1
|
Chen SY, Chen YC, Liu TY, Chang KC, Chang SS, Wu N, Lee Wu D, Dunlap RK, Chan CJ, Yang JS, Liao CC, Tsai FJ. Novel Genes Associated with Atrial Fibrillation and the Predictive Models for AF Incorporating Polygenic Risk Scores and PheWAS-Derived Risk Factors. Can J Cardiol 2024:S0828-282X(24)00851-1. [PMID: 39142603 DOI: 10.1016/j.cjca.2024.07.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Revised: 07/26/2024] [Accepted: 07/26/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Atrial fibrillation (AF), the most common atrial arrhythmia, presents varied clinical manifestations. Despite the identification of genetic loci associated with AF, particularly in specific populations, research within Asian ethnicities remains limited. This study aimed to develop predictive models for AF using AF-associated single-nucleotide polymorphisms (SNPs) from a Genome-Wide Association Study (GWAS) on a substantial cohort of Taiwanese individuals, evaluating the models' predictive efficacy. METHODS Involving 75,121 subjects, including 5,694 AF patients and 69,427 normal controls with GWAS data, the study merged polygenic risk scores (PRS) from AF-associated SNPs with Phenome-wide association study (PheWAS)-derived risk factors. Advanced statistical and machine learning techniques were employed to develop and evaluate AF predictive models for discrimination and calibration. RESULTS The study identified the top 30 significant SNPs associated with AF, predominantly on chromosomes 10 and 16, implicating genes like NEURL1, SH3PXD2A, INA, NT5C2, STN1, and ZFHX3. Notably, INA, NT5C2, and STN1 were newly linked to AF. The GWAS predictive power using PRS-CS analysis for AF exhibited an area under the curve (AUC) of 0.600 (P < 0.001), improving to 0.855 (P < 0.001) after adjusting for age and gender. PheWAS analysis showed the top 10 diseases associated with these genes were circulatory system diseases. CONCLUSIONS Integrating genetic and phenotypic data enhanced the accuracy and clinical relevance of AF predictive models. The findings suggest promise for refining AF risk assessment, enabling personalized interventions, and reducing AF-related morbidity and mortality burdens.
Collapse
Affiliation(s)
- Shih-Yin Chen
- School of Chinese Medicine, China Medical University, Taichung, 404, Taiwan; Genetics Center, Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan
| | - Yu-Chia Chen
- Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, 404, Taiwan
| | - Ting-Yuan Liu
- Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, 404, Taiwan
| | - Kuan-Cheng Chang
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung 404, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung 404, Taiwan
| | - Shih-Sheng Chang
- Division of Cardiovascular Medicine, Department of Medicine, China Medical University Hospital, Taichung 404, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung 404, Taiwan
| | - Ning Wu
- Department of Biological Sciences, Southeastern Oklahoma State University, Durant, OK 74701, USA
| | - Donald Lee Wu
- Department of Internal Medicine, University of Oklahoma Health Sciences Center, Tulsa, OK 74104, USA
| | - Rylee Kay Dunlap
- College of Osteopathic Medicine, Oklahoma State University Center for Health Sciences, Tulsa, OK 74107, USA
| | - Chia-Jung Chan
- Genetics Center, Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan
| | - Jai-Sing Yang
- Genetics Center, Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan
| | - Chi Chou Liao
- Genetics Center, Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan
| | - Fuu-Jen Tsai
- School of Chinese Medicine, China Medical University, Taichung, 404, Taiwan; Genetics Center, Department of Medical Research, China Medical University Hospital, Taichung 404, Taiwan; Department of Medical Genetics, China Medical University Hospital, Taichung 404, Taiwan.
