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Expression quantitative trait loci (eQTLs) are genomic regions that regulate gene expression. Here the authors provide a publicly available data set of exon-level eQTLs across the human brain. This includes many genome-wide association study (GWAS) hits for neurological and psychiatric disorders. Germ-line genetic control of gene expression occurs via expression quantitative trait loci (eQTLs). We present a large, exon-specific eQTL data set covering ten human brain regions. We found that cis -eQTL signals (within 1 Mb of their target gene) were numerous, and many acted heterogeneously among regions and exons. Co-regulation analysis of shared eQTL signals produced well-defined modules of region-specific co-regulated genes, in contrast to standard coexpression analysis of the same samples. We report cis -eQTL signals for 23.1% of catalogued genome-wide association study hits for adult-onset neurological disorders. The data set is publicly available via public data repositories and via http://www.braineac.org/ . Our study increases our understanding of the regulation of gene expression in the human brain and will be of value to others pursuing functional follow-up of disease-associated variants.

There is strong evidence to show that men and women differ in terms of neurodevelopment, neurochemistry and susceptibility to neurodegenerative and neuropsychiatric disease. The molecular basis of these differences remains unclear. Progress in this field has been hampered by the lack of genome-wide information on sex differences in gene expression and in particular splicing in the human brain. Here we address this issue by using post-mortem adult human brain and spinal cord samples originating from 137 neuropathologically confirmed control individuals to study whole-genome gene expression and splicing in 12 CNS regions. We show that sex differences in gene expression and splicing are widespread in adult human brain, being detectable in all major brain regions and involving 2.5% of all expressed genes. We give examples of genes where sex-biased expression is both disease-relevant and likely to have functional consequences, and provide evidence suggesting that sex biases in expression may reflect sex-biased gene regulatory structures. Men and women differ in terms of their neurochemistry, behaviour and susceptibility to disease. Here the authors show that sex differences in gene expression and splicing are widespread in adult human brain, and that sex-biased expression is likely to have functional consequences.

The eQTL Catalogue is an open database of uniformly processed human molecular quantitative trait loci (QTLs). We are continuously updating the resource to further increase its utility for interpreting genetic associations with complex traits. Over the past two years, we have increased the number of uniformly processed studies from 21 to 31 and added X chromosome QTLs for 19 compatible studies. We have also implemented Leafcutter to directly identify splice-junction usage QTLs in all RNA sequencing datasets. Finally, to improve the interpretability of transcript-level QTLs, we have developed static QTL coverage plots that visualise the association between the genotype and average RNA sequencing read coverage in the region for all 1.7 million fine mapped associations. To illustrate the utility of these updates to the eQTL Catalogue, we performed colocalisation analysis between vitamin D levels in the UK Biobank and all molecular QTLs in the eQTL Catalogue. Although most GWAS loci colocalised both with eQTLs and transcript-level QTLs, we found that visual inspection could sometimes be used to distinguish primary splicing QTLs from those that appear to be secondary consequences of large-effect gene expression QTLs. While these visually confirmed primary splicing QTLs explain just 6/53 of the colocalising signals, they are significantly less pleiotropic than eQTLs and identify a prioritised causal gene in 4/6 cases.

Understanding the complexity of the human brain transcriptome architecture is one of the most important human genetics study areas. Previous studies have applied expression quantitative trait loci (eQTL) analysis at the genome-wide level of the brain to understand the underlying mechanisms relating to neurodegenerative diseases, primarily at the transcript level. To increase the resolution of our understanding, the current study investigates multi/single-region, transcript/exon-level and cis versus trans -acting eQTL, across 10 regions of the human brain. Some of the key findings of this study are: (i) only a relatively small proportion of eQTLs will be detected, where the sensitivity is under 5%; (ii) when an eQTL is acting in multiple regions (MR-eQTL), it tends to have very similar effects on gene expression in each of these regions, as well as being cis -acting; (iii) trans -acting eQTLs tend to have larger effects on expression compared to cis -acting eQTLs and tend to be specific to a single region (SR-eQTL) of the brain; (iv) the cerebellum has a very large number of eQTLs that function exclusively in this region, compared with other regions of the brain; (v) importantly, an interactive visualisation tool (Shiny app) was developed to visualise the MR/SR-eQTL at transcript and exon levels.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Association studies have identified several signals at the LRRK2 locus for Parkinson's disease (PD), Crohn's disease (CD) and leprosy. However, little is known about the molecular mechanisms mediating these effects. To further characterize this locus, we fine-mapped the risk association in 5,802 PD and 5,556 controls using a dense genotyping array (ImmunoChip). Using samples from 134 post-mortem control adult human brains (UK Human Brain Expression Consortium), where up to ten brain regions were available per individual, we studied the regional variation, splicing and regulation of LRRK2. We found convincing evidence for a common variant PD association located outside of the LRRK2 protein coding region (rs117762348, A>G, P = 2.56×10(-8), case/control MAF 0.083/0.074, odds ratio 0.86 for the minor allele with 95% confidence interval [0.80-0.91]). We show that mRNA expression levels are highest in cortical regions and lowest in cerebellum. We find an exon quantitative trait locus (QTL) in brain samples that localizes to exons 32-33 and investigate the molecular basis of this eQTL using RNA-Seq data in n = 8 brain samples. The genotype underlying this eQTL is in strong linkage disequilibrium with the CD associated non-synonymous SNP rs3761863 (M2397T). We found two additional QTLs in liver and monocyte samples but none of these explained the common variant PD association at rs117762348. Our results characterize the LRRK2 locus, and highlight the importance and difficulties of fine-mapping and integration of multiple datasets to delineate pathogenic variants and thus develop an understanding of disease mechanisms.

