Prospective associations of coronary heart disease loci in African Americans using the MetaboChip: The PAGE study

Background Coronary heart disease (CHD) is a leading cause of morbidity and mortality in African Americans. However, there is a paucity of studies assessing genetic determinants of CHD in African Americans. We examined the association of published variants in CHD loci with incident CHD, attempted to fine map these loci, and characterize novel variants influencing CHD risk in African Americans. Methods and Results Up to 8,201 African Americans (including 546 first CHD events) were genotyped using the MetaboChip array in the Atherosclerosis Risk in Communities (ARIC) study and Women\u27s Health Initiative (WHI). We tested associations using Cox proportional hazard models in sex- and study-stratified analyses and combined results using meta-analysis. Among 44 validated CHD loci available in the array, we replicated and fine-mapped the SORT1 locus, and showed same direction of effects as reported in studies of individuals of European ancestry for SNPs in 22 additional published loci. We also identified a SNP achieving array wide significance (MYC: rs2070583, allele frequency 0.02, P = 8.1×10−8), but the association did not replicate in an additional 8,059 African Americans (577 events) from the WHI, HealthABC and GeneSTAR studies, and in a meta-analysis of 5 cohort studies of European ancestry (24,024 individuals including 1,570 cases of MI and 2,406 cases of CHD) from the CHARGE Consortium. Conclusions Our findings suggest that some CHD loci previously identified in individuals of European ancestry may be relevant to incident CHD in African Americans

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 37688 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 x10 -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. Copyright (C) 2019 Elsevier Ltd. All rights reserved.Peer reviewe

Evaluating the harmonisation potential of diverse cohort datasets

Data discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation.A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts.Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity.Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools

Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes.

Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency-large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant-common phenotype associations-11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency-large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10(-06) (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10(-10). Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect

Finnish Parkinson's disease study integrating protein-protein interaction network data with exome sequencing analysis

Variants associated with Parkinson's disease (PD) have generally a small effect size and, therefore, large sample sizes or targeted analyses are required to detect significant associations in a whole exome sequencing (WES) study. Here, we used protein-protein interaction (PPI) information on 36 genes with established or suggested associations with PD to target the analysis of the WES data. We performed an association analysis on WES data from 439 Finnish PD subjects and 855 controls, and included a Finnish population cohort as the replication dataset with 60 PD subjects and 8214 controls. Single variant association (SVA) test in the discovery dataset yielded 11 candidate variants in seven genes, but the associations were not significant in the replication cohort after correction for multiple testing. Polygenic risk score using variants rs2230288 and rs2291312, however, was associated to PD with odds ratio of 2.7 (95% confidence interval 1.4-5.2; p < 2.56e-03). Furthermore, an analysis of the PPI network revealed enriched clusters of biological processes among established and candidate genes, and these functional networks were visualized in the study. We identified novel candidate variants for PD using a gene prioritization based on PPI information, and described why these variants may be involved in the pathogenesis of PD

Additional rare variant analysis in Parkinson's Disease cases with and without known pathogenic mutations: evidence for oligogenic inheritance

Oligogenic inheritance implies a role for several genetic factors in disease etiology. We studied oligogenic inheritance in Parkinson’s (PD) by assessing the potential burden of additional rare variants in established Mendelian genes and/or GBA, in individuals with and without a primary pathogenic genetic cause in two large independent cohorts totaling 7,900 PD cases and 6,166 controls. An excess (≥30%) of cases with a recognized primary genetic cause had ≥1 additional rare variants in Mendelian PD genes, as compared with no known mutation PD cases (17%) and unaffected controls (16%), supporting our hypothesis. Carriers of additional Mendelian gene variants have younger ages at onset (AAO). The effect of additional Mendelian variants in LRRK2 G2019S mutation carriers, of which ATP13A2 variation is particularly common, may account for some of the variation in penetrance. About 10% of no known mutation PD cases harbor a rare GBA variant compared to known pathogenic mutation PD cases (8%) and controls (5%), with carriers having earlier AAOs. Together, the data suggest that the oligogenic inheritance of rare Mendelian variants may be important in patient with a primary pathogenic cause, whereas GBA increases risk across all forms of PD. This study highlights potential genetic complexity of Mendelian PD. The identification of potential modifying variants provides new insights into disease mechanisms by potentially separating relevant from benign variants and by the interaction between genes in specific pathways. In the future this may be relevant to genetic testing and counselling of PD patients and their families

