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Li G, He Q, Sun M, Ma Z, Zhao H, Wang Y, Feng Z, Li T, Chu J, Hu W, Chen X, Han Q, Sun N, Liu X, Sun H, Shen Y. Association of healthy lifestyle factors and genetic liability with bipolar disorder: Findings from the UK Biobank. J Affect Disord 2024; 364:279-285. [PMID: 39137837 DOI: 10.1016/j.jad.2024.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 06/16/2024] [Accepted: 08/09/2024] [Indexed: 08/15/2024]
Abstract
BACKGROUND The interplay between genetic and lifestyle factors in the development of bipolar disorder (BD) remains unclear. METHODS A cohort study was carried out on 365,517 participants from the UK Biobank. Lifestyle scores, based on smoking, physical activity, diet, alcohol consumption, sedentary behavior, sleep duration, and social contact, were grouped as favorable (scores 6-7), intermediate (scores 4-5), or unfavorable (scores 0-3). The BD polygenic risk score (PRS) was also categorized into high, intermediate, and low-risk groups using PRS tertiles. Cox regression models determined hazard ratios (HRs) and 95 % confidence intervals (CIs) for BD. RESULTS During the 12.9-year follow-up, 529 individuals developed BD. Comparing those with favorable lifestyles to those with unfavorable participants, the HR of developing BD was 3.28 (95 % CI, 2.76-3.89). Similarly, individuals with a high PRS had a risk of 3.20 (95 % CI, 2.83-3.63) compared to those with a low PRS. Notably, individuals with both a high PRS and an unfavorable lifestyle had a significantly higher risk of BD (HR = 6.31, 95 % CI, 4.14-9.63) compared to those with a low PRS and a favorable lifestyle. Additionally, the interaction between PRS and lifestyle contributed an additional risk, with a relative excess risk of 1.74 (95 % CI, 0.40-3.07) and an attributable proportion due to the interaction of 0.37 (95 % CI, 0.16-0.58). CONCLUSIONS Our findings suggest that genetic liability for BD, measured as PRS, and lifestyle have an additive effect on the risk of developing BD. A favorable lifestyle was associated with a reduced risk of developing BD.
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Affiliation(s)
- Guoxian Li
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Qida He
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Mengtong Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Ze Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Hanqing Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Yu Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Zhaolong Feng
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Tongxing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Jiadong Chu
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Wei Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Xuanli Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Qiang Han
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Na Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China
| | - Xiaoqin Liu
- The National Centre for Register-based Research, Aarhus University, Denmark
| | - Hongpeng Sun
- Department of Department of Child Health, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China.
| | - Yueping Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, 199 Renai Road, Suzhou 215123, PR China.
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2
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Ao L, van Heemst D, Luo J, Teder-Laving M, Mägi R, Frikke-Schmidt R, Willems van Dijk K, Noordam R. Large-scale genome-wide interaction analyses on multiple cardiometabolic risk factors to identify age-specific genetic risk factors. GeroScience 2024:10.1007/s11357-024-01348-0. [PMID: 39322921 DOI: 10.1007/s11357-024-01348-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2024] [Accepted: 09/08/2024] [Indexed: 09/27/2024] Open
Abstract
The genetic landscape of cardiometabolic risk factors has been explored extensively. However, insight in the effects of genetic variation on these risk factors over the life course is sparse. Here, we performed genome-wide interaction studies (GWIS) on different cardiometabolic risk factors to identify age-specific genetic risks. This study included 270,276 unrelated European-ancestry participants from the UK Biobank (54.2% women, a median age of 58 [interquartile range (IQR): 50, 63] years). GWIS models with interaction terms between genetic variants and age were performed on apolipoprotein B (ApoB), low-density lipoprotein-cholesterol (LDL-C), log-transformed triglycerides (TG), body mass index (BMI) and systolic blood pressure (SBP). Replication was subsequently performed in the Copenhagen General Population Study (CGPS) and the Estonian Biobank (EstBB). Multiple lead variants were identified to have genome-wide significant interactions with age (Pinteraction < 1e - 08). In detail, rs429358 (tagging APOE4) was identified for ApoB (Pinteraction = 9.0e - 14) and TG (Pinteraction = 5.4e - 16). Three additional lead variants were identified for ApoB: rs11591147 (R46L in PCSK9, Pinteraction = 3.9e - 09), rs34601365 (near APOB, Pinteraction = 8.4e - 09) and rs17248720 (near LDLR, Pinteraction = 2.0e - 09). Effect sizes of the identified lead variants were generally closer to the null with increasing age. No variant-age interactions were identified for LDL-C, SBP and BMI. The significant interactions of rs429358 with age on ApoB and TG were replicated in both CGPS and EstBB. The majority of genetic effects on cardiometabolic risk factors remain relatively constant over age, with the noted exceptions of specific genetic effects on ApoB and TG.
