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Cho HW, Jin HS, Kim SS, Eom YB. Forensic height estimation using polygenic score in Korean population. Mol Genet Genomics 2024; 299:78. [PMID: 39120737 DOI: 10.1007/s00438-024-02172-z] [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: 07/31/2023] [Accepted: 07/30/2024] [Indexed: 08/10/2024]
Abstract
Height is known to be a classically heritable trait controlled by complex polygenic factors. Numerous height-associated genetic variants across the genome have been identified so far. It is also a representative of externally visible characteristics (EVC) for predicting appearance in forensic science. When biological evidence at a crime scene is deficient in identifying an individual, the examination of forensic DNA phenotyping using some genetic variants could be considered. In this study, we aimed to predict 'height', a representative forensic phenotype, by using a small number of genetic variants when short tandem repeat (STR) analysis is hard with insufficient biological samples. Our results not only replicated previous genetic signals but also indicated an upward trend in polygenic score (PGS) with increasing height in the validation and replication stages for both genders. These results demonstrate that the established SNP sets in this study could be used for height estimation in the Korean population. Specifically, since the PGS model constructed in this study targets only a small number of SNPs, it contributes to enabling forensic DNA phenotyping even at crime scenes with a minimal amount of biological evidence. To the best of our knowledge, this was the first study to evaluate a PGS model for height estimation in the Korean population using GWAS signals. Our study offers insight into the polygenic effect of height in East Asians, incorporating genetic variants from non-Asian populations.
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Affiliation(s)
- Hye-Won Cho
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, 31538, Chungnam, Republic of Korea
| | - Hyun-Seok Jin
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan, 31499, Chungnam, Republic of Korea
| | - Sung-Soo Kim
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan, 31499, Chungnam, Republic of Korea
| | - Yong-Bin Eom
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, 31538, Chungnam, Republic of Korea.
- Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, 22 Soonchunhyang-ro, Sinchang-myeon, Asan-si, 31538, Chungcheongnam-do, Republic of Korea.
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2
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Jones AC, Patki A, Srinivasasainagendra V, Tiwari HK, Armstrong ND, Chaudhary NS, Limdi NA, Hidalgo BA, Davis B, Cimino JJ, Khan A, Kiryluk K, Lange LA, Lange EM, Arnett DK, Young BA, Diamantidis CJ, Franceschini N, Wassertheil-Smoller S, Rich SS, Rotter JI, Mychaleckyj JC, Kramer HJ, Chen YDI, Psaty BM, Brody JA, de Boer IH, Bansal N, Bis JC, Irvin MR. Single-Ancestry versus Multi-Ancestry Polygenic Risk Scores for CKD in Black American Populations. J Am Soc Nephrol 2024:00001751-990000000-00377. [PMID: 39073889 DOI: 10.1681/asn.0000000000000437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 06/28/2024] [Indexed: 07/31/2024] Open
Abstract
Key Points
The predictive performance of an African ancestry–specific polygenic risk score (PRS) was comparable to a European ancestry–derived PRS for kidney traits.However, multi-ancestry PRSs outperform single-ancestry PRSs in Black American populations.Predictive accuracy of PRSs for CKD was improved with the use of race-free eGFR.
Background
CKD is a risk factor of cardiovascular disease and early death. Recently, polygenic risk scores (PRSs) have been developed to quantify risk for CKD. However, African ancestry populations are underrepresented in both CKD genetic studies and PRS development overall. Moreover, European ancestry–derived PRSs demonstrate diminished predictive performance in African ancestry populations.
Methods
This study aimed to develop a PRS for CKD in Black American populations. We obtained score weights from a meta-analysis of genome-wide association studies for eGFR in the Million Veteran Program and Reasons for Geographic and Racial Differences in Stroke Study to develop an eGFR PRS. We optimized the PRS risk model in a cohort of participants from the Hypertension Genetic Epidemiology Network. Validation was performed in subsets of Black participants of the Trans-Omics in Precision Medicine Consortium and Genetics of Hypertension Associated Treatment Study.
Results
The prevalence of CKD—defined as stage 3 or higher—was associated with the PRS as a continuous predictor (odds ratio [95% confidence interval]: 1.35 [1.08 to 1.68]) and in a threshold-dependent manner. Furthermore, including APOL1 risk status—a putative variant for CKD with higher prevalence among those of sub-Saharan African descent—improved the score's accuracy. PRS associations were robust to sensitivity analyses accounting for traditional CKD risk factors, as well as CKD classification based on prior eGFR equations. Compared with previously published PRS, the predictive performance of our PRS was comparable with a European ancestry–derived PRS for kidney traits. However, single-ancestry PRSs were less predictive than multi-ancestry–derived PRSs.
Conclusions
In this study, we developed a PRS that was significantly associated with CKD with improved predictive accuracy when including APOL1 risk status. However, PRS generated from multi-ancestry populations outperformed single-ancestry PRS in our study.
