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Annevelink CE, Westra J, Sala-Vila A, Harris WS, Tintle NL, Shearer GC. A Genome-Wide Interaction Study of Erythrocyte ω-3 Polyunsaturated Fatty Acid Species and Memory in the Framingham Heart Study Offspring Cohort. J Nutr 2024; 154:1640-1651. [PMID: 38141771 DOI: 10.1016/j.tjnut.2023.12.035] [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: 05/23/2023] [Revised: 12/14/2023] [Accepted: 12/19/2023] [Indexed: 12/25/2023] Open
Abstract
BACKGROUND Cognitive decline, and more specifically Alzheimer's disease, continues to increase in prevalence globally, with few, if any, adequate preventative approaches. Several tests of cognition are utilized in the diagnosis of cognitive decline that assess executive function, short- and long-term memory, cognitive flexibility, and speech and motor control. Recent studies have separately investigated the genetic component of both cognitive health, using these measures, and circulating fatty acids. OBJECTIVES We aimed to examine the potential moderating effect of main species of ω-3 polyunsaturated fatty acids (PUFAs) on an individual's genetically conferred risk of cognitive decline. METHODS The Offspring cohort from the Framingham Heart Study was cross-sectionally analyzed in this genome-wide interaction study (GWIS). Our sample included all individuals with red blood cell ω-3 PUFA, genetic, cognitive testing (via Trail Making Tests [TMTs]), and covariate data (N = 1620). We used linear mixed effects models to predict each of the 3 cognitive measures (TMT A, TMT B, and TMT D) by each ω-3 PUFA, single nucleotide polymorphism (SNP) (0, 1, or 2 minor alleles), ω-3 PUFA by SNP interaction term, and adjusting for sex, age, education, APOE ε4 genotype status, and kinship (relatedness). RESULTS Our analysis identified 31 unique SNPs from 24 genes reaching an exploratory significance threshold of 1×10-5. Fourteen of the 24 genes have been previously associated with the brain/cognition, and 5 genes have been previously associated with circulating lipids. Importantly, 8 of the genes we identified, DAB1, SORCS2, SERINC5, OSBPL3, CPA6, DLG2, MUC19, and RGMA, have been associated with both cognition and circulating lipids. We identified 22 unique SNPs for which individuals with the minor alleles benefit substantially from increased ω-3 fatty acid concentrations and 9 unique SNPs for which the common homozygote benefits. CONCLUSIONS In this GWIS of ω-3 PUFA species on cognitive outcomes, we identified 8 unique genes with plausible biology suggesting individuals with specific polymorphisms may have greater potential to benefit from increased ω-3 PUFA intake. Additional replication in prospective settings with more diverse samples is needed.
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Affiliation(s)
- Carmen E Annevelink
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States
| | - Jason Westra
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States
| | - Aleix Sala-Vila
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States; Cardiovascular Risk and Nutrition, Hospital del Mar Research Institute, Barcelona, Spain; Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - William S Harris
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States; Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, United States
| | - Nathan L Tintle
- Fatty Acid Research Institute (FARI), Sioux Falls, SD, United States; Department of Population Health Nursing Science, College of Nursing, University of Illinois Chicago, Chicago, IL, United States
| | - Gregory C Shearer
- Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States.