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Affiliation(s)
- Linjun Ao
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands.
| | - Diana van Heemst
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden, the Netherlands
| | - Jiao Luo
- Department of Clinical Biochemistry, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Ruth Frikke-Schmidt
- Department of Clinical Biochemistry, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- The Copenhagen General Population Study, Herlev and Gentofte Hospital, Herlev, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, the Netherlands
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Raymond Noordam
- Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden, the Netherlands
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3
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Pazokitoroudi A, Liu Z, Dahl A, Zaitlen N, Rosset S, Sankararaman S. A scalable and robust variance components method reveals insights into the architecture of gene-environment interactions underlying complex traits. Am J Hum Genet 2024; 111:1462-1480. [PMID: 38866020 PMCID: PMC11267529 DOI: 10.1016/j.ajhg.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 05/15/2024] [Accepted: 05/15/2024] [Indexed: 06/14/2024] Open
Abstract
Understanding the contribution of gene-environment interactions (GxE) to complex trait variation can provide insights into disease mechanisms, explain sources of heritability, and improve genetic risk prediction. While large biobanks with genetic and deep phenotypic data hold promise for obtaining novel insights into GxE, our understanding of GxE architecture in complex traits remains limited. We introduce a method to estimate the proportion of trait variance explained by GxE (GxE heritability) and additive genetic effects (additive heritability) across the genome and within specific genomic annotations. We show that our method is accurate in simulations and computationally efficient for biobank-scale datasets. We applied our method to common array SNPs (MAF ≥1%), fifty quantitative traits, and four environmental variables (smoking, sex, age, and statin usage) in unrelated white British individuals in the UK Biobank. We found 68 trait-E pairs with significant genome-wide GxE heritability (p<0.05/200) with a ratio of GxE to additive heritability of ≈6.8% on average. Analyzing ≈8 million imputed SNPs (MAF ≥0.1%), we documented an approximate 28% increase in genome-wide GxE heritability compared to array SNPs. We partitioned GxE heritability across minor allele frequency (MAF) and local linkage disequilibrium (LD) values, revealing that, like additive allelic effects, GxE allelic effects tend to increase with decreasing MAF and LD. Analyzing GxE heritability near genes highly expressed in specific tissues, we find significant brain-specific enrichment for body mass index (BMI) and basal metabolic rate in the context of smoking and adipose-specific enrichment for waist-hip ratio (WHR) in the context of sex.
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Affiliation(s)
- Ali Pazokitoroudi
- Department of Computer Science, UCLA, Los Angeles, CA, USA; Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Zhengtong Liu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Andrew Dahl
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Noah Zaitlen
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; Department of Neurology, UCLA, Los Angeles, CA, USA
| | - Saharon Rosset
- Department of Statistics, Tel-Aviv University, Tel-Aviv, Israel
| | - Sriram Sankararaman
- Department of Computer Science, UCLA, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
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4
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Motsinger-Reif AA, Reif DM, Akhtari FS, House JS, Campbell CR, Messier KP, Fargo DC, Bowen TA, Nadadur SS, Schmitt CP, Pettibone KG, Balshaw DM, Lawler CP, Newton SA, Collman GW, Miller AK, Merrick BA, Cui Y, Anchang B, Harmon QE, McAllister KA, Woychik R. Gene-environment interactions within a precision environmental health framework. CELL GENOMICS 2024; 4:100591. [PMID: 38925123 PMCID: PMC11293590 DOI: 10.1016/j.xgen.2024.100591] [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: 11/30/2023] [Revised: 03/26/2024] [Accepted: 06/02/2024] [Indexed: 06/28/2024]
Abstract
Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.
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Affiliation(s)
- Alison A Motsinger-Reif
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA.
| | - David M Reif
- Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Farida S Akhtari
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - John S House
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - C Ryan Campbell
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kyle P Messier
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA; Predictive Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David C Fargo
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Tiffany A Bowen
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Srikanth S Nadadur
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Charles P Schmitt
- Office of the Scientific Director, Office of Data Science, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kristianna G Pettibone
- Program Analysis Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - David M Balshaw
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Cindy P Lawler
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Shelia A Newton
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Gwen W Collman
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA; Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Aubrey K Miller
- Office of Scientific Coordination, Planning and Evaluation, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - B Alex Merrick
- Mechanistic Toxicology Branch, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Yuxia Cui
- Exposure, Response, and Technology Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Benedict Anchang
- Biostatistics and Computational Biology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Quaker E Harmon
- Epidemiology Branch, Division of Intramural Research, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Kimberly A McAllister
- Genes, Environment, and Health Branch, Division of Extramural Research and Training, National Institute of Environmental Health Sciences, Durham, NC, USA
| | - Rick Woychik
- Office of the Director, National Institute of Environmental Health Sciences, Durham, NC, USA
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5
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Francis M, Westerman KE, Manning AK, Ye K. Gene-vegetarianism interactions in calcium, estimated glomerular filtration rate, and testosterone identified in genome-wide analysis across 30 biomarkers. PLoS Genet 2024; 20:e1011288. [PMID: 38990837 PMCID: PMC11239071 DOI: 10.1371/journal.pgen.1011288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 05/03/2024] [Indexed: 07/13/2024] Open
Abstract
We examined the associations of vegetarianism with metabolic biomarkers using traditional and genetic epidemiology. First, we addressed inconsistencies in self-reported vegetarianism among UK Biobank participants by utilizing data from two dietary surveys to find a cohort of strict European vegetarians (N = 2,312). Vegetarians were matched 1:4 with nonvegetarians for non-genetic association analyses, revealing significant effects of vegetarianism in 15 of 30 biomarkers. Cholesterol measures plus vitamin D were significantly lower in vegetarians, while triglycerides were higher. A genome-wide association study revealed no genome-wide significant (GWS; 5×10-8) associations with vegetarian behavior. We performed genome-wide gene-vegetarianism interaction analyses for the biomarkers, and detected a GWS interaction impacting calcium at rs72952628 (P = 4.47×10-8). rs72952628 is in MMAA, a B12 metabolic pathway gene; B12 has major deficiency potential in vegetarians. Gene-based interaction tests revealed two significant genes, RNF168 in testosterone (P = 1.45×10-6) and DOCK4 in estimated glomerular filtration rate (eGFR) (P = 6.76×10-7), which have previously been associated with testicular and renal traits, respectively. These nutrigenetic findings indicate genotype can modify the associations between vegetarianism and health outcomes.
