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1. Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations

Author

Liu, Shuai, Zhu, Jingjing, Zhong, Hua, Wu, Chong, Xue, Haoran, Darst, Burcu F., Guo, Xiuqing, Durda, Peter, Tracy, Russell P., Liu, Yongmei, Johnson, W. Craig, Taylor, Kent D., Manichaikul, Ani W., Goodarzi, Mark O., Gerszten, Robert E., Clish, Clary B., Chen, Yii-Der Ida, Highland, Heather, Haiman, Christopher A., Gignoux, Christopher R., Lange, Leslie, Conti, David V., Raffield, Laura M., Wilkens, Lynne, Marchand, Loïc Le, North, Kari E., Young, Kristin L., Loos, Ruth J., Buyske, Steve, Matise, Tara, Peters, Ulrike, Kooperberg, Charles, Reiner, Alexander P., Yu, Bing, Boerwinkle, Eric, Sun, Quan, Rooney, Mary R., Echouffo-Tcheugui, Justin B., Daviglus, Martha L., Qi, Qibin, Mancuso, Nicholas, Li, Changwei, Deng, Youping, Manning, Alisa, Meigs, James B., Rich, Stephen S., Rotter, Jerome I., and Wu, Lang

Published
2024
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5. Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Author

Wang, Yuxuan, Selvaraj, Margaret, Li, Xihao, Li, Zilin, Holdcraft, Jacob, Arnett, Donna, Bis, Joshua, Blangero, John, Boerwinkle, Eric, Bowden, Donald, Cade, Brian, Carlson, Jenna, Carson, April, Chen, Yii-Der, Curran, Joanne, de Vries, Paul, Dutcher, Susan, Ellinor, Patrick, Floyd, James, Fornage, Myriam, Freedman, Barry, Gabriel, Stacey, Germer, Soren, Gibbs, Richard, Guo, Xiuqing, He, Jiang, Heard-Costa, Nancy, Hildalgo, Bertha, Hou, Lifang, Irvin, Marguerite, Joehanes, Roby, Kaplan, Robert, Kardia, Sharon, Kelly, Tanika, Kim, Ryan, Kooperberg, Charles, Kral, Brian, Levy, Daniel, Li, Changwei, Liu, Chunyu, Lloyd-Jone, Don, Loos, Ruth, Mahaney, Michael, Martin, Lisa, Mathias, Rasika, Minster, Ryan, Mitchell, Braxton, Montasser, May, Morrison, Alanna, Murabito, Joanne, Naseri, Take, OConnell, Jeffrey, Palmer, Nicholette, Preuss, Michael, Psaty, Bruce, Raffield, Laura, Rao, Dabeeru, Redline, Susan, Reiner, Alexander, Rich, Stephen, Ruepena, Muagututia, Sheu, Wayne, Smith, Jennifer, Smith, Albert, Tiwari, Hemant, Tsai, Michael, Viaud-Martinez, Karine, Wang, Zhe, Yanek, Lisa, Zhao, Wei, Lin, Xihong, Natarajan, Pradeep, Peloso, Gina, and Rotter, Jerome

Subjects
association, blood lipid, cholesterol, lncRNA, rare variants, whole-genome sequencing, Humans, RNA, Long Noncoding, Genome-Wide Association Study, Precision Medicine, Whole Genome Sequencing, Lipids, Polymorphism, Single Nucleotide
Abstract

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Published
2023

19. Development and Validation of a Two-Step Predictive Risk Stratification Model for Coronavirus Disease 2019 In-hospital Mortality: A Multicenter Retrospective Cohort Study.