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2
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Laguzzi F, Åkesson A, Marklund M, Qian F, Gigante B, Bartz TM, Bassett JK, Birukov A, Campos H, Hirakawa Y, Imamura F, Jäger S, Lankinen M, Murphy RA, Senn M, Tanaka T, Tintle N, Virtanen JK, Yamagishi K, Allison M, Brouwer IA, De Faire U, Eiriksdottir G, Ferrucci L, Forouhi NG, Geleijnse JM, Hodge AM, Kimura H, Laakso M, Risérus U, van Westing AC, Bandinelli S, Baylin A, Giles GG, Gudnason V, Iso H, Lemaitre RN, Ninomiya T, Post WS, Psaty BM, Salonen JT, Schulze MB, Tsai MY, Uusitupa M, Wareham NJ, Oh SW, Wood AC, Harris WS, Siscovick D, Mozaffarian D, Leander K. Role of Polyunsaturated Fat in Modifying Cardiovascular Risk Associated With Family History of Cardiovascular Disease: Pooled De Novo Results From 15 Observational Studies. Circulation 2024; 149:305-316. [PMID: 38047387 PMCID: PMC10798593 DOI: 10.1161/circulationaha.123.065530] [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: 05/10/2023] [Accepted: 10/25/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND It is unknown whether dietary intake of polyunsaturated fatty acids (PUFA) modifies the cardiovascular disease (CVD) risk associated with a family history of CVD. We assessed interactions between biomarkers of low PUFA intake and a family history in relation to long-term CVD risk in a large consortium. METHODS Blood and tissue PUFA data from 40 885 CVD-free adults were assessed. PUFA levels ≤25th percentile were considered to reflect low intake of linoleic, alpha-linolenic, and eicosapentaenoic/docosahexaenoic acids (EPA/DHA). Family history was defined as having ≥1 first-degree relative who experienced a CVD event. Relative risks with 95% CI of CVD were estimated using Cox regression and meta-analyzed. Interactions were assessed by analyzing product terms and calculating relative excess risk due to interaction. RESULTS After multivariable adjustments, a significant interaction between low EPA/DHA and family history was observed (product term pooled RR, 1.09 [95% CI, 1.02-1.16]; P=0.01). The pooled relative risk of CVD associated with the combined exposure to low EPA/DHA, and family history was 1.41 (95% CI, 1.30-1.54), whereas it was 1.25 (95% CI, 1.16-1.33) for family history alone and 1.06 (95% CI, 0.98-1.14) for EPA/DHA alone, compared with those with neither exposure. The relative excess risk due to interaction results indicated no interactions. CONCLUSIONS A significant interaction between biomarkers of low EPA/DHA intake, but not the other PUFA, and a family history was observed. This novel finding might suggest a need to emphasize the benefit of consuming oily fish for individuals with a family history of CVD.
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Affiliation(s)
- Federica Laguzzi
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine (F.L., A.A., U.D.F., K.L.), Karolinska Institutet, Stockholm, Sweden
| | - Agneta Åkesson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine (F.L., A.A., U.D.F., K.L.), Karolinska Institutet, Stockholm, Sweden
| | - Matti Marklund
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (M.M., W.S.P)
- The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, Australia (M.M.)
| | - Frank Qian
- Section of Cardiovascular Medicine, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, MA (F.Q.)
- Department of Nutrition (F.Q.), Boston, MA
| | - Bruna Gigante
- Cardiovascular Medicine Unit, Department of Medicine Solna (B.G.), Karolinska Institutet, Stockholm, Sweden
| | - Traci M. Bartz
- Cardiovascular Health Research Unit, Departments of Biostatistics (T.M.B.), University of Washington, Seattle
- Medicine (T.M.B., R.N.L., B.M.P.), University of Washington, Seattle
| | - Julie K. Bassett
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia (J.K.B., A.M.H., G.G.G.)
| | - Anna Birukov
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal (A.K.B., S.J., M.B.S.)
- German Center for Diabetes Research, Neuherberg (A.K.B., S.J., M.B.S.)
| | - Hannia Campos
- Harvard T.H. Chan School of Public Health (H.C.), Boston, MA
| | - Yoichiro Hirakawa
- Departments of Epidemiology and Public Health and Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan (Y.H., T.N.)
| | - Fumiaki Imamura
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, UK (F.I., N.G.F., N.J.W.)
| | - Susanne Jäger
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal (A.K.B., S.J., M.B.S.)
| | - Maria Lankinen
- Institutes of Public Health and Clinical Nutrition (M. Lankinen, J.K.V., M.U.), University of Eastern Finland, Kuopio
| | - Rachel A. Murphy
- Cancer Control Research, BC Cancer Agency, Vancouver, Canada (R.A.M.)
- School of Population and Public Health, University of British Columbia, Vancouver, Canada (R.A.M.)
| | - Mackenzie Senn
- United States Department of Agriculture/Agricultural Research Service Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX (M.S., A.C.W.)
- Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany (M.B.S.)
| | - Toshiko Tanaka
- Longitudinal Study Section, National Institute on Aging, Baltimore, MD (T.T., L.F.)
| | - Nathan Tintle
- Fatty Acid Research Institute, Sioux Falls, SD (N.T., W.S.H.)