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Affiliation(s)
- Michael Francis
- Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
| | - Kenneth E. Westerman
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, Massachusetts, United States of America
| | - Alisa K. Manning
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, United States of America
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, Massachusetts, United States of America
| | - Kaixiong Ye
- Institute of Bioinformatics, University of Georgia, Athens, Georgia, United States of America
- Department of Genetics, University of Georgia, Athens, Georgia, United States of America
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6
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Tiezzi F, Goda K, Morgante F. Using lifestyle information in polygenic modeling of blood pressure traits: a simple method to reduce bias. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.05.597631. [PMID: 38895222 PMCID: PMC11185601 DOI: 10.1101/2024.06.05.597631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Complex traits are determined by the effects of multiple genetic variants, multiple environmental factors, and potentially their interaction. Predicting complex trait phenotypes from genotypes is a fundamental task in quantitative genetics that was pioneered in agricultural breeding for selection purposes. However, it has recently become important in human genetics. While prediction accuracy for some human complex traits is appreciable, this remains low for most traits. A promising way to improve prediction accuracy is by including not only genetic information but also environmental information in prediction models. However, environmental factors can, in turn, be genetically determined. This phenomenon gives rise to a correlation between the genetic and environmental components of the phenotype, which violates the assumption of independence between the genetic and environmental components of most statistical methods for polygenic modeling. In this work, we investigated the impact of including 27 lifestyle variables as well as genotype information (and their interaction) for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals in UK Biobank. The 27 lifestyle variables were included as either raw variables or adjusted by genetic and other non-genetic factors. The results show that including both lifestyle and genetic data improved prediction accuracy compared to using either piece of information alone. Both prediction accuracy and bias can improve substantially for some traits when the models account for the lifestyle variables after their proper adjustment. Our work confirms the utility of including environmental information in polygenic models of complex traits and highlights the importance of proper handling of the environmental variables.
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Affiliation(s)
- Francesco Tiezzi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
| | - Khushi Goda
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
| | - Fabio Morgante
- Center for Human Genetics, Clemson University, Greenwood, SC, USA
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA
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7
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Boye C, Nirmalan S, Ranjbaran A, Luca F. Genotype × environment interactions in gene regulation and complex traits. Nat Genet 2024; 56:1057-1068. [PMID: 38858456 DOI: 10.1038/s41588-024-01776-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 04/25/2024] [Indexed: 06/12/2024]
Abstract
Genotype × environment interactions (GxE) have long been recognized as a key mechanism underlying human phenotypic variation. Technological developments over the past 15 years have dramatically expanded our appreciation of the role of GxE in both gene regulation and complex traits. The richness and complexity of these datasets also required parallel efforts to develop robust and sensitive statistical and computational approaches. Although our understanding of the genetic architecture of molecular and complex traits has been maturing, a large proportion of complex trait heritability remains unexplained. Furthermore, there are increasing efforts to characterize the effect of environmental exposure on human health. We therefore review GxE in human gene regulation and complex traits, advocating for a comprehensive approach that jointly considers genetic and environmental factors in human health and disease.
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Affiliation(s)
- Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Shreya Nirmalan
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Ali Ranjbaran
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, MI, US.
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI, US.
- Department of Biology, University of Rome "Tor Vergata", Rome, Italy.
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8
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Herrera-Luis E, Benke K, Volk H, Ladd-Acosta C, Wojcik GL. Gene-environment interactions in human health. Nat Rev Genet 2024:10.1038/s41576-024-00731-z. [PMID: 38806721 DOI: 10.1038/s41576-024-00731-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/03/2024] [Indexed: 05/30/2024]
Abstract
Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.
