Periodic Reporting for period 1 - RMCmplxPheno (Recurrent miscarriage as a complex phenotype: Harnessing large-scale clinical data to uncover underlying biological pathways)
Reporting period: 2022-07-01 to 2023-12-31
The brain is integral to controlling the endocrine system, including the production and regulation of reproductive hormones. In particular, three regions of the brain; the hypothalamus – which produces gonadotropin-releasing hormone (GnRH); the pituitary gland – which produces several gonadotropins in response to GnRH; and the olfactory bulbs – through which the GnRH releasing nerves extend during fetal development. Morphological variation in these regions has been shown to have consequences to reproductive health across a spectrum of severity. At the common end of the spectrum, variation in the size of the pituitary gland correlates with sex-steroid concentrations. At the pathological end of the spectrum, individuals with Kallmann syndrome - a condition characterised by late or absent onset of puberty, infertility, and anosmia – have hypoplasic or absent olfactory bulbs.
Aims:
1. Identify image-derived phenotypes from MRI images of the brain to identify novel phenotypes associated with reproductive hormone regulation.
2. Identify biological pathways underlying the variation in brain morphology of endocrine regions using genetic analysis.
3. Understand how genetics is affecting endocrine brain morphology at a granular resolution using high-dimensional analyses.
Aims:
1. Identify image-derived phenotypes from MRI images of the brain to identify novel phenotypes associated with reproductive hormone regulation.
2. Identify biological pathways underlying the variation in brain morphology of endocrine regions using genetic analysis.
3. Understand how genetics is affecting endocrine brain morphology at a granular resolution using high-dimensional analyses.
Data was used from within the UK Biobank. Usable magnetic resonance imaging (MRI) data of the brain were available from 37,330 individuals, each coupled with genetic information and extensive electronic healthcare records. From the imaging data, measures relating to four regions have been extracted, the regions included: hypothalamus (HT), pituitary gland (PG), olfactory bulbs (OB), and hypothalamus grey matter (HTGM). This work was done in collaboration with Professor Stephen Smith’s group at the University of Oxford. For each of these regions, numerous quantitative image-derived phenotypes were extracted relating to both volume and intensity. The extraction of each of these phenotypes was done at two different confidence thresholds, 0 and 0.3. Going forward with analysis, I utilised the more stringent 0.3 threshold to ensure confidence in the values. Further I decided to focus on volumetric measures. This was to ensure biological interpretability and streamline analysis to enable adequate bandwidth for analytical follow-up.
Distribution of these values was assessed across sex and age groups within the population.
Firstly genome-wide association analyses (GWAS) were run for each of the four volumetric phenotypes relating to the HT, PG, OB, and HTGM. GWAS were run on the most densely populated well-mixed population which here largely aligns with those that self-identify as White-European. Age, sex, assessment centre, genetic principal components and a set of technical measures relating to the MRI acquisition (table position, head movement etc.) were included as covariates in an additive linear model. Following selection for independent loci using conditional joint analysis, >50 significantly associated loci were discovered to be associated with one or more of the phenotypes. All GWAS were deemed well controlled for residual population structure and inflation upon assessment of linkage-disequilibrium scores. Phenome-wide association studies were run across each of the genetic discovery sets using ICD-10 codes from within the UK Biobank, discovering three statistically significant associations between disease codes and SNPs associated with HTGM volume. Sexual dimorphism was seen in the effect sizes and a statistically significant sex difference was seen in the effect size of two genetic variants, one associated with HT volume, and the other associated with PG volume. Heritability of each trait was also calculated and interestingly the heritability varied across the sex-stratified populations. Exome-wide association analysis was further run, with a single gene found to be statistically significantly associated with hypothalamus volume.
To fully take advantage of the dimensionality of the data available in the original source images from which the image-derived phenotypes were extracted, an extension of the genetic analysis was completed. We took the set of genetic variants discovered during the four GWAS and built linear models to interrogate their associations with structural volume differences at voxel-level. The volume differences between individuals were captured with Jacobian determinant maps, which were estimated between each individual and a reference average brain. Each voxel in these maps represents an expansion or contraction at a spatial location relative to a reference average brain. Different statistical methods were trialed that encoded genotypes as continuous and discrete variables. This analysis allowed us to visualise associations between the discovered genetic variants and the morphology of the brain at a high spatial resolution, indicating effects at additional brain regions not captured by the previously extracted IDPs. Interpretation of this was aided by collaborators at the university of Oxford and the University of Heidelberg.
Distribution of these values was assessed across sex and age groups within the population.
Firstly genome-wide association analyses (GWAS) were run for each of the four volumetric phenotypes relating to the HT, PG, OB, and HTGM. GWAS were run on the most densely populated well-mixed population which here largely aligns with those that self-identify as White-European. Age, sex, assessment centre, genetic principal components and a set of technical measures relating to the MRI acquisition (table position, head movement etc.) were included as covariates in an additive linear model. Following selection for independent loci using conditional joint analysis, >50 significantly associated loci were discovered to be associated with one or more of the phenotypes. All GWAS were deemed well controlled for residual population structure and inflation upon assessment of linkage-disequilibrium scores. Phenome-wide association studies were run across each of the genetic discovery sets using ICD-10 codes from within the UK Biobank, discovering three statistically significant associations between disease codes and SNPs associated with HTGM volume. Sexual dimorphism was seen in the effect sizes and a statistically significant sex difference was seen in the effect size of two genetic variants, one associated with HT volume, and the other associated with PG volume. Heritability of each trait was also calculated and interestingly the heritability varied across the sex-stratified populations. Exome-wide association analysis was further run, with a single gene found to be statistically significantly associated with hypothalamus volume.
To fully take advantage of the dimensionality of the data available in the original source images from which the image-derived phenotypes were extracted, an extension of the genetic analysis was completed. We took the set of genetic variants discovered during the four GWAS and built linear models to interrogate their associations with structural volume differences at voxel-level. The volume differences between individuals were captured with Jacobian determinant maps, which were estimated between each individual and a reference average brain. Each voxel in these maps represents an expansion or contraction at a spatial location relative to a reference average brain. Different statistical methods were trialed that encoded genotypes as continuous and discrete variables. This analysis allowed us to visualise associations between the discovered genetic variants and the morphology of the brain at a high spatial resolution, indicating effects at additional brain regions not captured by the previously extracted IDPs. Interpretation of this was aided by collaborators at the university of Oxford and the University of Heidelberg.
The work within this project expanded our knowledge of the genetics and hence underlying biology of endocrine brain morphology. There were further several links between brain morphology and endocrine biology that require further exploration and research. By expanding our understanding of the biology underlying endocrine regulation - which in turn has many health implications including fertility, reproductive diseases and metabolic diseases, we hope to identify novel therapeutic targets for treatments to improve patient experience. It would be beneficial for this work to be replicated in a comparable cohort, however the availability of such data is currently limited.