- Department of Population Health Nursing Science, University of Illinois – Chicago (N.T.)
| | - Jyrki K. Virtanen
- Institutes of Public Health and Clinical Nutrition (M. Lankinen, J.K.V., M.U.), University of Eastern Finland, Kuopio
| | - Kazumasa Yamagishi
- Department of Public Health Medicine, Institute of Medicine (K.Y., H.K.), University of Tsukuba, Japan
- Health Services Research and Development Center (K.Y., H.K.), University of Tsukuba, Japan
| | - Matthew Allison
- Department of Family Medicine, University of California, San Diego, La Jolla (M.A.)
| | - Ingeborg A. Brouwer
- Department of Health Sciences, Faculty of Science, Vrije Universiteit Amsterdam, The Netherlands (I.A.B.)
- Amsterdam Public Health Research Institute, The Netherlands (I.A.B.)
| | - Ulf De Faire
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine (F.L., A.A., U.D.F., K.L.), Karolinska Institutet, Stockholm, Sweden
| | | | - Luigi Ferrucci
- Longitudinal Study Section, National Institute on Aging, Baltimore, MD (T.T., L.F.)
| | - Nita G. Forouhi
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, UK (F.I., N.G.F., N.J.W.)
| | - Johanna M. Geleijnse
- Division of Human Nutrition and Health, Wageningen University and Research, The Netherlands (J.M.G., A.C.v.W.)
| | - Allison M Hodge
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia (J.K.B., A.M.H., G.G.G.)
- Centre for Epidemiology and Biostatistics, University of Melbourne, Victoria, Australia (A.M.H., G.G.G.)
| | - Hitomi Kimura
- Department of Public Health Medicine, Institute of Medicine (K.Y., H.K.), University of Tsukuba, Japan
- Health Services Research and Development Center (K.Y., H.K.), University of Tsukuba, Japan
| | - Markku Laakso
- Clinical Medicine, Internal Medicine (M. Laakso), University of Eastern Finland, Kuopio
- Kuopio University Hospital (M. Laakso), University of Eastern Finland, Kuopio
| | - Ulf Risérus
- Department of Public Health and Caring Sciences, Clinical Nutrition and Metabolism, Uppsala University, Sweden (M.M., U.R)
| | - Anniek C. van Westing
- United States Department of Agriculture/Agricultural Research Service Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX (M.S., A.C.W.)
- Division of Human Nutrition and Health, Wageningen University and Research, The Netherlands (J.M.G., A.C.v.W.)
| | - Stefania Bandinelli
- Geriatric Unit, Azienda Unità Sanitaria Locale Toscana Centro, Florence, Italy (S.B.)
| | - Ana Baylin
- University of Michigan School of Public Health, Ann Arbor (A. Baylin)
| | - Graham G. Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia (J.K.B., A.M.H., G.G.G.)
- Centre for Epidemiology and Biostatistics, University of Melbourne, Victoria, Australia (A.M.H., G.G.G.)
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Victoria, Australia (G.G.G.)
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur (G.E., V.G.)
- Faculty of Medicine, University of Iceland, Reykjavik (V.G.)
| | - Hiroyasu Iso
- Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Suita, Japan (H.I.)
- Institute for Global Health Policy Research, Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo, Japan (H.I.)
| | | | - Toshiharu Ninomiya
- Departments of Epidemiology and Public Health and Medicine and Clinical Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan (Y.H., T.N.)
| | - Wendy S. Post
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (M.M., W.S.P)
- Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (W.S.P.)
| | - Bruce M. Psaty
- Medicine (T.M.B., R.N.L., B.M.P.), University of Washington, Seattle
- Epidemiology (B.M.P.), University of Washington, Seattle
- Health Systems and Population Health (B.M.P.), University of Washington, Seattle
| | - Jukka T. Salonen
- Metabolic Analytical Services Oy, Helsinki, Finland (J.T.S.)
- University of Helsinki, the Faculty of Medicine, Department of Public Health, Finland (J.T.S.)
| | - Matthias B. Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal (A.K.B., S.J., M.B.S.)
