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Ghiasi M, Chang C, Shafrir AL, Vitonis AF, Sasamoto N, Vazquez AI, DiVasta AD, Upson K, Sieberg CB, Terry KL, Holzman CB, Missmer SA. Subgroups of pelvic pain are differentially associated with endometriosis and inflammatory comorbidities: a latent class analysis. Pain 2024:00006396-990000000-00563. [PMID: 38563996 DOI: 10.1097/j.pain.0000000000003218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 01/15/2024] [Indexed: 04/04/2024]
Abstract
ABSTRACT Chronic pelvic pain is heterogeneous with potentially clinically informative subgroups. We aimed to identify subgroups of pelvic pain based on symptom patterns and investigate their associations with inflammatory and chronic pain-related comorbidities. Latent class analysis (LCA) identified subgroups of participants (n = 1255) from the Adolescence to Adulthood (A2A) cohort. Six participant characteristics were included in the LCA: severity, frequency, and impact on daily activities of both menstruation-associated (cyclic) and non-menstruation-associated (acyclic) pelvic pain. Three-step LCA quantified associations between LC subgroups, demographic and clinical variables, and 18 comorbidities (10 with prevalence ≥10%). Five subgroups were identified: none or minimal (23%), moderate cyclic only (28%), severe cyclic only (20%), moderate or severe acyclic plus moderate cyclic (9%), and severe acyclic plus severe cyclic (21%). Endometriosis prevalence within these 5 LCA-pelvic pain-defined subgroups ranged in size from 4% in "none or minimal pelvic pain" to 24%, 72%, 70%, and 94%, respectively, in the 4 pain subgroups, with statistically significant odds of membership only for the latter 3 subgroups. Migraines were associated with significant odds of membership in all 4 pelvic pain subgroups relative to those with no pelvic pain (adjusted odds ratios = 2.92-7.78), whereas back, joint, or leg pain each had significantly greater odds of membership in the latter 3 subgroups. Asthma or allergies had three times the odds of membership in the most severe pain group. Subgroups with elevated levels of cyclic or acyclic pain are associated with greater frequency of chronic overlapping pain conditions, suggesting an important role for central inflammatory and immunological mechanisms.
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Affiliation(s)
- Marzieh Ghiasi
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI, United States
| | - Chi Chang
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI, United States
- Office of Medical Education Research and Development, Michigan State University, East Lansing, MI, United States
| | - Amy L Shafrir
- Division of Adolescent and Young Adult Medicine, Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
- Boston Center for Endometriosis, Boston Children's Hospital and Brigham and Women's Hospital, Boston, MA, United States
| | - Allison F Vitonis
- Boston Center for Endometriosis, Boston Children's Hospital and Brigham and Women's Hospital, Boston, MA, United States
- Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Naoko Sasamoto
- Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | - Amy D DiVasta
- Division of Adolescent and Young Adult Medicine, Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
- Boston Center for Endometriosis, Boston Children's Hospital and Brigham and Women's Hospital, Boston, MA, United States
| | - Kristen Upson
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI, United States
| | - Christine B Sieberg
- Biobehavioral Pain Innovations Lab, Department of Psychiatry & Behavioral Sciences, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
- Pain & Affective Neuroscience Center, Department of Anesthesiology, Critical Care, & Pain Medicine, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Kathryn L Terry
- Obstetrics and Gynecology Epidemiology Center, Department of Obstetrics, Gynecology, and Reproductive Biology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Claudia B Holzman
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI, United States
| | - Stacey A Missmer
- Division of Adolescent and Young Adult Medicine, Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
- Boston Center for Endometriosis, Boston Children's Hospital and Brigham and Women's Hospital, Boston, MA, United States
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States
- Department of Obstetrics, Gynecology, and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
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Zoh RS, Esteves BH, Yu X, Fairchild AJ, Vazquez AI, Chapple AG, Brown AW, George B, Gordon D, Landsittel D, Gadbury GL, Pavela G, de Los Campos G, Mestre LM, Allison DB. Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research. Obes Rev 2023; 24:e13635. [PMID: 37667550 PMCID: PMC10825777 DOI: 10.1111/obr.13635] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 03/29/2023] [Accepted: 07/24/2023] [Indexed: 09/06/2023]
Abstract
It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine.
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Affiliation(s)
- Roger S Zoh
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | | | - Xiaoxin Yu
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Amanda J Fairchild
- Department of Psychology, University of South Carolina, Columbia, South Carolina, USA
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, Lansing, Michigan, USA
| | - Andrew G Chapple
- Biostatistics Program, School of Public Health, LSU Health Sciences Center, New Orleans, Louisiana, USA
| | - Andrew W Brown
- Department of Applied Health Science, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Brandon George
- College of Population Health, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Derek Gordon
- Department of Genetics, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Douglas Landsittel
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
| | - Gary L Gadbury
- Department of Statistics, Kansas State University, Manhattan, Kansa, USA
| | - Greg Pavela
- Department of Health Behavior, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Gustavo de Los Campos
- Departments of Epidemiology & Biostatistics and Statistics & Probability, IQ - Institute for Quantitative Health Science and Engineering, Michigan State University, Lansing, Michigan, USA
| | - Luis M Mestre
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
| | - David B Allison
- Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, Indiana, USA
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Hall MS, Holt VL, Holzman C, Vazquez AI, Harris HR, As-Sanie S, Upson K. Breastfeeding history and adenomyosis risk using a novel case-control study design. Fertil Steril 2023; 119:644-652. [PMID: 36563837 PMCID: PMC10079609 DOI: 10.1016/j.fertnstert.2022.12.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/04/2022] [Accepted: 12/13/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVE To evaluate the association between breastfeeding history, including lifetime exclusive breastfeeding, and risk of adenomyosis. DESIGN We used data from a case-control study designed with 2 control groups to address the challenge of selecting noncases for a valid epidemiologic study when cases are identified by hysterectomy. The case-control study was conducted among premenopausal and postmenopausal enrollees aged 18-59 years in a large, integrated health care system in western Washington state. PATIENT(S) Cases were enrollees with incident, pathology-confirmed adenomyosis diagnosed during 2001-2006 (n = 386). The 2 control groups were as follows: (1) randomly selected age-matched enrollees with intact uteri ("population controls," n = 323) and (2) hysterectomy controls (n = 233). INTERVENTION(S) Data on breastfeeding history were collected by in-person interviews. For each reported live birth, participants were asked whether they breastfed, along with infant age at supplemental feeding introduction and breastfeeding discontinuation. MAIN OUTCOME MEASURE(S) Among participants with at least 1 live birth (330 cases, 246 population controls, and 198 hysterectomy controls), we used unconditional logistic regression to estimate adjusted odds ratios and 95% confidence intervals (CIs) for the associations between the following: (1) ever breastfeeding, (2) ever breastfeeding for ≥8 weeks, (3) lifetime breastfeeding, and (4) lifetime exclusive breastfeeding and risk of adenomyosis. Analyses were adjusted for age, reference year, smoking, education, and parity. RESULT(S) In analyses comparing cases with population controls, we observed a 40% decreased odds of adenomyosis with a history of ever breastfeeding (adjusted odds ratio, 0.6; 95% CI, 0.3-1.0) and breastfeeding for ≥8 weeks (adjusted odds ratio, 0.6; 95% CI, 0.4-0.8). The strongest associations, 60%-70% decreased odds of adenomyosis, were observed with ≥12 months of lifetime breastfeeding (vs. <3 months) (adjusted odds ratio, 0.4; 95% CI, 0.2-0.6) and 9 to <12 months of lifetime exclusive breastfeeding (vs. <3 months) (adjusted odds ratio, 0.3; 95% CI, 0.2-0.6), comparing cases to population controls. In analyses using hysterectomy controls, we observed similar patterns of associations slightly attenuated in magnitude. CONCLUSION(S) Breastfeeding history was associated with a 40% decreased odds of adenomyosis, a condition that can confer substantial morbidity and requires hysterectomy for definitive treatment. The consistency of our findings with that of a previous study lends support that breastfeeding may modify risk of adenomyosis.
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Affiliation(s)
- Mandy S Hall
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, Michigan.
| | - Victoria L Holt
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington
| | - Claudia Holzman
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, Michigan
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, Michigan; Institute for Quantitative Health Science and Engineering (IQ), Michigan State University, East Lansing, Michigan
| | - Holly R Harris
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington; Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Sawsan As-Sanie
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor, Michigan
| | - Kristen Upson
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, Michigan
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Sumpter NA, Takei R, Cadzow M, Topless RKG, Phipps-Green AJ, Murphy R, de Zoysa J, Watson H, Qasim M, Lupi AS, Abhishek A, Andrés M, Crișan TO, Doherty M, Jacobsson L, Janssen M, Jansen TL, Joosten LAB, Kapetanovic M, Lioté F, Matsuo H, McCarthy GM, Perez-Ruiz F, Riches P, Richette P, Roddy E, Stiburkova B, So A, Tausche AK, Torres RJ, Uhlig T, Major TJ, Stamp LK, Dalbeth N, Choi HK, Vazquez AI, Leask MP, Reynolds RJ, Merriman TR. Association of Gout Polygenic Risk Score With Age at Disease Onset and Tophaceous Disease in European and Polynesian Men With Gout. Arthritis Rheumatol 2022; 75:816-825. [PMID: 36281732 DOI: 10.1002/art.42393] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 09/19/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE To determine whether a gout polygenic risk score (PRS) is associated with age at gout onset and tophaceous disease in European, East Polynesian, and West Polynesian men and women with gout. METHODS A 19-variant gout PRS was produced in 7 European gout cohorts (N = 4,016), 2 East Polynesian gout cohorts (N = 682), and 1 West Polynesian gout cohort (N = 490). Sex-stratified regression models were used to estimate the relationship between the PRS and age at gout onset and tophaceous disease. RESULTS The PRS was associated with earlier age at gout onset in men (β = -3.61 in years per unit PRS [95% confidence interval (95% CI) -4.32, -2.90] in European men; β = -6.35 [95% CI -8.91, -3.80] in East Polynesian men; β = -3.51 [95% CI -5.46, -1.57] in West Polynesian men) but not in women (β = 0.07 [95% CI -2.32, 2.45] in European women; β = 0.20 [95% CI -7.21, 7.62] in East Polynesian women; β -3.33 [95% CI -9.28, 2.62] in West Polynesian women). The PRS showed a positive association with tophaceous disease in men (odds ratio [OR] for the association 1.15 [95% CI 1.00, 1.31] in European men; OR 2.60 [95% CI 1.66, 4.06] in East Polynesian men; OR 1.53 [95% CI 1.07, 2.19] in West Polynesian men) but not in women (OR for the association 0.68 [95% CI 0.42, 1.10] in European women; OR 1.45 [95% CI 0.39, 5.36] in East Polynesian women). The PRS association with age at gout onset was robust to the removal of ABCG2 variants from the PRS in European and East Polynesian men (β = -2.42 [95% CI -3.37, -1.46] and β = -6.80 [95% CI -10.06, -3.55], respectively) but not in West Polynesian men (β = -1.79 [95% CI -4.74, 1.16]). CONCLUSION Genetic risk variants for gout also harbor risk for earlier age at gout onset and tophaceous disease in European and Polynesian men. Our findings suggest that earlier gout onset involves the accumulation of gout risk alleles in men but perhaps not in women, and that this genetic risk is shared across multiple ancestral groups.