Author

Li, Yang, Kong, Yanlei, Ebell, Mark, Martinez, Leonardo, Cai, Xinyan, Lennon, Robert, Tarn, Derjung, Mainous, Arch, Zgierska, Aleksandra, Barrett, Bruce, Tuan, Wen-Jan, Maloy, Kevin, Goyal, Munish, Krist, Alex, Gal, Tamas, Sung, Meng-Hsuan, Li, Changwei, Jin, Yier, and Shen, Ye

Subjects
COVID-19, multicenter cohort study, prognostic score, time-and cost-saving tool, two-step
Abstract

OBJECTIVES: An accurate prognostic score to predict mortality for adults with COVID-19 infection is needed to understand who would benefit most from hospitalizations and more intensive support and care. We aimed to develop and validate a two-step score system for patient triage, and to identify patients at a relatively low level of mortality risk using easy-to-collect individual information. DESIGN: Multicenter retrospective observational cohort study. SETTING: Four health centers from Virginia Commonwealth University, Georgetown University, the University of Florida, and the University of California, Los Angeles. PATIENTS: Coronavirus Disease 2019-confirmed and hospitalized adult patients. MEASUREMENTS AND MAIN RESULTS: We included 1,673 participants from Virginia Commonwealth University (VCU) as the derivation cohort. Risk factors for in-hospital death were identified using a multivariable logistic model with variable selection procedures after repeated missing data imputation. A two-step risk score was developed to identify patients at lower, moderate, and higher mortality risk. The first step selected increasing age, more than one pre-existing comorbidities, heart rate >100 beats/min, respiratory rate ≥30 breaths/min, and SpO2

Published
2022

21. Direct wavefront sensing from extended objects: a general sensor

Author

Li, Changwei, Lu, Yanting, and Zhang, Sijiong

Subjects
Physics - Optics, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Wavefront sensing from an extended object is a challenging task since the phase to be sensed is disturbed by the phase generated from the structure of the extended object. To address this problem, a general wavefront sensor was proposed. The hardware of the sensor consists of a field lens, a collimating lens, a lenslet array, and a camera. The idea for its algorithm is to eliminate the phase caused by the extended object and reconstruct the point spread function through each lenslet. As a result, the scenario of wavefront sensing from an extended object has been converted to the conventional one from a point source. Numerical simulations and experiments both verify the feasibility and the accuracy of the proposed sensor., Comment: 11 pages, 10 figures; to be submitted

Published
2021
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27. Gene-educational attainment interactions in a multi-ancestry genome-wide meta-analysis identify novel blood pressure loci.

Author

de Las Fuentes, Lisa, Sung, Yun Ju, Noordam, Raymond, Winkler, Thomas, Feitosa, Mary F, Schwander, Karen, Bentley, Amy R, Brown, Michael R, Guo, Xiuqing, Manning, Alisa, Chasman, Daniel I, Aschard, Hugues, Bartz, Traci M, Bielak, Lawrence F, Campbell, Archie, Cheng, Ching-Yu, Dorajoo, Rajkumar, Hartwig, Fernando P, Horimoto, ARVR, Li, Changwei, Li-Gao, Ruifang, Liu, Yongmei, Marten, Jonathan, Musani, Solomon K, Ntalla, Ioanna, Rankinen, Tuomo, Richard, Melissa, Sim, Xueling, Smith, Albert V, Tajuddin, Salman M, Tayo, Bamidele O, Vojinovic, Dina, Warren, Helen R, Xuan, Deng, Alver, Maris, Boissel, Mathilde, Chai, Jin-Fang, Chen, Xu, Christensen, Kaare, Divers, Jasmin, Evangelou, Evangelos, Gao, Chuan, Girotto, Giorgia, Harris, Sarah E, He, Meian, Hsu, Fang-Chi, Kühnel, Brigitte, Laguzzi, Federica, Li, Xiaoyin, Lyytikäinen, Leo-Pekka, Nolte, Ilja M, Poveda, Alaitz, Rauramaa, Rainer, Riaz, Muhammad, Rueedi, Rico, Shu, Xiao-Ou, Snieder, Harold, Sofer, Tamar, Takeuchi, Fumihiko, Verweij, Niek, Ware, Erin B, Weiss, Stefan, Yanek, Lisa R, Amin, Najaf, Arking, Dan E, Arnett, Donna K, Bergmann, Sven, Boerwinkle, Eric, Brody, Jennifer A, Broeckel, Ulrich, Brumat, Marco, Burke, Gregory, Cabrera, Claudia P, Canouil, Mickaël, Chee, Miao Li, Chen, Yii-Der Ida, Cocca, Massimiliano, Connell, John, de Silva, H Janaka, de Vries, Paul S, Eiriksdottir, Gudny, Faul, Jessica D, Fisher, Virginia, Forrester, Terrence, Fox, Ervin F, Friedlander, Yechiel, Gao, He, Gigante, Bruna, Giulianini, Franco, Gu, Chi Charles, Gu, Dongfeng, Harris, Tamara B, He, Jiang, Heikkinen, Sami, Heng, Chew-Kiat, Hunt, Steven, Ikram, M Arfan, Irvin, Marguerite R, Kähönen, Mika, and Kavousi, Maryam