- German Center for Diabetes Research, Neuherberg (A.K.B., S.J., M.B.S.)
| | - Michael Y. Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis (M.Y.T.)
| | - Matti Uusitupa
- Institutes of Public Health and Clinical Nutrition (M. Lankinen, J.K.V., M.U.), University of Eastern Finland, Kuopio
| | - Nicholas J. Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge School of Clinical Medicine, UK (F.I., N.G.F., N.J.W.)
| | - Seung-Won Oh
- Department of Family Medicine, Seoul National University College of Medicine, and Healthcare System Gangnam Center, Seoul National University Hospital, Republic of Korea (S.W.O.)
| | - Alexis C. Wood
- United States Department of Agriculture/Agricultural Research Service Children’s Nutrition Research Center, Baylor College of Medicine, Houston, TX (M.S., A.C.W.)
| | - William S. Harris
- Fatty Acid Research Institute, Sioux Falls, SD (N.T., W.S.H.)
- Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls (W.S.H.)
| | | | - Dariush Mozaffarian
- Food Is Medicine Institute, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA (D.M.)
| | - Karin Leander
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine (F.L., A.A., U.D.F., K.L.), Karolinska Institutet, Stockholm, Sweden
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3
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Haycock PC, Borges MC, Burrows K, Lemaitre RN, Harrison S, Burgess S, Chang X, Westra J, Khankari NK, Tsilidis KK, Gaunt T, Hemani G, Zheng J, Truong T, O’Mara TA, Spurdle AB, Law MH, Slager SL, Birmann BM, Saberi Hosnijeh F, Mariosa D, Amos CI, Hung RJ, Zheng W, Gunter MJ, Davey Smith G, Relton C, Martin RM. Design and quality control of large-scale two-sample Mendelian randomization studies. Int J Epidemiol 2023; 52:1498-1521. [PMID: 38587501 PMCID: PMC10555669 DOI: 10.1093/ije/dyad018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/10/2023] [Indexed: 03/27/2024] Open
Abstract
Background Mendelian randomization (MR) studies are susceptible to metadata errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control (QC) pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods We collated summary association statistics from fatty acid and cancer genome-wide association studies (GWAS) and subjected the collated data to a comprehensive QC pipeline. We identified metadata errors through comparison of study-specific statistics to external reference data sets (the National Human Genome Research Institute-European Bioinformatics Institute GWAS catalogue and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: (i) GWAS hits for fatty acids, (ii) GWAS hits for cancer and (iii) a 1000 genomes reference set. Results We collated summary data from 6 fatty acid and 54 cancer GWAS. Metadata errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (11.6%). After resolving metadata errors and analytical issues, we created a data set of 219 842 genetic associations with 90 cancer types, generated in analyses of 566 665 cancer cases and 1 622 374 controls. Conclusions In this large MR collaboration, 11.6% of included studies were affected by a substantial metadata error or analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of downstream MR analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).
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Affiliation(s)
- Philip C Haycock
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Maria Carolina Borges
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kimberley Burrows
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Sean Harrison
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Xuling Chang
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Khoo Teck Puat—National University Children's Medical Institute, National University Health System, Singapore, Singapore
| | - Jason Westra
- Department of Mathematics, Statistics, and Computer Science, Dordt College, Sioux Center, IA, USA
| | - Nikhil K Khankari
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kostas K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Tom Gaunt
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Therese Truong
- Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, Team “Exposome, Heredity, Cancer and Health”, CESP, Villejuif, France
| | - Tracy A O’Mara
- Genetics and Computational Biology Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Medicine, Faculty of Health Sciences, University of Queensland, Brisbane, Australia
| | - Amanda B Spurdle
- Genetics and Computational Biology Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Medicine, Faculty of Health Sciences, University of Queensland, Brisbane, Australia
| | - Matthew H Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, Australia
| | - Susan L Slager
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Brenda M Birmann
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Daniela Mariosa
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC), Lyon, France
| | - Christopher I Amos
- Dan L Duncan Comprehensive Cancer Center Baylor College of Medicine, Houston, USA
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health and University of Toronto, Toronto, Canada
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Marc J Gunter
- Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC), Lyon, France
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Caroline Relton
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Richard M Martin
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre at University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol, UK
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Westra J, Annevelink C, Orchard T, Hou L, Harris WS, O'Connell TD, Shearer G, Tintle N. Genome-wide association study of Red Blood Cell fatty acids in the Women's Health Initiative Memory Study. Prostaglandins Leukot Essent Fatty Acids 2023; 194:102577. [PMID: 37285607 PMCID: PMC10320552 DOI: 10.1016/j.plefa.2023.102577] [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: 04/01/2023] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023]
Abstract
Despite their widespread associations with a wide variety of disease phenotypes, the genetics of red blood cell fatty acids remains understudied. We present one of the first genome-wide association studies of red blood cell fatty acid levels, using the Women's Health Initiative Memory study - a prospective cohort of N = 7,479 women aged 65-79. Approximately 9 million SNPs were measured directly or imputed and, in separate linear models adjusted for age and genetic principal components of ethnicity, SNPs were used to predict 28 different fatty acids. SNPs were considered genome-wide significant using a standard genome-wide significance level of p < 1 × 10-8. Twelve separate loci were identified, seven of which replicated results of a prior RBC-FA GWAS. Of the five novel loci, two have functional annotations directly related to fatty acids (ELOVL6 and ACSL6). While overall explained variation is low, the twelve loci identified provide strong evidence of direct relationships between these genes and fatty acid levels. Further studies are needed to establish and confirm the biological mechanisms by which these genes may directly contribute to fatty acid levels.
