Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Abstract

Pubertal timing varies considerably and has been associated with a range of health outcomes in later life. To elucidate the underlying biological mechanisms, we performed multi-ancestry genetic analyses in ~800,000 women, identifying 1,080 independent signals associated with age at menarche. Collectively these loci explained 11% of the trait variance in an independent sample, with women at the top and bottom 1% of polygenic risk exhibiting a ~11 and ~14-fold higher risk of delayed and precocious pubertal development, respectively. These common variant analyses were supported by exome sequence analysis of ~220,000 women, identifying several genes, including rare loss of function variants inZNF483which abolished the impact of polygenic risk. Next, we implicated 660 genes in pubertal development using a combination ofin silicovariant-to-gene mapping approaches and integration with dynamic gene expression data from mouse embryonic GnRH neurons. This included an uncharacterized G-protein coupled receptorGPR83, which we demonstrate amplifies signaling ofMC3R, a key sensor of nutritional status. Finally, we identified several genes, including ovary-expressed genes involved in DNA damage response that co-localize with signals associated with menopause timing, leading us to hypothesize that the ovarian reserve might signal centrally to trigger puberty. Collectively these findings extend our understanding of the biological complexity of puberty timing and highlight body size dependent and independent mechanisms that potentially link reproductive timing to later life disease.

Population scale whole genome sequencing provides novel insights into cardiometabolic health

“…(Supplementary Table 5). Using a variant to gene mapping method 18 (see Methods), GWAS signals at both the RIF1 and TNK2 loci could be confidently linked to the function of these genes, e.g. we observed colocalisation between eQTLs for both RIF1 and TNK2, with decreased expression corresponding to increased BMI, directionally concordant with their rare PTV effects (Supplementary Table 5).…”

“…The previously identified genes were annotated if their start or end sites were within 500kb up-or downstream of GWAS signals in the two meta-analyses, using the NCBI RefSeq gene map for GRCh37, and overlayed with further supporting functional dataset information. For further details about the specific application of this method, see Kentistou et al 18 25…”

Profiling human hypothalamic neurons reveals a candidate combination drug therapy for weight loss

“…Given the central role that POMC neurons play in body weight regulation, we next asked which of our identified hPOMC-enriched genes have human genetic support for a direct involvement in obesity. Specifically, we employed a recently developed 'GWAS to Genes' pipeline (Kentistou et al 2023) that prioritises candidate causal genes proximal to GWAS association signals. We ran this on a GWAS meta-analysis (up to N=806,834) for adult BMI (Yengo et al 2018), in addition to a measure of recalled childhood body size (N=444,345) in UK Biobank (Bycroft et al 2018) which has previously been validated against objectively measured childhood BMI (Felix et al 2016).…”

“…In this study, we considered genes likely to be associated with obesity by a pipeline that considers GWAS, eQTL, and enhancer mapping data (Kentistou et al 2023). The causality of genes prioritised in this manner is uncertain, and in the future it would be interesting to test the effects of other genes associated with obesity by population exome and genome sequencing data, as well as genes nominated by familial or cohort studies of severe obesity.…”

“…Using scRNAseq, we first established that hPOMC neurons are transcriptionally similar to their counterparts in the human neonatal hypothalamus (W.-K. Huang et al 2021). We then performed genetic association analysis using a recently described variant to gene mapping approach that leverages large-scale population-based datasets to identify enriched genes that are associated with childhood and adult human obesity (Kentistou et al 2023). Among the candidate genes, we prioritised those that had drugs approved for use in humans that were likely to cross the blood-brain-barrier for testing in hPOMC neurons and in mouse models of diet-induced obesity (DIO) to enable their potentially rapid clinical translation.…”