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Affiliation(s)
- Esther Herrera-Luis
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kelly Benke
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Heather Volk
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Christine Ladd-Acosta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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9
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Zhu X, Yang Y, Lorincz-Comi N, Li G, Bentley AR, de Vries PS, Brown M, Morrison AC, Rotimi CN, Gauderman WJ, Rao DC, Aschard H. An approach to identify gene-environment interactions and reveal new biological insight in complex traits. Nat Commun 2024; 15:3385. [PMID: 38649715 PMCID: PMC11035594 DOI: 10.1038/s41467-024-47806-3] [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: 09/22/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Gen Li
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Michael Brown
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Dabeeru C Rao
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015, Paris, France
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
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10
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Zhou H, McPeek MS. Overcoming the "feast or famine" effect: improved interaction testing in genome-wide association studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580168. [PMID: 38405994 PMCID: PMC10888770 DOI: 10.1101/2024.02.13.580168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
In genetic association analysis of complex traits, detection of interaction (either GxG or GxE) can help to elucidate the genetic architecture and biological mechanisms underlying the trait. Detection of interaction in a genome-wide association study (GWAS) can be methodologically challenging for various reasons, including a high burden of multiple comparisons when testing for epistasis between all possible pairs of a set of genomewide variants, as well as heteroscedasticity effects occurring in the presence of GxG or GxE interaction. In this paper, we address the problem of an even more striking phenomenon that we call the "feast or famine" effect that occurs when testing interaction in a genomewide context. As we verify, even in a simplified setting in which there is no interaction at all (and so no heteroscedasticity), in a GWAS to detect GxG or GxE interaction with a fixed genetic variant or environmental factor, the distribution of the genome-wide p-values under the null hypothesis is not the i.i.d. uniform one that is commonly assumed. Using standard methods, even if all SNPs are independent, some GWASs will have systematically underinflated p-values ("feast"), and others will have systematically overinflated p-values ("famine"), which can lead to false detection of interaction, reduced power, inconsistent results across studies, and failure to replicate true signal. This startling phenomenon is specific to detection of interaction in a GWAS, and it may partly explain why such detection has so far proved challenging and difficult to replicate. We show theoretically that the key cause of this phenomenon is which variables are conditioned on in the analysis, and this suggests an approach to correct the problem by changing the way the conditioning is done. Using this insight, we have developed the TINGA method to adjust the interaction test statistics to make their p-values closer to uniform under the null hypothesis. In simulations we show that TINGA both controls type 1 error and improves power. TINGA allows for covariates and population structure through use of a linear mixed model and accounts for heteroscedasticity. We apply TINGA to detection of epistasis in a study of flowering time in Arabidopsis thaliana.
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Affiliation(s)
- Huanlin Zhou
- Department of Statistics, The University of Chicago, Chicago, Illinois, U.S.A
| | - Mary Sara McPeek
- Department of Statistics, The University of Chicago, Chicago, Illinois, U.S.A
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, U.S.A
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11
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Robinson GE, Bliss R, Hudson ME. The genomic case against genetic determinism. PLoS Biol 2024; 22:e3002510. [PMID: 38412239 PMCID: PMC10898719 DOI: 10.1371/journal.pbio.3002510] [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: 02/29/2024] Open
Abstract
Animal studies reveal that the molecular wiring of the brain can be altered by heredity, the environment, and their interaction. A deeper molecular understanding of these interactions could be a potent antidote to societal concerns of genetic determinism for human behavior, but this requires a paradigm that extends beyond traditional genome-wide association study (GWAS).
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Affiliation(s)
- Gene E. Robinson
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana Champaign, Urbana, Illinois, United States of America
- Department of Entomology, University of Illinois Urbana Champaign, Urbana, Illinois, United States of America
- Neuroscience Program, University of Illinois Urbana Champaign, Urbana, Illinois, United States of America
| | - Rina Bliss
- Department of Sociology, Rutgers University, New Brunswick, New Jersey, United States of America
| | - Matthew E. Hudson
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana Champaign, Urbana, Illinois, United States of America
- Department of Crop Sciences, University of Illinois Urbana Champaign, Urbana, Illinois, United States of America
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12
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George SHL, Medina-Rivera A, Idaghdour Y, Lappalainen T, Gallego Romero I. Increasing diversity of functional genetics studies to advance biological discovery and human health. Am J Hum Genet 2023; 110:1996-2002. [PMID: 37995684 PMCID: PMC10716434 DOI: 10.1016/j.ajhg.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 10/25/2023] [Accepted: 10/25/2023] [Indexed: 11/25/2023] Open
Abstract
In this perspective we discuss the current lack of genetic and environmental diversity in functional genomics datasets. There is a well-described Eurocentric bias in genetic and functional genomic research that has a clear impact on the benefit this research can bring to underrepresented populations. Current research focused on genetic variant-to-function experiments aims to identify molecular QTLs, but the lack of data from genetically diverse individuals has limited analyses to mostly populations of European ancestry. Although some efforts have been established to increase diversity in functional genomic studies, much remains to be done to consistently generate data for underrepresented populations from now on. We discuss the major barriers for this continuity and suggest actionable insights, aiming to empower research and researchers from underserved populations.