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Affiliation(s)
- Alana C Jones
- Medical Scientist Training Program, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Vinodh Srinivasasainagendra
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nicole D Armstrong
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Ninad S Chaudhary
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Nita A Limdi
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Bertha A Hidalgo
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Brittney Davis
- Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - James J Cimino
- Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University Medical Center, New York, New York
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, Colorado
| | - Ethan M Lange
- Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, Colorado
| | - Donna K Arnett
- Office of the Provost, University of South Carolina, Columbia, South Carolina
| | - Bessie A Young
- Division of Nephrology, University of Washington, Seattle, Washington
| | | | - Nora Franceschini
- Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sylvia Wassertheil-Smoller
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, New York, New York
| | - Stephen S Rich
- Department of Genome Sciences, University of Virginia, Charlottesville, Virginia
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomic and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, California
| | - Josyf C Mychaleckyj
- Department of Genome Sciences, University of Virginia, Charlottesville, Virginia
| | - Holly J Kramer
- Departments of Public Health Sciences and Medicine, Loyola University Medical Center, Taywood, Illinois
| | - Yii-Der I Chen
- Department of Pediatrics, The Institute for Translational Genomic and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbort-UCLA Medical Center, Torrance, California
| | - Bruce M Psaty
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Ian H de Boer
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Nisha Bansal
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, Washington
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, Washington
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
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3
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Lu X, Xie T, van Faassen M, Kema IP, van Beek AP, Xu X, Huo X, Wolffenbuttel BHR, van Vliet-Ostaptchouk JV, Nolte IM, Snieder H. Effects of endocrine disrupting chemicals and their interactions with genetic risk scores on cardiometabolic traits. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:169972. [PMID: 38211872 DOI: 10.1016/j.scitotenv.2024.169972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/04/2024] [Accepted: 01/04/2024] [Indexed: 01/13/2024]
Abstract
Ubiquitous non-persistent endocrine disrupting chemicals (EDCs) have inconsistent associations with cardiometabolic traits. Additionally, large-scale genome-wide association studies (GWASs) have yielded many genetic risk variants for cardiometabolic traits and diseases. This study aimed to investigate the associations between a wide range of EDC exposures (parabens, bisphenols, and phthalates) and 14 cardiometabolic traits and whether these are moderated by their respective genetic risk scores (GRSs). Data were from 1074 participants aged 18 years or older of the Lifelines Cohort Study, a large population-based biobank. GRSs for 14 cardiometabolic traits were calculated based on genome-wide significant common variants from recent GWASs. The concentrations of 15 EDCs in 24-hour urine were measured by isotope dilution liquid chromatography tandem mass spectrometry technology. The main effects of trait-specific GRSs and each of the EDC exposures and their interaction effects on the 14 cardiometabolic traits were examined in multiple linear regression. The present study confirmed significant main effects for all GRSs on their corresponding cardiometabolic trait. Regarding the main effects of EDC exposures, 26 out of 280 EDC-trait tests were significant with explained variances ranging from 0.43 % (MMP- estimated glomerular filtration rate (eGFR)) to 2.37 % (PrP-waist-hip ratio adjusted body mass index (WHRadjBMI)). We confirmed the association of MiBP and MBzP with WHRadjBMI and body mass index (BMI), and showed that parabens, bisphenol F, and many other phthalate metabolites significantly contributed to the variance of WHRadjBMI, BMI, high-density lipoprotein (HDL), eGFR, fasting glucose (FG), and diastolic blood pressure (DBP). Only one association between BMI and bisphenol F was nominally significantly moderated by the GRS explaining 0.36 % of the variance. However, it did not survive multiple testing correction. We showed that non-persistent EDC exposures exerted effects on BMI, WHRadjBMI, HDL, eGFR, FG, and DBP. However no evidence for a modulating role of GRSs was found.
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Affiliation(s)
- Xueling Lu
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands; Laboratory of Environmental Medicine and Developmental Toxicology, Shantou University Medical College, 515041, Guangdong, China
| | - Tian Xie
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Martijn van Faassen
- Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Ido P Kema
- Department of Laboratory Medicine, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - André P van Beek
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Xijin Xu
- Laboratory of Environmental Medicine and Developmental Toxicology, Shantou University Medical College, 515041, Guangdong, China
| | - Xia Huo
- Laboratory of Environmental Medicine and Developmental Toxicology, Guangdong Key Laboratory of Environmental Pollution and Health, School of Environment, Jinan University, 510632, Guangdong, China
| | - Bruce H R Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Jana V van Vliet-Ostaptchouk
- Department of Genetics, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, 9713 GZ Groningen, the Netherlands.