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Affiliation(s)
- Nicholas A Sumpter
- Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, and Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Riku Takei
- Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham
| | - Murray Cadzow
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Ruth K G Topless
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | | | - Rinki Murphy
- Department of Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Janak de Zoysa
- Department of Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Huti Watson
- Ngāti Porou Hauora Trust, Te Puia Springs, New Zealand
| | | | - Alexa S Lupi
- Department of Epidemiology and Biostatistics, Michigan State University, and The Institute for Quantitative Health Science & Engineering, Michigan State University, East Lansing, Michigan
| | - Abhishek Abhishek
- Academic Rheumatology, School of Medicine, University of Nottingham, Nottingham City Hospital, Nottingham, UK, and GlobalGoutGenetics Consortium
| | - Mariano Andrés
- GlobalGoutGenetics Consortium, and Department of Rheumatology, Alicante General University Hospital-ISABIAL, Miguel Hernandez University, Alicante, Spain
| | - Tania O Crișan
- GlobalGoutGenetics Consortium, and Department of Medical Genetics, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Michael Doherty
- Academic Rheumatology, School of Medicine, University of Nottingham, Nottingham City Hospital, Nottingham, UK, and GlobalGoutGenetics Consortium
| | - Lennart Jacobsson
- GlobalGoutGenetics Consortium, and Department of Rheumatology & Inflammation Research, Institute of Medicine, The Sahlgrenska Academy, University of Gothenburg, Sweden
| | - Matthijs Janssen
- GlobalGoutGenetics Consortium, and Complex Gout Expert Centre, Department of Rheumatology, Viecuri Medical Centre, Venlo, The Netherlands
| | - Tim L Jansen
- GlobalGoutGenetics Consortium, and Complex Gout Expert Centre, Department of Rheumatology, Viecuri Medical Centre, Venlo, The Netherlands
| | - Leo A B Joosten
- GlobalGoutGenetics Consortium, and Department of Medical Genetics, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania, and Department of Internal Medicine, Radboud Institute of Molecular Life Sciences (RIMLS), Radboud University Medical Center, Nijmegen, The Netherlands
| | - Meliha Kapetanovic
- GlobalGoutGenetics Consortium, and Lund University and Skåne University Hospital, Lund, Sweden
| | - Frédéric Lioté
- Department of Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Hirotaka Matsuo
- GlobalGoutGenetics Consortium, and Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Saitama, Japan
| | - Geraldine M McCarthy
- GlobalGoutGenetics Consortium, and Mater Misericordiae University Hospital and University College, Dublin, Ireland
| | - Fernando Perez-Ruiz
- GlobalGoutGenetics Consortium, and Rheumatology Division, Osakidetza, OSI EE-Cruces, Cruces University Hospital, Barakaldo, Biocruces-Bizkaia Health Research Institute, Barakaldo, and the Medicine Department of the Medicine School of the University of the Basque Country, Leioa, Spain
| | - Philip Riches
- GlobalGoutGenetics Consortium, and IGC, University of Edinburgh, Scotland
| | - Pascal Richette
- GlobalGoutGenetics Consortium, and Hôpital Lariboisière, Assistance Publique-Hopitaux de Paris, and INSERM UMR-1132 and Université Paris Cité, Paris, France
| | - Edward Roddy
- GlobalGoutGenetics Consortium, and School of Medicine, Keele University, Keele, Staffordshire, UK
| | - Blanka Stiburkova
- GlobalGoutGenetics Consortium, and Department of Pediatrics and Inherited Metabolic Disorders, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic, and Institute of Rheumatology, Prague, Czech Republic
| | - Alexander So
- GlobalGoutGenetics Consortium, and Service of Rheumatology, Department of Musculoskeletal Medicine, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Anne-Kathrin Tausche
- GlobalGoutGenetics Consortium, and Division of Rheumatology, University Clinic Carl Gustav Carus at the TU Dresden, Dresden, Germany
| | - Rosa J Torres
- GlobalGoutGenetics Consortium, and Department of Biochemistry, La Paz University Hospital Health Research Institute (FIBHULP), IdiPaz, and Center for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, Madrid, Spain
| | - Till Uhlig
- GlobalGoutGenetics Consortium, and Center for Treatment of Rheumatic and Musculoskeletal Diseases (REMEDY), Diakonhjemmet Hospital, Oslo, Norway
| | - Tanya J Major
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Lisa K Stamp
- GlobalGoutGenetics Consortium, and Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Nicola Dalbeth
- Department of Medicine, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand, and GlobalGoutGenetics Consortium
| | - Hyon K Choi
- Clinical Epidemiology Unit, Massachusetts General Hospital, Boston
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, and The Institute for Quantitative Health Science & Engineering, Michigan State University, East Lansing, Michigan
| | - Megan P Leask
- Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham
| | - Richard J Reynolds
- Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham
| | - Tony R Merriman
- Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Department of Biochemistry, University of Otago, Dunedin, New Zealand, and GlobalGoutGenetics Consortium
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5
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Lupi AS, Sumpter NA, Leask MP, O'Sullivan J, Fadason T, de Los Campos G, Merriman TR, Reynolds RJ, Vazquez AI. Local genetic covariance between serum urate and kidney function estimated with Bayesian multitrait models. G3 (Bethesda) 2022; 12:6649732. [PMID: 35876900 PMCID: PMC9434310 DOI: 10.1093/g3journal/jkac158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 06/05/2022] [Indexed: 11/13/2022]
Abstract
Hyperuricemia (serum urate >6.8 mg/dl) is associated with several cardiometabolic and renal diseases, such as gout and chronic kidney disease. Previous studies have examined the shared genetic basis of chronic kidney disease and hyperuricemia in humans either using single-variant tests or estimating whole-genome genetic correlations between the traits. Individual variants typically explain a small fraction of the genetic correlation between traits, thus the ability to map pleiotropic loci is lacking power for available sample sizes. Alternatively, whole-genome estimates of genetic correlation indicate a moderate correlation between these traits. While useful to explain the comorbidity of these traits, whole-genome genetic correlation estimates do not shed light on what regions may be implicated in the shared genetic basis of traits. Therefore, to fill the gap between these two approaches, we used local Bayesian multitrait models to estimate the genetic covariance between a marker for chronic kidney disease (estimated glomerular filtration rate) and serum urate in specific genomic regions. We identified 134 overlapping linkage disequilibrium windows with statistically significant covariance estimates, 49 of which had positive directionalities, and 85 negative directionalities, the latter being consistent with that of the overall genetic covariance. The 134 significant windows condensed to 64 genetically distinct shared loci which validate 17 previously identified shared loci with consistent directionality and revealed 22 novel pleiotropic genes. Finally, to examine potential biological mechanisms for these shared loci, we have identified a subset of the genomic windows that are associated with gene expression using colocalization analyses. The regions identified by our local Bayesian multitrait model approach may help explain the association between chronic kidney disease and hyperuricemia.
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Affiliation(s)
- Alexa S Lupi
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.,Institute for Quantitative Health Science and Engineering, Systems Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Nicholas A Sumpter
- Department of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Megan P Leask
- Department of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA.,Department of Biochemistry, University of Otago, Dunedin 9016, New Zealand
| | - Justin O'Sullivan
- Liggins Institute, The University of Auckland, Auckland 1142, New Zealand
| | - Tayaza Fadason
- Liggins Institute, The University of Auckland, Auckland 1142, New Zealand
| | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.,Institute for Quantitative Health Science and Engineering, Systems Biology, Michigan State University, East Lansing, MI 48824, USA.,Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
| | - Tony R Merriman
- Department of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Richard J Reynolds
- Department of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.,Institute for Quantitative Health Science and Engineering, Systems Biology, Michigan State University, East Lansing, MI 48824, USA
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Gonzalez-Reymundez A, Grueneberg A, Lu G, Alves FC, Rincon G, Vazquez AI. MOSS: multi-omic integration with sparse value decomposition. Bioinformatics 2022; 38:2956-2958. [PMID: 35561193 PMCID: PMC9113319 DOI: 10.1093/bioinformatics/btac179] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 03/07/2022] [Accepted: 03/23/2022] [Indexed: 02/03/2023] Open
Abstract
SUMMARY This article presents multi-omic integration with sparse value decomposition (MOSS), a free and open-source R package for integration and feature selection in multiple large omics datasets. This package is computationally efficient and offers biological insight through capabilities, such as cluster analysis and identification of informative omic features. AVAILABILITY AND IMPLEMENTATION https://CRAN.R-project.org/package=MOSS. SUPPLEMENTARY INFORMATION Supplementary information can be found at https://github.com/agugonrey/GonzalezReymundez2021.
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Affiliation(s)
| | - Alexander Grueneberg
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA
| | - Guanqi Lu
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA
| | - Filipe Couto Alves
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA
| | - Gonzalo Rincon
- Genus PLC Inc., Genome Sciences R&D, De Forest, WI 53532, USA
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA
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7
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Aguate FM, Vazquez AI, Merriman TR, de Los Campos G. Mapping pleiotropic loci using a fast-sequential testing algorithm. Eur J Hum Genet 2021; 29:1762-1773. [PMID: 34145383 PMCID: PMC8633382 DOI: 10.1038/s41431-021-00911-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/27/2021] [Accepted: 05/19/2021] [Indexed: 02/07/2023] Open
Abstract
Pleiotropy (i.e., genes with effects on multiple traits) leads to genetic correlations between traits and contributes to the development of many syndromes. Identifying variants with pleiotropic effects on multiple health-related traits can improve the biological understanding of gene action and disease etiology, and can help to advance disease-risk prediction. Sequential testing is a powerful approach for mapping genes with pleiotropic effects. However, the existing methods and the available software do not scale to analyses involving millions of SNPs and large datasets. This has limited the adoption of sequential testing for pleiotropy mapping at large scale. In this study, we present a sequential test and software that can be used to test pleiotropy in large systems of traits with biobank-sized data. Using simulations, we show that the methods implemented in the software are powerful and have adequate type-I error rate control. To demonstrate the use of the methods and software, we present a whole-genome scan in search of loci with pleiotropic effects on seven traits related to metabolic syndrome (MetS) using UK-Biobank data (n~300 K distantly related white European participants). We found abundant pleiotropy and report 170, 44, and 18 genomic regions harboring SNPs with pleiotropic effects in at least two, three, and four of the seven traits, respectively. We validate our results using previous studies documented in the GWAS-catalog and using data from GTEx. Our results confirm previously reported loci and lead to several novel discoveries that link MetS-related traits through plausible biological pathways.
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Affiliation(s)
- Fernando M Aguate
- Department of Epidemiology & Biostatistics, IQ - Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.
| | - Ana I Vazquez
- Department of Epidemiology & Biostatistics, IQ - Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Tony R Merriman
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Gustavo de Los Campos
- Department of Epidemiology & Biostatistics, IQ - Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.
- Department of Statistics & Probability, Michigan State University, East Lansing, MI, USA.
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8
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Behring M, Ye Y, Elkholy A, Bajpai P, Agarwal S, Kim H, Ojesina AI, Wiener HW, Manne U, Shrestha S, Vazquez AI. Immunophenotype-associated gene signature in ductal breast tumors varies by receptor subtype, but the expression of individual signature genes remains consistent. Cancer Med 2021; 10:5712-5720. [PMID: 34189853 PMCID: PMC8366080 DOI: 10.1002/cam4.4095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/25/2021] [Accepted: 05/10/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND In silico deconvolution of invasive immune cell infiltration in bulk breast tumors helps characterize immunophenotype, expands treatment options, and influences survival endpoints. In this study, we identify the differential expression (DE) of the LM22 signature to classify immune-rich and -poor breast tumors and evaluate immune infiltration by receptor subtype and lymph node metastasis. METHODS Using publicly available data, we applied the CIBERSORT algorithm to estimate immune cells infiltrating the tumor into immune-rich and immune-poor groups. We then tested the association of receptor subtype and nodal status with immune-rich/poor phenotype. We used DE to test individual signature genes and over-representation analysis for related pathways. RESULTS CCL19 and CXCL9 expression differed between rich/poor signature groups regardless of subtype. Overexpression of CHI3L2 and FES was observed in triple negative breast cancers (TNBCs) relative to other subtypes in immune-rich tumors. Non-signature genes, LYZ, C1QB, CORO1A, EVI2B, GBP1, PSMB9, and CD52 were consistently overexpressed in immune-rich tumors, and SCUBE2 and GRIA2 were associated with immune-poor tumors. Immune-rich tumors had significant upregulation of genes/pathways while none were identified in immune-poor tumors. CONCLUSIONS Overall, the proportion of immune-rich/poor tumors differed by subtype; however, a subset of 10 LM22 genes that marked immune-rich status remained the same across subtype. Non-LM22 genes differentially expressed between the phenotypes suggest that the biologic processes responsible for immune-poor phenotype are not yet well characterized.
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MESH Headings
- Biomarkers, Tumor/metabolism
- Breast Neoplasms/genetics
- Breast Neoplasms/immunology
- Breast Neoplasms/pathology
- Carcinoma, Ductal, Breast/genetics
- Carcinoma, Ductal, Breast/immunology
- Carcinoma, Ductal, Breast/pathology
- Datasets as Topic
- Female
- Gene Expression Profiling
- Gene Expression Regulation, Neoplastic/immunology
- Humans
- Immunophenotyping
- Lymphocytes, Tumor-Infiltrating/immunology
- Lymphocytes, Tumor-Infiltrating/metabolism
- Up-Regulation/immunology
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Affiliation(s)
- Michael Behring
- Department of EpidemiologyUniversity of Alabama at BirminghamBirminghamALUSA
- Department of Pathology and SurgeryUniversity of Alabama at BirminghamBirminghamALUSA
| | - Yuanfan Ye
- Department of EpidemiologyUniversity of Alabama at BirminghamBirminghamALUSA
| | - Amr Elkholy
- Department of Pathology and SurgeryUniversity of Alabama at BirminghamBirminghamALUSA
| | - Prachi Bajpai
- Department of Pathology and SurgeryUniversity of Alabama at BirminghamBirminghamALUSA
| | - Sumit Agarwal
- Department of Pathology and SurgeryUniversity of Alabama at BirminghamBirminghamALUSA
| | - Hyung‐Gyoon Kim
- Department of Pathology and SurgeryUniversity of Alabama at BirminghamBirminghamALUSA
| | - Akinyemi I. Ojesina
- Department of EpidemiologyUniversity of Alabama at BirminghamBirminghamALUSA
- Comprehensive Cancer CenterUniversity of Alabama at BirminghamBirminghamALUSA
| | - Howard W Wiener
- Department of EpidemiologyUniversity of Alabama at BirminghamBirminghamALUSA
| | - Upender Manne
- Department of Pathology and SurgeryUniversity of Alabama at BirminghamBirminghamALUSA
- Comprehensive Cancer CenterUniversity of Alabama at BirminghamBirminghamALUSA
| | - Sadeep Shrestha
- Department of EpidemiologyUniversity of Alabama at BirminghamBirminghamALUSA
| | - Ana I. Vazquez
- Department of Epidemiology and BiostatisticsMichigan State UniversityEast LansingMIUSA
- Institute for Quantitative Health Science & EngineeringEast LansingMIUSA
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9
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Rovere G, de Los Campos G, Lock AL, Worden L, Vazquez AI, Lee K, Tempelman RJ. Prediction of fatty acid composition using milk spectral data and its associations with various mid-infrared spectral regions in Michigan Holsteins. J Dairy Sci 2021; 104:11242-11258. [PMID: 34275636 DOI: 10.3168/jds.2021-20267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/28/2021] [Indexed: 11/19/2022]
Abstract
Fatty acid composition in milk is not only reflective of nutritional quality but also potentially predictive of other attributes (e. g. including the cow's energy balance and its relative output of methane emissions). Furthermore, a higher ratio of long-chain to short-chain fatty acids or mean carbon number has been associated with negative energy balance in dairy cows, whereas enhanced nutritional properties have been generally associated with higher levels of unsaturation. We set out to directly compare Bayesian regression strategies with partial least squares for the prediction of various milk fatty acids using Fourier-transform infrared spectrum data on 777 milk samples taken from 579 cows on 4 Michigan dairy herds between 5 and 90 d in milk. We also set out to identify those spectral regions that might be associated with fatty acids and whether carbon number or level of unsaturation might contribute to the strength of these associations. These associations were based on adaptively clustered windows of wavenumbers to mitigate the distorting effects of severe multicollinearity on marginal associations involving individual wavenumbers. In general, Bayesian regression methods, particularly the variable selection method BayesB, outperformed partial least squares regression for cross-validation prediction accuracy for both individual fatty acids and fatty acid groups. Strong signals for wavenumber associations using BayesB were well distributed throughout the mid-infrared spectrum, particularly between 910 and 3,998 cm-1. Carbon number appeared to be linearly related to strength of wavenumber associations for 38 moderately to highly predicted fatty acids within the spectral regions of 2,286 to 2,376 and 2,984 to 3,100 cm-1, whereas nonlinear associations were determined within 1,141 to 1,205; 1,570 to 1,630; and 1,727 to 1,768 cm-1. However, no such associations were detected with level of unsaturation. Spectral regions where there were significant relationships between strength of association and carbon number may be useful targets for inferring the relative proportion of long-chain to short-chain fatty acids, and hence energy balance.
