Linkage analysis of alcoholism-related electrophysiological phenotypes: genome scans with microsatellites compared to single-nucleotide polymorphisms

P300 amplitude is an electrophysiological quantitative trait that is correlated with both alcoholism and smoking status. Using the Collaborative Study on the Genetics of Alcoholism data, we performed model-free linkage analysis to investigate the relationship between alcoholism, P300 amplitude, and habitual smoking. We also analyzed the effect of parent-of-origin on alcoholism, and utilized both microsatellites (MS) markers and single-nucleotide polymorphisms (SNPs). We found significant evidence of linkage for alcoholism to chromosome 10; inclusion of P300 amplitude as a covariate provided additional evidence of linkage to chromosome 12. This same region on chromosome 12 showed some evidence for a parent-of-origin effect. We found evidence of linkage for the P300 phenotype to chromosome 7 in non-smokers, and to chromosome 17 in alcoholics. The effects of alcoholism and habitual smoking on P300 amplitude appear to have separate genetic determinants. Overall, there were few differences between MS and SNP genome scans. The use of covariates and parent-of-origin effects allowed detection of linkage not seen otherwise

Obtaining the Weyl tensor from the Bel-Robinson tensor

The algebraic study of the Bel-Robinson tensor proposed and initiated in a previous work (Gen. Relativ. Gravit. {\bf 41}, see ref [11]) is achieved. The canonical form of the different algebraic types is obtained in terms of Bel-Robinson eigen-tensors. An algorithmic determination of the Weyl tensor from the Bel-Robinson tensor is presented.Comment: 21 page

Evidence for association between the HLA-DQA locus and abdominal aortic aneurysms in the Belgian population: a case control study

BACKGROUND: Chronic inflammation and autoimmunity likely contribute to the pathogenesis of abdominal aortic aneurysms (AAAs). The aim of this study was to investigate the role of autoimmunity in the etiology of AAAs using a genetic association study approach with HLA polymorphisms. METHODS: HLA-DQA1, -DQB1, -DRB1 and -DRB3-5 alleles were determined in 387 AAA cases (180 Belgian and 207 Canadian) and 426 controls (269 Belgian and 157 Canadian) by a PCR and single-strand oligonucleotide probe hybridization assay. RESULTS: We observed a potential association with the HLA-DQA1 locus among Belgian males (empirical p = 0.027, asymptotic p = 0.071). Specifically, there was a significant difference in the HLA-DQA1*0102 allele frequencies between AAA cases (67/322 alleles, 20.8%) and controls (44/356 alleles, 12.4%) in Belgian males (empirical p = 0.019, asymptotic p = 0.003). In haplotype analyses, marginally significant association was found between AAA and haplotype HLA-DQA1-DRB1 (p = 0.049 with global score statistics and p = 0.002 with haplotype-specific score statistics). CONCLUSION: This study showed potential evidence that the HLA-DQA1 locus harbors a genetic risk factor for AAAs suggesting that autoimmunity plays a role in the pathogenesis of AAAs

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)

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

The Map Problem: A Comparison of Genetic and Sequence-Based Physical Maps

The genetic order of autosomal genome-scan markers from Marshfield panels 9 and 10 were compared with their physical order, on the basis of the assembled nonredundant human genome sequence from the Human Genome Project–Santa Cruz (HGP-sc; October 2000 and April 2001 releases) and Celera (CEL; February 2001 release) databases. The genetic order of 96% of the markers on the Marshfield map for panel 10 is supported by a likelihood ratio of ⩾3 (odds ratio of 1,000:1). Inconsistencies with the genetic panel 10 map were found for 5% and 2% of the markers in the CEL and HGP-sc sequences, respectively. These inconsistencies consisted of both positional and chromosomal-assignment disagreements. For the majority of these inconsistent markers, the genetic order was supported by a likelihood ratio of ⩾3, and the physical order in the other assembly matched the genetic order. The majority of the inconsistencies between the physical- and genetic-map order point to errors in the physical-map order. A Web site is made available that displays inconsistencies for genetic markers from Marshfield panels 9 and 10 between their genetic-map positions and sequence-based physical-map positions, as well as inconsistencies between their sequence-based physical position. This Web site also contains genetic-map distances, physical-map positions from the Celera and Human Genome Project sequence, and likelihood-ratio support for the genetic maps

Variations in the <i>FRA10AC1</i> Fragile Site and 15q21 Are Associated with Cerebrospinal Fluid Aβ_1-42 Level

<div><p>Proteolytic fragments of amyloid and post-translational modification of tau species in Cerebrospinal fluid (CSF) as well as cerebral amyloid deposition are important biomarkers for Alzheimer’s Disease. We conducted genome-wide association study to identify genetic factors influencing CSF biomarker level, cerebral amyloid deposition, and disease progression. The genome-wide association study was performed via a meta-analysis of two non-overlapping discovery sample sets to identify genetic variants other than <i>APOE</i> ε4 predictive of the CSF biomarker level (Aβ_1–42, t-Tau, p-Tau_181P, t-Tau:Aβ_1–42 ratio, and p-Tau_181P:Aβ_1–42 ratio) in patients enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Loci passing a genome-wide significance threshold of <i>P</i> < 5 x 10⁻⁸ were followed-up for replication in an independent sample set. We also performed joint meta-analysis of both discovery sample sets together with the replication sample set. In the discovery phase, we identified variants in <i>FRA10AC1</i> associated with CSF Aβ_1–42 level passing the genome-wide significance threshold (directly genotyped SNV rs10509663 <i>P</i>_FE = 1.1 x 10⁻⁹, imputed SNV rs116953792 <i>P</i>_FE = 3.5 x 10⁻¹⁰), rs116953792 (<i>P</i>_one-sided = 0.04) achieved replication. This association became stronger in the joint meta-analysis (directly genotyped SNV rs10509663 <i>P</i>_FE = 1.7 x 10⁻⁹, imputed SNV rs116953792 <i>P</i>_FE = 7.6 x 10⁻¹¹). Additionally, we identified locus 15q21 (imputed SNV rs1503351 <i>P</i>_FE = 4.0 x 10⁻⁸) associated with CSF Aβ_1–42 level. No other variants passed the genome-wide significance threshold for other CSF biomarkers in either the discovery sample sets or joint analysis. Gene set enrichment analyses suggested that targeted genes mediated by miR-33, miR-146, and miR-193 were enriched in various GWAS analyses. This finding is particularly important because CSF biomarkers confer disease susceptibility and may be predictive of the likelihood of disease progression in Alzheimer’s Disease.</p></div

Inrich Analysis Results (P_corrected < 0.05).

<p>Inrich Analysis Results (P_corrected < 0.05).</p