Background Hearing loss is a clinically and genetically heterogeneous disorder. Mutations in the DFNB1 locus have been reported to be the most common cause of autosomal recessive non-syndromic hearing loss worldwide. Apart from DFNB1 , many other loci and their underlying genes have also been identified and the basis of our study was to provide a comprehensive introduction to the delineation of the molecular basis of non-syndromic hearing loss in the Saudi Arabian population. This was performed by screening DFNB1 and to initiate prioritized linkage analysis or homozygosity mapping for a pilot number of families in which DFNB1 has been excluded. Methods Individuals from 130 families of Saudi Arabian tribal origin diagnosed with an autosomal recessive non-syndromic sensorineural hearing loss were screened for mutations at the DFNB1 locus by direct sequencing. If negative, genome wide linkage analysis or homozygosity mapping were performed using Affymetrix GeneChip ® Human Mapping 250K/6.0 Arrays to identify regions containing any known-deafness causing genes that were subsequently sequenced. Results Our results strongly indicate that DFNB1 only accounts for 3% of non-syndromic hearing loss in the Saudi Arabian population of ethnic ancestry. Prioritized linkage analysis or homozygosity mapping in five separate families established that their hearing loss was caused by five different known-deafness causing genes thus confirming the genetic heterogeneity of this disorder in the kingdom. Conclusion The overall results of this study are highly suggestive that underlying molecular basis of autosomal recessive non-syndromic deafness in Saudi Arabia is very genetically heterogeneous. In addition, we report that the preliminary results indicate that there does not seem to be any common or more prevalent loci, genes or mutations in patients with autosomal recessive non-syndromic hearing loss in patients of Saudi Arabian tribal origin.

Point mutations in LRRK2 cause autosomal dominant Parkinson's disease. Despite extensive efforts to determine the mechanism of cell death in patients with LRRK2 mutations, the aetiology of LRRK2 PD is not well understood. To examine possible alterations in gene expression linked to the presence of LRRK2 mutations, we carried out a case versus control analysis of global gene expression in three systems: fibroblasts isolated from LRRK2 mutation carriers and healthy, non-mutation carrying controls; brain tissue from G2019S mutation carriers and controls; and HEK293 inducible LRRK2 wild type and mutant cell lines. No significant alteration in gene expression was found in these systems following correction for multiple testing. These data suggest that any alterations in basal gene expression in fibroblasts or cell lines containing mutations in LRRK2 are likely to be quantitatively small. This work suggests that LRRK2 is unlikely to play a direct role in modulation of gene expression, although it remains possible that this protein can influence mRNA expression under pathogenic cicumstances.

Parkinson’s disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson’s disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson’s disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson’s disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson’s disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson’s disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson’s disease drug development. There is currently no disease-modifying treatment for Parkinson’s disease, a common neurodegenerative disorder. Here, the authors use genetic variation associated with gene and protein expression to find putative drug targets for Parkinson’s disease using Mendelian randomization of the druggable genome.

Background Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used R software package for the generation of gene co-expression networks (GCN). WGCNA generates both a GCN and a derived partitioning of clusters of genes (modules). We propose k-means clustering as an additional processing step to conventional WGCNA, which we have implemented in the R package km2gcn (k-means to gene co-expression network, https://github.com/juanbot/km2gcn ). Results We assessed our method on networks created from UKBEC data (10 different human brain tissues), on networks created from GTEx data (42 human tissues, including 13 brain tissues), and on simulated networks derived from GTEx data. We observed substantially improved module properties, including: (1) few or zero misplaced genes; (2) increased counts of replicable clusters in alternate tissues (x3.1 on average); (3) improved enrichment of Gene Ontology terms (seen in 48/52 GCNs) (4) improved cell type enrichment signals (seen in 21/23 brain GCNs); and (5) more accurate partitions in simulated data according to a range of similarity indices. Conclusions The results obtained from our investigations indicate that our k-means method, applied as an adjunct to standard WGCNA, results in better network partitions. These improved partitions enable more fruitful downstream analyses, as gene modules are more biologically meaningful.