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Affiliation(s)
- Sophia H L George
- Department of Obstetrics, Gynecology and Reproductive Sciences, Miller School of Medicine, University of Miami, Miami, FL, USA; Sylvester Comprehensive Cancer Center, Miami, FL, USA.
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación Sobre El Genoma Humano, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Youssef Idaghdour
- Program in Biology, Division of Science and Mathematics, New York University Abu Dhabi, Abu Dhabi, UAE; Public Health Research Center, New York University Abu Dhabi, Abu Dhabi, UAE; Center for Genomics and Systems Biology, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Tuuli Lappalainen
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden; New York Genome Center, New York, NY, USA.
| | - Irene Gallego Romero
- Melbourne Integrative Genomics and School of BioSciences, University of Melbourne, Parkville, VIC, Australia; Center for Genomics, Evolution and Medicine, Institute of Genomics, University of Tartu, Tartu, Estonia
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13
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Hasbani NR, Westerman KE, Kwak SH, Chen H, Li X, Di Corpo D, Wessel J, Bis JC, Sarnowski C, Wu P, Bielak LF, Guo X, Heard-Costa N, Kinney GL, Mahaney MC, Montasser ME, Palmer ND, Raffield LM, Terry JG, Yanek LR, Bon J, Bowden DW, Brody JA, Duggirala R, Jacobs DR, Kalyani RR, Lange LA, Mitchell BD, Smith JA, Taylor KD, Carson AP, Curran JE, Fornage M, Freedman BI, Gabriel S, Gibbs RA, Gupta N, Kardia SLR, Kral BG, Momin Z, Newman AB, Post WS, Viaud-Martinez KA, Young KA, Becker LC, Bertoni AG, Blangero J, Carr JJ, Pratte K, Psaty BM, Rich SS, Wu JC, Malhotra R, Peyser PA, Morrison AC, Vasan RS, Lin X, Rotter JI, Meigs JB, Manning AK, de Vries PS. Type 2 Diabetes Modifies the Association of CAD Genomic Risk Variants With Subclinical Atherosclerosis. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e004176. [PMID: 38014529 PMCID: PMC10843644 DOI: 10.1161/circgen.123.004176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 09/29/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Individuals with type 2 diabetes (T2D) have an increased risk of coronary artery disease (CAD), but questions remain about the underlying pathology. Identifying which CAD loci are modified by T2D in the development of subclinical atherosclerosis (coronary artery calcification [CAC], carotid intima-media thickness, or carotid plaque) may improve our understanding of the mechanisms leading to the increased CAD in T2D. METHODS We compared the common and rare variant associations of known CAD loci from the literature on CAC, carotid intima-media thickness, and carotid plaque in up to 29 670 participants, including up to 24 157 normoglycemic controls and 5513 T2D cases leveraging whole-genome sequencing data from the Trans-Omics for Precision Medicine program. We included first-order T2D interaction terms in each model to determine whether CAD loci were modified by T2D. The genetic main and interaction effects were assessed using a joint test to determine whether a CAD variant, or gene-based rare variant set, was associated with the respective subclinical atherosclerosis measures and then further determined whether these loci had a significant interaction test. RESULTS Using a Bonferroni-corrected significance threshold of P<1.6×10-4, we identified 3 genes (ATP1B1, ARVCF, and LIPG) associated with CAC and 2 genes (ABCG8 and EIF2B2) associated with carotid intima-media thickness and carotid plaque, respectively, through gene-based rare variant set analysis. Both ATP1B1 and ARVCF also had significantly different associations for CAC in T2D cases versus controls. No significant interaction tests were identified through the candidate single-variant analysis. CONCLUSIONS These results highlight T2D as an important modifier of rare variant associations in CAD loci with CAC.
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Affiliation(s)
- Natalie R Hasbani
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
| | - Kenneth E Westerman
- Department of Medicine, Clinical and Translation Epidemiology Unit (K.E.W., A.K.M.), Massachusetts General Hospital, Boston
- Programs in Metabolism and Medical and Population Genetics (K.E.W., J.B.M., A.K.M.), Broad Institute, Cambridge
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, South Korea (S.H.K.)
| | - Han Chen
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
- School of Biomedical Informatics, Center for Precision Health (H.C.), The University of Texas Health Science Center at Houston
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health (X. Li, X. Lin), Boston University School of Public Health, MA
| | - Daniel Di Corpo
- Department of Biostatistics (D.D., P.W.), Boston University School of Public Health, MA
| | - Jennifer Wessel
- Department of Epidemiology, Fairbanks School of Public Health, Indianapolis, IN (J.W.)
| | - Joshua C Bis
- Department of Medicine, Cardiovascular Health Research Unit (J.C.B., J.A.B., B.M.P.), University of Washington, Seattle
| | - Chloè Sarnowski
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
| | - Peitao Wu
- Department of Biostatistics (D.D., P.W.), Boston University School of Public Health, MA
| | - Lawrence F Bielak
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-University of California Los Angeles Medical Center, Torrance (X.G., K.D.T.)