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Affiliation(s)
- G Rovere
- Department of Animal Science, Michigan State University, East Lansing 48824-1225; Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824-1225
| | - G de Los Campos
- Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824-1225; Department of Statistics and Probability, Michigan State University, East Lansing 48824-1225
| | - A L Lock
- Department of Animal Science, Michigan State University, East Lansing 48824-1225
| | - L Worden
- Department of Animal Science, Michigan State University, East Lansing 48824-1225
| | - A I Vazquez
- Department of Epidemiology and Biostatistics, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824-1225
| | - K Lee
- Michigan State University Extension, Lake City, MI 49651
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing 48824-1225.
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10
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Reynolds RJ, Irvin MR, Bridges SL, Kim H, Merriman TR, Arnett DK, Singh JA, Sumpter NA, Lupi AS, Vazquez AI. Genetic correlations between traits associated with hyperuricemia, gout, and comorbidities. Eur J Hum Genet 2021; 29:1438-1445. [PMID: 33637890 DOI: 10.1038/s41431-021-00830-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 12/06/2020] [Accepted: 02/10/2021] [Indexed: 01/26/2023] Open
Abstract
Hypertension, obesity, chronic kidney disease and type 2 diabetes are comorbidities that have very high prevalence among persons with hyperuricemia (serum urate > 6.8 mg/dL) and gout. Here we use multivariate genetic models to test the hypothesis that the co-association of traits representing hyperuricemia and its comorbidities is genetically based. Using Bayesian whole-genome regression models, we estimated the genetic marker-based variance and the covariance between serum urate, serum creatinine, systolic blood pressure (SBP), blood glucose and body mass index (BMI) from two independent family-based studies: The Framingham Heart Study-FHS and the Hypertension Genetic Epidemiology Network study-HyperGEN. The main genetic findings that replicated in both FHS and HyperGEN, were (1) creatinine was genetically correlated only with urate and (2) BMI was genetically correlated with urate, SBP, and glucose. The environmental covariance among the traits was generally highest for trait pairs involving BMI. The genetic overlap of traits representing the comorbidities of hyperuricemia and gout appears to cluster in two separate axes of genetic covariance. Because creatinine is genetically correlated with urate but not with metabolic traits, this suggests there is one genetic module of shared loci associated with hyperuricemia and chronic kidney disease. Another module of shared loci may account for the association of hyperuricemia and metabolic syndrome. This study provides a clear quantitative genetic basis for the clustering of comorbidities with hyperuricemia.
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Affiliation(s)
- Richard J Reynolds
- Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA.
| | - M Ryan Irvin
- Department of Epidemiology, UAB, Birmingham, AL, USA
| | - S Louis Bridges
- Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Hwasoon Kim
- Duke Clinical Research Institute, Durham, NC, USA
| | - Tony R Merriman
- Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA.,Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Donna K Arnett
- College of Public Health, University of Kentucky, Lexington, KY, USA
| | - Jasvinder A Singh
- Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA.,Birmingham VA Medical Center, Birmingham, AL, USA
| | - Nicholas A Sumpter
- Department of Medicine, Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Alexa S Lupi
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.,Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA
| | - Ana I Vazquez
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA. .,Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
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11
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Toledo-Alvarado H, Pérez-Cabal MA, Tempelman RJ, Cecchinato A, Bittante G, de Los Campos G, Vazquez AI. Association between days open and milk spectral data in dairy cows. J Dairy Sci 2021; 104:3665-3675. [PMID: 33455800 DOI: 10.3168/jds.2020-19031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 10/22/2020] [Indexed: 11/19/2022]
Abstract
Data on 19,489 Brown Swiss cows reared in northeastern Italy were used to associate absorbances of individual wavenumbers within the mid-infrared range with days open (DO). Different postcalving days in milk (DIM) intervals were studied to determine the most informative milk sampling periods for predicting DO. Milk samples were analyzed using a MilkoScan (Foss Electric, Hillerød, Denmark) Fourier-transform infrared (FTIR) spectrometer for 1,060 wavenumbers (wn) ranging from 5,011 to 925 cm-1. To determine DO, we considered an insemination to lead to conception when there was no return of heat (i.e., no successive insemination) and the cow had a subsequent calving date whereby gestation length was required to be within ±30 d of 290 d. Only milk records within the first 90 DIM were considered. Associations were inferred by (1) fitting linear regression models between the DO and each individual wavenumber or milk component, and (2) fitting a Bayesian regression model that included the complete FTIR spectral data. The effects of including systematic effects (parity number, year-season, herd) in the model on these associations were also studied. These analyses were performed for the complete data (5-90 DIM) and for data stratified by DIM period (5 to 30, 31 to 60, and 61 to 90 DIM). Overall, regions of wavenumbers of the milk FTIR spectra that were associated with DO included wn 2,973 to 2,830 cm-1 [related to fat-B (C-H stretch)], wn 2,217 to 1,769 cm-1 [related to fat-A (C = O stretch)], wn 1,546 cm-1 (related to protein), wn 1,465 cm-1 (related to urea and fat), wn 1,399 to 1,245 cm-1 (related to acetone), and wn 1,110 cm-1 (related to lactose). Estimated effects depended on the DIM period, with milk samples drawn during DIM intervals 31 to 60 d and 61 to 90 d being most strongly associated with DO. These DIM intervals are also typically most associated with negative energy balance and peak lactation.
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Affiliation(s)
- H Toledo-Alvarado
- Department of Genetics and Biostatistics, Faculty of Veterinary Medicine and Zootechnics, National Autonomous University of Mexico, 04510, Mexico City, Mexico; Department of Animal Production, Complutense University of Madrid, 28040 Madrid, Spain.
| | - M A Pérez-Cabal
- Department of Animal Production, Complutense University of Madrid, 28040 Madrid, Spain
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing 48824
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020, Legnaro PD, Italy
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020, Legnaro PD, Italy
| | - G de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824
| | - A I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824
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12
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de los Campos G, Pook T, Gonzalez-Reymundez A, Simianer H, Mias G, Vazquez AI. ANOVA-HD: Analysis of variance when both input and output layers are high-dimensional. PLoS One 2020; 15:e0243251. [PMID: 33315963 PMCID: PMC7735570 DOI: 10.1371/journal.pone.0243251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 11/17/2020] [Indexed: 11/21/2022] Open
Abstract
Modern genomic data sets often involve multiple data-layers (e.g., DNA-sequence, gene expression), each of which itself can be high-dimensional. The biological processes underlying these data-layers can lead to intricate multivariate association patterns. We propose and evaluate two methods to determine the proportion of variance of an output data set that can be explained by an input data set when both data panels are high dimensional. Our approach uses random-effects models to estimate the proportion of variance of vectors in the linear span of the output set that can be explained by regression on the input set. We consider a method based on an orthogonal basis (Eigen-ANOVA) and one that uses random vectors (Monte Carlo ANOVA, MC-ANOVA) in the linear span of the output set. Using simulations, we show that the MC-ANOVA method gave nearly unbiased estimates. Estimates produced by Eigen-ANOVA were also nearly unbiased, except when the shared variance was very high (e.g., >0.9). We demonstrate the potential insight that can be obtained from the use of MC-ANOVA and Eigen-ANOVA by applying these two methods to the study of multi-locus linkage disequilibrium in chicken (Gallus gallus) genomes and to the assessment of inter-dependencies between gene expression, methylation, and copy-number-variants in data from breast cancer tumors from humans (Homo sapiens). Our analyses reveal that in chicken breeding populations ~50,000 evenly-spaced SNPs are enough to fully capture the span of whole-genome-sequencing genomes. In the study of multi-omic breast cancer data, we found that the span of copy-number-variants can be fully explained using either methylation or gene expression data and that roughly 74% of the variance in gene expression can be predicted from methylation data.
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Affiliation(s)
- Gustavo de los Campos
- Epidemiology & Biostatistics, Michigan State University, East Lansing, MI, United States of America
- Statistics & Probability, Michigan State University, East Lansing, MI, United States of America
- Institute for Quantitative Health Science and Engineering, East Lansing, MI, United States of America
| | - Torsten Pook
- Department of Animal Sciences, Center for Integrated Breeding Research, University of Goettingen, Goettingen, Germany
| | - Agustin Gonzalez-Reymundez
- Genetics and Genome Sciences Graduate Program, Michigan State University, East Lansing, MI, United States of America
| | - Henner Simianer
- Department of Animal Sciences, Center for Integrated Breeding Research, University of Goettingen, Goettingen, Germany
| | - George Mias
- Institute for Quantitative Health Science and Engineering, East Lansing, MI, United States of America
- Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States of America
| | - Ana I. Vazquez
- Epidemiology & Biostatistics, Michigan State University, East Lansing, MI, United States of America
- Institute for Quantitative Health Science and Engineering, East Lansing, MI, United States of America
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13
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Leask MP, Sumpter NA, Lupi AS, Vazquez AI, Reynolds RJ, Mount DB, Merriman TR. The Shared Genetic Basis of Hyperuricemia, Gout, and Kidney Function. Semin Nephrol 2020; 40:586-599. [DOI: 10.1016/j.semnephrol.2020.12.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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14
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Martinez-Castillero M, Toledo-Alvarado H, Pegolo S, Vazquez AI, de Los Campos G, Varona L, Finocchiaro R, Bittante G, Cecchinato A. Genetic parameters for fertility traits assessed in herds divergent in milk energy output in Holstein-Friesian, Brown Swiss, and Simmental cattle. J Dairy Sci 2020; 103:11545-11558. [PMID: 33222858 DOI: 10.3168/jds.2020-18934] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 09/02/2020] [Indexed: 11/19/2022]
Abstract
In this study, we aimed to investigate differences in the genetics of fertility traits (heritability of traits and correlations between traits in divergent environments) in dairy cows of different production levels defined on the basis of the herd-average daily milk energy output (herd-dMEO). Data were obtained from Holstein-Friesian (n = 37,359 for fertility traits, 381,334 for dMEO), Brown Swiss (n = 79,638 for fertility traits, 665,697 for dMEO), and Simmental cows (n = 63,048 for fertility traits, 448,445 for dMEO) reared in northeastern Italy. Fertility traits under study were interval from calving to first service, interval from first service to conception, days open, calving interval, calving rate, and nonreturn rate at d 56. We classified herds into low and high productivity based on the herd-average dMEO (inferred using mixed effects models). We estimated genetic parameters using Bayesian bivariate animal models, where expressions of a phenotype in the low and high dMEO herds were taken as being different-albeit correlated-traits. Fertility traits were more favorable in Simmental than in Holstein-Friesian cows, whereas for all traits, Holstein-Friesian had the highest estimates of intraherd heritability [ranging from 0.021 (0.006-0.038) to 0.126 (0.10-0.15)] and Simmental the lowest [ranging from 0.008 (0.001-0.017) to 0.101 (0.08-0.12)]. The genetic correlations between fertility traits and dMEO were moderate and unfavorable, ranging, in absolute values, from 0.527 (0.37-0.68) to 0.619 (0.50-0.73) in Holstein-Friesian; from 0.339 (0.20-0.47) to 0.556 (0.45-0.66) in Brown Swiss; and from 0.340 (0.10-0.60) to 0.475 (0.33-0.61) in Simmental cattle. The only exception was the nonreturn rate at d 56, which had weak genetic correlations with dMEO in all 3 breeds. The herd correlations between fertility and dMEO tended to be modest and favorable and the residual correlations modest and variable. The heritability of fertility traits tended to be greater in the low dMEO than in the high dMEO herds in the case of the Holstein-Friesians, but not in the case of the Brown Swiss or Simmentals. The additive genetic correlations between fertility traits in the low and high dMEO herds were always lower than 1 [0.329 (-0.17 to 0.85) to 0.934 (0.86 to 0.99)] for all traits considered in all breeds. The correlation was particularly low for the threshold characters and the interval from first service to conception in Holstein-Friesian, suggesting that the relative performances of genotypes vary significantly between herds of different dMEO levels. Although there was large variability in the estimates, results might support making separate genetic evaluations of fertility in the different herd production groups. Our results also indicate that Simmental, a dual-purpose breed, has higher fertility and lower environmental sensitivity than Holstein-Friesian, with Brown Swiss being intermediate.
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Affiliation(s)
- M Martinez-Castillero
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020, Legnaro PD, Italy
| | - H Toledo-Alvarado
- Department of Genetics and Biostatistics, School of Veterinary Medicine and Zootechnics, National Autonomous University of Mexico, Ciudad Universitaria, 0451, Mexico City, México
| | - S Pegolo
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020, Legnaro PD, Italy.
| | - A I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, 909 Fee Road, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, 775 Woodlot Drive, East Lansing 48824
| | - G de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, 909 Fee Road, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, 775 Woodlot Drive, East Lansing 48824; Department of Statistics and Probability, Michigan State University, 619 Red Cellar Road, East Lansing 48824
| | - L Varona
- Unidad de Genética Cuantitativa y Mejora Animal, Instituto Agroalimentario de Aragón (IA2), Universidad de Zaragoza, Calle de Miguel Servet, 177, 50013, Zaragoza, Zaragoza, Spain
| | - R Finocchiaro
- Associazione Nazionale Allevatori bovini della razza Frisona e Jersey Italiana (ANAFIJ), Via Bergamo 292, 26100 Cremona, Italy
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020, Legnaro PD, Italy
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell' Università 16, 35020, Legnaro PD, Italy
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15
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Mukerjee S, Gonzalez-Reymundez A, Lunt SY, Vazquez AI. DNA Methylation and Gene Expression with Clinical Covariates Explain Variation in Aggressiveness and Survival of Pancreatic Cancer Patients. Cancer Invest 2020; 38:502-506. [PMID: 32935594 DOI: 10.1080/07357907.2020.1812079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Pancreatic cancer (PC) is associated with a high mortality rate. We explored the interindividual variation of cancer outcomes, attributable to DNA methylation, gene expression, and clinical factors among PC patients. We aim to determine whether we could differentiate subjects with greater nodal involvement, higher cancer staging, and subsequent survival. We modeled every response variable as a function of a linear predictor involving the effects of clinical variables, methylation, and gene expression in a Bayesian framework. Our results highlight the overall importance of wide-spread alterations in methylation and gene expression patterns associated with survival, nodal metastasis, and staging.
