Trait-associated genetic variants affect complex phenotypes primarily via regulatory mechanisms on the transcriptome. To investigate the genetics of gene expression, we performed cis- and trans-expression quantitative trait locus (eQTL) analyses using blood-derived expression from 31,684 individuals through the eQTLGen Consortium. We detected cis-eQTL for 88% of genes, and these were replicable in numerous tissues. Distal trans-eQTL (detected for 37% of 10,317 trait-associated variants tested) showed lower replication rates, partially due to low replication power and confounding by cell type composition. However, replication analyses in single-cell RNA-seq data prioritized intracellular trans-eQTL. Trans-eQTL exerted their effects via several mechanisms, primarily through regulation by transcription factors. Expression of 13% of the genes correlated with polygenic scores for 1,263 phenotypes, pinpointing potential drivers for those traits. In summary, this work represents a large eQTL resource, and its results serve as a starting point for in-depth interpretation of complex phenotypes.

Parkinson's disease (PD) is characterized pathologically by the loss of dopaminergic (DA) neurons in the substantia nigra (SN). Whether cell types beyond DA neurons in the SN show vulnerability in PD remains unclear. Through transcriptomic profiling of 315,867 high-quality single nuclei in the SN from individuals with and without PD, we identified cell clusters representing various neuron types, glia, endothelial cells, pericytes, fibroblasts, and T cells and investigated cell type-dependent alterations in gene expression in PD. Notably, a unique neuron cluster marked by the expression of RIT2, a PD risk gene, also displayed vulnerability in PD. We validated RIT2-enriched neurons in midbrain organoids and the mouse SN. Our results demonstrated distinct transcriptomic signatures of the RIT2-enriched neurons in the human SN and implicated reduced RIT2 expression in the pathogenesis of PD. Our study sheds light on the diversity of cell types, including DA neurons, in the SN and the complexity of molecular and cellular changes associated with PD pathogenesis.

Women with autoimmune and inflammatory aetiologies can exhibit reduced fecundity. TNFAIP3 is a master negative regulator of inflammation, and has been linked to many inflammatory conditions by genome wide associations studies, however its role in fertility remains unknown. Here we show that mice harbouring a mild Tnfaip3 reduction-of-function coding variant (Tnfaip3I325N) that reduces the threshold for inflammatory NF-κB activation, exhibit reduced fecundity. Sub-fertility in Tnfaip3I325N mice is associated with irregular estrous cycling, low numbers of ovarian secondary follicles, impaired mammary gland development and insulin resistance. These pathological features are associated with infertility in human subjects. Transplantation of Tnfaip3I325N ovaries, mammary glands or pancreatic islets into wild-type recipients rescued estrous cycling, mammary branching and hyperinsulinemia respectively, pointing towards a cell-extrinsic hormonal mechanism. Examination of hypothalamic brain sections revealed increased levels of microglial activation with reduced levels of luteinizing hormone. TNFAIP3 coding variants may offer one contributing mechanism for the cause of sub-fertility observed across otherwise healthy populations as well as for the wide variety of auto-inflammatory conditions to which TNFAIP3 is associated. Further, TNFAIP3 represents a molecular mechanism that links heightened immunity with neuronal inflammatory homeostasis. These data also highlight that tuning-up immunity with TNFAIP3 comes with the potentially evolutionary significant trade-off of reduced fertility.

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the 'transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.