) that explain as little as 0.8% of the variance in facial traits. The association between each of the ~2.5 million SNPs (exposure variables) and facial symmetry, averageness and sexual dimorphism (outcome variables) will be independently tested in the ALSPAC cohort using linear additive regression. SNP associations that exceed the standard significance threshold for genome-wide significance (p less than 5 x 10 -8)37, will be identified and replicated independently in additional cohorts, making up the second replication phase for the GWA study.

To determine which genes (and pathways) are most likely associated with these facial traits we will conduct a range of post-hoc analyses including (a) assigning SNPs to genes, (b) epistasis modelling and (c) pathway analyses. Briefly, multiple genes are ascribed to each SNP and these genes are then prioritised using epistasis modelling and pathway analysis38, allowing us to further identify which biological processes regulate these facial traits. In addition, we will calculate heritability estimates for each of the three facial traits; do a GCTA analysis to estimate how much of the phenotypic variance in the facial traits are explained by common SNPs; test the relationship between admixture and the facial traits (especially averageness); test the relationship between genome-wide heterozygosity and the facial traits (especially symmetry and averageness) using the ~2000 ALSPAC individuals who have whole genome sequencing data; and test the association between previously imputed classical HLA alleles and these facial traits separately for males and females. All GWA analyses will be conducted at the University of Bristol.

The second aim is to determine the association between health measures (exposure variables) and facial symmetry, averageness and sexual dimorphism (outcome variables). The health measures will be divided into prenatal risk factors (e.g. parental age, presence of gestational diabetes) and childhood health measures (e.g. body mass index, blood pressure and self-reported health). All three facial traits are generally assumed to indicate good health27, but studies testing these assumptions have suffered from various methodological drawbacks, including small sample sizes (~N=40-200). The size and quality of the ALSPAC data set, especially the wide range of physiological measurements, provides us with the ideal opportunity to test the association between health indices and these three facial traits in male and female faces respectively.

The third aim is to determine whether SNPs associated with these facial traits are also associated with traits proposed to indicate overall condition, specifically increased height and body mass index (BMI; within the healthy BMI range). To do so we will test the relationship between allelic scores of SNPs for the three facial traits, height and BMI.

To accomplish these aims we kindly request access to the following data in the ALSPAC dataset

Concept

Specific measure

Person

Source

Time point

Prenatal risk

Maternal age

Mother

Q

8-42 wks gestation

Prenatal risk

Paternal age

Mother

Q

12 wks gestation

Prenatal risk

Height & weight

Mother

C

1st trimester

Prenatal risk

Gestational diabetes

Mother

C

1st-3rd trimester

Prenatal risk

Blood pressure

Mother

C

1st-3rd trimester

Prenatal risk

Smoking

Mother

Q

18 wks gestation

Prenatal risk

Alcohol use

Mother

Q

18 wks gestation

Prenatal risk

General health

Mother

Q

32 wks gestation

Childhood health

Hospital admittance

Child

Q

4 wks-69 mths

Childhood health

General health

Child

Q

6-91 mths

Childhood health

Specific health problems (e.g. coughing)

Child

Q

4 wks-91 mths

Childhood health

Height & weight

Child

C

4 mths-17 yrs

Childhood health

Fat and lean mass from DXA scan

Child

C

9-17 yrs

Childhood health

Lung function

Child

C

61 mths-17 yrs

Childhood health

Grip strength

Child

C

11 yrs

Childhood health

Blood pressure & pulse rate

Child

C

37 mths-17 yrs

Childhood health

Blood pressure after exercise

Child

C

9-17 yrs

Confounding

Pubertal development

Child

Q

175 mths (or PUB7)

Images

Facial scans

Child

C

15 yrs

Genetic

Genome wide association data

Child

C

-

questionnaire=Q; clinic=C; weeks=wks; months=mths; years=yrs

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