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Affiliation(s)
- Shyamali Mukerjee
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, USA
| | - Agustin Gonzalez-Reymundez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, USA.,Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Sophia Y Lunt
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, USA.,Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan, USA
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, USA.,Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, USA
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16
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Balmant KM, Noble JD, C Alves F, Dervinis C, Conde D, Schmidt HW, Vazquez AI, Barbazuk WB, Campos GDL, Resende MFR, Kirst M. Xylem systems genetics analysis reveals a key regulator of lignin biosynthesis in Populus deltoides. Genome Res 2020; 30:1131-1143. [PMID: 32817237 PMCID: PMC7462072 DOI: 10.1101/gr.261438.120] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 07/13/2020] [Indexed: 02/01/2023]
Abstract
Despite the growing resources and tools for high-throughput characterization and analysis of genomic information, the discovery of the genetic elements that regulate complex traits remains a challenge. Systems genetics is an emerging field that aims to understand the flow of biological information that underlies complex traits from genotype to phenotype. In this study, we used a systems genetics approach to identify and evaluate regulators of the lignin biosynthesis pathway in Populus deltoides by combining genome, transcriptome, and phenotype data from a population of 268 unrelated individuals of P. deltoides The discovery of lignin regulators began with the quantitative genetic analysis of the xylem transcriptome and resulted in the detection of 6706 and 4628 significant local- and distant-eQTL associations, respectively. Among the locally regulated genes, we identified the R2R3-MYB transcription factor MYB125 (Potri.003G114100) as a putative trans-regulator of the majority of genes in the lignin biosynthesis pathway. The expression of MYB125 in a diverse population positively correlated with lignin content. Furthermore, overexpression of MYB125 in transgenic poplar resulted in increased lignin content, as well as altered expression of genes in the lignin biosynthesis pathway. Altogether, our findings indicate that MYB125 is involved in the control of a transcriptional coexpression network of lignin biosynthesis genes during secondary cell wall formation in P. deltoides.
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Affiliation(s)
- Kelly M Balmant
- School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611, USA
| | - Jerald D Noble
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, Gainesville, Florida 32611, USA
| | - Filipe C Alves
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824, USA
| | - Christopher Dervinis
- School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611, USA
| | - Daniel Conde
- School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611, USA
| | - Henry W Schmidt
- School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611, USA
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824, USA
| | - William B Barbazuk
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, Gainesville, Florida 32611, USA
- Department of Biology, University of Florida, Gainesville, Florida 32611, USA
- Genetics Institute, University of Florida, Gainesville, Florida 32611, USA
| | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824, USA
- Statistics Department, Michigan State University, East Lansing, Michigan 48824, USA
| | - Marcio F R Resende
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, Gainesville, Florida 32611, USA
- Horticulture Sciences Department, University of Florida, Gainesville, Florida 32611, USA
| | - Matias Kirst
- School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611, USA
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, Gainesville, Florida 32611, USA
- Genetics Institute, University of Florida, Gainesville, Florida 32611, USA
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17
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Jiang Y, Chiu CY, Yan Q, Chen W, Gorin MB, Conley YP, Lakhal-Chaieb ML, Cook RJ, Amos CI, Wilson AF, Bailey-Wilson JE, McMahon FJ, Vazquez AI, Yuan A, Zhong X, Xiong M, Weeks DE, Fan R. Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration. J Am Stat Assoc 2020; 116:531-545. [PMID: 34321704 PMCID: PMC8315575 DOI: 10.1080/01621459.2020.1799809] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 07/09/2020] [Accepted: 07/17/2020] [Indexed: 10/23/2022]
Abstract
Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
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Affiliation(s)
- Yingda Jiang
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Chi-Yang Chiu
- Division of Biostatistics, Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Qi Yan
- Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh at The University of Pittsburgh, Pittsburgh, PA
| | - Wei Chen
- Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh at The University of Pittsburgh, Pittsburgh, PA
| | - Michael B. Gorin
- Department of Ophthalmology, David Geffen School of Medicine, UCLA Stein Eye Institute, Los Angeles, CA
| | - Yvette P. Conley
- Department of Health Promotion and Development, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | | | - Richard J. Cook
- Department of Statistics and Actuarial Science, Waterloo, ON, Canada
| | | | - Alexander F. Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Joan E. Bailey-Wilson
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
| | - Francis J. McMahon
- Human Genetics Branch and Genetic Basis of Mood and Anxiety Disorders Section, National Institute of Mental Health, NIH, Bethesda, MD
| | - Ana I. Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI
| | - Ao Yuan
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
| | - Xiaogang Zhong
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
| | - Momiao Xiong
- Human Genetics Center, University of Texas, Houston, TX
| | - Daniel E. Weeks
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Ruzong Fan
- Computational and Statistical Genomics Branch, National Human Genome Research Institute, NIH, Baltimore, MD
- Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC
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18
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Funkhouser SA, Vazquez AI, Steibel JP, Ernst CW, Los Campos GD. Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions. Genetics 2020; 215:231-241. [PMID: 32198180 PMCID: PMC7198271 DOI: 10.1534/genetics.120.303120] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2019] [Accepted: 03/01/2020] [Indexed: 11/18/2022] Open
Abstract
Many complex human traits exhibit differences between sexes. While numerous factors likely contribute to this phenomenon, growing evidence from genome-wide studies suggest a partial explanation: that males and females from the same population possess differing genetic architectures. Despite this, mapping gene-by-sex (G×S) interactions remains a challenge likely because the magnitude of such an interaction is typically and exceedingly small; traditional genome-wide association techniques may be underpowered to detect such events, due partly to the burden of multiple test correction. Here, we developed a local Bayesian regression (LBR) method to estimate sex-specific SNP marker effects after fully accounting for local linkage-disequilibrium (LD) patterns. This enabled us to infer sex-specific effects and G×S interactions either at the single SNP level, or by aggregating the effects of multiple SNPs to make inferences at the level of small LD-based regions. Using simulations in which there was imperfect LD between SNPs and causal variants, we showed that aggregating sex-specific marker effects with LBR provides improved power and resolution to detect G×S interactions over traditional single-SNP-based tests. When using LBR to analyze traits from the UK Biobank, we detected a relatively large G×S interaction impacting bone mineral density within ABO, and replicated many previously detected large-magnitude G×S interactions impacting waist-to-hip ratio. We also discovered many new G×S interactions impacting such traits as height and body mass index (BMI) within regions of the genome where both male- and female-specific effects explain a small proportion of phenotypic variance (R2 < 1 × 10-4), but are enriched in known expression quantitative trait loci.
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Affiliation(s)
- Scott A Funkhouser
- Institute for Behavioral Genetics, The University of Colorado, Boulder, Colorado 80309
- Genetics Graduate Program, Michigan State University, East Lansing, Michigan 48824
| | - Ana I Vazquez
- Departments of Epidemiology and Biostatistics and Statistics and Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, 48824
| | - Juan P Steibel
- Department of Animal Science, Michigan State University, East Lansing, Michigan, 48824
| | - Catherine W Ernst
- Department of Animal Science, Michigan State University, East Lansing, Michigan, 48824
| | - Gustavo de Los Campos
- Departments of Epidemiology and Biostatistics and Statistics and Probability, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan, 48824
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19
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Yu L, Teoh ST, Ensink E, Ogrodzinski MP, Yang C, Vazquez AI, Lunt SY. Cysteine catabolism and the serine biosynthesis pathway support pyruvate production during pyruvate kinase knockdown in pancreatic cancer cells. Cancer Metab 2019; 7:13. [PMID: 31893043 PMCID: PMC6937848 DOI: 10.1186/s40170-019-0205-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 12/06/2019] [Indexed: 12/11/2022] Open
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with limited treatment options. Pyruvate kinase, especially the M2 isoform (PKM2), is highly expressed in PDAC cells, but its role in pancreatic cancer remains controversial. To investigate the role of pyruvate kinase in pancreatic cancer, we knocked down PKM2 individually as well as both PKM1 and PKM2 concurrently (PKM1/2) in cell lines derived from a KrasG12D/-; p53-/- pancreatic mouse model. Methods We used liquid chromatography tandem mass spectrometry (LC-MS/MS) to determine metabolic profiles of wildtype and PKM1/2 knockdown PDAC cells. We further used stable isotope-labeled metabolic precursors and LC-MS/MS to determine metabolic pathways upregulated in PKM1/2 knockdown cells. We then targeted metabolic pathways upregulated in PKM1/2 knockdown cells using CRISPR/Cas9 gene editing technology. Results PDAC cells are able to proliferate and continue to produce pyruvate despite PKM1/2 knockdown. The serine biosynthesis pathway partially contributed to pyruvate production during PKM1/2 knockdown: knockout of phosphoglycerate dehydrogenase in this pathway decreased pyruvate production from glucose. In addition, cysteine catabolism generated ~ 20% of intracellular pyruvate in PDAC cells. Other potential sources of pyruvate include the sialic acid pathway and catabolism of glutamine, serine, tryptophan, and threonine. However, these sources did not provide significant levels of pyruvate in PKM1/2 knockdown cells. Conclusion PKM1/2 knockdown does not impact the proliferation of pancreatic cancer cells. The serine biosynthesis pathway supports conversion of glucose to pyruvate during pyruvate kinase knockdown. However, direct conversion of serine to pyruvate was not observed during PKM1/2 knockdown. Investigating several alternative sources of pyruvate identified cysteine catabolism for pyruvate production during PKM1/2 knockdown. Surprisingly, we find that a large percentage of intracellular pyruvate comes from cysteine. Our results highlight the ability of PDAC cells to adaptively rewire their metabolic pathways during knockdown of a key metabolic enzyme.
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Affiliation(s)
- Lei Yu
- 1Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI USA
| | - Shao Thing Teoh
- 1Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI USA
| | - Elliot Ensink
- 1Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI USA
| | - Martin P Ogrodzinski
- 1Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI USA.,2Department of Physiology, Michigan State University, East Lansing, MI USA
| | - Che Yang
- 1Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI USA
| | - Ana I Vazquez
- 3Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI USA.,4The Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI USA
| | - Sophia Y Lunt
- 1Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI USA.,5Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI USA
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20
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Behring M, Vazquez AI, Cui X, Irvin MR, Ojesina AI, Agarwal S, Manne U, Shrestha S. Gain of function in somatic TP53 mutations is associated with immune-rich breast tumors and changes in tumor-associated macrophages. Mol Genet Genomic Med 2019; 7:e1001. [PMID: 31637877 PMCID: PMC6900370 DOI: 10.1002/mgg3.1001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 09/04/2019] [Accepted: 09/05/2019] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Somatic mutations in TP53 are present in 20%-30% of all breast tumors. While there are numerous population-based analyses of TP53, yet none have examined the relationship between somatic mutations in TP53 and tumor invasive immune cells. METHODS Clinical and genetic data from 601 women drawn from The Cancer Genome Atlas (TCGA) were used to test the association between somatic TP53 mutation and immune-rich or immune-poor tumor status; determined using the CIBERSORT-based gene expression signature of 22 immune cell types. Our validation dataset, the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), used a pathologist-determined measure of lymphocyte infiltration. RESULTS Within TP53-mutated samples, a mutation at codon p.R175H was shown to be present at higher frequency in immune-rich tumors. In validation analysis, any somatic mutation in TP53 was associated with immune-rich status, and the mutation at p.R175H had a significant association with tumor-invasive lymphocytes. TCGA-only analysis of invasive immune cell type identified an increase in M0 macrophages associated with p.R175H. CONCLUSIONS These findings suggest that TP53 somatic mutations, particularly at codon p.R175H, are enriched in tumors with infiltrating immune cells. Our results confirm recent research showing inflammation-related gain of function in specific TP53 mutations.
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Affiliation(s)
- Michael Behring
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.,Department of Pathology and Surgery, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ana I Vazquez
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA.,Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA
| | - Xiangqin Cui
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Akinyemi I Ojesina
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.,HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA.,Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sumit Agarwal
- Department of Pathology and Surgery, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Upender Manne
- Department of Pathology and Surgery, University of Alabama at Birmingham, Birmingham, AL, USA.,Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Sadeep Shrestha
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.,Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL, USA
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21
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Joo J, Williamson SA, Vazquez AI, Fernandez JR, Bray MS. The influence of 15-week exercise training on dietary patterns among young adults. Int J Obes (Lond) 2019; 43:1681-1690. [PMID: 30659257 PMCID: PMC6639161 DOI: 10.1038/s41366-018-0299-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 11/18/2018] [Accepted: 11/29/2018] [Indexed: 12/17/2022]
Abstract
BACKGROUND/OBJECTIVES Little is currently known about how exercise may influence dietary patterns and/or food preferences. The present study aimed to examine the effect of a 15-week exercise training program on overall dietary patterns among young adults. SUBJECTS/METHODS This study consisted of 2680 young adults drawn from the Training Intervention and Genetics of Exercise Response (TIGER) study. Subjects underwent 15 weeks of aerobic exercise training, and exercise duration, intensity, and dose were recorded for each session using computerized heart rate monitors. In total, 4355 dietary observations with 102 food items were collected using a self-administered food frequency questionnaire before and after exercise training (n = 2476 at baseline; n = 1859 at 15 weeks). Dietary patterns were identified using a Bayesian sparse latent factor model. Changes in dietary pattern preferences were evaluated based on the pre/post-training differences in dietary pattern scores, accounting for the effects of gender, race/ethnicity, and BMI. RESULTS Within each of the seven dietary patterns identified, most dietary pattern scores were decreased following exercise training, consistent with increased voluntary regulation of food intake. A longer duration of exercise was associated with decreased preferences for the western (β: -0.0793; 95% credible interval: -0.1568, -0.0017) and snacking (β: -0.1280; 95% credible interval: -0.1877, -0.0637) patterns, while a higher intensity of exercise was linked to an increased preference for the prudent pattern (β: 0.0623; 95% credible interval: 0.0159, 0.1111). Consequently, a higher dose of exercise was related to a decreased preference for the snacking pattern (β: -0.0023; 95% credible interval: -0.0042, -0.0004) and an increased preference for the prudent pattern (β: 0.0029; 95% credible interval: 0.0009, 0.0048). CONCLUSIONS The 15-week exercise training appeared to motivate young adults to pursue healthier dietary preferences and to regulate their food intake.
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Affiliation(s)
- Jaehyun Joo
- Department of Nutritional Sciences, The University of Texas at Austin, Austin, TX, USA
| | - Sinead A Williamson
- Departments of Information, Risk, & Operations Management and Statistics & Data Science, The University of Texas at Austin, Austin, TX, USA
| | - Ana I Vazquez
- Department of Epidemiology & Biostatistics and Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Jose R Fernandez
- Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Molly S Bray
- Department of Nutritional Sciences, The University of Texas at Austin, Austin, TX, USA.