Integration of GWAS SNPs and tissue specific expression profiling reveal discrete eQTLs for human traits in blood and brain

Our knowledge of the transcriptome has become much more complex since the days of the central dogma of molecular biology. We now know that splicing takes place to create potentially thousands of isoforms from a single gene, and we know that RNA does not always faithfully recapitulate DNA if RNA editing occurs. Collectively, these observations show that the transcriptome is amazingly rich with intricate regulatory mechanisms for overall gene expression, splicing, and RNA editing. Genetic variability can play a role in controlling gene expression, which can be identified by examining expression quantitative trait loci (eQTLs). eQTLs are genomic regions where genetic variants, including single nucleotide polymorphisms (SNPs) show a statistical association with expression of mRNA transcripts. In humans, many SNPs are also associated with disease, and have been identified using genome wide association studies (GWAS) but the biological effects of those SNPs are usually not known. If SNPs found in GWAS are also found in eQTLs, then one could hypothesize that expression levels may contribute to disease risk. Performing eQTL analysis with GWAS SNPs in both blood and brain, specifically the frontal cortex and the cerebellum, we found both shared and tissue unique eQTLS. The identification of tissue-unique eQTLs supports the argument that choice of tissue type is important in eQTL studies (Paper I). Aging is a complex process with the mechanisms underlying aging still being poorly defined. There is evidence that the transcriptome changes with age, and hence we used the brain dataset from our first paper as a discovery set, with an additional replication dataset, to investigate any aging-gene expression associations. We found evidence that many genes were associated with aging. We further found that there were more statically significant expression changes in the frontal cortex versus the cerebellum, indicating that brain regions may age at different rates. As the brain is a heterogeneous tissue including both neurons and non-neuronal cells, we used LCM to capture Purkinje cells as a representative neuronal type and repeated the age analysis. Looking at the discovery, replication and Purkinje cell datasets we found five genes with strong, replicated evidence of age-expression associations (Paper II). Being able to capture and quantify the depth of the transcriptome has been a lengthy process starting with methods that could only measure a single gene to genome-wide techniques such as microarray. A recently developed technology, RNA-Seq, shows promise in its ability to capture expression, splicing, and editing and with its broad dynamic range quantification is accurate and reliable. RNA-Seq is, however, data intensive and a great deal of computational expertise is required to fully utilize the strengths of this method. We aimed to create a small, well-controlled, experiment in order to test the performance of this relatively new technology in the brain. We chose embryonic versus adult cerebral cortex, as mice are genetically homogenous and there are many known differences in gene expression related to brain development that we could use as benchmarks for analysis testing. We found a large number of differences in total gene expression between embryonic and adult brain. Rigorous technical and biological validation illustrated the accuracy and dynamic range of RNA-Seq. We were also able to interrogate differences in exon usage in the same dataset. Finally we were able to identify and quantify both well-known and novel A-to-I edit sites. Overall this project helped us develop the tools needed to build usable pipelines for RNA-Seq data processing (Paper III). Our studies in the developing brain (Paper III) illustrated that RNA-Seq was a useful unbiased method for investigating RNA editing. To extend this further, we utilized a genetically modified mouse model to study the transcriptomic role of the RNA editing enzyme ADAR2. We found that ADAR2 was important for editing of the coding region of mRNA as a large proportion of RNA editing sites in coding regions had a statistically significant decrease in editing percentages in Adar2 -/-Gria2 R/R mice versus controls. However, despite indications in the literature that ADAR2 may also be involved in splicing and expression regulatory machinery we found no changes in gene expression or exon utilization in Adar2 -/-Gria2 R/R mice as compared to their littermate controls (Paper IV). In our final study, based on the methods developed in Papers III and IV, we revisited the idea of age related gene expression associations from Paper II. We used a subset of human frontal cortices for RNA sequencing. Interestingly we found more gene expression changes with aging compared to the previous data using microarrays in Paper II. When the significant gene lists were analysed for gene ontology enrichment, we found that there was a large number of downregulated genes involved in synaptic function while those that were upregulated had enrichment in immune function. This dataset illustrates that the aging brain may be predisposed to the processes found in neurodegenerative diseases (Paper V)