Search

We report a genome-wide association study of facial features in >6000 Latin Americans based on automatic landmarking of 2D portraits and testing for association with inter-landmark distances. We detected significant associations (P-value <5 × 10−8) at 42 genome regions, nine of which have been previously reported. In follow-up analyses, 26 of the 33 novel regions replicate in East Asians, Europeans, or Africans, and one mouse homologous region influences craniofacial morphology in mice. The novel region in 1q32.3 shows introgression from Neanderthals and we find that the introgressed tract increases nasal height (consistent with the differentiation between Neanderthals and modern humans). Novel regions include candidate genes and genome regulatory elements previously implicated in craniofacial development, and show preferential transcription in cranial neural crest cells. The automated approach used here should simplify the collection of large study samples from across the world, facilitating a cosmopolitan characterization of the genetics of facial features.

This report will discuss the process of collecting skin tone measurements from a diverse group of participants and analyse the accuracies of machine-based readings through comparing with reference measurements. The machines usually take the readings of the skin tone level by generating the images and analysing them with various statistical techniques to read the information received. Most methods used today can be completed using machines and technology and we have taken advantage of this by obtaining image samples of the participants faces or/and parts of their arms and have the image processed by self-created algorithm to get the colour values of each image sample for every sample given. Instead of the results received being based on societies ethnicities the colours are received in RGB (red, green, and blue) reflectance values with a median number between 0 and 255 for each image sample processed. Using this information, we can observe the accuracy that is assumed for the machine to have when reading the skin tone of a range of skin tones and use the results as a basis to improve accuracy in machines when it comes to recognising those of a darker skin tone removing the Caucasian bias within them.

In the area of statistical genetics, classical genome-wide association studies (GWAS) assess the association between a biological characteristic and genetic variants, working with one variant at a time in a regression model, and reporting the most significant associations. These studies test genetic markers individually, even though the data may exhibit multivariate structure due to the way genes are transmitted together from the parents to the offspring. Despite considering covariates like age and sex in the model, the classical GWAS does not account for the joint effects of genetic variants. Moreover, when multiple genetic variants within a gene have small effects on a phenotype, testing them individually can lack statistical power, but testing them together in a joint model can be more useful in pooling together all the evidence. In this thesis, I reviewed different multivariate testing procedures in joint multivariate model settings, explored their properties, and demonstrated them in further real-life database applications, such as enhancing statistical power by conditioning on major variants. I studied the mathematical properties of various multivariate test procedures, particularly within the context of multiple linear regression. Considering the theoretical aspect as well as their availability in literature, I adapt various multivariate test procedures for canonical correlation in multiple regression settings. These procedures have been demonstrated to asymptotically follow the chi-square distribution. Importantly, these test procedures exhibit asymptotic equivalence among themselves and with the Wald test statistic. This indicates that the Wald test statistic may be sufficient for future studies, given its equivalence to the multivariate test procedures. In many cases, there are known databases of major genetic variants that have a substantial effect on the trait. In such situations, it makes sense statistically to condition on these major variants to impro