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22
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Rovere G, de Los Campos G, Tempelman RJ, Vazquez AI, Miglior F, Schenkel F, Cecchinato A, Bittante G, Toledo-Alvarado H, Fleming A. A landscape of the heritability of Fourier-transform infrared spectral wavelengths of milk samples by parity and lactation stage in Holstein cows. J Dairy Sci 2018; 102:1354-1363. [PMID: 30580946 DOI: 10.3168/jds.2018-15109] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 09/28/2018] [Indexed: 11/19/2022]
Abstract
Fourier-transform near- and mid-infrared (FTIR) milk spectral data are routinely collected in many countries worldwide. Establishing an optimal strategy to use spectral data in genetic evaluations requires knowledge of the heritabilities of individual FTIR wavelength absorbances. Previous FTIR heritability estimates have been based on relatively small sample sizes and have not considered the possibility that heritability may vary across parities and stages of the lactation. We used data from ∼370,000 test-day records of Canadian Holstein cows to produce a landscape of the heritability of FTIR spectra, 1,060 wavelengths in the near- and mid-infrared spectrum (5,011-925 cm-1), by parity and month of the lactation (mo 1 to 3 and mo 1 to 6, respectively). The 2 regions of the spectrum associated with absorption of electromagnetic energy by water molecules were estimated to have very high phenotypic variances, very low heritabilities, and very low proportion of variance explained by herd-year-season (HYS) subclasses. The near- or short-wavelength infrared (SWIR: 5,066-3,672 cm-1) region was also characterized by low heritability estimates, whereas the estimated proportion of the variance explained by HYS was high. The mid-wavelength infrared region (MWIR: 3,000-2,500 cm-1) and the transition between mid and long-wavelength infrared region (MWIR-LWIR: 1,500-925 cm-1) harbor several waves characterized by moderately high (≥0.4) heritabilities. Most of the high-heritability regions contained wavelengths that are reported to be associated with important milk metabolites and components. Interestingly, these 2 same regions tended to show more variability in heritabilities between parity and lactation stage. Second parity showed heritability patterns that were distinctly different from those of the first and third parities, whereas the first 2 mo of the lactation had clearly distinct heritability patterns compared with mo 3 to 6.
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Affiliation(s)
- G Rovere
- Department of Animal Science, Michigan State University, East Lansing 48824; Department of Epidemiology and Biostatistics, Michigan State University, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824.
| | - G de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824; Department of Statistics and Probability, Michigan State University, East Lansing 48824
| | - R J Tempelman
- Department of Animal Science, Michigan State University, East Lansing 48824
| | - A I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing 48824; Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing 48824
| | - F Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1; Canadian Dairy Network, Guelph, Ontario, Canada N1K 1E5
| | - F Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro, Italy
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro, Italy
| | - H Toledo-Alvarado
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, 35020 Legnaro, Italy
| | - A Fleming
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada, N1G 2W1; Canadian Dairy Network, Guelph, Ontario, Canada N1K 1E5
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23
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Behring M, Shrestha S, Manne U, Cui X, Gonzalez-Reymundez A, Grueneberg A, Vazquez AI. Integrated landscape of copy number variation and RNA expression associated with nodal metastasis in invasive ductal breast carcinoma. Oncotarget 2018; 9:36836-36848. [PMID: 30627325 PMCID: PMC6305147 DOI: 10.18632/oncotarget.26386] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 10/31/2018] [Indexed: 01/01/2023] Open
Abstract
Background Lymph node metastasis (NM) in breast cancer is a clinical predictor of patient outcomes, but how its genetic underpinnings contribute to aggressive phenotypes is unclear. Our objective was to create the first landscape analysis of CNV-associated NM in ductal breast cancer. To assess the role of copy number variations (CNVs) in NM, we compared CNVs and/or associated mRNA expression in primary tumors of patients with NM to those without metastasis. Results We found CNV loss in chromosomes 1, 3, 9, 18, and 19 and gains in chromosomes 5, 8, 12, 14, 16-17, and 20 that were associated with NM and replicated in both databases. In primary tumors, per-gene CNVs associated with NM were ten times more frequent than mRNA expression; however, there were few CNV-driven changes in mRNA expression that differed by nodal status. Overlapping regions of CNV changes and mRNA expression were evident for the CTAGE5 gene. In 8q12, 11q13-14, 20q1, and 17q14-24 regions, there were gene-specific gains in CNV-driven mRNA expression associated with NM. Methods Data on CNV and mRNA expression from the TCGA and the METABRIC consortium of breast ductal carcinoma were utilized to identify CNV-based features associated with NM. Within each dataset, associations were compared across omic platforms to identify CNV-driven variations in gene expression. Only replications across both datasets were considered as determinants of NM. Conclusions Gains in CTAGE5, NDUFC2, EIF4EBP1, and PSCA genes and their expression may aid in early diagnosis of metastatic breast carcinoma and have potential as therapeutic targets.
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Affiliation(s)
- Michael Behring
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.,Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Sadeep Shrestha
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Upender Manne
- Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35294, USA.,Department of Pathology and Surgery, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Xiangqin Cui
- Biostatistics Department, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Agustin Gonzalez-Reymundez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.,Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Alexander Grueneberg
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA.,Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
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24
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Joo J, Williamson SA, Vazquez AI, Fernandez JR, Bray MS. Advanced Dietary Patterns Analysis Using Sparse Latent Factor Models in Young Adults. J Nutr 2018; 148:1984-1992. [PMID: 30418566 PMCID: PMC6280002 DOI: 10.1093/jn/nxy188] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Accepted: 07/30/2018] [Indexed: 11/13/2022] Open
Abstract
Background Principal components analysis (PCA) has been the most widely used method for deriving dietary patterns to date. However, PCA requires arbitrary ad hoc decisions for selecting food variables in interpreting dietary patterns and does not easily accommodate covariates. Sparse latent factor models can be utilized to address these issues. Objective The objective of this study was to compare Bayesian sparse latent factor models with PCA for identifying dietary patterns among young adults. Methods Habitual food intake was estimated in 2730 sedentary young adults from the Training Interventions and Genetics of Exercise Response (TIGER) Study [aged 18-35 y; body mass index (BMI; in kg/m2): 26.5 ± 6.1] who exercised <30 min/wk during the previous 30 d without restricting caloric intake before study enrollment. A food-frequency questionnaire was used to generate the frequency intakes of 102 food items. Sparse latent factor modeling was applied to the standardized food intakes to derive dietary patterns, incorporating additional covariates (sex, race/ethnicity, and BMI). The identified dietary patterns via sparse latent factor modeling were compared with the PCA derived dietary patterns. Results Seven dietary patterns were identified in both PCA and sparse latent factor analysis. In contrast to PCA, the sparse latent factor analysis allowed the covariate information to be jointly accounted for in the estimation of dietary patterns in the model and offered probabilistic criteria to determine the foods relevant to each dietary pattern. The derived patterns from both methods generally described common dietary behaviors. Dietary patterns 1-4 had similar food subsets using both statistical approaches, but PCA had smaller sets of foods with more cross-loading elements between the 2 factors. Overall, the sparse latent factor analysis produced more interpretable dietary patterns, with fewer of the food items excluded from all patterns. Conclusion Sparse latent factor models can be useful in future studies of dietary patterns by reducing the intrinsic arbitrariness involving the choice of food variables in interpreting dietary patterns and incorporating covariates in the assessment of dietary patterns.
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Affiliation(s)
| | | | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI
| | - Jose R Fernandez
- Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL
| | - Molly S Bray
- Departments of Nutritional Sciences,Address correspondence to MSB (e-mail: )
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25
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Bernal Rubio YL, González-Reymúndez A, Wu KHH, Griguer CE, Steibel JP, de Los Campos G, Doseff A, Gallo K, Vazquez AI. Whole-Genome Multi-omic Study of Survival in Patients with Glioblastoma Multiforme. G3 (Bethesda) 2018; 8:3627-3636. [PMID: 30228192 PMCID: PMC6222579 DOI: 10.1534/g3.118.200391] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2018] [Accepted: 09/12/2018] [Indexed: 12/22/2022]
Abstract
Glioblastoma multiforme (GBM) has been recognized as the most lethal type of malignant brain tumor. Despite efforts of the medical and research community, patients' survival remains extremely low. Multi-omic profiles (including DNA sequence, methylation and gene expression) provide rich information about the tumor. These profiles are likely to reveal processes that may be predictive of patient survival. However, the integration of multi-omic profiles, which are high dimensional and heterogeneous in nature, poses great challenges. The goal of this work was to develop models for prediction of survival of GBM patients that can integrate clinical information and multi-omic profiles, using multi-layered Bayesian regressions. We apply the methodology to data from GBM patients from The Cancer Genome Atlas (TCGA, n = 501) to evaluate whether integrating multi-omic profiles (SNP-genotypes, methylation, copy number variants and gene expression) with clinical information (demographics as well as treatments) leads to an improved ability to predict patient survival. The proposed Bayesian models were used to estimate the proportion of variance explained by clinical covariates and omics and to evaluate prediction accuracy in cross validation (using the area under the Receiver Operating Characteristic curve, AUC). Among clinical and demographic covariates, age (AUC = 0.664) and the use of temozolomide (AUC = 0.606) were the most predictive of survival. Among omics, methylation (AUC = 0.623) and gene expression (AUC = 0.593) were more predictive than either SNP (AUC = 0.539) or CNV (AUC = 0.547). While there was a clear association between age and methylation, the integration of age, the use of temozolomide, and either gene expression or methylation led to a substantial increase in AUC in cross-validaton (AUC = 0.718). Finally, among the genes whose methylation was higher in aging brains, we observed a higher enrichment of these genes being also differentially methylated in cancer.
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Affiliation(s)
| | | | - Kuan-Han H Wu
- Department of Epidemiology and Biostatistics
- Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan, 48202
| | - Corinne E Griguer
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, Alabama, 35294
| | - Juan P Steibel
- Department of Animal Science and Department of Fisheries and Wildlife
| | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics
- Institute for Quantitative Health Science and Engineering
- Department of Statistics and Probability
| | - Andrea Doseff
- Department of Physiology
- Department of Pharmacology and Toxicology, Michigan State University, East Lansing, Michigan, 48823
| | | | - Ana I Vazquez
- Department of Epidemiology and Biostatistics
- Institute for Quantitative Health Science and Engineering
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Abstract
We construct genomic predictors for heritable but extremely complex human quantitative traits (height, heel bone density, and educational attainment) using modern methods in high dimensional statistics (i.e., machine learning). The constructed predictors explain, respectively, ∼40, 20, and 9% of total variance for the three traits, in data not used for training. For example, predicted heights correlate ∼0.65 with actual height; actual heights of most individuals in validation samples are within a few centimeters of the prediction. The proportion of variance explained for height is comparable to the estimated common SNP heritability from genome-wide complex trait analysis (GCTA), and seems to be close to its asymptotic value (i.e., as sample size goes to infinity), suggesting that we have captured most of the heritability for SNPs. Thus, our results close the gap between prediction R-squared and common SNP heritability. The ∼20k activated SNPs in our height predictor reveal the genetic architecture of human height, at least for common variants. Our primary dataset is the UK Biobank cohort, comprised of almost 500k individual genotypes with multiple phenotypes. We also use other datasets and SNPs found in earlier genome-wide association studies (GWAS) for out-of-sample validation of our results.
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Affiliation(s)
- Louis Lello
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan 48824
| | - Steven G Avery
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan 48824
| | - Laurent Tellier
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan 48824
- Cognitive Genomics Laboratory, Shenzhen Key Laboratory of Neurogenomics, China National GeneBank, BGI-Shenzhen, 518083, China
- Department of Biology, Functional Genetics, University of Copenhagen, DK-2200, Denmark
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824
| | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 48824
| | - Stephen D H Hsu
- Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan 48824
- Cognitive Genomics Laboratory, Shenzhen Key Laboratory of Neurogenomics, China National GeneBank, BGI-Shenzhen, 518083, China
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Toledo-Alvarado H, Vazquez AI, de los Campos G, Tempelman RJ, Gabai G, Cecchinato A, Bittante G. Changes in milk characteristics and fatty acid profile during the estrous cycle in dairy cows. J Dairy Sci 2018; 101:9135-9153. [DOI: 10.3168/jds.2018-14480] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 05/31/2018] [Indexed: 11/19/2022]
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Sun M, Vazquez AI, Reynolds RJ, Singh JA, Reeves M, Merriman TR, Gaffo AL, Los Campos GD. Untangling the complex relationships between incident gout risk, serum urate, and its comorbidities. Arthritis Res Ther 2018; 20:90. [PMID: 29720278 PMCID: PMC5932762 DOI: 10.1186/s13075-018-1558-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 03/06/2018] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Many gout comorbidities (e.g., hypertension) are correlated with serum urate. In this investigation, we identified risk factors (e.g., systolic blood pressure [SBP]), that (1) are associated with incident gout, (2) have effects on gout risk that cannot be fully explained by correlated differences in serum urate, and (3) may modulate the relationship between gout and serum urate. METHODS Using data from the Atherosclerosis Risk in Communities (ARIC) study, we estimated the unadjusted associations between gout and risk factors by calculating ORs and using chi-square tests. The adjusted associations were analyzed using logistic regression by sequentially adding (1) one risk factor at a time or (2) all risk factors, to a baseline model that includes serum urate only. Stepwise selection was used to select main effects. Two-way interactions of variables from the main effects model were also analyzed. RESULTS Average gout incidence was 2.7 per 1000 people per year. Serum urate was highly associated with incident gout, with odd ratios of 3.16 [95% CI 2.11, 4.76] and 25.9 [95% CI 17.2, 38.4] for moderately high (6-8 mg/dl) and high serum urate (> 8 mg/dl), relative to normal serum urate (< 6 mg/dl), respectively. Ethnicity and SBP were independently and additively associated with gout after accounting for serum urate levels. No significant interactions were found between serum urate and ethnicity or SBP. CONCLUSIONS Ethnicity and hypertension are predictive of gout risk, and the associations cannot be fully explained by serum urate. For serum urate levels near the crystallization threshold (6-8 mg/dl) African Americans and people with hypertension are at two to three times greater risk for developing gout. The gout risk for this group appears to increase before the onset of severe hyperuricemia.
