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      Proteomics Analysis of Genetic Liability of Abdominal Aortic Aneurysm Identifies Plasma Neogenin and Kit Ligand: The ARIC Study

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          Abstract

          Background:

          Genome-wide association studies have reported 23 gene loci related to abdominal aortic aneurysm (AAA)—a potentially lethal condition characterized by a weakened dilated vessel wall. This study aimed to identify proteomic signatures and pathways related to these risk loci to better characterize AAA genetic susceptibility.

          Methods:

          Plasma concentrations of 4870 proteins were determined using a DNA aptamer-based array. Linear regression analysis estimated the associations between the 23 risk alleles and plasma protein levels with adjustments for potential confounders in a race-stratified analysis of 1671 Black and 7241 White participants. Significant proteins were then evaluated for their prediction of clinical AAA (454 AAA events in 11 064 individuals), and those significantly associated with AAA were further interrogated using Mendelian randomization analysis.

          Results:

          Risk variants proximal to PSRC1-CELSR2-SORT1 , PCIF1-ZNF335-MMP9, RP11-136O12.2/TRIB1 , ZNF259/APOA5, IL6R , PCSK9 , LPA , and APOE were associated with 118 plasma proteins in Whites and 59 were replicated in Black participants. Novel associations with clinical AAA incidence were observed for kit ligand (HR, 0.59 [95% CI, 0.42–0.82] for top versus first quintiles) and neogenin (HR, 0.64 [95% CI, 0.46–0.88]) over a median 21.2-year follow-up; neogenin was also associated with ultrasound-detected asymptomatic AAA (N=4295; 57 asymptomatic AAA cases). Mendelian randomization inverse variance weighted estimates suggested that AAA risk is promoted by lower levels of kit ligand (OR per SD=0.67; P =1.4×10 −5 ) and neogenin (OR per SD=0.50; P =0.03).

          Conclusions:

          Low levels of neogenin and kit ligand may be novel risk factors for AAA development in potentially causal pathways. These findings provide insights and potential targets to reduce AAA susceptibility.

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          Most cited references52

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          Causal analysis approaches in Ingenuity Pathway Analysis

          Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. Availability: The causal analytics tools ‘Upstream Regulator Analysis', ‘Mechanistic Networks', ‘Causal Network Analysis' and ‘Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). Supplementary information: Supplementary material is available at Bioinformatics online.
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            Estimating glomerular filtration rate from serum creatinine and cystatin C.

            Estimates of glomerular filtration rate (GFR) that are based on serum creatinine are routinely used; however, they are imprecise, potentially leading to the overdiagnosis of chronic kidney disease. Cystatin C is an alternative filtration marker for estimating GFR. Using cross-sectional analyses, we developed estimating equations based on cystatin C alone and in combination with creatinine in diverse populations totaling 5352 participants from 13 studies. These equations were then validated in 1119 participants from 5 different studies in which GFR had been measured. Cystatin and creatinine assays were traceable to primary reference materials. Mean measured GFRs were 68 and 70 ml per minute per 1.73 m(2) of body-surface area in the development and validation data sets, respectively. In the validation data set, the creatinine-cystatin C equation performed better than equations that used creatinine or cystatin C alone. Bias was similar among the three equations, with a median difference between measured and estimated GFR of 3.9 ml per minute per 1.73 m(2) with the combined equation, as compared with 3.7 and 3.4 ml per minute per 1.73 m(2) with the creatinine equation and the cystatin C equation (P=0.07 and P=0.05), respectively. Precision was improved with the combined equation (interquartile range of the difference, 13.4 vs. 15.4 and 16.4 ml per minute per 1.73 m(2), respectively [P=0.001 and P 30% of measured GFR, 8.5 vs. 12.8 and 14.1, respectively [P<0.001 for both comparisons]). In participants whose estimated GFR based on creatinine was 45 to 74 ml per minute per 1.73 m(2), the combined equation improved the classification of measured GFR as either less than 60 ml per minute per 1.73 m(2) or greater than or equal to 60 ml per minute per 1.73 m(2) (net reclassification index, 19.4% [P<0.001]) and correctly reclassified 16.9% of those with an estimated GFR of 45 to 59 ml per minute per 1.73 m(2) as having a GFR of 60 ml or higher per minute per 1.73 m(2). The combined creatinine-cystatin C equation performed better than equations based on either of these markers alone and may be useful as a confirmatory test for chronic kidney disease. (Funded by the National Institute of Diabetes and Digestive and Kidney Diseases.).
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              Is Open Access

              Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

              The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes) 1 . In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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                Author and article information

                Contributors
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                Journal
                Arteriosclerosis, Thrombosis, and Vascular Biology
                ATVB
                Ovid Technologies (Wolters Kluwer Health)
                1079-5642
                1524-4636
                February 2023
                February 2023
                : 43
                : 2
                : 367-378
                Affiliations
                [1 ]Division of Epidemiology and Community Health (B.T.S., J.S.P., P.L.L., R.T.D., A.L., W.T.), University of Minnesota School of Public Health, Minneapolis.
                [2 ]Division of Computational Health Sciences, Department of Surgery (B.T.S.), University of Minnesota, Minneapolis.
                [3 ]Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, CA (F.L.N.).
                [4 ]Department of Epidemiology, Mailman School of Public Health, Columbia University, New York (R.T.D.).
                [5 ]Division of Biostatistics (W.G.), University of Minnesota School of Public Health, Minneapolis.
                [6 ]Department of Laboratory Medicine and Pathology (N.P.), University of Minnesota, Minneapolis.
                [7 ]Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center, School of Public Health, Houston (G.L.).
                [8 ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M.).
                [9 ]Welch Center for Prevention, Epidemiology and Clinical Research, Baltimore, MD (K.M.).
                [10 ]Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson (A.T.).
                Article
                10.1161/ATVBAHA.122.317984
                36579647
                9bcad08b-0c1a-40d5-ba90-358daa7baf3f
                © 2023
                History

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