| | | | - Gregory L Kinney
- Department of Epidemiology, University of Colorado School of Public Health, Aurora (G.L.K., K.A.Y.)
| | - Michael C Mahaney
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville (M.C.M., J.E.C., J. Blangero)
| | - May E Montasser
- Department of Medicine, Division of Endocrinology Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore (M.E.M., B.D.M.)
| | - Nicholette D Palmer
- Department of Biochemistry (N.D.P., D.W.B.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill (L.M.R.)
| | - James G Terry
- Department of Radiology, Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University Medical Center, Nashville, TN (J.G.T., J.J.C.)
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Jessica Bon
- Department of Medicine, Division of Pulmonary Allergy and Critical Care Medicine, University of Pittsburgh Medical Center, PA (J. Bon)
| | - Donald W Bowden
- Department of Biochemistry (N.D.P., D.W.B.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Jennifer A Brody
- Department of Medicine, Cardiovascular Health Research Unit (J.C.B., J.A.B., B.M.P.), University of Washington, Seattle
| | - Ravindranath Duggirala
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, McAllen (R.D.)
| | | | - Rita R Kalyani
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado, Aurora (L.A.L.)
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore (M.E.M., B.D.M.)
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, MD (B.D.M.)
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (L.F.B., J.A.S., S.L.R.K., P.A.P.)
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor (J.A.S.)
| | - Kent D Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-University of California Los Angeles Medical Center, Torrance (X.G., K.D.T.)
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson (A.P.C.)
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville (M.C.M., J.E.C., J. Blangero)
| | - Myriam Fornage
- Institute of Molecular Medicine (M.F.), The University of Texas Health Science Center at Houston
| | - Barry I Freedman
- Department of Internal Medicine, Section on Nephrology (B.I.F.), Wake Forest School of Medicine, Winston-Salem, NC
| | - Stacey Gabriel
- Genomics Platform (S.G., N.G.), Broad Institute, Cambridge
| | - Richard A Gibbs
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX (R.A.G., Z.M.)
| | - Namrata Gupta
- Genomics Platform (S.G., N.G.), Broad Institute, Cambridge
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (L.F.B., J.A.S., S.L.R.K., P.A.P.)
| | - Brian G Kral
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Zeineen Momin
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX (R.A.G., Z.M.)
| | - Anne B Newman
- Department of Epidemiology, University of Pittsburgh School of Public Health, PA (A.B.N.)
| | - Wendy S Post
- Division of Cardiology, Johns Hopkins Medicine, Baltimore, MD (W.S.P.)
| | | | - Kendra A Young
- Department of Epidemiology, University of Colorado School of Public Health, Aurora (G.L.K., K.A.Y.)
| | - Lewis C Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (L.R.Y., R.R.K., B.G.K., L.C.B.)
| | - Alain G Bertoni
- Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC (A.G.B.)
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville (M.C.M., J.E.C., J. Blangero)
| | - John J Carr
- Department of Radiology, Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University Medical Center, Nashville, TN (J.G.T., J.J.C.)
| | - Katherine Pratte
- Department of Biostatistics, National Jewish Health, Denver, CO (K.P.)
| | - Bruce M Psaty
- Department of Medicine, Cardiovascular Health Research Unit (J.C.B., J.A.B., B.M.P.), University of Washington, Seattle
- Department of Epidemiology (B.M.P.), University of Washington, Seattle
- Department of Health Systems and Population Health (B.M.P.), University of Washington, Seattle
| | | | - Joseph C Wu
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville (J.C.W.)
- Department of Medicine, Division of Cardiovascular Medicine, Stanford Cardiovascular Institute, Stanford University School of Medicine (J.C.W.), Stanford University, CA
| | - Rajeev Malhotra
- Division of Cardiology (R.M.), Massachusetts General Hospital, Boston
- Department of Radiology Molecular Imaging Program at Stanford (R.M.), Stanford University, CA
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor (L.F.B., J.A.S., S.L.R.K., P.A.P.)
| | - Alanna C Morrison
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
| | - Ramachandran S Vasan
- Framingham Heart Study, MA (N.H.-C., R.S.V.)
- Department of Quantitative and Qualitative Health Sciences, University of Texas Health San Antonio School of Public Health (R.S.V.)
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health (X. Li, X. Lin), Boston University School of Public Health, MA
| | | | - James B Meigs
- Division of General Internal Medicine (J.B.M.), Massachusetts General Hospital, Boston
- Programs in Metabolism and Medical and Population Genetics (K.E.W., J.B.M., A.K.M.), Broad Institute, Cambridge
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Alisa K Manning
- Department of Medicine, Clinical and Translation Epidemiology Unit (K.E.W., A.K.M.), Massachusetts General Hospital, Boston
- Programs in Metabolism and Medical and Population Genetics (K.E.W., J.B.M., A.K.M.), Broad Institute, Cambridge
- Department of Medicine, Harvard Medical School, Boston, MA (K.E.W., J.B.M., A.K.M.)
| | - Paul S de Vries
- Department of Epidemiology Human Genetics and Environmental Sciences, Human Genetics Center, The University of Texas Health Science Center at Houston School of Public Health (N.R.H., H.C., C.S., A.C.M., P.S.d.V.)
