250 research outputs found

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

    Get PDF
    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

    Polygenic risk of Parkinson disease is correlated with disease age at onset

    Get PDF
    OBJECTIVE: We have investigated the polygenic architecture of Parkinson disease (PD) and have also explored the potential relationship between an individual's polygenic risk score and their disease age at onset. METHODS: This study used genotypic data from 4,294 cases and 10,340 controls obtained from the meta-analysis of PD genome-wide association studies. Polygenic score analysis was performed as previously described by the International Schizophrenia Consortium, testing whether the polygenic score alleles identified in 1 association study were significantly enriched in the cases relative to the controls of 3 independent studies. Linear regression was used to investigate the relationship between an individual's polygenic score for PD risk alleles and disease age at onset. RESULTS: Our polygenic score analysis has identified significant evidence for a polygenic component enriched in the cases of each of 3 independent PD genome-wide association cohorts (minimum p = 3.76 × 10(-6) ). Further analysis identified compelling evidence that the average polygenic score in patients with an early disease age at onset was significantly higher than in those with a late age at onset (p = 0.00014). INTERPRETATION: This provides strong support for a large polygenic contribution to the overall heritable risk of PD and also suggests that early onset forms of the illness are not exclusively caused by highly penetrant Mendelian mutations, but can also be contributed to by an accumulation of common polygenic alleles with relatively low effect sizes

    Artificial intelligence for dementia genetics and omics

    Get PDF
    Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine

    Prospective Associations of Coronary Heart Disease Loci in African Americans Using the MetaboChip: The PAGE Study

    Get PDF
    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's 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 and single-base gene-editing functional validation of a cis-EPO variant as a genetic predictor for EPO-increasing therapies

    Get PDF
    Hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs) are currently under clinical development for treating anemia in chronic kidney disease (CKD), but it is important to monitor their cardiovascular safety. Genetic variants can be used as predictors to help inform the potential risk of adverse effects associated with drug treatments. We therefore aimed to use human genetics to help assess the risk of adverse cardiovascular events associated with therapeutically altered EPO levels to help inform clinical trials studying the safety of HIF-PHIs. By performing a genome-wide association meta-analysis of EPO (n = 6,127), we identified a cis-EPO variant (rs1617640) lying in the EPO promoter region. We validated this variant as most likely causal in controlling EPO levels by using genetic and functional approaches, including single-base gene editing. Using this variant as a partial predictor for therapeutic modulation of EPO and large genome-wide association data in Mendelian randomization tests, we found no evidence (at p < 0.05) that genetically predicted long-term rises in endogenous EPO, equivalent to a 2.2-unit increase, increased risk of coronary artery disease (CAD, OR [95% CI] = 1.01 [0.93, 1.07]), myocardial infarction (MI, OR [95% CI] = 0.99 [0.87, 1.15]), or stroke (OR [95% CI] = 0.97 [0.87, 1.07]). We could exclude increased odds of 1.15 for cardiovascular disease for a 2.2-unit EPO increase. A combination of genetic and functional studies provides a powerful approach to investigate the potential therapeutic profile of EPO-increasing therapies for treating anemia in CKD

    Deletions at 22q11.2 in idiopathic Parkinson's disease: a combined analysis of genome-wide association data.

    Get PDF
    BACKGROUND: Parkinson's disease has been reported in a small number of patients with chromosome 22q11.2 deletion syndrome. In this study, we screened a series of large, independent Parkinson's disease case-control studies for deletions at 22q11.2. METHODS: We used data on deletions spanning the 22q11.2 locus from four independent case-control Parkinson's disease studies (UK Wellcome Trust Case Control Consortium 2, Dutch Parkinson's Disease Genetics Consortium, US National Institute on Aging, and International Parkinson's Disease Genomics Consortium studies), which were independent of the original reports of chromosome 22q11.2 deletion syndrome. We did case-control association analysis to compare the proportion of 22q11.2 deletions found, using the Fisher's exact test for the independent case-control studies and the Mantel-Haenszel test for the meta-analyses. We retrieved clinical details of patients with Parkinson's disease who had 22q11.2 deletions from the medical records of these patients. FINDINGS: We included array-based copy number variation data from 9387 patients with Parkinson's disease and 13 863 controls. Eight patients with Parkinson's disease and none of the controls had 22q11.2 deletions (p=0·00082). In the 8451 patients for whom age at onset data were available, deletions at 22q11.2 were associated with Parkinson's disease age at onset (Mann-Whitney U test p=0·001). Age at onset of Parkinson's disease was lower in patients carrying a 22q11.2 deletion (median 37 years, 95% CI 32·0-55·5; mean 42·1 years [SD 11·9]) than in those who did not carry a deletion (median 61 years, 95% CI 60·5-61·0; mean 60·3 years [SD 12·8]). A 22q11.2 deletion was present in more patients with early-onset (p<0·0001) and late-onset Parkinson's disease (p=0·016) than in controls, and in more patients with early-onset than late-onset Parkinson's disease (p=0·005). INTERPRETATION: Clinicians should be alert to the possibility of 22q11.2 deletions in patients with Parkinson's disease who have early presentation or features associated with the chromosome 22q11.2 deletion syndrome, or both. FUNDING: UK Medical Research Council, UK Wellcome Trust, Parkinson's UK, Patrick Berthoud Trust, National Institutes of Health, "Investissements d'Avenir" ANR-10-IAIHU-06, Dutch Parkinson Foundation (Parkinson Vereniging), Neuroscience Campus Amsterdam, National Institute for Health Research, National Institute on Aging, National Institutes of Health.UK Medical Research Council, UK Wellcome Trust, Parkinson's UK, Patrick Berthoud Trust, National Institutes of Health, “Investissements d'Avenir” ANR-10-IAIHU-06, Dutch Parkinson Foundation (Parkinson Vereniging), Neuroscience Campus Amsterdam, National Institute for Health Research, National Institute on Aging, National Institutes of Health.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/S1474-4422(16)00071-
    corecore