The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and ...aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components and phenotypic heteroskedasticity. GENESIS integrates cohesively with other R/Bioconductor packages to build a complete genomic analysis workflow entirely within the R environment.
https://bioconductor.org/packages/GENESIS; vignettes included.
Supplementary data are available at Bioinformatics online.
Obstructive sleep apnea (OSA) is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. There is strong clinical and epidemiologic evidence ...supporting the importance of genetic factors influencing OSA but limited data implicating specific genes.
To search for rare variants contributing to OSA severity.
Leveraging high-depth genomic sequencing data from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and imputed genotype data from multiple population-based studies, we performed linkage analysis in the CFS (Cleveland Family Study), followed by multistage gene-based association analyses in independent cohorts for apnea-hypopnea index (AHI) in a total of 7,708 individuals of European ancestry.
Linkage analysis in the CFS identified a suggestive linkage peak on chromosome 7q31 (LOD = 2.31). Gene-based analysis identified 21 noncoding rare variants in
(Caveolin-1) associated with lower AHI after accounting for multiple comparisons (
= 7.4 × 10
). These noncoding variants together significantly contributed to the linkage evidence (
< 10
). Follow-up analysis revealed significant associations between these variants and increased
expression, and increased
expression in peripheral monocytes was associated with lower AHI (
= 0.024) and higher minimum overnight oxygen saturation (
= 0.007).
Rare variants in
, a membrane-scaffolding protein essential in multiple cellular and metabolic functions, are associated with higher
gene expression and lower OSA severity, suggesting a novel target for modulating OSA severity.
Mitochondrial DNA copy number (mtDNA-CN) has been associated with a variety of aging-related diseases, including all-cause mortality. However, the mechanism by which mtDNA-CN influences disease is ...not currently understood. One such mechanism may be through regulation of nuclear gene expression via the modification of nuclear DNA (nDNA) methylation.
To investigate this hypothesis, we assessed the relationship between mtDNA-CN and nDNA methylation in 2507 African American (AA) and European American (EA) participants from the Atherosclerosis Risk in Communities (ARIC) study. To validate our findings, we assayed an additional 2528 participants from the Cardiovascular Health Study (CHS) (N = 533) and Framingham Heart Study (FHS) (N = 1995). We further assessed the effect of experimental modification of mtDNA-CN through knockout of TFAM, a regulator of mtDNA replication, via CRISPR-Cas9.
Thirty-four independent CpGs were associated with mtDNA-CN at genome-wide significance (P < 5 × 10
). Meta-analysis across all cohorts identified six mtDNA-CN-associated CpGs at genome-wide significance (P < 5 × 10
). Additionally, over half of these CpGs were associated with phenotypes known to be associated with mtDNA-CN, including coronary heart disease, cardiovascular disease, and mortality. Experimental modification of mtDNA-CN demonstrated that modulation of mtDNA-CN results in changes in nDNA methylation and gene expression of specific CpGs and nearby transcripts. Strikingly, the "neuroactive ligand receptor interaction" KEGG pathway was found to be highly overrepresented in the ARIC cohort (P = 5.24 × 10
), as well as the TFAM knockout methylation (P = 4.41 × 10
) and expression (P = 4.30 × 10
) studies.
These results demonstrate that changes in mtDNA-CN influence nDNA methylation at specific loci and result in differential expression of specific genes that may impact human health and disease via altered cell signaling.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
ABSTRACT
Introduction: Patients with self‐limited statin‐related myopathy improve spontaneously when statins are stopped. In contrast, patients with statin‐associated autoimmune myopathy have ...autoantibodies recognizing 3‐hydroxy‐3‐methyl‐glutaryl‐coenzyme A reductase (HMGCR) and usually require immunosuppressive therapy to control their disease. On initial presentation, it can sometimes be difficult to distinguish between these 2 diseases, as both present with muscle pain, weakness, and elevated serum creatine kinase (CK) levels. The goal of this study was to determine whether patients with severe self‐limited statin‐related myopathy also make anti‐HMGCR autoantibodies. Methods: We screened 101 subjects with severe self‐limited cerivastatin‐related myopathy for anti‐HMGCR autoantibodies. Results: No patient with severe self‐limited cerivastatin‐related myopathy had anti‐HMGCR autoantibodies. Conclusion: Anti‐HMGCR autoantibody testing can be used to help differentiate whether a patient has self‐limited myopathy due to cerivastatin or autoimmune statin‐associated myopathy; these findings may apply to other statins as well. Muscle Nerve 54: 142–144, 2016
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Statins can be associated with myopathy. We have undertaken a genomewide association study (GWAS) to discover and validate genetic risk factors for statin‐induced myopathy in a “real‐world” setting. ...One hundred thirty‐five patients with statin myopathy recruited via the UK Clinical Practice Research Datalink were genotyped using the Illumina OmniExpress Exome version 1.0 Bead Chip and compared with the Wellcome Trust Case‐Control Consortium (n = 2,501). Nominally statistically significant single nucleotide polymorphism (SNP) signals in the GWAS (P < 5 × 10−5) were further evaluated in several independent cohorts (comprising 332 cases and 449 drug‐tolerant controls). Only one (rs4149056/c.521C>T in the SLCO1B1 gene) SNP was genomewide significant in the severe myopathy (creatine kinase > 10 × upper limit of normal or rhabdomyolysis) group (P = 2.55 × 10−9; odds ratio 5.15; 95% confidence interval 3.13–8.45). The association with SLCO1B1 was present for several statins and replicated in the independent validation cohorts. The data highlight the role of SLCO1B1 c.521C>T SNP as a replicable genetic risk factor for statin myopathy. No other novel genetic risk factors with a similar effect size were identified.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury
. These substances are used ...across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries
. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The most prominent risk factor for atrial fibrillation (AF) is chronological age; however, underlying mechanisms are unexplained. Algorithms using epigenetic modifications to the human genome ...effectively predict chronological age. Chronological and epigenetic predicted ages may diverge in a phenomenon referred to as epigenetic age acceleration (EAA), which may reflect accelerated biological aging. We sought to evaluate for associations between epigenetic age measures and incident AF.
