1,721,075 research outputs found
VA Million Veteran Program (MVP) Acknowledgements.
No full textVA Million Veteran Program (MVP) Acknowledgements.</p
Correction to: The power of genetic diversity in genome-wide association studies of lipids (Nature, (2021), 600, 7890, (675-679), 10.1038/s41586-021-04064-3)
No full textCorrection to: Naturehttps://doi.org/10.1038/s41586-021-04064-3 Published online 9 December 2021 In the version of this article initially published, Noha A. Yousri (Department of Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar and Department of Computer and Systems Engineering, Alexandria University, Egypt) and Steven C. Hunt (Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA and Department of Genetic Medicine, Weill Cornell Medicine-Qatar, Doha, Qatar) were not included in the author list. In addition, Hieab H. H. Adams (Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands and Department of Clinical Genetics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands) was shown with an incorrect second affiliation in the HTML and PDF versions of the article. Finally, in the HTML version, Cristen J. Willer was mistakenly listed with an extra affiliation (Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD), King Abdulaziz University, Jeddah, Saudi Arabia). The authors and affiliations have been corrected in the HTML and PDF versions of the article
Genetic and clinical determinants of abdominal aortic diameter: genome-wide association studies, exome array data and Mendelian randomization study
Full text linkProgressive dilation of the infrarenal aortic diameter is a consequence of the ageing process and is considered the main determinant of abdominal aortic aneurysm (AAA). We aimed to investigate the genetic and clinical determinants of abdominal aortic diameter (AAD). We conducted a meta-analysis of genome-wide association studies in 10 cohorts (n = 13 542) imputed to the 1000 Genome Project reference panel including 12 815 subjects in the discovery phase and 727 subjects [Partners Biobank cohort 1 (PBIO)] as replication. Maximum anterior-posterior diameter of the infrarenal aorta was used as AAD. We also included exome array data (n = 14 480) from seven epidemiologic studies. Single-variant and gene-based associations were done using SeqMeta package. A Mendelian randomization analysis was applied to investigate the causal effect of a number of clinical risk factors on AAD. In genome-wide association study (GWAS) on AAD, rs74448815 in the intronic region of LDLRAD4 reached genome-wide significance (beta = -0.02, SE = 0.004, P-value = 2.10 × 10-8). The association replicated in the PBIO1 cohort (P-value = 8.19 × 10-4). In exome-array single-variant analysis (P-value threshold = 9 × 10-7), the lowest P-value was found for rs239259 located in SLC22A20 (beta = 0.007, P-value = 1.2 × 10-5). In the gene-based analysis (P-value threshold = 1.85 × 10-6), PCSK5 showed an association with AAD (P-value = 8.03 × 10-7). Furthermore, in Mendelian randomization analyses, we found evidence for genetic association of pulse pressure (beta = -0.003, P-value = 0.02), triglycerides (beta = -0.16, P-value = 0.008) and height (beta = 0.03, P-value < 0.0001), known risk factors for AAA, consistent with a causal association with AAD. Our findings point to new biology as well as highlighting gene regions in mechanisms that have previously been implicated in the genetics of other vascular diseases
The power of genetic diversity in genome-wide association studies of lipids
No full textIncreased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice
Common and rare variant analyses combined with single-cell multiomics reveal cell-type-specific molecular mechanisms of COVID-19 severity
No full textThe determinants of severe COVID-19 in non-elderly adults are poorly understood, which limits opportunities for early intervention and treatment. Here we present novel machine learning frameworks for identifying common and rare disease-associated genetic variation, which outperform conventional approaches. By integrating single-cell multiomics profiling of human lungs to link genetic signals to cell-type-specific functions, we have discovered and validated over 1,000 risk genes underlying severe COVID-19 across 19 cell types. Identified risk genes are overexpressed in healthy lungs but relatively downregulated in severely diseased lungs. Genetic risk for severe COVID-19, within both common and rare variants, is particularly enriched in natural killer (NK) cells, which places these immune cells upstream in the pathogenesis of severe disease. Mendelian randomization indicates that failed NKG2D-mediated activation of NK cells leads to critical illness. Network analysis further links multiple pathways associated with NK cell activation, including type-I-interferon-mediated signalling, to severe COVID-19. Our rare variant model, PULSE, enables sensitive prediction of severe disease in non-elderly patients based on whole-exome sequencing; individualized predictions are accurate independent of age and sex, and are consistent across multiple populations and cohorts. Risk stratification based on exome sequencing has the potential to facilitate post-exposure prophylaxis in at-risk individuals, potentially based around augmentation of NK cell function. Overall, our study characterizes a comprehensive genetic landscape of COVID-19 severity and provides novel insights into the molecular mechanisms of severe disease, leading to new therapeutic targets and sensitive detection of at-risk individuals
Genomics and phenomics of body mass index reveals a complex disease network