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14
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Pham DT, Westerman KE, Pan C, Chen L, Srinivasan S, Isganaitis E, Vajravelu ME, Bacha F, Chernausek S, Gubitosi-Klug R, Divers J, Pihoker C, Marcovina SM, Manning AK, Chen H. Re-analysis and meta-analysis of summary statistics from gene-environment interaction studies. Bioinformatics 2023; 39:btad730. [PMID: 38039147 PMCID: PMC10724851 DOI: 10.1093/bioinformatics/btad730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/26/2023] [Accepted: 11/30/2023] [Indexed: 12/03/2023] Open
Abstract
MOTIVATION statistics from genome-wide association studies enable many valuable downstream analyses that are more efficient than individual-level data analysis while also reducing privacy concerns. As growing sample sizes enable better-powered analysis of gene-environment interactions, there is a need for gene-environment interaction-specific methods that manipulate and use summary statistics. RESULTS We introduce two tools to facilitate such analysis, with a focus on statistical models containing multiple gene-exposure and/or gene-covariate interaction terms. REGEM (RE-analysis of GEM summary statistics) uses summary statistics from a single, multi-exposure genome-wide interaction study to derive analogous sets of summary statistics with arbitrary sets of exposures and interaction covariate adjustments. METAGEM (META-analysis of GEM summary statistics) extends current fixed-effects meta-analysis models to incorporate multiple exposures from multiple studies. We demonstrate the value and efficiency of these tools by exploring alternative methods of accounting for ancestry-related population stratification in genome-wide interaction study in the UK Biobank as well as by conducting a multi-exposure genome-wide interaction study meta-analysis in cohorts from the diabetes-focused ProDiGY consortium. These programs help to maximize the value of summary statistics from diverse and complex gene-environment interaction studies. AVAILABILITY AND IMPLEMENTATION REGEM and METAGEM are open-source projects freely available at https://github.com/large-scale-gxe-methods/REGEM and https://github.com/large-scale-gxe-methods/METAGEM.
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Affiliation(s)
- Duy T Pham
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Kenneth E Westerman
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, United States
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
| | - Cong Pan
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
| | - Ling Chen
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, United States
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
| | - Shylaja Srinivasan
- Department of Pediatrics, University of California, San Francisco, CA 94158, United States
| | - Elvira Isganaitis
- Research Division, Joslin Diabetes Center, Boston, MA 02115, United States
| | - Mary Ellen Vajravelu
- Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA 15224, United States
| | - Fida Bacha
- Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, United States
| | - Steve Chernausek
- Department of Pediatrics, The University of Oklahoma College of Medicine, Oklahoma City, OK 73117, United States
| | - Rose Gubitosi-Klug
- Department of Pediatrics, Case Western Reserve University, Cleveland, OH 44106, United States
| | - Jasmin Divers
- Department of Foundations of Medicine, New York University, New York, NY 10016, United States
| | - Catherine Pihoker
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA 98105, United States
| | - Santica M Marcovina
- Northwest Lipid Metabolism and Diabetes Research Laboratories, Department of Medicine, University of Washington, Seattle, WA 98105, United States
| | - Alisa K Manning
- Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA 02114, United States
- Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 02142, United States
- Department of Medicine, Harvard Medical School, Boston, MA 02115, United States
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States
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15
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Zhu X, Yang Y, Lorincz-Comi N, Li G, Bentley A, de Vries PS, Brown M, Morrison AC, Rotimi C, James Gauderman W, Rao DC, Aschard H. A new Approach to Identify Gene-Environment Interactions and Reveal New Biological Insight in Complex traits. RESEARCH SQUARE 2023:rs.3.rs-3338723. [PMID: 37886448 PMCID: PMC10602131 DOI: 10.21203/rs.3.rs-3338723/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the CHARGE Gene-Lifestyle Interactions Working Group has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identified and confirmed 5 loci (6 independent signals) interacting with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrated that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated contribution ranges from 1.76% to 14.05% of SNP heritability of serum lipids in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Gen Li
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA
| | - Amy Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Michael Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - W. James Gauderman
- Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - DC Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015 Paris, France
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16
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McNeill A. 2022: the year that was in the European Journal of Human Genetics. Eur J Hum Genet 2023; 31:131-133. [PMID: 36750730 PMCID: PMC9905485 DOI: 10.1038/s41431-023-01283-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Affiliation(s)
- Alisdair McNeill
- Department of Neuroscience, The University of Sheffield, Sheffield, UK.
- Sheffield Clinical Genetics Department, Sheffield Children's Hospital NHS Foundation Trust, Sheffield, UK.