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Jiang X, Zai CC, Dimick MK, Kennedy JL, Young LT, Birmaher B, Goldstein BI. Psychiatric Polygenic Risk Scores Across Youth With Bipolar Disorder, Youth at High Risk for Bipolar Disorder, and Controls. J Am Acad Child Adolesc Psychiatry 2024:S0890-8567(24)00062-5. [PMID: 38340895 DOI: 10.1016/j.jaac.2023.12.009] [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] [Received: 07/05/2023] [Revised: 11/23/2023] [Accepted: 02/01/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVE There is a pronounced gap in knowledge regarding polygenic underpinnings of youth bipolar disorder (BD). This study aimed to compare polygenic risk scores (PRSs) in youth with BD, youth at high clinical and/or familial risk for BD (HR), and controls. METHOD Participants were 344 youths of European ancestry (13-20 years old), including 136 youths with BD, 121 HR youths, and 87 controls. PRSs for BD, schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were constructed using independent genome-wide summary statistics from adult cohorts. Multinomial logistic regression was used to examine the association between each PRS and diagnostic status (BD vs HR vs controls). All genetic analyses controlled for age, sex, and 2 genetic principal components. RESULTS The BD group showed significantly higher BD-PRS than the control group (odds ratio = 1.54, 95% CI = 1.13-2.10, p = .006), with the HR group numerically intermediate. BD-PRS explained 7.9% of phenotypic variance. PRSs for schizophrenia, major depressive disorder, and attention-deficit/hyperactivity disorder were not significantly different among groups. In the BD group, BD-PRS did not significantly differ in relation to BD subtype, age of onset, psychosis, or family history of BD. CONCLUSION BD-PRS derived from adult genome-wide summary statistics is elevated in youth with BD. Absence of significant between-group differences in PRSs for other psychiatric disorders supports the specificity of BD-PRS in youth. These findings add to the biological validation of BD in youth and could have implications for early identification and diagnosis. To enhance clinical utility, future genome-wide association studies that focus specifically on early-onset BD are warranted, as are studies integrating additional genetic and environmental factors. DIVERSITY & INCLUSION STATEMENT We worked to ensure sex and gender balance in the recruitment of human participants. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.
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Affiliation(s)
- Xinyue Jiang
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada
| | - Clement C Zai
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada; Tanenbaum Centre for Pharmacogenetics, Psychiatric Neurogenetics Section, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Mikaela K Dimick
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
| | - James L Kennedy
- University of Toronto, Toronto, Ontario, Canada; Tanenbaum Centre for Pharmacogenetics, Psychiatric Neurogenetics Section, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - L Trevor Young
- University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Boris Birmaher
- Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; University of Toronto, Toronto, Ontario, Canada.
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5
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Jefferis J, Hudson R, Lacaze P, Bakshi A, Hawley C, Patel C, Mallett A. Monogenic and polygenic concepts in chronic kidney disease (CKD). J Nephrol 2024; 37:7-21. [PMID: 37989975 PMCID: PMC10920206 DOI: 10.1007/s40620-023-01804-8] [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: 05/08/2023] [Accepted: 10/11/2023] [Indexed: 11/23/2023]
Abstract
Kidney function is strongly influenced by genetic factors with both monogenic and polygenic factors contributing to kidney function. Monogenic disorders with primarily autosomal dominant inheritance patterns account for 10% of adult and 50% of paediatric kidney diseases. However, kidney function is also a complex trait with polygenic architecture, where genetic factors interact with environment and lifestyle factors. Family studies suggest that kidney function has significant heritability at 35-69%, capturing complexities of the genome with shared environmental factors. Genome-wide association studies estimate the single nucleotide polymorphism-based heritability of kidney function between 7.1 and 20.3%. These heritability estimates, measuring the extent to which genetic variation contributes to CKD risk, indicate a strong genetic contribution. Polygenic Risk Scores have recently been developed for chronic kidney disease and kidney function, and validated in large populations. Polygenic Risk Scores show correlation with kidney function but lack the specificity to predict individual-level changes in kidney function. Certain kidney diseases, such as membranous nephropathy and IgA nephropathy that have significant genetic components, may benefit most from polygenic risk scores for improved risk stratification. Genetic studies of kidney function also provide a potential avenue for the development of more targeted therapies and interventions. Understanding the development and validation of genomic scores is required to guide their implementation and identify the most appropriate potential implications in clinical practice. In this review, we provide an overview of the heritability of kidney function traits in population studies, explore both monogenic and polygenic concepts in kidney disease, with a focus on recently developed polygenic risk scores in kidney function and chronic kidney disease, and review specific diseases which are most amenable to incorporation of genomic scores.
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Affiliation(s)
- Julia Jefferis
- Genetic Health Queensland, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia.
- Faculty of Medicine, University of Queensland, Brisbane, Australia.
