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Diego VP, Manusov EG, Almeida M, Laston S, Ortiz D, Blangero J, Williams-Blangero S. Statistical Genetic Approaches to Investigate Genotype-by-Environment Interaction: Review and Novel Extension of Models. Genes (Basel) 2024; 15:547. [PMID: 38790175 PMCID: PMC11121143 DOI: 10.3390/genes15050547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/17/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
Statistical genetic models of genotype-by-environment (G×E) interaction can be divided into two general classes, one on G×E interaction in response to dichotomous environments (e.g., sex, disease-affection status, or presence/absence of an exposure) and the other in response to continuous environments (e.g., physical activity, nutritional measurements, or continuous socioeconomic measures). Here we develop a novel model to jointly account for dichotomous and continuous environments. We develop the model in terms of a joint genotype-by-sex (for the dichotomous environment) and genotype-by-social determinants of health (SDoH; for the continuous environment). Using this model, we show how a depression variable, as measured by the Beck Depression Inventory-II survey instrument, is not only underlain by genetic effects (as has been reported elsewhere) but is also significantly determined by joint G×Sex and G×SDoH interaction effects. This model has numerous applications leading to potentially transformative research on the genetic and environmental determinants underlying complex diseases.
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Affiliation(s)
- Vincent P. Diego
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA; (E.G.M.); (M.A.); (S.L.); (J.B.); (S.W.-B.)
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
| | - Eron G. Manusov
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA; (E.G.M.); (M.A.); (S.L.); (J.B.); (S.W.-B.)
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
| | - Marcio Almeida
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA; (E.G.M.); (M.A.); (S.L.); (J.B.); (S.W.-B.)
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
| | - Sandra Laston
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA; (E.G.M.); (M.A.); (S.L.); (J.B.); (S.W.-B.)
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
| | - David Ortiz
- Department of Family Medicine, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA;
| | - John Blangero
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA; (E.G.M.); (M.A.); (S.L.); (J.B.); (S.W.-B.)
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
| | - Sarah Williams-Blangero
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA; (E.G.M.); (M.A.); (S.L.); (J.B.); (S.W.-B.)
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX 78520, USA
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Diego VP, Manusov EG, Mao X, Almeida M, Peralta JM, Curran JE, Mahaney MC, Göring H, Blangero J, Williams-Blangero S. Metabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study. Front Genet 2024; 15:1240462. [PMID: 38495670 PMCID: PMC10940335 DOI: 10.3389/fgene.2024.1240462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 02/08/2024] [Indexed: 03/19/2024] Open
Abstract
Background: Socioeconomic Status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for metabolic disease. We analyzed data from the Mexican American Family Studies (MAFS) to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an essential determinant of variation in risk factors for metabolic syndrome (MS). Methods: We employed a maximum likelihood estimation of the decomposition of variance components to detect GxE interaction. After excluding individuals with diabetes and individuals on medication for diabetes, hypertension, or dyslipidemia, we analyzed 12 MS risk factors: fasting glucose (FG), fasting insulin (FI), 2-h glucose (2G), 2-h insulin (2I), body mass index (BMI), waist circumference (WC), leptin (LP), high-density lipoprotein-cholesterol (HDL-C), triglycerides (TG), total serum cholesterol (TSC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Our SES variable used a combined score of Duncan's socioeconomic index and education years. Heterogeneity in the additive genetic variance across the SES continuum and a departure from unity in the genetic correlation coefficient were taken as evidence of GxE interaction. Hypothesis tests were conducted using standard likelihood ratio tests. Results: We found evidence of GxE for fasting glucose, 2-h glucose, 2-h insulin, BMI, and triglycerides. The genetic effects underlying the insulin/glucose metabolism component of MS are upregulated at the lower end of the SES spectrum. We also determined that the household variance for systolic blood pressure decreased with increasing SES. Conclusion: These results show a significant change in the GxE interaction underlying the major components of MS in response to changes in socioeconomic status. Further mRNA sequencing studies will identify genes and canonical gene pathways to support our molecular-level hypotheses.