Measures for 4 epigenetic clocks (Horvath, Hannum, DNA methylation DNAm PhenoAge, and DNAm GrimAge) and an epigenetic predictor of PAI-1 (plasminogen activator inhibitor-1) levels (ie, DNAm PAI-1) were determined for study participants from 3 population-based cohort studies. Cox models evaluated for associations with incident AF and results were combined via random-effects meta-analyses. Two-sample summary-level Mendelian randomization analyses evaluated for associations between genetic instruments of the EAA measures and AF.
Among 5600 participants (mean age, 65.5 years; female, 60.1%; Black, 50.7%), there were 905 incident AF cases during a mean follow-up of 12.9 years. Unadjusted analyses revealed all 4 epigenetic clocks and the DNAm PAI-1 predictor were associated with statistically significant higher hazards of incident AF, though the magnitudes of their point estimates were smaller relative to the associations observed for chronological age. The pooled EAA estimates for each epigenetic measure, with the exception of Horvath EAA, were associated with incident AF in models adjusted for chronological age, race, sex, and smoking variables. After multivariable adjustment for additional known AF risk factors that could also potentially function as mediators, pooled EAA measures for 2 clocks remained statistically significant. Five-year increases in EAA measures for DNAm GrimAge and DNAm PhenoAge were associated with 19% (adjusted hazard ratio HR, 1.19 95% CI, 1.09-1.31;
<0.01) and 15% (adjusted HR, 1.15 95% CI, 1.05-1.25;
<0.01) higher hazards of incident AF, respectively. Mendelian randomization analyses for the 5 EAA measures did not reveal statistically significant associations with AF.
Our study identified adjusted associations between EAA measures and incident AF, suggesting that biological aging plays an important role independent of chronological age, though a potential underlying causal relationship remains unclear. These aging processes may be modifiable and not constrained by the immutable factor of time.
BACKGROUND: The majority of out-of-hospital cardiac arrests (OHCAs) occur among individuals in the general population, for whom there is no established strategy to identify risk. In this study, we ...assess the use of electronic health record (EHR) data to identify OHCA in the general population and define salient factors contributing to OHCA risk. METHODS: The analytical cohort included 2366 individuals with OHCA and 23 660 age- and sex-matched controls receiving health care at the University of Washington. Comorbidities, electrocardiographic measures, vital signs, and medication prescription were abstracted from the EHR. The primary outcome was OHCA. Secondary outcomes included shockable and nonshockable OHCA. Model performance including area under the receiver operating characteristic curve and positive predictive value were assessed and adjusted for observed rate of OHCA across the health system. RESULTS: There were significant differences in demographic characteristics, vital signs, electrocardiographic measures, comorbidities, and medication distribution between individuals with OHCA and controls. In external validation, discrimination in machine learning models (area under the receiver operating characteristic curve 0.80–0.85) was superior to a baseline model with conventional cardiovascular risk factors (area under the receiver operating characteristic curve 0.66). At a specificity threshold of 99%, correcting for baseline OHCA incidence across the health system, positive predictive value was 2.5% to 3.1% in machine learning models compared with 0.8% for the baseline model. Longer corrected QT interval, substance abuse disorder, fluid and electrolyte disorder, alcohol abuse, and higher heart rate were identified as salient predictors of OHCA risk across all machine learning models. Established cardiovascular risk factors retained predictive importance for shockable OHCA, but demographic characteristics (minority race, single marital status) and noncardiovascular comorbidities (substance abuse disorder) also contributed to risk prediction. For nonshockable OHCA, a range of salient predictors, including comorbidities, habits, vital signs, demographic characteristics, and electrocardiographic measures, were identified. CONCLUSIONS: In a population-based case–control study, machine learning models incorporating readily available EHR data showed reasonable discrimination and risk enrichment for OHCA in the general population. Salient factors associated with OCHA risk were myriad across the cardiovascular and noncardiovascular spectrum. Public health and tailored strategies for OHCA prediction and prevention will require incorporation of this complexity.
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