Increased brain expression of GPNMB is associated with genome wide significant risk for Parkinson's disease on chromosome 7p15.3.

Genome wide association studies (GWAS) for Parkinson's disease (PD) have previously revealed a significant association with a locus on chromosome 7p15.3, initially designated as the glycoprotein non-metastatic melanoma protein B (GPNMB) locus. In this study, the functional consequences of this association on expression were explored in depth by integrating different expression quantitative trait locus (eQTL) datasets (Braineac, CAGEseq, GTEx, and Phenotype-Genotype Integrator (PheGenI)). Top risk SNP rs199347 eQTLs demonstrated increased expressions of GPNMB, KLHL7, and NUPL2 with the major allele (AA) in brain, with most significant eQTLs in cortical regions, followed by putamen. In addition, decreased expression of the antisense RNA KLHL7-AS1 was observed in GTEx. Furthermore, rs199347 is an eQTL with long non-coding RNA (AC005082.12) in human tissues other than brain. Interestingly, transcript-specific eQTLs in immune-related tissues (spleen and lymphoblastoid cells) for NUPL2 and KLHL7-AS1 were observed, which suggests a complex functional role of this eQTL in specific tissues, cell types at specific time points. Significantly increased expression of GPNMB linked to rs199347 was consistent across all datasets, and taken in combination with the risk SNP being located within the GPNMB gene, these results suggest that increased expression of GPNMB is the causative link explaining the association of this locus with PD. However, other transcript eQTLs and subsequent functional roles cannot be excluded. This highlights the importance of further investigations to understand the functional interactions between the coding genes, antisense, and non-coding RNA species considering the tissue and cell-type specificity to understand the underlying biological mechanisms in PD.

High-resolution arrays reveal burden of copy number variations on Parkinson disease genes associated with increased disease risk in random cohorts.

BACKGROUND: Parkinson disease (PD) is a neurological disease responsible for a considerable rate of mortality and morbidity in the society. Since the symptoms of the disease appear much later than the actual onset of neuron degeneration, a majority of cases remain undiagnosed until the manifestation of the symptoms. OBJECTIVES: In order to investigate the existence of such susceptibility in the population, we analyzed Copy Number Variation (CNV) influences on PD genes in 1715 individuals from 12 different populations. RESULTS: Overall, 16 CNV-PD genes, 3 known to be causal and 13 associated, were found to be significantly enriched. PARK2, was under heavy burden with ~1% of the population containing CNV in the exonic region. The impact of these genes on the genome and disease pathway was analyzed using several genome analysis tools. Protein interaction network of CNV-PD genes revealed a complex interaction of molecules forming a major hub by the α-Synuclein, whose direct interactors, LRRK2, PARK2 and ATP13A2 are under CNV influence. CONCLUSIONS: We hypothesize that CNVs may not be the initiating event in the pathogenesis of PD and remain latent until additional secondary hits are acquired and also propose novel genes that may fall under the PD pathway which contribute in pathogenesis.

Type 2 diabetes mellitus disease risk genes identified by genome wide copy number variation scan in normal populations.

AIMS: To identify the role of copy number variations (CNVs) on disease risk genes and its effect on disease phenotypes in type 2 diabetes mellitus (T2DM) in 12 random populations using high throughput arrays. METHODS: CNV analysis was carried out on a total of 1715 individuals from 12 populations, from ArrayExpress Archive of the European Bioinformatics Institute along with our subjects using Affymetrix Genome Wide SNP 6.0 array. CNV effect on T2DM genes were analyzed using several bioinformatics tools and a molecular protein interaction network was constructed to identify the disease mechanism altered by the CNVs. RESULTS: Analysis showed 34.4% of the total population to be under CNV burden for T2DM, with 83 disease causal and associated genes being under CNV influence. Hotspots were identified on chromosomes 22, 12, 6, 19 and 11.Overlap studies with case cohorts revealed significant disease risk genes such as EGFR, E2F1, PPP1R3A, HLA and TSPAN8. CONCLUSIONS: CNVs play a significant role in predisposing T2DM in normal cohorts and contribute to the phenotypic effects. Thus, CNVs should be considered as one of the major contributors in predisposition of the disease.

Copy number variations burden on miRNA genes reveals layers of complexities involved in the regulation of pathways and phenotypic expression.

MicroRNAs are involved in post-transcriptional down-regulation of gene expression. Variations in miRNA genes can severely affect downstream-regulated genes and their pathways. However, population-specific burden of CNVs on miRNA genes and the complexities created towards the phenotype is not known. From a total of 44109 CNVs investigated from 1715 individuals across 12 populations using high-throughput arrays, 4007 miRNA-CNVs (∼ 9%) consisting 6542 (∼ 5%) miRNA genes with a total of 333 (∼ 5%) singleton miRNA genes were identified. We found miRNA-CNVs across the genomes of individuals showing multiple hits in many targets, co-regulated under the same pathway. This study proposes four mechanisms unraveling the many complexities in miRNA genes, targets and co-regulated miRNA genes towards establishment of phenotypic diversity.

Impact of copy number variations burden on coding genome in humans using integrated high resolution arrays.

Copy number variations (CNVs) alter the transcriptional and translational levels of genes by disrupting the coding structure and this burden of CNVs seems to be a significant contributor to phenotypic variations. Therefore it was necessary to assess the complexities of CNV burden on the coding genome. A total of 1715 individuals from 12 populations were used for CNV analysis in the present investigation. Analysis was performed using Affymetrix Genome-Wide Human SNP Array 6·0 chip and CytoScan High-Density arrays. CNVs were more frequently observed in the coding region than in the non-coding region. CNVs were observed vastly more frequently in the coding region than the non-coding region. CNVs were found to be enriched in the regions containing functional genes (83-96%) compared with the regions containing pseudogenes (4-17%). CNVs across the genome of an individual showed multiple hits across many genes, whose proteins interact physically and function under the same pathway. We identified varying numbers of proteins and degrees of interactions within protein complexes of single individual genomes. This study represents the first draft of a population-specific CNV genes map as well as a cross-populational map. The complex relationship of CNVs on genes and their physically interacting partners unravels many complexities involved in phenotype expression. This study identifies four mechanisms contributing to the complexities caused by the presence of multiple CNVs across many genes in the coding part of the genome.