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Affiliation(s)
- Vincent P. Diego
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Eron G. Manusov
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Xi Mao
- Department of Economics, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Marcio Almeida
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Juan M. Peralta
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Joanne E. Curran
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Michael C. Mahaney
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Harald Göring
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - John Blangero
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Sarah Williams-Blangero
- South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
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Howard T, Almieda M, Diego V, Viel K, Luu B, Haack K, Raja R, Ameri A, Chitlur M, Rydz N, Lillicrap D, Watts R, Kessler C, Ramsey C, Dinh L, Kim B, Powell J, Peralta J, Bouls R, Abraham S, Shen YM, Murillo C, Mead H, Lehmann P, Fine E, Escobar M, Kumar S, Williams-Blangero S, Kasper C, Almasy L, Cole S, Blangero J, Konkle B. A Scan of Pleiotropic Immune Mediated Disease Genes Identifies Novel Determinants of Baseline FVIII Inhibitor Status in Hemophilia-A. RESEARCH SQUARE 2023:rs.3.rs-3371095. [PMID: 37886476 PMCID: PMC10602130 DOI: 10.21203/rs.3.rs-3371095/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Hemophilia-A (HA) is caused by heterogeneous loss-of-function factor (F)VIII gene (F8)-mutations and deficiencies in plasma-FVIII-activity that impair intrinsic-pathway-mediated coagulation-amplification. The standard-of-care for severe-HA-patients is regular infusions of therapeutic-FVIII-proteins (tFVIIIs) but ~30% develop neutralizing-tFVIII-antibodies called "FVIII-inhibitors (FEIs)" and become refractory. We used the PATH study and ImmunoChip to scan immune-mediated-disease (IMD)-genes for novel and/or replicated genomic-sequence-variations associated with baseline-FEI-status while accounting for non-independence of data due to genetic-relatedness and F8-mutational-heterogeneity. The baseline-FEI-status of 450 North American PATH subjects-206 with black-African-ancestry and 244 with white-European-ancestry-was the dependent variable. The F8-mutation-data and a genetic-relatedness matrix were incorporated into a binary linear-mixed model of genetic association with baseline-FEI-status. We adopted a gene-centric-association-strategy to scan, as candidates, pleiotropic-IMD-genes implicated in the development of either ³2 autoimmune-/autoinflammatory-disorders (AADs) or ³1 AAD and FEIs. Baseline-FEI-status was significantly associated with SNPs assigned to NOS2A (rs117382854; p=3.2E-6) and B3GNT2 (rs10176009; p=5.1E-6), which have functions in anti-microbial-/-tumoral-immunity. Among IMD-genes implicated in FEI-risk previously, we identified strong associations with CTLA4 assigned SNPs (p=2.2E-5). The F8-mutation-effect underlies ~15% of the total heritability for baseline-FEI-status. Additive genetic heritability and SNPs in IMD-genes account for >50% of the patient-specific variability in baseline-FEI-status. Race is a significant determinant independent of F8-mutation-effects and non-F8-genetics.
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Affiliation(s)
- Tom Howard
- University of Texas Rio Grande Valley School of Medicine
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Diego VP, Manusov EG, Mao X, Curran JE, Göring H, Almeida M, Mahaney MC, Peralta JM, Blangero J, Williams-Blangero S. Genotype-by-socioeconomic status interaction influences heart disease risk scores and carotid artery thickness in Mexican Americans: the predominant role of education in comparison to household income and socioeconomic index. Front Genet 2023; 14:1132110. [PMID: 37795246 PMCID: PMC10547145 DOI: 10.3389/fgene.2023.1132110] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 07/17/2023] [Indexed: 10/06/2023] Open
Abstract
Background: Socioeconomic status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for cardiovascular disease (CVD). We analyzed Mexican American Family Studies (MAFS) data to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an important determinant of variation in CVD risk factors. Methods: We employed a linear mixed model to investigate GxE in Mexican American extended families. We studied two proxies for CVD [Pooled Cohort Equation Risk Scores/Framingham Risk Scores (FRS/PCRS) and carotid artery intima-media thickness (CA-IMT)] in relation to socioeconomic status as determined by Duncan's Socioeconomic Index (SEI), years of education, and household income. Results: We calculated heritability for FRS/PCRS and carotid artery intima-media thickness. There was evidence of GxE due to additive genetic variance heterogeneity and genetic correlation for FRS, PCRS, and CA-IMT measures for education (environment) but not for household income or SEI. Conclusion: The genetic effects underlying CVD are dynamically modulated at the lower end of the SES spectrum. There is a significant change in the genetic architecture underlying the major components of CVD in response to changes in education.
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Affiliation(s)
- Vincent P. Diego
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- School of Medicine, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Eron G. Manusov
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- School of Medicine, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Xi Mao
- Department of Economics, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Joanne E. Curran
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- School of Medicine, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Harald Göring
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- School of Medicine, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Marcio Almeida
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- School of Medicine, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Michael C. Mahaney
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- School of Medicine, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Juan M. Peralta
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- School of Medicine, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - John Blangero
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- School of Medicine, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Sarah Williams-Blangero
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, TX, United States
- School of Medicine, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, United States
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Manusov EG, Diego VP, Abrego E, Herklotz K, Almeida M, Mao X, Laston S, Blangero J, Williams-Blangero S. Gene-by-Environment Interaction in Non-Alcoholic Fatty Liver Disease and Depression: The Role of Hepatic Transaminases. MEDICAL RESEARCH ARCHIVES 2023; 11:10.18103/mra.v11i9.4408. [PMID: 38698891 PMCID: PMC11064892 DOI: 10.18103/mra.v11i9.4408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Non-alcoholic fatty liver disease (NAFLD) encompasses a range of liver conditions, from benign fatty accumulation to severe fibrosis. The global prevalence of NAFLD has risen to 25-30%, with variations across ethnic groups. NAFLD may advance to hepatocellular carcinoma, increases cardiovascular risk, is associated with chronic kidney disease, and is an independent metabolic disease risk factor. Assessment methods for liver health include liver biopsy, magnetic resonance imaging, ultrasound, and vibration-controlled transient elastography (VCTE by FibroScan). Hepatic transaminases are cost-effective and minimally invasive liver health assessment methods options. This study focuses on the interaction between genetic factors underlying the traits (hepatic transaminases and the FibroScan results) on the one hand and the environment (depression) on the other. We examined 525 individuals at risk for metabolic disorders. We utilized variance components models and likelihood-based statistical inference to examine potential GxE interactions in markers of NAFLD, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), and the AST/ALT ratio, and Vibration-Controlled Transient Elastography (VCTE by FibroScan). We calculated the Fibroscan-AST (FAST) score (a score that identifies the risk of progressive non-alcoholic steatohepatitis (NASH) and screened for depression using the Beck Depression Inventory-II (BDI-II). We identified significant G × E interactions for AST/ALT ratio × BDI-II, but not AST, ALT, or the FAST score. Our findings support that genetic factors play a role in hepatic transaminases, especially the AST/ALT ratio, with depression influencing this relationship. These insights contribute to understanding the complex interplay of genetics, environment, and liver health, potentially guiding future personalized interventions.