Subjects
Lifelines Cohort Study, Psychiatry, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

Educational attainment is widely used as a surrogate for socioeconomic status (SES). Low SES is a risk factor for hypertension and high blood pressure (BP). To identify novel BP loci, we performed multi-ancestry meta-analyses accounting for gene-educational attainment interactions using two variables, "Some College" (yes/no) and "Graduated College" (yes/no). Interactions were evaluated using both a 1 degree of freedom (DF) interaction term and a 2DF joint test of genetic and interaction effects. Analyses were performed for systolic BP, diastolic BP, mean arterial pressure, and pulse pressure. We pursued genome-wide interrogation in Stage 1 studies (N = 117 438) and follow-up on promising variants in Stage 2 studies (N = 293 787) in five ancestry groups. Through combined meta-analyses of Stages 1 and 2, we identified 84 known and 18 novel BP loci at genome-wide significance level (P

Published
2021

43. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Author

Noordam, Raymond, Bos, Maxime M, Wang, Heming, Winkler, Thomas W, Bentley, Amy R, Kilpeläinen, Tuomas O, de Vries, Paul S, Sung, Yun Ju, Schwander, Karen, Cade, Brian E, Manning, Alisa, Aschard, Hugues, Brown, Michael R, Chen, Han, Franceschini, Nora, Musani, Solomon K, Richard, Melissa, Vojinovic, Dina, Aslibekyan, Stella, Bartz, Traci M, de Las Fuentes, Lisa, Feitosa, Mary, Horimoto, Andrea R, Ilkov, Marjan, Kho, Minjung, Kraja, Aldi, Li, Changwei, Lim, Elise, Liu, Yongmei, Mook-Kanamori, Dennis O, Rankinen, Tuomo, Tajuddin, Salman M, van der Spek, Ashley, Wang, Zhe, Marten, Jonathan, Laville, Vincent, Alver, Maris, Evangelou, Evangelos, Graff, Maria E, He, Meian, Kühnel, Brigitte, Lyytikäinen, Leo-Pekka, Marques-Vidal, Pedro, Nolte, Ilja M, Palmer, Nicholette D, Rauramaa, Rainer, Shu, Xiao-Ou, Snieder, Harold, Weiss, Stefan, Wen, Wanqing, Yanek, Lisa R, Adolfo, Correa, Ballantyne, Christie, Bielak, Larry, Biermasz, Nienke R, Boerwinkle, Eric, Dimou, Niki, Eiriksdottir, Gudny, Gao, Chuan, Gharib, Sina A, Gottlieb, Daniel J, Haba-Rubio, José, Harris, Tamara B, Heikkinen, Sami, Heinzer, Raphaël, Hixson, James E, Homuth, Georg, Ikram, M Arfan, Komulainen, Pirjo, Krieger, Jose E, Lee, Jiwon, Liu, Jingmin, Lohman, Kurt K, Luik, Annemarie I, Mägi, Reedik, Martin, Lisa W, Meitinger, Thomas, Metspalu, Andres, Milaneschi, Yuri, Nalls, Mike A, O'Connell, Jeff, Peters, Annette, Peyser, Patricia, Raitakari, Olli T, Reiner, Alex P, Rensen, Patrick CN, Rice, Treva K, Rich, Stephen S, Roenneberg, Till, Rotter, Jerome I, Schreiner, Pamela J, Shikany, James, Sidney, Stephen S, Sims, Mario, Sitlani, Colleen M, Sofer, Tamar, Strauch, Konstantin, Swertz, Morris A, Taylor, Kent D, and Uitterlinden, André G

Subjects
Humans, Lipids, Chromosome Mapping, Sleep, Phylogeny, Polymorphism, Single Nucleotide, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Young Adult, Genetic Loci, Polymorphism, Single Nucleotide, and over
Abstract

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles.