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Affiliation(s)
- Jason Westra
- Fatty Acid Research Institute, Sioux Falls, SD, United States of America
| | - Carmen Annevelink
- Department of Nutrition, Penn State University, State College, PA, United States of America
| | - Tonya Orchard
- Human Nutrition Program, Department of Human Sciences, Ohio State University, Columbus, OH, United States of America
| | - Lifang Hou
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States of America
| | - William S Harris
- Fatty Acid Research Institute, Sioux Falls, SD, United States of America; Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, United States of America
| | - Timothy D O'Connell
- Department of Integrative Biology and Physiology, University of Minnesota, Minneapolis, MN, United States of America
| | - Gregory Shearer
- Department of Nutrition, Penn State University, State College, PA, United States of America
| | - Nathan Tintle
- Fatty Acid Research Institute, Sioux Falls, SD, United States of America; Department of Population Health Nursing Science, College of Nursing, University of Illinois - Chicago, Chicago, IL, United States of America.
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5
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Buckner T, Johnson RK, Vanderlinden LA, Carry PM, Romero A, Onengut-Gumuscu S, Chen WM, Kim S, Fiehn O, Frohnert BI, Crume T, Perng W, Kechris K, Rewers M, Norris JM. Genome-wide analysis of oxylipins and oxylipin profiles in a pediatric population. Front Nutr 2023; 10:1040993. [PMID: 37057071 PMCID: PMC10086335 DOI: 10.3389/fnut.2023.1040993] [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: 09/09/2022] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
Background Oxylipins are inflammatory biomarkers derived from omega-3 and-6 fatty acids implicated in inflammatory diseases but have not been studied in a genome-wide association study (GWAS). The aim of this study was to identify genetic loci associated with oxylipins and oxylipin profiles to identify biologic pathways and therapeutic targets for oxylipins. Methods We conducted a GWAS of plasma oxylipins in 316 participants in the Diabetes Autoimmunity Study in the Young (DAISY). DNA samples were genotyped using the TEDDY-T1D Exome array, and additional variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) multi-ancestry reference panel. Principal components analysis of 36 plasma oxylipins was used to capture oxylipin profiles. PC1 represented linoleic acid (LA)- and alpha-linolenic acid (ALA)-related oxylipins, and PC2 represented arachidonic acid (ARA)-related oxylipins. Oxylipin PC1, PC2, and the top five loading oxylipins from each PC were used as outcomes in the GWAS (genome-wide significance: p < 5×10-8). Results The SNP rs143070873 was associated with (p < 5×10-8) the LA-related oxylipin 9-HODE, and rs6444933 (downstream of CLDN11) was associated with the LA-related oxylipin 13 S-HODE. A locus between MIR1302-7 and LOC100131146, rs10118380 and an intronic variant in TRPM3 were associated with the ARA-related oxylipin 11-HETE. These loci are involved in inflammatory signaling cascades and interact with PLA2, an initial step to oxylipin biosynthesis. Conclusion Genetic loci involved in inflammation and oxylipin metabolism are associated with oxylipin levels.