| |
Collapse
|
2
|
Garner T, Clayton P, Højby M, Murray P, Stevens A. Gene Expression Signatures Predict First-Year Response to Somapacitan Treatment in Children With Growth Hormone Deficiency. J Clin Endocrinol Metab 2024; 109:1214-1221. [PMID: 38066644 PMCID: PMC11031233 DOI: 10.1210/clinem/dgad717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Indexed: 04/21/2024]
Abstract
CONTEXT The pretreatment blood transcriptome predicts growth response to daily growth hormone (GH) therapy with high accuracy. OBJECTIVE Investigate response prediction using pretreatment transcriptome in children with GH deficiency (GHD) treated with once-weekly somapacitan, a novel long-acting GH. METHODS REAL4 is a randomized, multinational, open-label, active-controlled parallel group phase 3 trial, comprising a 52-week main phase and an ongoing 3-year safety extension (NCT03811535). A total of 128/200 treatment-naïve prepubertal children with GHD consented to baseline blood transcriptome profiling. They were randomized 2:1 to subcutaneous somapacitan (0.16 mg/kg/week) or daily GH (0.034 mg/kg/day). Differential RNA-seq analysis and machine learning were used to predict therapy response. RESULTS 121/128 samples passed quality control. Children treated with somapacitan (n = 76) or daily GH (n = 45) were categorized based on fastest and slowest growing quartiles at week 52. Prediction of height velocity (HV; cm/year) was excellent for both treatments (out of bag [OOB] area under curve [AUC]: 0.98-0.99; validation AUC: 0.83-0.84), as was prediction of secondary markers of growth response: HV standard deviation score (SDS) (0.99-1.0; 0.75-0.78), change from baseline height SDS (ΔHSDS) (0.98-1.0; 0.61-0.75), and change from baseline insulin-like growth factor-I SDS (ΔIGF-I SDS) (0.96-1.0; 0.85-0.88). Genes previously identified as predictive of GH therapy response were consistently better at predicting the fastest growers in both treatments in this study (OOB AUC: 0.93-0.97) than the slowest (0.67-0.85). CONCLUSION Pretreatment transcriptome predicts first-year growth response in somapacitan-treated children with GHD. A common set of genes can predict the treatment response to both once-weekly somapacitan and conventional daily GH. This approach could potentially be developed into a clinically applicable pretreatment test to improve clinical management.
Collapse
Affiliation(s)
- Terence Garner
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine and Health, University of Manchester and Manchester Academic Health Science Centre, Manchester, M13 9WL, UK
| | - Peter Clayton
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine and Health, University of Manchester and Manchester Academic Health Science Centre, Manchester, M13 9WL, UK
- Department of Paediatric Endocrinology, Royal Manchester Children's Hospital, Manchester, M13 9WL, UK
| | - Michael Højby
- Novo Nordisk, Clinical Drug Development, 2860 Søborg, Denmark
| | - Philip Murray
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine and Health, University of Manchester and Manchester Academic Health Science Centre, Manchester, M13 9WL, UK
- Department of Paediatric Endocrinology, Royal Manchester Children's Hospital, Manchester, M13 9WL, UK
| | - Adam Stevens
- Division of Developmental Biology and Medicine, Faculty of Biology, Medicine and Health, University of Manchester and Manchester Academic Health Science Centre, Manchester, M13 9WL, UK
| |
Collapse
|
3
|
Brockett JS, Manalo T, Zein-Sabatto H, Lee J, Fang J, Chu P, Feng H, Patil D, Davidson P, Ogan K, Master VA, Pattaras JG, Roberts DL, Bergquist SH, Reyna MA, Petros JA, Lerit DA, Arnold RS. A missense SNP in the tumor suppressor SETD2 reduces H3K36me3 and mitotic spindle integrity in Drosophila. Genetics 2024; 226:iyae015. [PMID: 38290049 PMCID: PMC10990431 DOI: 10.1093/genetics/iyae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 01/11/2024] [Accepted: 01/11/2024] [Indexed: 02/01/2024] Open
Abstract
Mutations in SETD2 are among the most prevalent drivers of renal cell carcinoma (RCC). We identified a novel single nucleotide polymorphism (SNP) in SETD2, E902Q, within a subset of RCC patients, which manifests as both an inherited or tumor-associated somatic mutation. To determine if the SNP is biologically functional, we used CRISPR-based genome editing to generate the orthologous mutation within the Drosophila melanogaster Set2 gene. In Drosophila, the homologous amino acid substitution, E741Q, reduces H3K36me3 levels comparable to Set2 knockdown, and this loss is rescued by reintroduction of a wild-type Set2 transgene. We similarly uncovered significant defects in spindle morphogenesis, consistent with the established role of SETD2 in methylating α-Tubulin during mitosis to regulate microtubule dynamics and maintain genome stability. These data indicate the Set2 E741Q SNP affects both histone methylation and spindle integrity. Moreover, this work further suggests the SETD2 E902Q SNP may hold clinical relevance.