- Kidney Health Service, Royal Brisbane and Women's Hospital, Brisbane, Australia.
| | - Rebecca Hudson
- Faculty of Medicine, University of Queensland, Brisbane, Australia
- Kidney Health Service, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Paul Lacaze
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Andrew Bakshi
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Carmel Hawley
- Department of Nephrology, Princess Alexandra Hospital, Woolloongabba, QLD, Australia
- Australasian Kidney Trials Network, The University of Queensland, Brisbane, QLD, Australia
- Translational Research Institute, Brisbane, QLD, Australia
| | - Chirag Patel
- Genetic Health Queensland, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - Andrew Mallett
- Institutional for Molecular Bioscience and Faculty of Medicine, The University of Queensland, Saint Lucia, Australia.
- Department of Renal Medicine, Townsville University Hospital, Douglas, QLD, Australia.
- College of Medicine and Dentistry, James Cook University, Douglas, QLD, Australia.
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Lee MP, Dimos SF, Raffield LM, Wang Z, Ballou AF, Downie CG, Arehart CH, Correa A, de Vries PS, Du Z, Gignoux CR, Gordon-Larsen P, Guo X, Haessler J, Howard AG, Hu Y, Kassahun H, Kent ST, Lopez JAG, Monda KL, North KE, Peters U, Preuss MH, Rich SS, Rhodes SL, Yao J, Yarosh R, Tsai MY, Rotter JI, Kooperberg CL, Loos RJF, Ballantyne C, Avery CL, Graff M. Ancestral diversity in lipoprotein(a) studies helps address evidence gaps. Open Heart 2023; 10:e002382. [PMID: 37648373 PMCID: PMC10471864 DOI: 10.1136/openhrt-2023-002382] [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: 06/16/2023] [Accepted: 08/04/2023] [Indexed: 09/01/2023] Open
Abstract
INTRODUCTION The independent and causal cardiovascular disease risk factor lipoprotein(a) (Lp(a)) is elevated in >1.5 billion individuals worldwide, but studies have prioritised European populations. METHODS Here, we examined how ancestrally diverse studies could clarify Lp(a)'s genetic architecture, inform efforts examining application of Lp(a) polygenic risk scores (PRS), enable causal inference and identify unexpected Lp(a) phenotypic effects using data from African (n=25 208), East Asian (n=2895), European (n=362 558), South Asian (n=8192) and Hispanic/Latino (n=8946) populations. RESULTS Fourteen genome-wide significant loci with numerous population specific signals of large effect were identified that enabled construction of Lp(a) PRS of moderate (R2=15% in East Asians) to high (R2=50% in Europeans) accuracy. For all populations, PRS showed promise as a 'rule out' for elevated Lp(a) because certainty of assignment to the low-risk threshold was high (88.0%-99.9%) across PRS thresholds (80th-99th percentile). Causal effects of increased Lp(a) with increased glycated haemoglobin were estimated for Europeans (p value =1.4×10-6), although inverse effects in Africans and East Asians suggested the potential for heterogeneous causal effects. Finally, Hispanic/Latinos were the only population in which known associations with coronary atherosclerosis and ischaemic heart disease were identified in external testing of Lp(a) PRS phenotypic effects. CONCLUSIONS Our results emphasise the merits of prioritising ancestral diversity when addressing Lp(a) evidence gaps.
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Affiliation(s)
- Moa P Lee
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sofia F Dimos
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Laura M Raffield
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anna F Ballou
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Carolina G Downie
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Christopher H Arehart
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Adolfo Correa
- Department of Population Health Science, The University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Paul S de Vries
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Zhaohui Du
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Christopher R Gignoux
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Xiuqing Guo
- Department of Pediatrics, UCLA Medical Center, Los Angeles, California, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Annie Green Howard
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yao Hu
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Helina Kassahun
- Global Development, Amgen Inc, Thousand Oaks, California, USA
| | - Shia T Kent
- Center for Observational Research, Amgen Inc, Thousand Oaks, California, USA
| | | | - Keri L Monda
- Center for Observational Research, Amgen Inc, Thousand Oaks, California, USA
| | - Kari E North
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stephen S Rich
- University of Virginia School of Medicine, Charlottesville, Virginia, USA
| | - Shannon L Rhodes
- Center for Observational Research, Amgen Inc, Thousand Oaks, California, USA
| | - Jie Yao
- Department of Pediatrics, UCLA Medical Center, Los Angeles, California, USA
| | - Rina Yarosh
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Michael Y Tsai
- Department of Laboratory Medicine & Pathology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jerome I Rotter
- Department of Pediatrics, UCLA Medical Center, Los Angeles, California, USA
| | - Charles L Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Kobenhavn, Denmark
| | - Christie Ballantyne
- Department of Medicine, Section of Cardiology, Baylor College of Medicine, Houston, Texas, USA
| | - Christy L Avery
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mariaelisa Graff
- Department of Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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7