Published
2019

44. Novel serum metabolites associate with cognition phenotypes among Bogalusa Heart Study participants

Author

Shi, Mengyao, Bazzano, Lydia A, He, Jiang, Gu, Xiaoying, Li, Changwei, Li, Shengxu, Yaffe, Kristine, Kinchen, Jason M, Stuchlik, Patrick, Mi, Xuenan, Nierenberg, Jovia L, Razavi, Alexander C, and Kelly, Tanika N

Subjects
Digestive Diseases, Good Health and Well Being, Cognition, Cross-Sectional Studies, Female, Humans, Longitudinal Studies, Male, Metabolome, Metabolomics, Middle Aged, Phenotype, metabolomics, metabolite network, dementia, Alzheimer's disease, cognition, Alzheimer’s disease, Biochemistry and Cell Biology, Physiology, Oncology and Carcinogenesis, Developmental Biology
Abstract

BackgroundMetabolomics study provides an opportunity to identify novel molecular determinants of altered cognitive function.MethodsDuring 2013 to 2016 Bogalusa Heart Study (BHS) visit, 1,177 participants underwent untargeted, ultrahigh performance liquid chromatography-tandem mass spectroscopy metabolomics profiling. Global cognition and five cognition domains were also assessed. The cross-sectional associations of single metabolites with cognition were tested using multiple linear regression models. Weighted correlation network analysis was used to examine the covariable-adjusted correlations of modules of co-abundant metabolites with cognition. Analyses were conducted in the overall sample and according to both ethnicity and sex.ResultsFive known metabolites and two metabolite modules robustly associated with cognition across overall and stratified analyses. Two metabolites were from lipid sub-pathways including fatty acid metabolism [9-hydroxystearate; minimum P-value (min-P)=1.11×10-5], and primary bile acid metabolism (glyco-alpha-muricholate; min-P=4.10×10-5). One metabolite from the glycogen metabolism sub-pathway (maltose; min-P=9.77×10-6), one from the polyamine metabolism sub-pathway (N-acetyl-isoputreanine; min-P=1.03×10-5), and one from the purine metabolism sub-pathway (7-methylguanine; min-P=1.19×10-5) were also identified. Two metabolite modules reflecting bile acid metabolism and androgenic steroids correlated with cognition (min-P=5.00×10-4 and 3.00×10-3, respectively).ConclusionThe novel associations of 5 known metabolites and 2 metabolite modules with cognition provide insights into the physiological mechanisms regulating cognitive function.