Collapse
Affiliation(s)
- Jovan S Brockett
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Tad Manalo
- Department of Urology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Hala Zein-Sabatto
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jina Lee
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Junnan Fang
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Philip Chu
- Department of Urology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Harry Feng
- Department of Urology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Dattatraya Patil
- Department of Urology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Priscilla Davidson
- Department of Urology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Kenneth Ogan
- Department of Urology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Viraj A Master
- Department of Urology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - John G Pattaras
- Department of Urology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - David L Roberts
- Emory University Department of Medicine, Division of General Internal Medicine, Atlanta, GA 30322, USA
| | - Sharon H Bergquist
- Emory University Department of Medicine, Division of General Internal Medicine, Atlanta, GA 30322, USA
| | - Matthew A Reyna
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - John A Petros
- Department of Urology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Dorothy A Lerit
- Department of Cell Biology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Rebecca S Arnold
- Department of Urology, Emory University School of Medicine, Atlanta, GA 30322, USA
- Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| |
Collapse
|
4
|
Banack HR, Mayeda ER, Naimi AI, Fox MP, Whitcomb BW. Collider Stratification Bias I: Principles and Structure. Am J Epidemiol 2024; 193:238-240. [PMID: 37814490 DOI: 10.1093/aje/kwad203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 08/24/2023] [Accepted: 10/06/2023] [Indexed: 10/11/2023] Open
|
5
|
Parker WA, Vigneault DM, Yang I, Bratt A, Marquardt AC, Sharifi H, Guo HH. Opportunistic Screening for Atrial Fibrillation on Routine Chest Computed Tomography. J Thorac Imaging 2023; 38:270-277. [PMID: 36917506 DOI: 10.1097/rti.0000000000000702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
PURPOSE Quantitative biomarkers from chest computed tomography (CT) can facilitate the incidental detection of important diseases. Atrial fibrillation (AFib) substantially increases the risk for comorbid conditions including stroke. This study investigated the relationship between AFib status and left atrial enlargement (LAE) on CT. MATERIALS AND METHODS A total of 500 consecutive patients who had undergone nongated chest CTs were included, and left atrium maximal axial cross-sectional area (LA-MACSA), left atrium anterior-posterior dimension (LA-AP), and vertebral body cross-sectional area (VB-Area) were measured. Height, weight, age, sex, and diagnosis of AFib were obtained from the medical record. Parametric statistical analyses and receiver operating characteristic curves were performed. Machine learning classifiers were run with clinical risk factors and LA measurements to predict patients with AFib. RESULTS Eighty-five patients with a diagnosis of AFib were identified. Mean LA-MACSA and LA-AP were significantly larger in patients with AFib than in patients without AFib (28.63 vs. 20.53 cm 2 , P <0.000001; 4.34 vs. 3.5 cm, P <0.000001, respectively), both with area under the curves (AUCs) of 0.73. Multivariable logistic regression analysis including age, sex, and VB-Area with LA-MACSA improved the AUC for predicting AFib (AUC=0.77). An LA-MACSA threshold of 30 cm 2 demonstrated high specificity for AFib diagnosis at 92% and sensitivity of 48%, and LA-AP threshold at 4.5 cm demonstrated 90% specificity and 42% sensitivity. A Bayesian machine learning model using age, sex, height, body surface area, and LA-MACSA predicted AFib with an AUC of 0.743. CONCLUSIONS LA-MACSA or LA-AP can be rapidly measured from routine chest CT, and when >30 cm 2 and >4.5 cm, respectively, are specific indicators to predict patients at increased risk for AFib.