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Pérez-Gimeno G, Seral-Cortes M, Sabroso-Lasa S, Esteban LM, Lurbe E, Béghin L, Gottrand F, Meirhaeghe A, Muntaner M, Kafatos A, Molnár D, Leclercq C, Widhalm K, Kersting M, Nova E, Salazar-Tortosa DF, Gonzalez-Gross M, Breidenassel C, Sinningen K, De Ruyter T, Labayen I, Rupérez AI, Bueno-Lozano G, Moreno LA. Development of a genetic risk score to predict the risk of hypertension in European adolescents from the HELENA study. Front Cardiovasc Med 2023; 10:1118919. [PMID: 37324619 PMCID: PMC10267871 DOI: 10.3389/fcvm.2023.1118919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 05/11/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction From genome wide association study (GWAS) a large number of single nucleotide polymorphisms (SNPs) have previously been associated with blood pressure (BP) levels. A combination of SNPs, forming a genetic risk score (GRS) could be considered as a useful genetic tool to identify individuals at risk of developing hypertension from early stages in life. Therefore, the aim of our study was to build a GRS being able to predict the genetic predisposition to hypertension (HTN) in European adolescents. Methods Data were extracted from the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) cross-sectional study. A total of 869 adolescents (53% female), aged 12.5-17.5, with complete genetic and BP information were included. The sample was divided into altered (≥130 mmHg for systolic and/or ≥80 mmHg for diastolic) or normal BP. Based on the literature, a total of 1.534 SNPs from 57 candidate genes related with BP were selected from the HELENA GWAS database. Results From 1,534 SNPs available, An initial screening of SNPs univariately associated with HTN (p < 0.10) was established, to finally obtain a number of 16 SNPs significantly associated with HTN (p < 0.05) in the multivariate model. The unweighted GRS (uGRS) and weighted GRS (wGRS) were estimated. To validate the GRSs, the area under the curve (AUC) was explored using ten-fold internal cross-validation for uGRS (0.802) and wGRS (0.777). Further covariates of interest were added to the analyses, obtaining a higher predictive ability (AUC values of uGRS: 0.879; wGRS: 0.881 for BMI z-score). Furthermore, the differences between AUCs obtained with and without the addition of covariates were statistically significant (p < 0.05). Conclusions Both GRSs, the uGRS and wGRS, could be useful to evaluate the predisposition to hypertension in European adolescents.
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Affiliation(s)
- Gloria Pérez-Gimeno
- Growth, Exercise, NUtrition and Development (GENUD), Research Group, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Universidad de Zaragoza, Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Miguel Seral-Cortes
- Growth, Exercise, NUtrition and Development (GENUD), Research Group, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Universidad de Zaragoza, Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Sergio Sabroso-Lasa
- Genetic and Molecular Epidemiology Group (GMEG), Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | | | - Empar Lurbe
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- INCLIVA Biomedical Research Institute, Pediatric Department, Consorcio Hospital General, University of Valencia, Valencia, Spain
| | - Laurent Béghin
- Université Lille, Inserm, CHU Lille, INFINITE—Institute for Translational Research in Inflammation, Lille, France
| | - Frederic Gottrand
- Université Lille, Inserm, CHU Lille, INFINITE—Institute for Translational Research in Inflammation, Lille, France
| | - Aline Meirhaeghe
- Risk Factors and Molecular Determinants of Aging-Related Diseases (RID-AGE), Centre Hosp. Univ Lille, Institut Pasteur de Lille, Université de Lille, Lille, France
| | - Manon Muntaner
- Risk Factors and Molecular Determinants of Aging-Related Diseases (RID-AGE), Centre Hosp. Univ Lille, Institut Pasteur de Lille, Université de Lille, Lille, France
| | - Anthony Kafatos
- Department of Social Medicine, Preventive Medicine and Nutrition Clinic, University of Crete School of Medicine, Heraklion, Greece
| | - Dénes Molnár
- Department of Pediatrics, University of Pecs, Pecs, Hungary
| | - Catherine Leclercq
- INRAN, National Research Institute for Food and Nutrition, Food and Nutrition Research Centre-Council for Agricultural Research and Economics, Rome, Italy
| | - Kurt Widhalm
- Division of Clinical Nutrition and Prevention, Department of Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Mathilde Kersting
- Departement of Nutrition—Human Nutrition, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Esther Nova
- Department of Metabolism and Nutrition, Institute of Food Science and Technology and Nutrition (ICTAN), CSIC, Madrid, Spain
| | - Diego F. Salazar-Tortosa
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, United States
- PROFITH ‘PROmoting FITness and Health Through Physical Activity’ Research Group, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
| | - Marcela Gonzalez-Gross
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- ImFine Research Group, Department of Health and Human Performance, Facultad de Ciencias de la Actividad Física y del Deporte-INEF, Universidad Politécnica de Madrid, Madrid, Spain
| | - Christina Breidenassel
- Departement of Nutrition—Human Nutrition, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
- ImFine Research Group, Department of Health and Human Performance, Facultad de Ciencias de la Actividad Física y del Deporte-INEF, Universidad Politécnica de Madrid, Madrid, Spain
| | - Kathrin Sinningen
- Research Department of Child Nutrition, University Hospital of Pediatrics and Adolescent Medicine, St. Josef-Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Thaïs De Ruyter
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Idoia Labayen
- Department of Health Sciences, Institute for Innovation & Sustainable Food Chain Development, Public University of Navarra, Pamplona, Spain
| | - Azahara I. Rupérez
- Growth, Exercise, NUtrition and Development (GENUD), Research Group, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Universidad de Zaragoza, Zaragoza, Spain
| | - Gloria Bueno-Lozano
- Growth, Exercise, NUtrition and Development (GENUD), Research Group, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Universidad de Zaragoza, Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Luis A. Moreno
- Growth, Exercise, NUtrition and Development (GENUD), Research Group, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Universidad de Zaragoza, Zaragoza, Spain
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
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Drouet DE, Liu S, Crawford DC. Assessment of multi-population polygenic risk scores for lipid traits in African Americans. PeerJ 2023; 11:e14910. [PMID: 37214096 PMCID: PMC10198155 DOI: 10.7717/peerj.14910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/25/2023] [Indexed: 05/24/2023] Open
Abstract
Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits (HDL-C, LDL-C, triglycerides, and total cholesterol) conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip (n = 3,254). Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRSLDL-Cwas nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.