Published
2019

45. Multiancestry Genome-Wide Association Study of Lipid Levels Incorporating Gene-Alcohol Interactions

Author

de Vries, Paul S, Brown, Michael R, Bentley, Amy R, Sung, Yun J, Winkler, Thomas W, Ntalla, Ioanna, Schwander, Karen, Kraja, Aldi T, Guo, Xiuqing, Franceschini, Nora, Cheng, Ching-Yu, Sim, Xueling, Vojinovic, Dina, Huffman, Jennifer E, Musani, Solomon K, Li, Changwei, Feitosa, Mary F, Richard, Melissa A, Noordam, Raymond, Aschard, Hugues, Bartz, Traci M, Bielak, Lawrence F, Deng, Xuan, Dorajoo, Rajkumar, Lohman, Kurt K, Manning, Alisa K, Rankinen, Tuomo, Smith, Albert V, Tajuddin, Salman M, Evangelou, Evangelos, Graff, Mariaelisa, Alver, Maris, Boissel, Mathilde, Chai, Jin Fang, Chen, Xu, Divers, Jasmin, Gandin, Ilaria, Gao, Chuan, Goel, Anuj, Hagemeijer, Yanick, Harris, Sarah E, Hartwig, Fernando P, He, Meian, Horimoto, Andrea RVR, Hsu, Fang-Chi, Jackson, Anne U, Kasturiratne, Anuradhani, Komulainen, Pirjo, Kühnel, Brigitte, Laguzzi, Federica, Lee, Joseph H, Luan, Jian'an, Lyytikäinen, Leo-Pekka, Matoba, Nana, Nolte, Ilja M, Pietzner, Maik, Riaz, Muhammad, Said, M Abdullah, Scott, Robert A, Sofer, Tamar, Stančáková, Alena, Takeuchi, Fumihiko, Tayo, Bamidele O, van der Most, Peter J, Varga, Tibor V, Wang, Yajuan, Ware, Erin B, Wen, Wanqing, Yanek, Lisa R, Zhang, Weihua, Zhao, Jing Hua, Afaq, Saima, Amin, Najaf, Amini, Marzyeh, Arking, Dan E, Aung, Tin, Ballantyne, Christie, Boerwinkle, Eric, Broeckel, Ulrich, Campbell, Archie, Canouil, Mickaël, Charumathi, Sabanayagam, Chen, Yii-Der Ida, Connell, John M, de Faire, Ulf, de las Fuentes, Lisa, de Mutsert, Renée, de Silva, H Janaka, Ding, Jingzhong, Dominiczak, Anna F, Duan, Qing, Eaton, Charles B, Eppinga, Ruben N, Faul, Jessica D, Fisher, Virginia, Forrester, Terrence, Franco, Oscar H, Friedlander, Yechiel, Ghanbari, Mohsen, and Giulianini, Franco

Subjects
Epidemiology, Health Sciences, Substance Misuse, Alcoholism, Alcohol Use and Health, Human Genome, Prevention, Genetics, Aetiology, 2.1 Biological and endogenous factors, Cardiovascular, Adolescent, Adult, Aged, Alcohol Drinking, Cholesterol, HDL, Cholesterol, LDL, Female, Genome-Wide Association Study, Genotype, Humans, Life Style, Lipids, Male, Middle Aged, Phenotype, Racial Groups, Triglycerides, Vascular Endothelial Growth Factor B, Young Adult, alcohol consumption, cholesterol, gene-environment interactions, gene-lifestyle interactions, genome-wide association studies, lipids, triglycerides, InterAct Consortium, Lifelines Cohort, Groningen, The Netherlands, Mathematical Sciences, Medical and Health Sciences
Abstract

A person's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multiancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in stage 1 (genome-wide discovery) and 66 studies in stage 2 (focused follow-up), for a total of 394,584 individuals from 5 ancestry groups. Analyses covered the period July 2014-November 2017. Genetic main effects and interaction effects were jointly assessed by means of a 2-degrees-of-freedom (df) test, and a 1-df test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in stage 1 and were evaluated in stage 2, followed by combined analyses of stage 1 and stage 2. In the combined analysis of stages 1 and 2, a total of 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2-df tests, of which 18 were novel. No genome-wide-significant associations were found testing the interaction effect alone. The novel loci included several genes (proprotein convertase subtilisin/kexin type 5 (PCSK5), vascular endothelial growth factor B (VEGFB), and apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1) complementation factor (A1CF)) that have a putative role in lipid metabolism on the basis of existing evidence from cellular and experimental models.