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Affiliation(s)
- Eron G Manusov
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Vincent P Diego
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Edward Abrego
- The University of Texas Rio Grande Valley, School of Medicine, Edinburg Texas
| | - Kathryn Herklotz
- The University of Texas Rio Grande Valley, School of Medicine, Edinburg Texas
| | - Marcio Almeida
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Xi Mao
- Department of Economics, University of Texas Rio Grande Valley, Brownsville, TX 78520
| | - Sandra Laston
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - John Blangero
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Sarah Williams-Blangero
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States
- South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
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Zheng H, Ye Y, Huang H, Huang C, Gao W, Wang M, Li W, Zhou R, Jiang J, Wang S, Yu C, Lv J, Wu X, Huang X, Cao W, Yan Y, Zheng K, Wu T, Li L. A pedigree-based cohort to study the genetic risk factors for cardiometabolic diseases: study design, baseline characteristics and preliminary results. Front Public Health 2023; 11:1189993. [PMID: 37521988 PMCID: PMC10374840 DOI: 10.3389/fpubh.2023.1189993] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 06/26/2023] [Indexed: 08/01/2023] Open
Abstract
Background We initiated the Fujian Tulou Pedigree-based Cohort (FTPC) as the integration of extended pedigrees and prospective cohort to clarify the genetic and environmental risk factors of cardiometabolic diseases. Methods FTPC was carried out in Nanjing County, Fujian Province, China from August 2015 to December 2017 to recruit probands with the same surnames and then enroll their first-degree and more distant relatives. The participants were asked to complete questionnaire interview, physical examination, and blood collection. According to the local genealogical booklets and family registry, we reconstructed extended pedigrees to estimate the heritability of cardiometabolic traits. The follow-up of FTPC is scheduled every 5 years in the future. Results The baseline survey interviewed 2,727 individuals in two clans. A total of 1,563 adult subjects who completed all baseline examinations were used to reconstruct pedigrees and 452 extended pedigrees were finally identified, including one seven-generation pedigree, two five-generation pedigrees, 23 four-generation pedigrees, 186 three-generation pedigrees, and 240 two-generation pedigrees. The average age of the participants was 57.4 years, with 43.6% being males. The prevalence of hypertension, diabetes and dyslipidemia in FTPC were 49.2, 10.0, and 45.2%, respectively. Based on the pedigree structure, the heritability of systolic blood pressure, diastolic blood pressure, fast blood glucose, total cholesterol, triglyceride, high-density lipoprotein, and low-density lipoprotein was estimated at 0.379, 0.306, 0.386, 0.452, 0.568, 0.852, and 0.387, respectively. Conclusion As an extended pedigree cohort in China, FTPC will provide an important source to study both genetic and environmental risk factors prospectively.
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Affiliation(s)
- Hongchen Zheng
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Laboratory of Genetics, Peking University Cancer Hospital and Institute, Beijing, China
| | - Ying Ye
- Department of Local Diseases Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Hui Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Chunlan Huang
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing, China
| | - Wenjing Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Mengying Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Wenyong Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Ren Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jin Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Siyue Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
| | - Xiaoling Wu
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing, China
| | - Xiaoming Huang
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing, China
| | - Weihua Cao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yansheng Yan
- Department of Local Diseases Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Kuicheng Zheng
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Tao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Key Laboratory of Reproductive Health, Ministry of Health, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
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7
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Manusov EG, Diego VP, Sheikh K, Laston S, Blangero J, Williams-Blangero S. Non-alcoholic Fatty Liver Disease and Depression: Evidence for Genotype × Environment Interaction in Mexican Americans. Front Psychiatry 2022; 13:936052. [PMID: 35845438 PMCID: PMC9283683 DOI: 10.3389/fpsyt.2022.936052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 06/13/2022] [Indexed: 11/17/2022] Open
Abstract
This study examines the impact of G × E interaction effects on non-alcoholic fatty liver disease (NAFLD) among Mexican Americans in the Rio Grande Valley (RGV) of South Texas. We examined potential G × E interaction using variance components models and likelihood-based statistical inference in the phenotypic expression of NAFLD, including hepatic steatosis and hepatic fibrosis (identified using vibration controlled transient elastography and controlled attenuation parameter measured by the FibroScan Device). We screened for depression using the Beck Depression Inventory-II (BDI-II). We identified significant G × E interactions for hepatic fibrosis × BDI-II. These findings provide evidence that genetic factors interact with depression to influence the expression of hepatic fibrosis.