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Affiliation(s)
- Mengying Sun
- Department of Epidemiology and Biostatistics, Michigan State University, 220 Trowbridge Rd, East Lansing, MI 48824, USA.,The Institute for Quantitative Health Science and Engineering, Michigan State University, 220 Trowbridge Rd, East Lansing, MI 48824, USA
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, 220 Trowbridge Rd, East Lansing, MI 48824, USA.,The Institute for Quantitative Health Science and Engineering, Michigan State University, 220 Trowbridge Rd, East Lansing, MI 48824, USA
| | - Richard J Reynolds
- Division of Clinical Immunology and Rheumatology, University of Alabama Birmingham (UAB), 1825 University Blvd., Birmingham, AL 35294, USA
| | - Jasvinder A Singh
- Division of Clinical Immunology and Rheumatology, University of Alabama Birmingham (UAB), 1825 University Blvd., Birmingham, AL 35294, USA
| | - Mathew Reeves
- Department of Epidemiology and Biostatistics, Michigan State University, 220 Trowbridge Rd, East Lansing, MI 48824, USA
| | - Tony R Merriman
- Biochemistry Department, School of Biomedical Sciences, University of Otago, 710 Cumberland St., Dunedin, 9054, New Zealand
| | - Angelo L Gaffo
- Division of Clinical Immunology and Rheumatology, University of Alabama Birmingham (UAB), 1825 University Blvd., Birmingham, AL 35294, USA
| | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, 220 Trowbridge Rd, East Lansing, MI 48824, USA. .,The Institute for Quantitative Health Science and Engineering, Michigan State University, 220 Trowbridge Rd, East Lansing, MI 48824, USA. .,Department of Probability and Statistics, Michigan State University, 220 Trowbridge Rd, East Lansing, MI 48824, USA.
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Toledo-Alvarado H, Vazquez AI, de los Campos G, Tempelman RJ, Bittante G, Cecchinato A. Diagnosing pregnancy status using infrared spectra and milk composition in dairy cows. J Dairy Sci 2018; 101:2496-2505. [DOI: 10.3168/jds.2017-13647] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 11/08/2017] [Indexed: 01/01/2023]
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Pickens CA, Vazquez AI, Jones AD, Fenton JI. Obesity, adipokines, and C-peptide are associated with distinct plasma phospholipid profiles in adult males, an untargeted lipidomic approach. Sci Rep 2017; 7:6335. [PMID: 28740130 PMCID: PMC5524758 DOI: 10.1038/s41598-017-05785-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 06/05/2017] [Indexed: 12/12/2022] Open
Abstract
Obesity is associated with dysregulated lipid metabolism and adipokine secretion. Our group has previously reported obesity and adipokines are associated with % total fatty acid (FA) differences in plasma phospholipids. The objective of our current study was to identify in which complex lipid species (i.e., phosphatidylcholine, sphingolipids, etc) these FA differences occur. Plasma lipidomic profiling (n = 126, >95% Caucasian, 48–65 years) was performed using chromatographic separation and high resolution tandem mass spectrometry. The responses used in the statistical analyses were body mass index (BMI), waist circumference (WC), serum adipokines, cytokines, and a glycemic marker. High-dimensional statistical analyses were performed, all models were adjusted for age and smoking, and p-values were adjusted for false discovery. In Bayesian models, the lipidomic profiles (over 1,700 lipids) accounted for >60% of the inter-individual variation of BMI, WC, and leptin in our population. Across statistical analyses, we report 51 individual plasma lipids were significantly associated with obesity. Obesity was inversely associated lysophospholipids and ether linked phosphatidylcholines. In addition, we identify several unreported lipids associated with obesity that are not present in lipid databases. Taken together, these results provide new insights into the underlying biology associated with obesity and reveal new potential pathways for therapeutic targeting.
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Affiliation(s)
- C Austin Pickens
- Department of Food Science and Human Nutrition, Michigan State University, 469 Wilson Road, East Lansing, MI 48824, USA
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, 909 Fee Road, East Lansing, MI 48824, USA
| | - A Daniel Jones
- Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Road, East Lansing, MI 48824, USA.,Department of Chemistry, Michigan State University, 578 S Shaw Lane, East Lansing, MI 48824, USA
| | - Jenifer I Fenton
- Department of Food Science and Human Nutrition, Michigan State University, 469 Wilson Road, East Lansing, MI 48824, USA.
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Bray MS, Herring MP, Dishman RK, O’Connor DP, Jackson AS, Vazquez AI. Genome-wide Association For Exercise Tolerance In The TIGER Study. Med Sci Sports Exerc 2017. [DOI: 10.1249/01.mss.0000517065.40263.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Fahrenkrog AM, Neves LG, Resende MFR, Vazquez AI, de Los Campos G, Dervinis C, Sykes R, Davis M, Davenport R, Barbazuk WB, Kirst M. Genome-wide association study reveals putative regulators of bioenergy traits in Populus deltoides. New Phytol 2017; 213:799-811. [PMID: 27596807 DOI: 10.1111/nph.14154] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 07/13/2016] [Indexed: 05/18/2023]
Abstract
Genome-wide association studies (GWAS) have been used extensively to dissect the genetic regulation of complex traits in plants. These studies have focused largely on the analysis of common genetic variants despite the abundance of rare polymorphisms in several species, and their potential role in trait variation. Here, we conducted the first GWAS in Populus deltoides, a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits, and common and low-frequency single-nucleotide polymorphisms detected by targeted resequencing of 18 153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. Our results suggest that both common and low-frequency variants need to be considered for a comprehensive understanding of the genetic regulation of complex traits, particularly in species that carry large numbers of rare polymorphisms. These polymorphisms may be critical for the development of specialized plant feedstocks for bioenergy.
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Affiliation(s)
- Annette M Fahrenkrog
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL, 32611, USA
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, PO Box 110690, Gainesville, FL, 32610, USA
| | - Leandro G Neves
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL, 32611, USA
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, PO Box 110690, Gainesville, FL, 32610, USA
| | - Márcio F R Resende
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL, 32611, USA
- Genetics and Genomics Graduate Program, University of Florida, PO Box 103610, Gainesville, FL, 32610, USA
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, 909 Fee Road, East Lansing, MI, 48824, USA
| | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, 909 Fee Road, East Lansing, MI, 48824, USA
- Statistics Department, Michigan State University, 619 Red Cedar Road, MI, 48824, USA
| | - Christopher Dervinis
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL, 32611, USA
| | - Robert Sykes
- National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA
| | - Mark Davis
- National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO, 80401, USA
| | - Ruth Davenport
- Biology Department, University of Florida, PO Box 118525, Gainesville, FL, 32611, USA
| | - William B Barbazuk
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, PO Box 110690, Gainesville, FL, 32610, USA
- Biology Department, University of Florida, PO Box 118525, Gainesville, FL, 32611, USA
- University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL, 32611, USA
| | - Matias Kirst
- School of Forest Resources and Conservation, University of Florida, PO Box 110410, Gainesville, FL, 32611, USA
- Plant Molecular and Cellular Biology Graduate Program, University of Florida, PO Box 110690, Gainesville, FL, 32610, USA
- University of Florida Genetics Institute, University of Florida, PO Box 103610, Gainesville, FL, 32611, USA
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Vazquez AI, Veturi Y, Behring M, Shrestha S, Kirst M, Resende MFR, de Los Campos G. Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole-Genome Multiomic Profiles. Genetics 2016; 203:1425-38. [PMID: 27129736 PMCID: PMC4937492 DOI: 10.1534/genetics.115.185181] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2015] [Accepted: 04/12/2015] [Indexed: 11/18/2022] Open
Abstract
Whole-genome multiomic profiles hold valuable information for the analysis and prediction of disease risk and progression. However, integrating high-dimensional multilayer omic data into risk-assessment models is statistically and computationally challenging. We describe a statistical framework, the Bayesian generalized additive model ((BGAM), and present software for integrating multilayer high-dimensional inputs into risk-assessment models. We used BGAM and data from The Cancer Genome Atlas for the analysis and prediction of survival after diagnosis of breast cancer. We developed a sequence of studies to (1) compare predictions based on single omics with those based on clinical covariates commonly used for the assessment of breast cancer patients (COV), (2) evaluate the benefits of combining COV and omics, (3) compare models based on (a) COV and gene expression profiles from oncogenes with (b) COV and whole-genome gene expression (WGGE) profiles, and (4) evaluate the impacts of combining multiple omics and their interactions. We report that (1) WGGE profiles and whole-genome methylation (METH) profiles offer more predictive power than any of the COV commonly used in clinical practice (e.g., subtype and stage), (2) adding WGGE or METH profiles to COV increases prediction accuracy, (3) the predictive power of WGGE profiles is considerably higher than that based on expression from large-effect oncogenes, and (4) the gain in prediction accuracy when combining multiple omics is consistent. Our results show the feasibility of omic integration and highlight the importance of WGGE and METH profiles in breast cancer, achieving gains of up to 7 points area under the curve (AUC) over the COV in some cases.
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Affiliation(s)
- Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824
| | - Yogasudha Veturi
- Biostatistics Department, University of Alabama at Birmingham, Alabama 35294
| | - Michael Behring
- Comprehensive Cancer Center, University of Alabama at Birmingham, Alabama 35294 Department of Epidemiology, University of Alabama at Birmingham, Alabama 35294
| | - Sadeep Shrestha
- Department of Epidemiology, University of Alabama at Birmingham, Alabama 35294
| | - Matias Kirst
- School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611 University of Florida Genetics Institute, University of Florida, Gainesville, Florida 32611
| | - Marcio F R Resende
- School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32611 University of Florida Genetics Institute, University of Florida, Gainesville, Florida 32611
| | - Gustavo de Los Campos
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824 Statistics Department, Michigan State University, East Lansing, Michigan 48824
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de Los Campos G, Veturi Y, Vazquez AI, Lehermeier C, Pérez-Rodríguez P. Incorporating Genetic Heterogeneity in Whole-Genome Regressions Using Interactions. J Agric Biol Environ Stat 2015; 20:467-490. [PMID: 26660276 PMCID: PMC4666286 DOI: 10.1007/s13253-015-0222-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Accepted: 09/16/2015] [Indexed: 11/22/2022]
Abstract
Naturally and artificially selected populations usually exhibit some degree of stratification. In Genome-Wide Association Studies and in Whole-Genome Regressions (WGR) analyses, population stratification has been either ignored or dealt with as a potential confounder. However, systematic differences in allele frequency and in patterns of linkage disequilibrium can induce sub-population-specific effects. From this perspective, structure acts as an effect modifier rather than as a confounder. In this article, we extend WGR models commonly used in plant and animal breeding to allow for sub-population-specific effects. This is achieved by decomposing marker effects into main effects and interaction components that describe group-specific deviations. The model can be used both with variable selection and shrinkage methods and can be implemented using existing software for genomic selection. Using a wheat and a pig breeding data set, we compare parameter estimates and the prediction accuracy of the interaction WGR model with WGR analysis ignoring population stratification (across-group analysis) and with a stratified (i.e., within-sub-population) WGR analysis. The interaction model renders trait-specific estimates of the average correlation of effects between sub-populations; we find that such correlation not only depends on the extent of genetic differentiation in allele frequencies between groups but also varies among traits. The evaluation of prediction accuracy shows a modest superiority of the interaction model relative to the other two approaches. This superiority is the result of better stability in performance of the interaction models across data sets and traits; indeed, in almost all cases, the interaction model was either the best performing model or it performed close to the best performing model.
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Affiliation(s)
- Gustavo de Los Campos
- Department of Epidemiology & Biostatistics, Michigan State University, 909 Fee Road, Room B601, East Lansing, MI 48824 USA ; Department of Statistics & Probability, Michigan State University, 619 Red Cedar Rd., East Lansing, MI 48824 USA
| | - Yogasudha Veturi
- University of Alabama at Birmingham, Ryals Public Health Bldg. 443, Birmingham, AL 35294 USA
| | - Ana I Vazquez
- Department of Epidemiology & Biostatistics, Michigan State University, 909 Fee Road, Room B601, East Lansing, MI 48824 USA
| | - Christina Lehermeier
- Department of Plant Breeding, Technische Universität München, Liesel-Beckmann-Str. 2, 85354 Freising, Germany
| | - Paulino Pérez-Rodríguez
- Colegio de Postgraduados, Km. 36.5, Carretera Mexico, Montecillo, 56230 Texcoco, Estado de México Mexico
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Reynolds RJ, Vazquez AI, Srinivasasainagendra V, Klimentidis YC, Bridges SL, Allison DB, Singh JA. Serum urate gene associations with incident gout, measured in the Framingham Heart Study, are modified by renal disease and not by body mass index. Rheumatol Int 2015; 36:263-70. [PMID: 26427508 DOI: 10.1007/s00296-015-3364-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Accepted: 09/17/2015] [Indexed: 02/04/2023]
Abstract
We hypothesized that serum urate-associated SNPs, individually or collectively, interact with BMI and renal disease to contribute to risk of incident gout. We measured the incidence of gout and associated comorbidities using the original and offspring cohorts of the Framingham Heart Study. We used direct and imputed genotypes for eight validated serum urate loci. We fit binomial regression models of gout incidence as a function of the covariates, age, type 2 diabetes, sex, and all main and interaction effects of the eight serum urate SNPs with BMI and renal disease. Models were also fit with a genetic risk score for serum urate levels which corresponds to the sum of risk alleles at the eight SNPs. Model covariates, age (P = 5.95E-06), sex (P = 2.46E-39), diabetes (P = 2.34E-07), BMI (P = 1.14E-11) and the SNPs, rs1967017 (P = 9.54E-03), rs13129697 (P = 4.34E-07), rs2199936 (P = 7.28E-03) and rs675209 (P = 4.84E-02) were all associated with incident gout. No BMI by SNP or BMI by serum urate genetic risk score interactions were statistically significant, but renal disease by rs1106766 was statistically significant (P = 6.12E-03). We demonstrated that minor alleles of rs1106766 (intergenic, INHBC) were negatively associated with the risk of incident gout in subjects without renal disease, but not for individuals with renal disease. These analyses demonstrate that a significant component of the risk of gout may involve complex interplay between genes and environment.
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Affiliation(s)
- Richard J Reynolds
- Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Faculty Office Tower 805B, 510 20th Street S, Birmingham, AL, 35294, USA
| | - Ana I Vazquez
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA
| | | | - Yann C Klimentidis
- Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA
| | - S Louis Bridges
- Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Faculty Office Tower 805B, 510 20th Street S, Birmingham, AL, 35294, USA
| | - David B Allison
- Nutrition Obesity Research Center, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jasvinder A Singh
- Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Faculty Office Tower 805B, 510 20th Street S, Birmingham, AL, 35294, USA.