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Affiliation(s)
- Teresa Buckner
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
- Department of Kinesiology, Nutrition, and Dietetics, University of Northern Colorado, Greeley, CO, United States
| | - Randi K. Johnson
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
- Department of Biomedical Informatics, CU School of Medicine, Anschutz Medical Campus, Aurora, CO, United States
| | - Lauren A. Vanderlinden
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
| | - Patrick M. Carry
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, CU School of Medicine, Anschutz Medical Campus, Aurora, CO, United States
| | - Alex Romero
- Department of Biomedical Informatics, CU School of Medicine, Anschutz Medical Campus, Aurora, CO, United States
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Wei-Min Chen
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States
| | - Soojeong Kim
- Department of Health Administration, Dongseo University, Busan, Republic of Korea
| | - Oliver Fiehn
- NIH-West Coast Metabolomics Center, University of California-Davis, Davis, CA, United States
| | - Brigitte I. Frohnert
- The Barbara Davis Center for Diabetes, CU School of Medicine, Anschutz Medical Campus, Aurora, CO, United States
| | - Tessa Crume
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
| | - Wei Perng
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
| | - Katerina Kechris
- Department of Biostatistics and Informatics, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
| | - Marian Rewers
- The Barbara Davis Center for Diabetes, CU School of Medicine, Anschutz Medical Campus, Aurora, CO, United States
| | - Jill M. Norris
- Department of Epidemiology, Colorado School of Public Health, Anschutz Medical Campus, Aurora, CO, United States
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6
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Zigarelli AM, Venera HM, Receveur BA, Wolf JM, Westra J, Tintle NL. Multimarker omnibus tests by leveraging individual marker summary statistics from large biobanks. Ann Hum Genet 2023; 87:125-136. [PMID: 36683423 DOI: 10.1111/ahg.12495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 12/24/2022] [Accepted: 01/04/2023] [Indexed: 01/24/2023]
Abstract
As biobanks become increasingly popular, access to genotypic and phenotypic data continues to increase in the form of precomputed summary statistics (PCSS). Widespread accessibility of PCSS alleviates many issues related to biobank data, including that of data privacy and confidentiality, as well as high computational costs. However, questions remain about how to maximally leverage PCSS for downstream statistical analyses. Here we present a novel method for testing the association of an arbitrary number of single nucleotide variants (SNVs) on a linear combination of phenotypes after adjusting for covariates for common multimarker tests (e.g., SKAT, SKAT-O) without access to individual patient-level data (IPD). We validate exact formulas for each method, and demonstrate their accuracy through simulation studies and an application to fatty acid phenotypic data from the Framingham Heart Study.
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Affiliation(s)
- Angela M Zigarelli
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Massachusetts, USA
| | - Hanna M Venera
- Division of Biostatistics, University of Michigan, Michigan, USA
| | - Brody A Receveur
- Department of Statistics, George Mason University, Virginia, USA
| | - Jack M Wolf
- Division of Biostatistics, University of Minnesota, Minnesota, USA
| | - Jason Westra
- Department of Math, Computer Science, and Statistics, Dordt University, Iowa, USA
| | - Nathan L Tintle
- Department of Population Health Nursing Sciences, University of Illinois Chicago, Chicago, Illinois, USA
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7
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Wolf JM, Barnard M, Xia X, Ryder N, Westra J, Tintle N. Computationally efficient, exact, covariate-adjusted genetic principal component analysis by leveraging individual marker summary statistics from large biobanks. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020; 25:719-730. [PMID: 31797641 PMCID: PMC6907735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The popularization of biobanks provides an unprecedented amount of genetic and phenotypic information that can be used to research the relationship between genetics and human health. Despite the opportunities these datasets provide, they also pose many problems associated with computational time and costs, data size and transfer, and privacy and security. The publishing of summary statistics from these biobanks, and the use of them in a variety of downstream statistical analyses, alleviates many of these logistical problems. However, major questions remain about how to use summary statistics in all but the simplest downstream applications. Here, we present a novel approach to utilize basic summary statistics (estimates from single marker regressions on single phenotypes) to evaluate more complex phenotypes using multivariate methods. In particular, we present a covariate-adjusted method for conducting principal component analysis (PCA) utilizing only biobank summary statistics. We validate exact formulas for this method, as well as provide a framework of estimation when specific summary statistics are not available, through simulation. We apply our method to a real data set of fatty acid and genomic data.
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Affiliation(s)
- Jack M Wolf
- Department of Mathematics, Statistics, and Computer Science, St. Olaf College, Northfield, MN 55057, USA,
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