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Affiliation(s)
- Eron Grant Manusov
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States.,School of Medicine, South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Vincent P Diego
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States.,School of Medicine, South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Khalid Sheikh
- School of Medicine, The University of Texas Rio Grande Valley, Edinburg, TX, United States
| | - Sandra Laston
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States.,School of Medicine, South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - John Blangero
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States.,School of Medicine, South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
| | - Sarah Williams-Blangero
- Department of Human Genetics, The University of Texas Rio Grande Valley, Brownsville, TX, United States.,School of Medicine, South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, United States
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Comparing empirical kinship derived heritability for imaging genetics traits in the UK biobank and human connectome project. Neuroimage 2021; 245:118700. [PMID: 34740793 PMCID: PMC8771206 DOI: 10.1016/j.neuroimage.2021.118700] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 10/15/2021] [Accepted: 10/30/2021] [Indexed: 11/22/2022] Open
Abstract
Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability – the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ~N2–3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 105 voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28.8 ± 3.7 years) and N = 37,432 (17,531 M/19,901 F; age = 63.7 ± 7.5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (102–4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0.63–0.76, p < 10−10). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org.
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Nayor M, Shen L, Hunninghake GM, Kochunov P, Barr RG, Bluemke DA, Broeckel U, Caravan P, Cheng S, de Vries PS, Hoffmann U, Kolossváry M, Li H, Luo J, McNally EM, Thanassoulis G, Arnett DK, Vasan RS. Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop. Circ Cardiovasc Imaging 2021; 14:e012943. [PMID: 34387095 PMCID: PMC8486340 DOI: 10.1161/circimaging.121.012943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Imaging genomics is a rapidly evolving field that combines state-of-the-art bioimaging with genomic information to resolve phenotypic heterogeneity associated with genomic variation, improve risk prediction, discover prevention approaches, and enable precision diagnosis and treatment. Contemporary bioimaging methods provide exceptional resolution generating discrete and quantitative high-dimensional phenotypes for genomics investigation. Despite substantial progress in combining high-dimensional bioimaging and genomic data, methods for imaging genomics are evolving. Recognizing the potential impact of imaging genomics on the study of heart and lung disease, the National Heart, Lung, and Blood Institute convened a workshop to review cutting-edge approaches and methodologies in imaging genomics studies, and to establish research priorities for future investigation. This report summarizes the presentations and discussions at the workshop. In particular, we highlight the need for increased availability of imaging genomics data in diverse populations, dedicated focus on less common conditions, and centralization of efforts around specific disease areas.
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Affiliation(s)
- Matthew Nayor
- Cardiology Division, Department of Medicine, Massachusetts
General Hospital, Harvard Medical School, Boston, MA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics,
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Gary M. Hunninghake
- Division of Pulmonary and Critical Care Medicine, Harvard
Medical School, Brigham and Women’s Hospital, Boston, MA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of
Psychiatry, University of Maryland School of Medicine, Baltimore, MD
| | - R. Graham Barr
- Department of Medicine and Department of Epidemiology,
Mailman School of Public Health, Columbia University Irving Medical Center, New
York, NY
| | - David A. Bluemke
- Department of Radiology, University of Wisconsin-Madison
School of Medicine and Public Health, Madison, WI
| | - Ulrich Broeckel
- Section of Genomic Pediatrics, Department of Pediatrics,
Medicine and Physiology, Children’s Research Institute and Genomic Sciences
and Precision Medicine Center, Medical College of Wisconsin, Milwaukee, WI
| | - Peter Caravan
- Institute for Innovation in Imaging, Athinoula A. Martinos
Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical
School, Charlestown, MA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute,
Cedars-Sinai Medical Center, Los Angeles, CA
| | - Paul S. de Vries
- Human Genetics Center, Department of Epidemiology, Human
Genetics, and Environmental Sciences, School of Public Health, The University of
Texas Health Science Center at Houston, Houston, TX
| | - Udo Hoffmann
- Department of Radiology, Harvard Medical School,