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Affiliation(s)
- Domenica E. Drouet
- Department of Medicine, Case Western Reserve University, Cleveland, OH, United States of America
| | - Shiying Liu
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
| | - Dana C. Crawford
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
- Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, United States of America
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9
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Jung H, Jung HU, Baek EJ, Chung JY, Kwon SY, Kang JO, Lim JE, Oh B. Investigation of heteroscedasticity in polygenic risk scores across 15 quantitative traits. Front Genet 2023; 14:1150889. [PMID: 37229196 PMCID: PMC10203621 DOI: 10.3389/fgene.2023.1150889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/17/2023] [Indexed: 05/27/2023] Open
Abstract
The polygenic risk score (PRS) could be used to stratify individuals with high risk of diseases and predict complex trait of individual in a population. Previous studies developed a PRS-based prediction model using linear regression and evaluated the predictive performance of the model using the R 2 value. One of the key assumptions of linear regression is that the variance of the residual should be constant at each level of the predictor variables, called homoscedasticity. However, some studies show that PRS models exhibit heteroscedasticity between PRS and traits. This study analyzes whether heteroscedasticity exists in PRS models of diverse disease-related traits and, if any, it affects the accuracy of PRS-based prediction in 354,761 Europeans from the UK Biobank. We constructed PRSs for 15 quantitative traits using LDpred2 and estimated the existence of heteroscedasticity between PRSs and 15 traits using three different tests of the Breusch-Pagan (BP) test, score test, and F test. Thirteen out of fifteen traits show significant heteroscedasticity. Further replication using new PRSs from the PGS catalog and independent samples (N = 23,620) from the UK Biobank confirmed the heteroscedasticity in ten traits. As a result, ten out of fifteen quantitative traits show statistically significant heteroscedasticity between the PRS and each trait. There was a greater variance of residuals as PRS increased, and the prediction accuracy at each level of PRS tended to decrease as the variance of residuals increased. In conclusion, heteroscedasticity was frequently observed in the PRS-based prediction models of quantitative traits, and the accuracy of the predictive model may differ according to PRS values. Therefore, prediction models using the PRS should be constructed by considering heteroscedasticity.
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Affiliation(s)
- Hyein Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | | | - Ju Yeon Chung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Shin Young Kwon
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
- Mendel, Seoul, Republic of Korea
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
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Westphal DS, Hauser M, Beckmann BM, Wolf CM, Hessling G, Oberhoffer-Fritz R, Wacker-Gussmann A. Fetal Bradycardia Caused by Monogenic Disorders-A Review of the Literature. J Clin Med 2022; 11:jcm11236880. [PMID: 36498454 PMCID: PMC9741304 DOI: 10.3390/jcm11236880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/03/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
Introduction: The standard obstetric definition of fetal bradycardia is a sustained fetal heart rate < 110 bpm over at least 10 min. Fetal bradycardia can be the first and only prenatal presentation of a heart disease. We present an overview on different genetic disorders that should be taken into consideration in case of diagnosed fetal bradycardia. Methods: A literature review was conducted using a PubMed- and OMIM-based search for monogenetic disorders causing fetal bradycardia in September 2022. Results: The review on the literature identified nine monogenic diseases that could lead to fetal bradycardia. Four of these disorders can be associated with extracardiac findings. Discussion: Genetic testing should be considered in cases with fetal bradycardia, especially in cases of additional extracardiac findings. Broad sequencing techniques and improved prenatal phenotyping could help to establish a diagnosis in an increasing number of cases.