Collapse
Affiliation(s)
| | | | - Issac Yang
- Stanford University School of Medicine, Stanford, CA
| | - Alex Bratt
- Stanford and Mayo Clinic Hospital, Rochester, MN
| | | | | | | |
Collapse
|
6
|
Seng LL, Liu CT, Wang J, Li J. Instrumental variable model average with applications in Mendelian randomization. Stat Med 2023; 42:3547-3567. [PMID: 37476915 DOI: 10.1002/sim.9819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 04/20/2023] [Accepted: 05/29/2023] [Indexed: 07/22/2023]
Abstract
Mendelian randomization is a technique used to examine the causal effect of a modifiable exposure on a trait using an observational study by utilizing genetic variants. The use of many instruments can help to improve the estimation precision but may suffer bias when the instruments are weakly associated with the exposure. To overcome the difficulty of high-dimensionality, we propose a model average estimator which involves using different subsets of instruments (single nucleotide polymorphisms, SNPs) to predict the exposure in the first stage, followed by weighting the submodels' predictions using penalization by common penalty functions such as least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). The model averaged predictions are then used as a genetically predicted exposure to obtain the estimation of the causal effect on the response in the second stage. The novelty of our model average estimator also lies in that it allows the number of submodels and the submodels' sizes to grow with the sample size. The practical performance of the estimator is examined in a series of numerical studies. We apply the proposed method on a real genetic dataset investigating the relationship between stature and blood pressure.
Collapse
Affiliation(s)
- Loraine Liping Seng
- Department of Statistics and Data Science, National University of Singapore, Singapore
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan
| | - Jingli Wang
- School of Statistics and Data Science, Nankai University, China
| | - Jialiang Li
- Department of Statistics and Data Science, National University of Singapore, Singapore
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore
| |
Collapse
|
7
|
Chmielewski PP, Kozieł S, Borysławski K. Do the short die young? Evidence from a large sample of deceased Polish adults. ANTHROPOLOGICAL REVIEW 2023. [DOI: 10.18778/1898-6773.86.1.07] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
Abstract
Body height is associated with various socioeconomic and health-related outcomes. Despite numerous studies, the relationship between stature and longevity remains uncertain. This study explores the association between self-reported height and lifespan. Data from 848,860 adults who died between 2004 and 2008 in Poland were collected. After excluding a small proportion of records due to missing data or errors, we examined records for 848,387 individuals (483,281 men, age range: 20–110 years; 365,106 women, age range: 20–112 years). Height was expressed as standardized residual variance derived from linear regression in order to eliminate the variance of year of birth on height. After the elimination of the cohort effect, five height classes were designated using centiles: very short, short, medium, tall and very tall. The differences between sexes and among classes were evaluated with two-way ANOVA and post hoc Tukey’s test. The effect size was assessed using partial eta squared (η2). Pearson’s r coefficients of correlation were calculated. The effect of sex on lifespan was nearly 17 times stronger than the effect of height. No correlation between height and lifespan was found. In conclusion, these findings do not support the hypothesis that taller people have a longevity advantage. We offer tentative explanations for the obtained results.
Collapse
|
8
|
Schooling CM, Zhao JV. Insights into Causal Cardiovascular Risk Factors from Mendelian Randomization. Curr Cardiol Rep 2023; 25:67-76. [PMID: 36640254 DOI: 10.1007/s11886-022-01829-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/07/2022] [Indexed: 01/15/2023]
Abstract
PURPOSE OF THE REVIEW This review summarizes major insights into causal risk factors for cardiovascular disease (CVD) by using Mendelian randomization (MR) to obtain unconfounded estimates, contextualized within its strengths and weaknesses. RECENT FINDINGS MR studies have confirmed the role of major CVD risk factors, including alcohol, smoking, adiposity, blood pressure, type 2 diabetes, lipids, and possibly inflammation, but added that the relation with alcohol is likely linear, confirmed the role of diastolic blood pressure, identified apolipoprotein B as the major target lipid, and foreshadowed results of some trials concerning anti-inflammatories. Identifying a healthy diet and the role of early life influences, such as birth weight, has proved more difficult. Use of MR has winnowed empirically driven hypotheses about CVD into a set of genetically validated targets of intervention. Greater inclusion of global diversity in genetic studies and the use of an overarching framework would enable even more informative MR studies.
Collapse
Affiliation(s)
- C M Schooling
- School of Public Health and Health Policy, City University of New York, 55 West 125th St, NY, 10027, New York, USA. .,School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| | - J V Zhao
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|