Massachusetts General Hospital, Boston, Massachusetts
| | - Márton Kolossváry
- Department of Radiology, Harvard Medical School,
Massachusetts General Hospital, Boston, Massachusetts
| | - Huiqing Li
- Division of Cardiovascular Sciences, National Heart,
Lung, and Blood Institute, Bethesda, MD
| | - James Luo
- Division of Cardiovascular Sciences, National Heart,
Lung, and Blood Institute, Bethesda, MD
| | - Elizabeth M. McNally
- Center for Genetic Medicine, Northwestern University
Feinberg School of Medicine, Chicago, IL
| | - George Thanassoulis
- Preventive and Genomic Cardiology, McGill University
Health Center and Research Institute, Montreal, Quebec, Canada
| | - Donna K. Arnett
- College of Public Health, University of Kentucky,
Lexington KY
| | - Ramachandran S. Vasan
- Sections of Preventive Medicine and Epidemiology, and
Cardiology, Department of Medicine, Department of Epidemiology, Boston University
Schools of Medicine and Public Health, and Center for Computing and Data Sciences,
Boston University, Boston, MA
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10
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Whole-exome sequencing in multiplex preeclampsia families identifies novel candidate susceptibility genes. J Hypertens 2020; 37:997-1011. [PMID: 30633125 DOI: 10.1097/hjh.0000000000002023] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Preeclampsia is a common and serious heritable disorder of human pregnancy. Although there have been notable successes in identification of maternal susceptibility genes a large proportion of the heritability of preeclampsia remains unaccounted for. It is has been postulated that rare variation may account for some of this missing heritability. In this study, we performed whole-exome sequencing (WES) in multiplex families to identify rare exonic risk variants. METHODS We conducted WES in 244 individuals from 34 Australian/New Zealand multiplex preeclampsia families. Variants were tested for association with preeclampsia using a threshold model and logistic regression. RESULTS We found significant association for two moderately rare missense variants, rs145743393 (Padj = 0.0032, minor allele frequency = 0.016) in the chromosome 1 open reading frame 35 (C1orf35) gene, and rs34270076 (Padj = 0.0128, minor allele frequency = 0.024) in the pyroglutamylated RFamide peptide receptor (QRFPR) gene. To replicate these associations we performed imputation in our Australian genome wide association scan for preeclampsia and found no significant exonic variants in either C1orf35 or QRFPR. However, 11 variants demonstrating nominal significance (P < 0.05) in the genomic region between QRFPR and annexin A5 (ANXA5) were identified. We further leveraged publicly available genome-wide available summary data from the UK Biobank to investigate association of these two variants with the underlying clinical phenotypes of preeclampsia and detected nominal association of the QRFPR variant (rs34270076, P = 0.03) with protein levels in females. CONCLUSION The study represents the first to use WES in multiplex families for preeclampsia and identifies two novel genes (QRFPR and C1orf35) not previously associated with preeclampsia and find nominal association of rs34270076 with protein levels, a key clinical feature of preeclampsia. We find further support for ANXA5 previously associated with pregnancy complications, including preeclampsia.
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11
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Doucet GE, Moser DA, Rodrigue A, Bassett DS, Glahn DC, Frangou S. Person-Based Brain Morphometric Similarity is Heritable and Correlates With Biological Features. Cereb Cortex 2020; 29:852-862. [PMID: 30462205 PMCID: PMC6319174 DOI: 10.1093/cercor/bhy287] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 10/25/2018] [Indexed: 11/29/2022] Open
Abstract
The characterization of the functional significance of interindividual variation in brain morphometry is a core aim of cognitive neuroscience. Prior research has focused on interindividual variation at the level of regional brain measures thus overlooking the fact that each individual brain is a person-specific ensemble of interdependent regions. To expand this line of inquiry we introduce the person-based similarity index (PBSI) for brain morphometry. The conceptual unit of the PBSI is the individual person’s brain structural profile which considers all relevant morphometric measures as features of a single vector. In 2 independent cohorts (total of 1756 healthy participants), we demonstrate the foundational validity of this approach by affirming that the PBSI scores for subcortical volume and cortical thickness in healthy individuals differ between men and women, are heritable, and robust to variation in neuroimaging parameters, sample composition, and regional brain morphometry. Moreover, the PBSI scores correlate with age, body mass index, and fluid intelligence. Collectively, these results suggest that the person-based measures of brain morphometry are biologically and functionally meaningful and have the potential to advance the study of human variation in multivariate brain imaging phenotypes in healthy and clinical populations.