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17
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Elgart M, Goodman MO, Isasi C, Chen H, Morrison AC, de Vries PS, Xu H, Manichaikul AW, Guo X, Franceschini N, Psaty BM, Rich SS, Rotter JI, Lloyd-Jones DM, Fornage M, Correa A, Heard-Costa NL, Vasan RS, Hernandez R, Kaplan RC, Redline S, Sofer T. Correlations between complex human phenotypes vary by genetic background, gender, and environment. Cell Rep Med 2022; 3:100844. [PMID: 36513073 PMCID: PMC9797952 DOI: 10.1016/j.xcrm.2022.100844] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 07/11/2022] [Accepted: 11/09/2022] [Indexed: 12/15/2022]
Abstract
We develop a closed-form Haseman-Elston estimator for genetic and environmental correlation coefficients between complex phenotypes, which we term HEc, that is as precise as GCTA yet ∼20× faster. We estimate genetic and environmental correlations between over 7,000 phenotype pairs in subgroups from the Trans-Omics in Precision Medicine (TOPMed) program. We demonstrate substantial differences in both heritabilities and genetic correlations for multiple phenotypes and phenotype pairs between individuals of self-reported Black, Hispanic/Latino, and White backgrounds. We similarly observe differences in many of the genetic and environmental correlations between genders. To estimate the contribution of genetics to the observed phenotypic correlation, we introduce "fractional genetic correlation" as the fraction of phenotypic correlation explained by genetics. Finally, we quantify the enrichment of correlations between phenotypic domains, each of which is comprised of multiple phenotypes. Altogether, we demonstrate that the observed correlations between complex human phenotypes depend on the genetic background of the individuals, their gender, and their environment.
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Affiliation(s)
- Michael Elgart
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Matthew O Goodman
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Carmen Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA; Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Huichun Xu
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA; Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Adolfo Correa
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Nancy L Heard-Costa
- Boston University and National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Ramachandran S Vasan
- Boston University and National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA; Preventive Medicine & Epidemiology, and Cardiovascular Medicine, Medicine, Boston University School of Medicine, and Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Ryan Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA; Fred Hutchinson Cancer Research Center, Division of Public Health Sciences, Seattle, WA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Majarian TD, Bentley AR, Laville V, Brown MR, Chasman DI, de Vries PS, Feitosa MF, Franceschini N, Gauderman WJ, Marchek C, Levy D, Morrison AC, Province M, Rao DC, Schwander K, Sung YJ, Rotimi CN, Aschard H, Gu CC, Manning AK. Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption. Front Genet 2022; 13:954713. [PMID: 36544485 PMCID: PMC9760722 DOI: 10.3389/fgene.2022.954713] [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: 05/27/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium's Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and 'omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (ALDH2) was associated with higher systolic blood pressure, and a larger effect was observed in smokers compared to non-smokers. In gene expression studies, this allele is associated with decreased expression of ALDH2, which is part of a major oxidative pathway. Other results show increased expression of ALDH2 among smokers. Oxidative stress is known to contribute to worsening blood pressure. Together these data support the hypothesis that rs6490056 reduces expression of ALDH2, which raises oxidative stress, leading to an increase in blood pressure, with a stronger effect among smokers, in whom the burden of oxidative stress is greater. Other genes for which the aggregation of data types suggest a potential mechanism include: GCNT4×current smoking (HDL), PTPRZ1×ever-smoking (HDL), SYN2×current smoking (pulse pressure), and TMEM116×ever-smoking (mean arterial pressure). This work demonstrates the utility of careful curation of summary-level data from a variety of sources to prioritize gene-lifestyle interaction loci for follow-up analyses.
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Affiliation(s)
- Timothy D. Majarian
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Amy R. Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, US National Institutes of Health, Bethesda, MD, United States
| | - Vincent Laville
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France
| | - Michael R. Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Daniel I. Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Mary F. Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - W. James Gauderman
- Biostatistics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, United States
| | - Casey Marchek
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, United States
| | - Daniel Levy
- The Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MA, United States
| | - Alanna C. Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Michael Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
| | - Dabeeru C. Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Karen Schwander
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, MO, United States
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, US National Institutes of Health, Bethesda, MD, United States
| | - Hugues Aschard
- Department of Computational Biology, Institut Pasteur, Université Paris Cité, Paris, France
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - C. Charles Gu
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, United States
| | - Alisa K. Manning
- Program in Metabolism, Broad Institute of MIT and Harvard, Cambridge, MA, United States
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, United States
- Department of Medicine and Harvard Medical School, Boston, MA, United States
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19
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McNeill A. What's new in genetics in June 2022? Eur J Hum Genet 2022; 30:633-634. [PMID: 35676534 DOI: 10.1038/s41431-022-01122-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- Alisdair McNeill
- Department of Neuroscience, The University of Sheffield, Sheffield, UK. .,Sheffield Clinical Genetics Department, Sheffield Children's Hospital NHS Foundation Trust, Sheffield, UK.
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