Published
2019

46. Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Author

Kilpeläinen, Tuomas O, Bentley, Amy R, Noordam, Raymond, Sung, Yun Ju, Schwander, Karen, Winkler, Thomas W, Jakupović, Hermina, Chasman, Daniel I, Manning, Alisa, Ntalla, Ioanna, Aschard, Hugues, Brown, Michael R, de Las Fuentes, Lisa, Franceschini, Nora, Guo, Xiuqing, Vojinovic, Dina, Aslibekyan, Stella, Feitosa, Mary F, Kho, Minjung, Musani, Solomon K, Richard, Melissa, Wang, Heming, Wang, Zhe, Bartz, Traci M, Bielak, Lawrence F, Campbell, Archie, Dorajoo, Rajkumar, Fisher, Virginia, Hartwig, Fernando P, Horimoto, Andrea RVR, Li, Changwei, Lohman, Kurt K, Marten, Jonathan, Sim, Xueling, Smith, Albert V, Tajuddin, Salman M, Alver, Maris, Amini, Marzyeh, Boissel, Mathilde, Chai, Jin Fang, Chen, Xu, Divers, Jasmin, Evangelou, Evangelos, Gao, Chuan, Graff, Mariaelisa, Harris, Sarah E, He, Meian, Hsu, Fang-Chi, Jackson, Anne U, Zhao, Jing Hua, Kraja, Aldi T, Kühnel, Brigitte, Laguzzi, Federica, Lyytikäinen, Leo-Pekka, Nolte, Ilja M, Rauramaa, Rainer, Riaz, Muhammad, Robino, Antonietta, Rueedi, Rico, Stringham, Heather M, Takeuchi, Fumihiko, van der Most, Peter J, Varga, Tibor V, Verweij, Niek, Ware, Erin B, Wen, Wanqing, Li, Xiaoyin, Yanek, Lisa R, Amin, Najaf, Arnett, Donna K, Boerwinkle, Eric, Brumat, Marco, Cade, Brian, Canouil, Mickaël, Chen, Yii-Der Ida, Concas, Maria Pina, Connell, John, de Mutsert, Renée, de Silva, H Janaka, de Vries, Paul S, Demirkan, Ayşe, Ding, Jingzhong, Eaton, Charles B, Faul, Jessica D, Friedlander, Yechiel, Gabriel, Kelley P, Ghanbari, Mohsen, Giulianini, Franco, Gu, Chi Charles, Gu, Dongfeng, Harris, Tamara B, He, Jiang, Heikkinen, Sami, Heng, Chew-Kiat, Hunt, Steven C, Ikram, M Arfan, Jonas, Jost B, Koh, Woon-Puay, Komulainen, Pirjo, and Krieger, Jose E

Subjects
Lifelines Cohort Study, Humans, Cholesterol, Lipids, Triglycerides, Calcium-Binding Proteins, Muscle Proteins, Microtubule-Associated Proteins, Membrane Proteins, Nerve Tissue Proteins, Transcription Factors, Exercise, Genotype, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, African Continental Ancestry Group, Asian Continental Ancestry Group, European Continental Ancestry Group, Hispanic Americans, Brazil, Female, Male, Lipid Metabolism, Cholesterol, LDL, Cholesterol, HDL, Genome-Wide Association Study, Young Adult, Genetic Loci, LIM-Homeodomain Proteins, and over, HDL, LDL
Abstract

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels.

Published
2019

47. Rubinstein-Taybi Syndrome: Clinical and Genetic Analyses of One Case and Literature Review

Author

Xiaomiao WAN, Ling LI, Changwei LIU, Chaohui SHEN, Liwen WU, Suxian ZHOU

Subjects
rubinstein-taybi syndrome, genetic techniques, dna mutational analysis, genetic association studies, whole exome sequencing, rare diseases, crebbp gene, Medicine
Abstract

Rubinstein-Taybi syndrome (RSTS) is a rare autosomal dominant genetic disorder characterized by mental retardation, broad thumbs, and hooked nose and so on. RSTS can cause multiple organ hypoplasia and multiple system hypoplasia (such as endocrine system, digestive system, urinary system, etc.) , and even involve the skin (such as pilomatrixomas, hirsutism, etc.) . Moreover, it has been recently reported to be associated with increased risk of cancer. However, relevant research about RSTS is still very limited. By use of clinical characteristics and genetic testing, only some cases can be diagnosed, and the diagnosis for many other cases additionally requires an in-depth analysis of etiology and pathogenesis of RSTS. This paper reports a suspected RSTS patient treated in the Affiliated Hospital of Guilin Medical University. Whole exome sequencing showed that the patient has a mutation in the CREBBP gene: c.3832G>A (p.Glu1278Lys) , but her parents do not have, indicating that it is a new mutation. By the analysis of the genetic testing result with clinical characteristics, the patient was diagnosed with RSTS. It is very difficult to make a diagnosis of RSTS. By providing a detailed description of clinical features of RSTS, this report is conducive to the study of genotype-phenotype correlation of RSTS, and to clinical diagnosis and treatment of RSTS.

Published
2022
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