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Ferragina A, de los Campos G, Vazquez AI, Cecchinato A, Bittante G. Bayesian regression models outperform partial least squares methods for predicting milk components and technological properties using infrared spectral data. J Dairy Sci 2015; 98:8133-51. [PMID: 26387015 DOI: 10.3168/jds.2014-9143] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2014] [Accepted: 07/06/2015] [Indexed: 11/19/2022]
Abstract
The aim of this study was to assess the performance of Bayesian models commonly used for genomic selection to predict "difficult-to-predict" dairy traits, such as milk fatty acid (FA) expressed as percentage of total fatty acids, and technological properties, such as fresh cheese yield and protein recovery, using Fourier-transform infrared (FTIR) spectral data. Our main hypothesis was that Bayesian models that can estimate shrinkage and perform variable selection may improve our ability to predict FA traits and technological traits above and beyond what can be achieved using the current calibration models (e.g., partial least squares, PLS). To this end, we assessed a series of Bayesian methods and compared their prediction performance with that of PLS. The comparison between models was done using the same sets of data (i.e., same samples, same variability, same spectral treatment) for each trait. Data consisted of 1,264 individual milk samples collected from Brown Swiss cows for which gas chromatographic FA composition, milk coagulation properties, and cheese-yield traits were available. For each sample, 2 spectra in the infrared region from 5,011 to 925 cm(-1) were available and averaged before data analysis. Three Bayesian models: Bayesian ridge regression (Bayes RR), Bayes A, and Bayes B, and 2 reference models: PLS and modified PLS (MPLS) procedures, were used to calibrate equations for each of the traits. The Bayesian models used were implemented in the R package BGLR (http://cran.r-project.org/web/packages/BGLR/index.html), whereas the PLS and MPLS were those implemented in the WinISI II software (Infrasoft International LLC, State College, PA). Prediction accuracy was estimated for each trait and model using 25 replicates of a training-testing validation procedure. Compared with PLS, which is currently the most widely used calibration method, MPLS and the 3 Bayesian methods showed significantly greater prediction accuracy. Accuracy increased in moving from calibration to external validation methods, and in moving from PLS and MPLS to Bayesian methods, particularly Bayes A and Bayes B. The maximum R(2) value of validation was obtained with Bayes B and Bayes A. For the FA, C10:0 (% of each FA on total FA basis) had the highest R(2) (0.75, achieved with Bayes A and Bayes B), and among the technological traits, fresh cheese yield R(2) of 0.82 (achieved with Bayes B). These 2 methods have proven to be useful instruments in shrinking and selecting very informative wavelengths and inferring the structure and functions of the analyzed traits. We conclude that Bayesian models are powerful tools for deriving calibration equations, and, importantly, these equations can be easily developed using existing open-source software. As part of our study, we provide scripts based on the open source R software BGLR, which can be used to train customized prediction equations for other traits or populations.
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Affiliation(s)
- A Ferragina
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
| | - G de los Campos
- Epidemiology and Biostatistics Department, Michigan State University, East Lansing 48824; Department of Statistics and Probability, Michigan State University, East Lansing 48824
| | - A I Vazquez
- Epidemiology and Biostatistics Department, Michigan State University, East Lansing 48824
| | - A Cecchinato
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy.
| | - G Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro, Italy
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Lebrón-Aldea D, Dhurandhar EJ, Pérez-Rodríguez P, Klimentidis YC, Tiwari HK, Vazquez AI. Integrated genomic and BMI analysis for type 2 diabetes risk assessment. Front Genet 2015; 6:75. [PMID: 25852736 PMCID: PMC4362394 DOI: 10.3389/fgene.2015.00075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 02/12/2015] [Indexed: 11/23/2022] Open
Abstract
Type 2 Diabetes (T2D) is a chronic disease arising from the development of insulin absence or resistance within the body, and a complex interplay of environmental and genetic factors. The incidence of T2D has increased throughout the last few decades, together with the occurrence of the obesity epidemic. The consideration of variants identified by Genome Wide Association Studies (GWAS) into risk assessment models for T2D could aid in the identification of at-risk patients who could benefit from preventive medicine. In this study, we build several risk assessment models, evaluated with two different classification approaches (Logistic Regression and Neural Networks), to measure the effect of including genetic information in the prediction of T2D. We used data from to the Original and the Offspring cohorts of the Framingham Heart Study, which provides phenotypic and genetic information for 5245 subjects (4306 controls and 939 cases). Models were built by using several covariates: gender, exposure time, cohort, body mass index (BMI), and 65 SNPs associated to T2D. We fitted Logistic Regressions and Bayesian Regularized Neural Networks and then assessed their predictive ability by using a ten-fold cross validation. We found that the inclusion of genetic information into the risk assessment models increased the predictive ability by 2%, when compared to the baseline model. Furthermore, the models that included BMI at the onset of diabetes as a possible effector, gave an improvement of 6% in the area under the curve derived from the ROC analysis. The highest AUC achieved (0.75) belonged to the model that included BMI, and a genetic score based on the 65 established T2D-associated SNPs. Finally, the inclusion of SNPs and BMI raised predictive ability in all models as expected; however, results from the AUC in Neural Networks and Logistic Regression did not differ significantly in their prediction accuracy.
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Affiliation(s)
- Dayanara Lebrón-Aldea
- Institute of Mathematics, School of Science and Technology, Universidad Metropolitana San Juan, Puerto Rico
| | - Emily J Dhurandhar
- Department of Health Behavior, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA
| | | | - Yann C Klimentidis
- Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona Tucson, AZ, USA
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA
| | - Ana I Vazquez
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA
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Shendre A, Wiener HW, Zhi D, Vazquez AI, Portman MA, Shrestha S. High-density genotyping of immune loci in Kawasaki disease and IVIG treatment response in European-American case-parent trio study. Genes Immun 2014; 15:534-42. [PMID: 25101798 PMCID: PMC4257866 DOI: 10.1038/gene.2014.47] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Revised: 06/24/2014] [Accepted: 06/25/2014] [Indexed: 12/04/2022]
Abstract
Kawasaki disease (KD) is a diffuse and acute small-vessel vasculitis observed in children, and has genetic and autoimmune components. We genotyped 112 case-parent trios of European decent (confirmed by ancestry informative markers) using the immunoChip array, and performed association analyses with susceptibility to KD and intravenous immunoglobulin (IVIG) non-response. KD susceptibility was assessed using the transmission disequilibrium test, whereas IVIG non-response was evaluated using multivariable logistic regression analysis. We replicated single-nucleotide polymorphisms (SNPs) in three gene regions (FCGR, CD40/CDH22 and HLA-DQB2/HLA-DOB) that have been previously associated with KD and provide support to other findings of several novel SNPs in genes with a potential pathway in KD pathogenesis. SNP rs838143 in the 3'-untranslated region of the FUT1 gene (2.7 × 10(-5)) and rs9847915 in the intergenic region of LOC730109 | BRD7P2 (6.81 × 10(-7)) were the top hits for KD susceptibility in additive and dominant models, respectively. The top hits for IVIG responsiveness were rs1200332 in the intergenic region of BAZ1A | C14orf19 (1.4 × 10(-4)) and rs4889606 in the intron of the STX1B gene (6.95 × 10(-5)) in additive and dominant models, respectively. Our study suggests that genes and biological pathways involved in autoimmune diseases have an important role in the pathogenesis of KD and IVIG response mechanism.
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Affiliation(s)
- Aditi Shendre
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | - Howard W. Wiener
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
| | - Degui Zhi
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL
| | - Ana I Vazquez
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL
| | - Michael A. Portman
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL
| | - Sadeep Shrestha
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL
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Dhurandhar EJ, Vazquez AI, Argyropoulos GA, Allison DB. Even modest prediction accuracy of genomic models can have large clinical utility. Front Genet 2014; 5:417. [PMID: 25506355 PMCID: PMC4246888 DOI: 10.3389/fgene.2014.00417] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2014] [Accepted: 11/07/2014] [Indexed: 11/17/2022] Open
Abstract
Whole Genome Prediction (WGP) jointly fits thousands of SNPs into a regression model to yield estimates for the contribution of markers to the overall variance of a particular trait, and for their associations with that trait. To date, WGP has offered only modest prediction accuracy, but in some cases even modest prediction accuracy may be useful. We provide an illustration of this using a theoretical simulation that used WGP to predict weight loss after bariatric surgery with moderate accuracy (R2 = 0.07) to assess the clinical utility of WGP despite these limitations. Prevention of Type 2 Diabetes (T2DM) post-surgery was considered the major outcome. Treating only patients above predefined threshold of predicted weight loss in our simulation, in the realistic context of finite resources for the surgery, significantly reduced lifetime risk of T2DM in the treatable population by selecting those most likely to succeed. Thus, our example illustrates how WGP may be clinically useful in some situations, and even with moderate accuracy, may provide a clear path for turning personalized medicine from theory to reality.
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Affiliation(s)
- Emily J Dhurandhar
- Department of Health Behavior, Nutrition Obesity Research Center, Office of Energetics, University of Alabama at Birmingham Birmingham, AL, USA
| | - Ana I Vazquez
- Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham Birmingham, AL, USA
| | - George A Argyropoulos
- Weis Center for Research, Institute of Obesity, Geisinger Health System Danville, PA, USA
| | - David B Allison
- School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA
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Klimentidis YC, Wineinger NE, Vazquez AI, de Los Campos G. Multiple metabolic genetic risk scores and type 2 diabetes risk in three racial/ethnic groups. J Clin Endocrinol Metab 2014; 99:E1814-8. [PMID: 24905067 PMCID: PMC4154088 DOI: 10.1210/jc.2014-1818] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
UNLABELLED CONTEXT/RATIONALE: Meta-analyses of genome-wide association studies have identified many single-nucleotide polymorphisms associated with various metabolic and cardiovascular traits, offering us the opportunity to learn about and capitalize on the links between cardiometabolic traits and type 2 diabetes (T2D). DESIGN In multiple datasets comprising over 30 000 individuals and 3 ethnic/racial groups, we calculated 17 genetic risk scores (GRSs) for glycemic, anthropometric, lipid, hemodynamic, and other traits, based on the results of recent trait-specific meta-analyses of genome-wide association studies, and examined associations with T2D risk. Using a training-testing procedure, we evaluated whether additional GRSs could contribute to risk prediction. RESULTS In European Americans, we find that GRSs for T2D, fasting glucose, fasting insulin, and body mass index are associated with T2D risk. In African Americans, GRSs for T2D, fasting insulin, and waist-to-hip ratio are associated with T2D. In Hispanic Americans, GRSs for T2D and body mass index are associated with T2D. We observed a trend among European Americans suggesting that genetic risk for hyperlipidemia is inversely associated with T2D risk. The use of additional GRSs resulted in only small changes in prediction accuracy in multiple independent validation datasets. CONCLUSIONS The analysis of multiple GRSs can shed light on T2D etiology and how it varies across ethnic/racial groups. Our findings using multiple GRSs are consistent with what is known about the differences in T2D pathogenesis across racial/ethnic groups. However, further work is needed to understand the putative inverse correlation of genetic risk for hyperlipidemia and T2D risk and to develop ethnic-specific GRSs.
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Affiliation(s)
- Yann C Klimentidis
- Mel and Enid Zuckerman College of Public Health (Y.C.K.), Division of Epidemiology and Biostatistics, University of Arizona, Tucson, Arizona 85724; Scripps Translational Science Institute (N.E.W.), La Jolla, California 92037; and Section on Statistical Genetics (A.I.V., G.d.l.C.), Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama 35294
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Aslibekyan S, Wiener HW, Wu G, Zhi D, Shrestha S, de Los Campos G, Vazquez AI. Estimating proportions of explained variance: a comparison of whole genome subsets. BMC Proc 2014; 8:S102. [PMID: 25519356 PMCID: PMC4143698 DOI: 10.1186/1753-6561-8-s1-s102] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Following the publication of the ENCODE project results, there has been increasing interest in investigating different areas of the chromosome and evaluating the relative contribution of each area to expressed phenotypes. This study aims to evaluate the contribution of variants, classified by minor allele frequency and gene annotation, to the observed interindividual differences. In this study, we fitted Bayesian linear regression models to data from Genetic Analysis Workshop 18 (n = 395) to estimate the variance of standardized and log-transformed systolic blood pressure that can be explained by subsets of genetic markers. Rare and very rare variants explained an overall higher proportion of the variance, as did markers located within a gene rather than flanking regions. The proportion of variance explained by rare and very rare variants decreased when we controlled for the number of markers, suggesting that the number of contributing rare alleles plays an important role in the genetic architecture of chronic disease traits. Our findings lend support to the "common disease, rare variant" hypothesis for systolic blood pressure and highlight allele frequency and functional annotation of a polymorphism as potentially crucial considerations in whole genome study designs.
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Affiliation(s)
- Stella Aslibekyan
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35205, USA
| | - Howard W Wiener
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35205, USA
| | - Guodong Wu
- Department of Biostatistics, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35205, USA
| | - Degui Zhi
- Department of Biostatistics, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35205, USA
| | - Sadeep Shrestha
- Department of Epidemiology, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35205, USA
| | - Gustavo de Los Campos
- Department of Biostatistics, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35205, USA
| | - Ana I Vazquez
- Department of Biostatistics, University of Alabama at Birmingham, 1665 University Blvd, Birmingham, AL 35205, USA
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Libby EF, Azrad M, Novak L, Vazquez AI, Wilson TR, Demark-Wahnefried W. Obesity is associated with higher 4E-BP1 expression in endometrial cancer. ACTA ACUST UNITED AC 2014; 2014:1-7. [PMID: 24639918 PMCID: PMC3955094 DOI: 10.2147/cbf.s53530] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
PURPOSE Obesity is associated with risk and prognosis of endometrial cancer (EC), and the mammalian target of rapamycin complex 1 (mTORC1) pathway may play an instrumental role. We sought to explore the associations between cellular proliferation, Akt, and 4E binding protein-1 (4E-BP1) (a downstream target of mTORC1), in obese and nonobese women with and without EC. METHODS Archival tissue-specimens from endometrial biopsies were grouped into two broad categories based on the observed disease behavior and similarities in tissue staining patterns: benign/hyperplasia (without cytologic atypia) (n=18) versus atypia (complex hyperplasia with cytologic atypia)/carcinoma (n=25). The characteristics of the study population, including height and weight to determine body mass index (BMI: kg/m2), were abstracted from medical records. Immunohistochemistry was used to assess the phosphorylated (p)Akt, p4E-BP1, and antigen Ki67. RESULTS Cytoplasmic and nuclear pAkt were significantly associated with cytoplasmic p4E-BP1 (ρ=+0.48, ρ=+0.50) (P<0.05) and nuclear p4E-BP1 (ρ=+0.40, ρ=+0.44) (P<0.05); cytoplasmic and nuclear p4E-BP1 were significantly associated with Ki67 (ρ=+0.46, ρ=+0.59) (P<0.05). Compared with the benign/hyperplasia group, the women with atypia/carcinoma had significantly higher cytoplasmic and nuclear p4E-BP1 and Ki67. This staining pattern was similar in obese women; however, in nonobese women, neither cytoplasmic nor nuclear p4E-BP1staining differed between benign/hyperplasia versus atypia/carcinoma. CONCLUSION The activation of 4E-BP1 was higher in the obese women with EC. Adiposity may be a key factor to consider in future studies investigating the role of 4E-BP1 as a biomarker and therapeutic target in EC.