No full textElevated body mass index (BMI) is heritable and associated with many health conditions that impact morbidity and mortality. The study of the genetic association of BMI across a broad range of common disease conditions offers the opportunity to extend current knowledge regarding the breadth and depth of adiposity-related diseases. We identify 906 (364 novel) and 41 (6 novel) genome-wide significant loci for BMI among participants of European (N~1.1 million) and African (N~100,000) ancestry, respectively. Using a BMI genetic risk score including 2446 variants, 316 diagnoses are associated in the Million Veteran Program, with 96.5% showing increased risk. A co-morbidity network analysis reveals seven disease communities containing multiple interconnected diseases associated with BMI as well as extensive connections across communities. Mendelian randomization analysis confirms numerous phenotypes across a breadth of organ systems, including conditions of the circulatory (heart failure, ischemic heart disease, atrial fibrillation), genitourinary (chronic renal failure), respiratory (respiratory failure, asthma), musculoskeletal and dermatologic systems that are deeply interconnected within and across the disease communities. This work shows that the complex genetic architecture of BMI associates with a broad range of major health conditions, supporting the need for comprehensive approaches to prevent and treat obesity
GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors
No full textObjective: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and crossvalidated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS metaanalysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. Methods: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. Results: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. Conclusions: This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death
Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure
No full textAssociation of Deployment Characteristics and Exposures With Persistent Ill Health Among 1990–1991 Gulf War Veterans in the VA Million Veteran Program
No full textBackground: Veterans of the 1990-1991 Gulf War have experienced excess health problems, most prominently the multisymptom condition Gulf War illness (GWI). The Department of Veterans Affairs (VA) Cooperative Studies Program #2006 Genomics of Gulf War Illness in Veterans project was established to address important questions concerning pathobiological and genetic aspects of GWI. The current study evaluated patterns of chronic ill health/GWI in the VA Million Veteran Program (MVP) Gulf War veteran cohort in relation to wartime exposures and key features of deployment, 27-30 years after Gulf War service.
Methods: MVP participants who served in the 1990-1991 Gulf War completed the MVP Gulf War Era Survey in 2018-2020. Survey responses provided detailed information on veterans\u27 health, Gulf War exposures, and deployment time periods and locations. Analyses determined associations of three defined GWI/ill health outcomes with Gulf War deployment characteristics and exposures.
Results: The final cohort included 14,103 veterans; demographic and military characteristics of the sample were similar to the full population of U.S. 1990-1991 Gulf War veterans. Overall, a substantial number of veterans experienced chronic ill health, as indicated by three defined outcomes: 49% reported their health as fair or poor, 31% met Centers for Disease Control and Prevention criteria for severe GWI, and 20% had been diagnosed with GWI by a healthcare provider. Health outcomes varied consistently with veterans\u27 demographic and military characteristics, and with exposures during deployment. All outcomes were most prevalent among youngest veterans (\u3c 50 years), Army and Marine Corps veterans, enlisted personnel (vs. officers), veterans located in Iraq and/or Kuwait for at least 7 days, and veterans who remained in theater from January/February 1991 through the summer of 1991. In multivariable models, GWI/ill health was most strongly associated with three exposures: chemical/biological warfare agents, taking pyridostigmine bromide pills, and use of skin pesticides.
Conclusions: Results from this large cohort indicate that GWI/chronic ill health continues to affect a large proportion of Gulf War veterans in patterns associated with 1990-1991 Gulf War deployment and exposures. Findings establish a foundation for comprehensive evaluation of genetic factors and deployment exposures in relation to GWI risk and pathobiology
Data Resource Profile: Nutrition Data in the VA Million Veteran Program
No full textIntroduction
The Department of Veterans Affairs (VA) Million Veteran Program (MVP) nutrition data is derived from dietary food/beverage intake information collected through a semiquantitative food frequency questionnaire (SFFQ).
Methods
Estimates of dietary energy, nutrient, and non-nutritive food components intakes data were derived from an extensively validated SFFQ, which assessed the habitual frequency of consumption of 61 food items, added sugar, fried food frequency, and 21 nutritional supplements over the 12 months preceding questionnaire administration.
Results
Complete nutrition data was available for 353,418 MVP participants as of 30th September 2021. Overall, 91.5% of MVP participants with nutrition data were male with an average age of 65.7 years at enrollment. Participants who completed the SFFQ were primarily White (82.5%), and Blacks accounted for 13.2% of the responders. Mean ± SD energy intake for 353, 418 MVP participants was 1428 ±616 kcal/day, which was 1434 ±617 kcal/day for males and 1364 ±601 kcal/day for females. Energy intake and information on 322 nutrients and non-nutritive food components is available through contact with MVP for research collaborations at www.research.va.gov/mvp.
Conclusions
The energy and nutrient data derived from MVP SFFQ are an invaluable resource for Veteran health and research. In conjunction with the MVP Lifestyle Survey, electronic health records, and genomic data, MVP nutrition data may be used to assess nutritional status and related risk factors, disease prevalence, and determinants of health that can provide scientific support for the development of evidence-based public health policy and health promotion programs and services for Veterans and general population
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