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Affiliation(s)
- Dominik S. Westphal
- Department of Internal Medicine I, Klinikum Rechts der Isar, School of Medicine and Health, Technical University Munich, 81675 Munich, Germany
- Institute of Human Genetics, Klinikum Rechts der Isar, School of Medicine and Health, Technical University Munich, 81675 Munich, Germany
- Correspondence:
| | | | - Britt-Maria Beckmann
- Institute of Legal Medicine, University Hospital Frankfurt, Goethe University, 60596 Frankfurt, Germany
| | - Cordula M. Wolf
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802 Munich, Germany
- Department of Congenital Heart Defects and Pediatric Cardiology, German Heart Center Munich, School of Medicine and Health, Technical University Munich, 80636 Munich, Germany
| | - Gabriele Hessling
- Department of Congenital Heart Defects and Pediatric Cardiology, German Heart Center Munich, School of Medicine and Health, Technical University Munich, 80636 Munich, Germany
| | - Renate Oberhoffer-Fritz
- Department of Congenital Heart Defects and Pediatric Cardiology, German Heart Center Munich, School of Medicine and Health, Technical University Munich, 80636 Munich, Germany
- Institute of Preventive Pediatrics, TUM Department of Sport and Health Sciences, Technical University Munich, 80992 Munich, Germany
| | - Annette Wacker-Gussmann
- Department of Congenital Heart Defects and Pediatric Cardiology, German Heart Center Munich, School of Medicine and Health, Technical University Munich, 80636 Munich, Germany
- Institute of Preventive Pediatrics, TUM Department of Sport and Health Sciences, Technical University Munich, 80992 Munich, Germany
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11
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Fuentes-Paez G, Escaramís G, Aguilar-Lacasaña S, Andrusaityte S, Brantsæter AL, Casas M, Charles MA, Chatzi L, Lepeule J, Grazuleviciene R, Gützkow KB, Heude B, Maitre L, Ruiz-Arenas C, Sunyer J, Urquiza J, Yang TC, Wright J, Vrijheid M, Vilor-Tejedor N, Bustamante M. Study of the Combined Effect of Maternal Tobacco Smoking and Polygenic Risk Scores on Birth Weight and Body Mass Index in Childhood. Front Genet 2022; 13:867611. [PMID: 35646076 PMCID: PMC9133473 DOI: 10.3389/fgene.2022.867611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Maternal smoking during pregnancy has adverse health effects on the offspring, including lower birth weight and increased risk for obesity. These outcomes are also influenced by common genetic polymorphisms. We aimed to investigate the combined effect of maternal smoking during pregnancy and genetic predisposition on birth weight and body mass index (BMI)-related traits in 1,086 children of the Human Early Life Exposome (HELIX) project.Methods: Maternal smoking during pregnancy was self-reported. Phenotypic traits were assessed at birth or at the age of 8 years. Ten polygenic risk scores (PRSs) per trait were calculated using the PRSice v2 program. For birth weight, we estimated two sets of PRSs based on two different base GWAS summary statistics: PRS-EGG, which includes HELIX children, and PRS-PanUK, which is completely independent. The best PRS per trait (highest R2) was selected for downstream analyses, and it was treated in continuous or categorized into three groups. Multivariate linear regression models were applied to evaluate the association of the explanatory variables with the traits of interest. The combined effect was evaluated by including an interaction term in the regression models and then running models stratified by the PRS group.Results: BMI-related traits were correlated among them but not with birth weight. A similar pattern was observed for their PRSs. On average, the PRSs explained ∼4% of the phenotypic variation, with higher PRS values related to higher trait values (p-value <5.55E-08). Sustained maternal smoking was associated with lower birth weight and higher BMI and related traits (p-value <2.99E-02). We identified a gene by environment (GxE) interaction for birth weight between sustained maternal smoking and the PRS-EGG in three groups (p-value interaction = 0.01), which was not replicated with the PRS-PanUK (p-value interaction = 0.341). Finally, we did not find any statistically significant GxE interaction for BMI-related traits (p-value interaction >0.237).Conclusion: Sustained maternal smoking and the PRSs were independently associated with birth weight and childhood BMI-related traits. There was low evidence of GxE interactions.
