The Role of SINE-VNTR-Alu (SVA) Retrotransposons in Shaping the Human Genome

Retrotransposons can alter the regulation of genes both transcriptionally and post-transcriptionally, through mechanisms such as binding transcription factors and alternative splicing of transcripts. SINE-VNTR-Alu (SVA) retrotransposons are the most recently evolved class of retrotransposable elements, found solely in primates, including humans. SVAs are preferentially found at genic, high GC loci, and have been termed "mobile CpG islands". We hypothesise that the ability of SVAs to mobilise, and their non-random distribution across the genome, may result in differential regulation of certain pathways. We analysed SVA distribution patterns across the human reference genome and identified over-representation of SVAs at zinc finger gene clusters. Zinc finger proteins are able to bind to and repress SVA function through transcriptional and epigenetic mechanisms, and the interplay between SVAs and zinc fingers has been proposed as a major feature of genome evolution. We describe observations relating to the clustering patterns of both reference SVAs and polymorphic SVA insertions at zinc finger gene loci, suggesting that the evolution of this network may be ongoing in humans. Further, we propose a mechanism to direct future research and validation efforts, in which the interplay between zinc fingers and their epigenetic modulation of SVAs may regulate a network of zinc finger genes, with the potential for wider transcriptional consequences

A SINE-VNTR-Alu in the LRIG2 Promoter Is Associated with Gene Expression at the Locus

The hominid SINE-VNTR-Alu (SVA) retrotransposons represent a repertoire of genomic variation which could have significant effects on genome function. A human-specific SVA in the promoter region of the gene leucine-rich repeats and immunoglobulin-like domains 2 (LRIG2), which we termed SVA_LRIG2, is a common retrotransposon insertion polymorphism (RIP), defined as an element which is polymorphic for its presence or absence in the genome. We hypothesised that this RIP might be associated with differential levels of expression of LRIG2. The RIP genotype of SVA_LRIG2 was determined in a subset of frontal cortex DNA samples from the North American Brain Expression Consortium (NABEC) cohort and was imputed for a larger set of that cohort. Utilising available frontal cortex total RNA-seq and CpG methylation data for this cohort, we observed that increased allele dosage of SVA_LRIG2 was non-significantly associated with a decrease in transcription from the region and significantly associated with increased methylation of the CpG probe nearest to SVA_LRIG2, i.e., SVA_LRIG2 is a significant methylation quantitative trait loci (mQTL) at the LRIG2 locus. These data are consistent with SVA_LRIG2 being a transcriptional regulator, which in part may involve epigenetic modulation.</jats:p

NeuroBooster Array: A Genome-Wide Genotyping Platform to Study Neurological Disorders Across Diverse Populations

Genome-wide genotyping platforms have the capacity to capture genetic variation across different populations, but there have been disparities in the representation of population-dependent genetic diversity. The motivation for pursuing this endeavor was to create a comprehensive genome-wide array capable of encompassing a wide range of neuro-specific content for the Global Parkinson's Genetics Program (GP2) and the Center for Alzheimer's and Related Dementias (CARD). CARD aims to increase diversity in genetic studies, using this array as a tool to foster inclusivity. GP2 is the first supported resource project of the Aligning Science Across Parkinson's (ASAP) initiative that aims to support a collaborative global effort aimed at significantly accelerating the discovery of genetic factors contributing to Parkinson's disease and atypical parkinsonism by generating genome-wide data for over 200,000 individuals in a multi-ancestry context. Here, we present the Illumina NeuroBooster array (NBA), a novel, high-throughput and cost-effective custom-designed content platform to screen for genetic variation in neurological disorders across diverse populations. The NBA contains a backbone of 1,914,934 variants (Infinium Global Diversity Array) complemented with custom content of 95,273 variants implicated in over 70 neurological conditions or traits with potential neurological complications. Furthermore, the platform includes over 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related GWAS loci across diverse populations. The NBA can identify low frequency variants and accurately impute over 15 million common variants from the latest release of the TOPMed Imputation Server as of August 2023 (reference of over 300 million variants and 90,000 participants). We envisage this valuable tool will standardize genetic studies in neurological disorders across different ancestral groups, allowing researchers to perform genetic research inclusively and at a global scale

