Morphometric traits are some of the most extensively-studied quantitative traits, with easy-to-measure exemplars-such as height and weight-collected in a variety of studies as a matter of routine. Recent efforts by national population cohorts, such as the UK Biobank, have-in some cases for the first time-made it possible for researchers to obtain information on other, harder-to-measure, morphometric traits, in a large population. The genetic data that is also available for large cohorts, such as the UK Biobank, has enabled the study of how such traits are genetically determined. Many morphometric traits have complex architectures, involving hundreds-or thousands-of genetic variants, most of which have small effects; it is generally not possible to detect these small effects in smaller cohorts, which lack sufficient power. The project described herein considers several morphometric aspects of the human skeleton in the context of variance in skeletal dimensions-e.g., the lengths of the long bones, hip (acetabular) width, shoulder width, and torso length-after correcting for overall body height. The aims of the project are: (1) to investigate the genetic components of the phenotypic architecture of skeletal dimensions relative to height; (2) to determine whether the same portions of the genome that affect skeletal structures during development are responsible for such relative variation; (3) to investigate the role sex-specific effects might play in such phenotypes; and (4) to demonstrate that there are novel ways to use existing data from cohorts in order to enable the investigation of heretofore unconsidered phenotypes, e.g., using image analysis methods with medical images to obtain morphometric measures of skeletal structures that can not be directly measured. Using image data from two population cohorts, Orcades and the UK Biobank, and a novel application of image processing, I have extracted the aforementioned skeletal measures from dual-energy, x-ray absorptiometry (DXA) images-typically used to measure bone mineral density. I then combined this phenotypic data with the cohorts' genetic data to investigate the genetic contribution to variation in these traits. My results show that there is a genetic basis for residual variation in skeletal measurements after accounting for height-with heritabilities ranging from 33% for radius length, to 58% for tibia length. They also show that this is driven by different variants than those affecting height-genetic correlations of the residual skeletal measurements with height range from −.0391 for fibula length, to .1621 for acetabular width-and that are located in genomic regions other than those involved in limb morphogenesis. There are also indications of associations with a particular trait that exist for one sex, and not the other, or that are much stronger for one sex. These may indicate an autosomal basis for sex differences in these traits-which may be due to a different set of loci than those affecting trait variation-although replication should be sought in another cohort. Pending replication of significant genetic associations, further study looking into potential confounding factors may be necessary to fully understand these differences. Finally, the work herein shows the benefit of collaboration with experts in other fields, such as image analysis, for genetic research. In this case it has made possible the derivation of novel phenotypes-that are of interest for research-from existing data that is otherwise not widely used. This kind of cross-disciplinary collaboration could help cohort maintainers increase the value of their resources, without having to expend more funds on additional equipment, or lab work.