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Affiliation(s)
- Georgina Fuentes-Paez
- Endocrine Regulatory Genomics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Geòrgia Escaramís
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Departament de Biomedicina, Institut de Neurociències, Universitat de Barcelona (UB), Barcelona, Spain
| | - Sofía Aguilar-Lacasaña
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Sandra Andrusaityte
- Department of Environmental Sciences, Vytautas Magnus University, Kaunas, Lithuania
| | - Anne Lise Brantsæter
- Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Maribel Casas
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Marie-Aline Charles
- Université de Paris Cité, Inserm, INRAE, Centre of Research in Epidemiology and StatisticS (CRESS), Paris, France
| | - Leda Chatzi
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Johanna Lepeule
- Inserm, CNRS, Team of Environmental Epidemiology Applied to Development and Respiratory Health, Institute for Advanced Biosciences, University Grenoble Alpes, Grenoble, France
| | | | - Kristine B. Gützkow
- Climate and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Barbara Heude
- Université de Paris Cité, Inserm, INRAE, Centre of Research in Epidemiology and StatisticS (CRESS), Paris, France
| | - Léa Maitre
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Carlos Ruiz-Arenas
- Genetics Unit, Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Enfermedades Raras (CIBERER), Barcelona, Spain
| | - Jordi Sunyer
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Fundació Institut Mar D'Investigacions Mèdiques (IMIM), Barcelona, Spain
| | - Jose Urquiza
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Tiffany C. Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, United Kingdom
| | - Martine Vrijheid
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Natàlia Vilor-Tejedor
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Spain
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, Netherlands
| | - Mariona Bustamante
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Childhood and Environment, ISGlobal, Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- *Correspondence: Mariona Bustamante,
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Vimaleswaran KS. GeNuIne (gene-nutrient interactions) Collaboration: towards implementing multi-ethnic population-based nutrigenetic studies of vitamin B 12 and D deficiencies and metabolic diseases. Proc Nutr Soc 2021; 80:1-11. [PMID: 34548115 DOI: 10.1017/s0029665121002822] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Gene-nutrient interactions (GeNuIne) collaboration, a large-scale collaborative project, has been initiated to investigate the impact of gene-nutrient interactions on cardiometabolic diseases using population-based studies from ethnically diverse populations. In this project, the relationship between deficiencies of vitamins B12 and D, and metabolic diseases was explored using a nutrigenetic approach. A genetic risk score (GRS) analysis was used to examine the combined effect of several genetic variations that have been shown to be associated with metabolic diseases and vitamin B12 and D deficiencies, respectively. In Sri Lankan, Indonesian and Brazilian populations, those carrying a high B12-GRS had an increased risk of metabolic diseases under the influence of dietary protein, fibre and carbohydrate intakes, respectively; however, in Asian Indians, genetically instrumented metabolic disease risk showed a significant association with low vitamin B12 status. With regards to nutrigenetic studies on vitamin D status, although high metabolic-GRS showed an interaction with dietary carbohydrate intake on vitamin D status, the study in Indonesian women demonstrated a vitamin D GRS-carbohydrate interaction on body fat percentage. In summary, these nutrigenetic studies from multiple ethnic groups have provided evidence for the influence of the dietary factors on the relationship between vitamin B12/D deficiency and metabolic outcomes. Furthermore, these studies highlight the existence of genetic heterogeneity in gene-diet interactions across ethnically diverse populations, which further implicates the significance of personalised dietary approaches for the prevention of these micronutrient deficiencies and metabolic diseases.
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Affiliation(s)
- Karani S Vimaleswaran
- Hugh Sinclair Unit of Human Nutrition, Department of Food and Nutritional Sciences, University of Reading, Reading, UK
- The Institute for Food, Nutrition, and Health (IFNH), University of Reading, Reading, UK
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13
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Trinder M, Brunham LR. Polygenic scores for dyslipidemia: the emerging genomic model of plasma lipoprotein trait inheritance. Curr Opin Lipidol 2021; 32:103-111. [PMID: 33395106 DOI: 10.1097/mol.0000000000000737] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW Contemporary polygenic scores, which summarize the cumulative contribution of millions of common single-nucleotide variants to a phenotypic trait, can have effects comparable to monogenic mutations. This review focuses on the emerging use of 'genome-wide' polygenic scores for plasma lipoproteins to define the etiology of clinical dyslipidemia, modify the severity of monogenic disease, and inform therapeutic options. RECENT FINDINGS Polygenic scores for low-density lipoprotein cholesterol (LDL-C), triglycerides, and high-density lipoprotein cholesterol are associated with severe hypercholesterolemia, hypertriglyceridemia, or hypoalphalipoproteinemia, respectively. These polygenic scores for LDL-C or triglycerides associate with risk of incident coronary artery disease (CAD) independent of polygenic scores designed specifically for CAD and may identify individuals that benefit most from lipid-lowering medication. Additionally, the severity of hypercholesterolemia and CAD associated with familial hypercholesterolemia-a common monogenic disorder-is modified by these polygenic factors. The current focus of polygenic scores for dyslipidemia is to design predictive polygenic scores for diverse populations and determining how these polygenic scores could be implemented and standardized for use in the clinic. SUMMARY Polygenic scores have shown early promise for the management of dyslipidemias, but several challenges need to be addressed before widespread clinical implementation to ensure that potential benefits are robust and reproducible, equitable, and cost-effective.
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Affiliation(s)
- Mark Trinder
- Centre for Heart Lung Innovation, University of British Columbia
- Experimental Medicine Program, University of British Columbia
| | - Liam R Brunham
- Centre for Heart Lung Innovation, University of British Columbia
- Experimental Medicine Program, University of British Columbia
- Department of Medicine, University of British Columbia
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
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