Longitudinal multi-dimensional investigation of metabolic and endocrine genetics

Genome-wide association studies (GWASs) in recent decades have revealed the genetic landscape and shared aetiology of common, complex traits across the spectrum of human phenotypes. In this work, I develop and apply statistical tools to interrogate the genetic basis of, and relationships between, metabolic and endocrine traits. I demonstrate that under-explored primary care electronic health records (EHRs), linked to massive biobank projects across the globe, are a valuable source of longitudinal and rare biomarker data for genetics studies. Using EHRs, I find a common missense variant in the APOE gene that is associated with weight-loss in adulthood, which replicates in three global biobanking cohorts of between 125,000 to 475,000 individuals each. While the heritability of weight-change is low ( 700,000 participants across seven global biobanks), to characterise the genetic contributions to these common but poorly understood phenotypes. I find 21 unique genetic loci for infertility, of which only six colocalise with reproductive hormone levels. While there is modest correlation between female infertility and heritable diseases of the reproductive tract, such as endometriosis (rG = 58%) and polycystic ovary syndrome (PCOS) (rG = 40%), I find no evidence for metabolic conditions such as obesity in the genetic aetiology of infertility. I explore these findings further through Mendelian Randomisation analyses to reveal heterogeneity in the genetically predicted causal effects of overall and central obesity on the risk of female reproductive conditions, including infertility, endometriosis, and PCOS, which may be partly genetically mediated by hormone levels. Through a range of genetics-based investigations, I outline the shared and distinct mechanisms of metabolic and endocrine disease in humans

Frameshift mutations at the C-terminus of HIST1H1E result in a specific DNA hypomethylation signature

BACKGROUND: We previously associated HIST1H1E mutations causing Rahman syndrome with a specific genome-wide methylation pattern. RESULTS: Methylome analysis from peripheral blood samples of six affected subjects led us to identify a specific hypomethylated profile. This "episignature" was enriched for genes involved in neuronal system development and function. A computational classifier yielded full sensitivity and specificity in detecting subjects with Rahman syndrome. Applying this model to a cohort of undiagnosed probands allowed us to reach diagnosis in one subject. CONCLUSIONS: We demonstrate an epigenetic signature in subjects with Rahman syndrome that can be used to reach molecular diagnosis

Rare and low-frequency coding variants alter human adult height

Height is a highly heritable, classic polygenic trait with ~700 common associated variants identified so far through genome - wide association studies . Here , we report 83 height - associated coding variants with lower minor allele frequenc ies ( range of 0.1 - 4.8% ) and effects of up to 2 16 cm /allele ( e.g. in IHH , STC2 , AR and CRISPLD2 ) , >10 times the average effect of common variants . In functional follow - up studies, rare height - increasing alleles of STC2 (+1 - 2 cm/allele) compromise d proteolytic inhibition of PAPP - A and increased cleavage of IGFBP - 4 in vitro , resulting in higher bioavailability of insulin - like growth factors . The se 83 height - associated variants overlap genes mutated in monogenic growth disorders and highlight new biological candidates ( e.g. ADAMTS3, IL11RA, NOX4 ) and pathways ( e.g . proteoglycan/ glycosaminoglycan synthesis ) involved in growth . Our results demonstrate that sufficiently large sample sizes can uncover rare and low - frequency variants of moderate to large effect associated with polygenic human phenotypes , and that these variants implicate relevant genes and pathways