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Affiliation(s)
- Gaelle E Doucet
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dominik A Moser
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amanda Rodrigue
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,Olin Neuropsychiatric Institute, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Leite JMRS, Soler JMP, Horimoto ARVR, Alvim RO, Pereira AC. Heritability and Sex-Specific Genetic Effects of Self-Reported Physical Activity in a Brazilian Highly Admixed Population. Hum Hered 2020; 84:151-158. [PMID: 32088709 PMCID: PMC7212208 DOI: 10.1159/000506007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 01/17/2020] [Indexed: 01/13/2023] Open
Abstract
INTRODUCTION The engagement in sports or habitual physical activity (PA) has shown an extensive protective role against multiple diseases such as cancer, obesity, and many others. Additionally, PA has also a significant impact on life quality, since it aids with managing stress, preserving cognitive function and memory, and preventing fractures in the elderly. OBJECTIVE Considering there has been multiple evidence showing that genetic variation underpins variation of PA-related traits, we aimed to estimate the heritability (h2) of these phenotypes in a sample from the Brazilian population and assess whether males and females differ in relation to those estimates. METHODS 2,027 participants from a highly admixed population from Baependi, MG, Brazil, had information regarding their PA and sedentary behavior (SB) phenotypes collected through a questionnaire (IPAQ-SF). After data cleaning and transformation procedures, we obtained four variables to be evaluated: total PA (TPA MET), walking time, (WK MET), moderate-vigorous PA (MVPA MET), and SB. A model selection procedure was performed using a single-step covariate inclusion approach. We tested for BMI, waist, hip and neck circumferences, smoking, and depression separately, and performed correlation tests among covariates. Linear mixed models, selection procedure, and the variance components approach to estimate h2 were implemented using SOLAR-Eclipse 8.3.1. RESULTS We obtained estimates of 0.221, 0.109, 0.226, and 0 for TPA MET, WK MET, MVPA MET, and SB, respectively. We found evidence for gene-sex interactions, with males having higher sex-specific heritabilities than females for TPA MET and MVPA MET. In addition, we found higher estimates of the genetic variance component in males than females for most phenotypes. DISCUSSION/CONCLUSION The heritability estimates presented in this work show a moderate heritable set of genetic factors affecting PA in a sample from the Brazilian population. The evaluation of the genetic variance component suggests segregating genetic factors in male individuals are more heterogeneous, which can explain why men globally tend to need to practice more intense PA than women to achieve similar health benefits. Hence, these findings have significant implications for the understanding of the genetic architecture of PA and might aid to promote health in the future.
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Affiliation(s)
| | | | - Andréa Roseli Vançan Russo Horimoto
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, São Paulo, Brazil
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Rafael O Alvim
- Department of Physiological Sciences, Federal University of Amazonas, Manaus, Brazil
| | - Alexandre C Pereira
- Laboratory of Genetics and Molecular Cardiology, Heart Institute, São Paulo, Brazil
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13
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Thompson EA. Correlations between relatives: From Mendelian theory to complete genome sequence. Genet Epidemiol 2019; 43:577-591. [PMID: 31045279 PMCID: PMC6559867 DOI: 10.1002/gepi.22206] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 03/04/2019] [Accepted: 03/25/2019] [Indexed: 12/19/2022]
Abstract
It is 100 years since R. A. Fisher proposed that a Mendelian model of genetic variant effects, additive over loci, could explain the patterns of observed phenotypic correlations between relatives. His loci were hypothetical and his model theoretical. It is only about 50 years since the first genetic markers allowed the detection of even variants with major effects on phenotype, and only 20 years since the development of single-nucleotide polymorphism technology provided dense markers over the genome. Then both mappings in defined pedigrees and population-based genome-wide association studies samples allowed the detection of multiple contributing variants of smaller effect. Finally, with methods based on genotypic correlations between individuals, or on allelic associations between loci, the additive heritability contributions of the genome can be estimated from large population samples. In this review we trace, from 1918 to 2018, the analysis of observed phenotypic correlations between relatives to estimate underlying genetic components of traits in human populations. As with studies from 1918 onward, we use height as the example trait where not only data are readily available, but where Fisher's model of large numbers of variants of infinitesimal effect appears to provide a good approximation to reality. However, we also trace the use of phenotypic and genotypic correlations between relatives in mapping causal variants and resolving genetic contributions to more complex human traits. With the availability of DNA sequence data, we can hope to not only estimate the total genetic contribution to a trait, but to resolve effects of individual genetic variants on biological function.
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14
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Szily M, Tarnoki AD, Tarnoki DL, Kovacs DT, Forgo B, Lee J, Kim E, Sung J, Kunos L, Meszaros M, Muller V, Bikov A. Genetic influences on the onset of obstructive sleep apnoea and daytime sleepiness: a twin study. Respir Res 2019; 20:125. [PMID: 31208424 PMCID: PMC6580623 DOI: 10.1186/s12931-019-1095-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 06/11/2019] [Indexed: 12/21/2022] Open
Abstract
Background Obstructive sleep apnoea (OSA) is one of the major sources of the excessive daily sleepiness, cognitive dysfunction, and it increases cardiovascular morbidity and mortality. Previous studies suggested a possible genetic influence, based on questionnaires but no objective genetic study was conducted to understand the exact variance underpinned by genetic factors. Methods Seventy-one Hungarian twin pairs involved from the Hungarian Twin Registry (48 monozygotic, MZ and 23 dizygotic, DZ pairs, mean age 51 ± 15 years) underwent overnight polysomnography (Somnoscreen Plus Tele PSG, Somnomedics GMBH, Germany). Apnoea hypopnea index (AHI), respiratory disturbance index (RDI) and oxygen desaturation index (ODI) were registered. Daytime sleepiness was measured with the Epworth Sleepiness Scale (ESS). Bivariate heritability analysis was applied. Results The prevalence of OSA was 41% in our study population. The heritability of the AHI, ODI and RDI ranged between 69% and 83%, while the OSA, defined by an AHI ≥5/h, was itself 73% heritable. The unshared environmental component explained the rest of the variance between 17% and 31%. Daytime sleepiness was mostly determined by the environment, and the variance was influenced in 34% by the additive genetic factors. These associations were present after additional adjustment for body mass index. Conclusion OSA and the indices of OSA severity are heritable, while daytime sleepiness is mostly influenced by environmental factors. Further studies should elucidate whether close relatives of patients with OSA may benefit from early family risk based screening.