A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci

Common variant genome-wide association studies (GWASs) have, to date, identified >24 risk loci for Parkinson's disease (PD). To discover additional loci, we carried out a GWAS comparing 6,476 PD cases with 302,042 controls, followed by a meta-analysis with a recent study of over 13,000 PD cases and 95,000 controls at 9,830 overlapping variants. We then tested 35 loci (P < 1 × 10−6) in a replication cohort of 5,851 cases and 5,866 controls. We identified 17 novel risk loci (P < 5 × 10−8) in a joint analysis of 26,035 cases and 403,190 controls. We used a neurocentric strategy to assign candidate risk genes to the loci. We identified protein-altering or cis–expression quantitative trait locus (cis-eQTL) variants in linkage disequilibrium with the index variant in 29 of the 41 PD loci. These results indicate a key role for autophagy and lysosomal biology in PD risk, and suggest potential new drug targets for PD

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms

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Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)

Genome-wide structural variant analysis identifies risk loci for non-Alzheimer’s dementias

We characterized the role of structural variants, a largely unexplored type of genetic variation, in two non-Alzheimer’s dementias, namely Lewy body dementia (LBD) and frontotemporal dementia (FTD)/amyotrophic lateral sclerosis (ALS). To do this, we applied an advanced structural variant calling pipeline (GATK-SV) to short-read whole-genome sequence data from 5,213 European-ancestry cases and 4,132 controls. We discovered, replicated, and validated a deletion in TPCN1 as a novel risk locus for LBD and detected the known structural variants at the C9orf72 and MAPT loci as associated with FTD/ALS. We also identified rare pathogenic structural variants in both LBD and FTD/ALS. Finally, we assembled a catalog of structural variants that can be mined for new insights into the pathogenesis of these understudied forms of dementia

Identification of candidate Parkinson disease genes by integrating genome-wide association study, expression, and epigenetic data sets

Importance Substantial genome-wide association study (GWAS) work in Parkinson disease (PD) has led to the discovery of an increasing number of loci shown reliably to be associated with increased risk of disease. Improved understanding of the underlying genes and mechanisms at these loci will be key to understanding the pathogenesis of PD. Objective To investigate what genes and genomic processes underlie the risk of sporadic PD. Design and Setting This genetic association study used the bioinformatic tools Coloc and transcriptome-wide association study (TWAS) to integrate PD case-control GWAS data published in 2017 with expression data (from Braineac, the Genotype-Tissue Expression [GTEx], and CommonMind) and methylation data (derived from UK Parkinson brain samples) to uncover putative gene expression and splicing mechanisms associated with PD GWAS signals. Candidate genes were further characterized using cell-type specificity, weighted gene coexpression networks, and weighted protein-protein interaction networks. Main Outcomes and Measures It was hypothesized a priori that some genes underlying PD loci would alter PD risk through changes to expression, splicing, or methylation. Candidate genes are presented whose change in expression, splicing, or methylation are associated with risk of PD as well as the functional pathways and cell types in which these genes have an important role. Results Gene-level analysis of expression revealed 5 genes (WDR6 [OMIM 606031], CD38 [OMIM 107270], GPNMB [OMIM 604368], RAB29 [OMIM 603949], and TMEM163 [OMIM 618978]) that replicated using both Coloc and TWAS analyses in both the GTEx and Braineac expression data sets. A further 6 genes (ZRANB3 [OMIM 615655], PCGF3 [OMIM 617543], NEK1 [OMIM 604588], NUPL2 [NCBI 11097], GALC [OMIM 606890], and CTSB [OMIM 116810]) showed evidence of disease-associated splicing effects. Cell-type specificity analysis revealed that gene expression was overall more prevalent in glial cell types compared with neurons. The weighted gene coexpression performed on the GTEx data set showed that NUPL2 is a key gene in 3 modules implicated in catabolic processes associated with protein ubiquitination and in the ubiquitin-dependent protein catabolic process in the nucleus accumbens, caudate, and putamen. TMEM163 and ZRANB3 were both important in modules in the frontal cortex and caudate, respectively, indicating regulation of signaling and cell communication. Protein interactor analysis and simulations using random networks demonstrated that the candidate genes interact significantly more with known mendelian PD and parkinsonism proteins than would be expected by chance. Conclusions and Relevance Together, these results suggest that several candidate genes and pathways are associated with the findings observed in PD GWAS studies