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Affiliation(s)
- Emily Falk Libby
- Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Maria Azrad
- Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Lea Novak
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ana I Vazquez
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Tamara R Wilson
- Department of Nutrition Sciences, University of Alabama at Birmingham, Birmingham, AL, USA
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Dawson JA, Dhurandhar EJ, Vazquez AI, Peng B, Allison DB. Propagation of obesity across generations: the roles of differential realized fertility and assortative mating by body mass index. Hum Hered 2013; 75:204-12. [PMID: 24081235 DOI: 10.1159/000352007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND/AIMS To quantify the extent to which the increase in obesity observed across recent generations of the American population is associated with the individual or combined effects of assortative mating (AM) for body mass index (BMI) and differential realized fertility by BMI. METHODS A Monte Carlo framework is formed and informed using data collected from the National Longitudinal Survey of Youth (NLSY). The model has 2 portions: one that generates childbirth events on an annual basis and another that produces a BMI for each child. Once the model is informed using the data, a reference distribution of offspring BMIs is simulated. We quantify the effects of our factors of interest by removing them from the model and comparing the resulting offspring BMI distributions with that of the baseline scenario. RESULTS An association between maternal BMI and number of offspring is evidenced in the NLSY data as well as the presence of AM. These 2 factors combined are associated with an increased mean BMI (+0.067, 95% CI: 0.056; 0.078), an increased BMI variance (+0.578, 95% CI: 0.418; 0.736) and an increased prevalence of obesity (RR 1.032, 95% CI: 1.023; 1.041) and BMIs >40 (RR 1.083, 95% CI: 1.053; 1.118) among offspring. CONCLUSION Our investigation suggests that both differential realized fertility and AM by BMI appear to play a role in the increasing prevalence of obesity in America.
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Affiliation(s)
- John A Dawson
- Office of Energetics, School of Public Health, University of Alabama at Birmingham, Birmingham, Ala., USA
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Klimentidis YC, Vazquez AI, de los Campos G, Allison DB, Dransfield MT, Thannickal VJ. Heritability of pulmonary function estimated from pedigree and whole-genome markers. Front Genet 2013; 4:174. [PMID: 24058366 PMCID: PMC3766834 DOI: 10.3389/fgene.2013.00174] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2013] [Accepted: 08/22/2013] [Indexed: 11/13/2022] Open
Abstract
Asthma and chronic obstructive pulmonary disease (COPD) are major worldwide health problems. Pulmonary function testing is a useful diagnostic tool for these diseases, and is known to be influenced by genetic and environmental factors. Previous studies have demonstrated that a substantial proportion of the variation in pulmonary function phenotypes can be explained by familial relationships. The availability of whole-genome single nucleotide polymorphism (SNP) data enables us to further evaluate the extent to which genetic factors account for variation in pulmonary function and to compare pedigree- to SNP-based estimates of heritability. Here, we employ methods developed in the animal breeding field to estimate the heritability of forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and the ratio of these two measures (FEV1/FVC) among subjects in the Framingham Heart Study dataset. We compare heritability estimates based on pedigree-based relationships to those based on genome-wide SNPs. We find that, in a family-based study, estimates of heritability using SNP data are nearly identical to estimates based on pedigree information, and range from 0.50 for FEV1 to 0.66 for FEV1/FVC. Therefore, we conclude that genetic factors account for a sizable proportion of inter-individual differences in pulmonary function, and that estimates of heritability based on SNP data are nearly identical to estimates based on pedigree data. Finally, our findings suggest a higher heritability for FEV1/FVC compared to either FEV1 or FVC.
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Affiliation(s)
- Yann C. Klimentidis
- Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of ArizonaTucson, AZ, USA
| | - Ana I. Vazquez
- Department of Biostatistics, Section on Statistical Genetics, University of Alabama at BirminghamBirmingham, AL, USA
| | - Gustavo de los Campos
- Department of Biostatistics, Section on Statistical Genetics, University of Alabama at BirminghamBirmingham, AL, USA
| | - David B. Allison
- Office of Energetics, School of Public Health, University of Alabama at BirminghamBirmingham, AL, USA
| | - Mark T. Dransfield
- Allergy and Critical Care Medicine, Department of Medicine, Division of Pulmonary, University of Alabama at BirminghamBirmingham, AL, USA
| | - Victor J. Thannickal
- Allergy and Critical Care Medicine, Department of Medicine, Division of Pulmonary, University of Alabama at BirminghamBirmingham, AL, USA
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de Los Campos G, Vazquez AI, Fernando R, Klimentidis YC, Sorensen D. Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genet 2013; 9:e1003608. [PMID: 23874214 PMCID: PMC3708840 DOI: 10.1371/journal.pgen.1003608] [Citation(s) in RCA: 221] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2012] [Accepted: 05/20/2013] [Indexed: 01/12/2023] Open
Abstract
Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction (G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations. However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the performance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage disequilibrium (LD) between markers and QTL, the prediction R-squared (R2) of G-BLUP reaches trait-heritability, asymptotically. However, under imperfect LD between markers and QTL, prediction R2 based on G-BLUP has a much lower upper bound. We show that the minimum decrease in prediction accuracy caused by imperfect LD between markers and QTL is given by (1−b)2, where b is the regression of marker-derived genomic relationships on those realized at causal loci. For pairs of related individuals, due to within-family disequilibrium, the patterns of realized genomic similarity are similar across the genome; therefore b is close to one inducing small decrease in R2. However, with distantly related individuals b reaches very low values imposing a very low upper bound on prediction R2. Our simulations suggest that for the analysis of data from unrelated individuals, the asymptotic upper bound on R2 may be of the order of 20% of the trait heritability. We show how PA can be enhanced with use of variable selection or differential shrinkage of estimates of marker effects. Despite great advances in genotyping technologies, the ability to predict complex traits and diseases remains limited. Increasing evidence suggests that many of these traits may be affected by a large number of small-effect genes that are difficult to detect in single-variant association studies. Whole-Genome Regression (WGR) methods can be used to confront this challenge and have exhibited good predictive power when applied to animal and plant breeding populations. WGR is receiving increased attention in the field of human genetics. However, human and breeding populations differ greatly in factors that can affect the performance of WGRs. Using theory, simulation and real data analysis, we study the predictive performance of the Genomic Best Linear Unbiased Predictor (G-BLUP), one of the most commonly used WGR methods. We derive upper bounds for the prediction accuracy of G-BLUP under perfect and imperfect LD between markers and genotypes at causal loci and validate such upper bounds using simulation and real data analysis. Imperfect LD between markers and causal loci can impose a very low upper bound on the prediction accuracy of G-BLUP, especially when data involve unrelated individuals. In this context, we propose and evaluate avenues for improving the predictive performance of G-BLUP.
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Affiliation(s)
- Gustavo de Los Campos
- Biostatistics Department, University of Alabama at Birmingham, Birmingham, Alabama, USA.
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Abstract
The BLR (Bayesian linear regression) package of R implements several Bayesian regression models for continuous traits. The package was originally developed for implementing the Bayesian LASSO (BL) of Park and Casella (J Am Stat Assoc 103(482):681-686, 2008), extended to accommodate fixed effects and regressions on pedigree using methods described by de los Campos et al. (Genetics 182(1):375-385, 2009). In 2010 we further developed the code into an R-package, reprogrammed some internal aspects of the algorithm in the C language to increase computational speed, and further documented the package (Plant Genome J 3(2):106-116, 2010). The first version of BLR was launched in 2010 and since then the package has been used for multiple publications and is being routinely used for genomic evaluations in some animal and plant breeding programs. In this article we review the models implemented by BLR and illustrate the use of the package with examples.
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Affiliation(s)
- Gustavo de Los Campos
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
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Vazquez AI, de los Campos G, Klimentidis YC, Rosa GJM, Gianola D, Yi N, Allison DB. A comprehensive genetic approach for improving prediction of skin cancer risk in humans. Genetics 2012; 192:1493-502. [PMID: 23051645 PMCID: PMC3512154 DOI: 10.1534/genetics.112.141705] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2012] [Accepted: 09/07/2012] [Indexed: 01/09/2023] Open
Abstract
Prediction of genetic risk for disease is needed for preventive and personalized medicine. Genome-wide association studies have found unprecedented numbers of variants associated with complex human traits and diseases. However, these variants explain only a small proportion of genetic risk. Mounting evidence suggests that many traits, relevant to public health, are affected by large numbers of small-effect genes and that prediction of genetic risk to those traits and diseases could be improved by incorporating large numbers of markers into whole-genome prediction (WGP) models. We developed a WGP model incorporating thousands of markers for prediction of skin cancer risk in humans. We also considered other ways of incorporating genetic information into prediction models, such as family history or ancestry (using principal components, PCs, of informative markers). Prediction accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) estimated in a cross-validation. Incorporation of genetic information (i.e., familial relationships, PCs, or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline model that accounted for nongenetic covariates to AUCs of 0.58 (pedigree), 0.62 (PCs), and 0.64 (WGP). In summary, prediction of skin cancer risk could be improved by considering genetic information and using a large number of single-nucleotide polymorphisms (SNPs) in a WGP model, which allows for the detection of patterns of genetic risk that are above and beyond those that can be captured using family history. We discuss avenues for improving prediction accuracy and speculate on the possible use of WGP to prospectively identify individuals at high risk.
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Affiliation(s)
- Ana I Vazquez
- Section on Statistical Genetics, Department of Biostatistics, University of Alabama, Birmingham, AL 35294, USA.
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Abstract
Genetic factors are believed to account for 25% of the interindividual differences in Years of Life (YL) among humans. However, the genetic loci that have thus far been found to be associated with YL explain a very small proportion of the expected genetic variation in this trait, perhaps reflecting the complexity of the trait and the limitations of traditional association studies when applied to traits affected by a large number of small-effect genes. Using data from the Framingham Heart Study and statistical methods borrowed largely from the field of animal genetics (whole-genome prediction, WGP), we developed a WGP model for the study of YL and evaluated the extent to which thousands of genetic variants across the genome examined simultaneously can be used to predict interindividual differences in YL. We find that a sizable proportion of differences in YL--which were unexplained by age at entry, sex, smoking and BMI--can be accounted for and predicted using WGP methods. The contribution of genomic information to prediction accuracy was even higher than that of smoking and body mass index (BMI) combined; two predictors that are considered among the most important life-shortening factors. We evaluated the impacts of familial relationships and population structure (as described by the first two marker-derived principal components) and concluded that in our dataset population structure explained partially, but not fully the gains in prediction accuracy obtained with WGP. Further inspection of prediction accuracies by age at death indicated that most of the gains in predictive ability achieved with WGP were due to the increased accuracy of prediction of early mortality, perhaps reflecting the ability of WGP to capture differences in genetic risk to deadly diseases such as cancer, which are most often responsible for early mortality in our sample.
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Affiliation(s)
- Gustavo de los Campos
- Section on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
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Pérez-Cabal MA, Vazquez AI, Gianola D, Rosa GJM, Weigel KA. Accuracy of Genome-Enabled Prediction in a Dairy Cattle Population using Different Cross-Validation Layouts. Front Genet 2012; 3:27. [PMID: 22403583 PMCID: PMC3288819 DOI: 10.3389/fgene.2012.00027] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2011] [Accepted: 02/13/2012] [Indexed: 11/26/2022] Open
Abstract
The impact of extent of genetic relatedness on accuracy of genome-enabled predictions was assessed using a dairy cattle population and alternative cross-validation (CV) strategies were compared. The CV layouts consisted of training and testing sets obtained from either random allocation of individuals (RAN) or from a kernel-based clustering of individuals using the additive relationship matrix, to obtain two subsets that were as unrelated as possible (UNREL), as well as a layout based on stratification by generation (GEN). The UNREL layout decreased the average genetic relationships between training and testing animals but produced similar accuracies to the RAN design, which were about 15% higher than in the GEN setting. Results indicate that the CV structure can have an important effect on the accuracy of whole-genome predictions. However, the connection between average genetic relationships across training and testing sets and the estimated predictive ability is not straightforward, and may depend also on the kind of relatedness that exists between the two subsets and on the heritability of the trait. For high heritability traits, close relatives such as parents and full-sibs make the greatest contributions to accuracy, which can be compensated by half-sibs or grandsires in the case of lack of close relatives. However, for the low heritability traits the inclusion of close relatives is crucial and including more relatives of various types in the training set tends to lead to greater accuracy. In practice, CV designs should resemble the intended use of the predictive models, e.g., within or between family predictions, or within or across generation predictions, such that estimation of predictive ability is consistent with the actual application to be considered.
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Makowsky R, Pajewski NM, Klimentidis YC, Vazquez AI, Duarte CW, Allison DB, de los Campos G. Beyond missing heritability: prediction of complex traits. PLoS Genet 2011; 7:e1002051. [PMID: 21552331 PMCID: PMC3084207 DOI: 10.1371/journal.pgen.1002051] [Citation(s) in RCA: 210] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2010] [Accepted: 03/02/2011] [Indexed: 01/25/2023] Open
Abstract
Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the "missing heritability" for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h(2) up to 0.83, R(2) up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R(2) values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10), given the heritability of the trait (∼ 0.80), substantial room for improvement remains.
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Affiliation(s)
- Robert Makowsky
- Department of Biostatistics, University of Alabama at Birmingham, Alabama, United States of America.
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