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Affiliation(s)
- Marcell Szily
- Department of Radiology, Semmelweis University, 78/A Ulloi street, 1082, Budapest, Hungary
| | - Adam D Tarnoki
- Department of Radiology, Semmelweis University, 78/A Ulloi street, 1082, Budapest, Hungary.
| | - David L Tarnoki
- Department of Radiology, Semmelweis University, 78/A Ulloi street, 1082, Budapest, Hungary
| | - Daniel T Kovacs
- Department of Radiology, Semmelweis University, 78/A Ulloi street, 1082, Budapest, Hungary
| | - Bianka Forgo
- Department of Radiology, Semmelweis University, 78/A Ulloi street, 1082, Budapest, Hungary
| | - Jooyeon Lee
- Complex Disease and Genome Epidemiology Branch, Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Eunae Kim
- Complex Disease and Genome Epidemiology Branch, Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Joohon Sung
- Complex Disease and Genome Epidemiology Branch, Department of Public Health Science, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Laszlo Kunos
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Martina Meszaros
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Veronika Muller
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
| | - Andras Bikov
- Department of Pulmonology, Semmelweis University, Budapest, Hungary
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15
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Rediscovering the value of families for psychiatric genetics research. Mol Psychiatry 2019; 24:523-535. [PMID: 29955165 PMCID: PMC7028329 DOI: 10.1038/s41380-018-0073-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 01/11/2018] [Accepted: 03/26/2018] [Indexed: 01/09/2023]
Abstract
As it is likely that both common and rare genetic variation are important for complex disease risk, studies that examine the full range of the allelic frequency distribution should be utilized to dissect the genetic influences on mental illness. The rate limiting factor for inferring an association between a variant and a phenotype is inevitably the total number of copies of the minor allele captured in the studied sample. For rare variation, with minor allele frequencies of 0.5% or less, very large samples of unrelated individuals are necessary to unambiguously associate a locus with an illness. Unfortunately, such large samples are often cost prohibitive. However, by using alternative analytic strategies and studying related individuals, particularly those from large multiplex families, it is possible to reduce the required sample size while maintaining statistical power. We contend that using whole genome sequence (WGS) in extended pedigrees provides a cost-effective strategy for psychiatric gene mapping that complements common variant approaches and WGS in unrelated individuals. This was our impetus for forming the "Pedigree-Based Whole Genome Sequencing of Affective and Psychotic Disorders" consortium. In this review, we provide a rationale for the use of WGS with pedigrees in modern psychiatric genetics research. We begin with a focused review of the current literature, followed by a short history of family-based research in psychiatry. Next, we describe several advantages of pedigrees for WGS research, including power estimates, methods for studying the environment, and endophenotypes. We conclude with a brief description of our consortium and its goals.
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Kochunov P, Patel B, Ganjgahi H, Donohue B, Ryan M, Hong EL, Chen X, Adhikari B, Jahanshad N, Thompson PM, Van't Ent D, den Braber A, de Geus EJC, Brouwer RM, Boomsma DI, Hulshoff Pol HE, de Zubicaray GI, McMahon KL, Martin NG, Wright MJ, Nichols TE. Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data. Front Neuroinform 2019; 13:16. [PMID: 30914942 PMCID: PMC6422938 DOI: 10.3389/fninf.2019.00016] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Accepted: 02/25/2019] [Indexed: 11/30/2022] Open
Abstract
Imaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods. Accelerated Permutation inference for ACE (APACE) and fast permutation heritability inference (FPHI), employ fast, non-iterative approximation-based methods. We performed this evaluation in a simulated twin-sibling pedigree and phenotypes and in diffusion tensor imaging (DTI) data from three twin-sibling cohorts, the human connectome project (HCP), netherlands twin register (NTR) and BrainSCALE projects provided as a part of the enhancing neuro imaging genetics analysis (ENIGMA) consortium. We observed that heritability estimate may differ depending on the underlying method and dataset. The heritability estimates from the two MLE approaches provided excellent agreement in both simulated and imaging data. The heritability estimates for two approximation approaches showed reduced heritability estimates in datasets with deviations from data normality. We propose a data homogenization approach (implemented in solar-eclipse; www.solar-eclipse-genetics.org) to improve the convergence of heritability estimates across different methods. The homogenization steps include consistent regression of any nuisance covariates and enforcing normality on the trait data using inverse Gaussian transformation. Under these conditions, the heritability estimates for simulated and DTI phenotypes produced converging heritability estimates regardless of the method. Thus, using these simple suggestions may help new heritability studies to provide outcomes that are comparable regardless of software package.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Binish Patel
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Habib Ganjgahi
- Department of Statistics, University of Oxford, Oxford, United Kingdom
| | - Brian Donohue
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Meghann Ryan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Elliot L Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Xu Chen
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Bhim Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, United States
| | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, CA, United States
| | - Dennis Van't Ent
- Department of Biological Psychology, VU University, Amsterdam, Netherlands
| | - Anouk den Braber
- Department of Biological Psychology, VU University, Amsterdam, Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, VU University, Amsterdam, Netherlands
| | - Rachel M Brouwer
- Department of Biological Psychology, VU University, Amsterdam, Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, Netherlands
| | - Hilleke E Hulshoff Pol
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Greig I de Zubicaray
- Faculty of Health, and Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Katie L McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | | | - Margaret J Wright
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.,Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Thomas E Nichols
- Big Data Institute, University of Oxford, Oxford, United Kingdom
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Abstract
BACKGROUND Although alterations in medial temporal lobe structures have been previously associated with use of cannabis, one of the most widely used illicit drugs, whether such alterations are a cause or effect of cannabis use has been unclear. METHODS In this cross-sectional observational study involving 404 twins/siblings, we have compared cortical thickness and surface area between groups of gender-matched sibling-pairs (concordant cannabis unexposed, concordant exposed and discordant for cannabis exposure) using permutation tests after controlling for potential confounds. Bi-variate polygenic model was used to assess the genetic and environmental contributions underlying cortical morphological phenotypes and frequency of cannabis use. RESULTS Cortical thickness of the right entorhinal cortex was significantly lower in the concordant exposed siblings compared to both discordant unexposed and discordant exposed groups [false discovery rate (FDR)-corrected, q < 0.05]. The association between the right entorhinal cortex thickness and frequency of cannabis use is due to the contribution of significant shared additive genetic (ρg = -0.19 ± 0.08; p = 0.02) factors but not unique environment (ρe = 0.05 ± 0.09; p = 0.53). Significantly lower surface area of the right entorhinal cortex in discordant exposed group compared with the discordant unexposed group furnishes preliminary evidence in support of causal effect of cannabis use (FDR-corrected, q < 0.05). However, bi-variate polygenic model-based analysis did not show any significant effect. CONCLUSIONS Shared genetic liability may underlie the association between cannabis exposure and thinner right entorhinal cortex. Prospective longitudinal studies are necessary to definitively disentangle the cause-effect relationships of cannabis use.
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Affiliation(s)
- Subhadip Paul
- Institute of Psychiatry,Psychology & Neuroscience,King's College London,UK
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18
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Blackburn NB, Porto A, Peralta JM, Blangero J. Heritability and genetic associations of triglyceride and HDL-C levels using pedigree-based and empirical kinships. BMC Proc 2018; 12:34. [PMID: 30263045 PMCID: PMC6157025 DOI: 10.1186/s12919-018-0133-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
The heritability of a phenotype is an estimation of the percent of variance in that phenotype that is attributable to additive genetic factors. Heritability is optimally estimated in family-based sample populations. Traditionally, this involves use of a pedigree-based kinship coefficient generated from the collected genealogical relationships between family members. An alternative, when dense genotype data are available, is to directly measure the empirical kinship between samples. This study compares the use of pedigree and empirical kinships in the GAW20 data set. Two phenotypes were assessed: triglyceride levels and high-density lipoprotein cholesterol (HDL-C) levels pre- and postintervention with the cholesterol-reducing drug fenofibrate. Using SOLAR (Sequential Oligogenic Linkage Analysis Routines), pedigree-based kinships and empirically calculated kinships (using IBDLD and LDAK) were used to calculate phenotype heritability. In addition, a genome-wide association study was conducted using each kinship model for each phenotype to identify genetic variants significantly associated with phenotypic variation. The variant rs247617 was significantly associated with HDL-C levels both pre- and post-fenofibrate intervention. Overall, the phenotype heritabilities calculated using pedigree based kinships or either of the empirical kinships generated using IBDLD or LDAK were comparable. Phenotype heritabilities estimated from empirical kinships generated using IBDLD were closest to the pedigree-based estimations. Given that there was not an appreciable amount of unknown relatedness between the pedigrees in this data set, a large increase in heritability in using empirical kinship was not expected, and our calculations support this. Importantly, these results demonstrate that when sufficient genotypic data are available, empirical kinship estimation is a practical alternative to using pedigree-based kinships.
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Affiliation(s)
- Nicholas B. Blackburn
- South Texas Diabetes and Obesity Institute, Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, One University Blvd., Modular Building #100, Brownsville, TX 78250 USA
| | - Arthur Porto
- South Texas Diabetes and Obesity Institute, Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, One University Blvd., Modular Building #100, Brownsville, TX 78250 USA
| | - Juan M. Peralta
- South Texas Diabetes and Obesity Institute, Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, One University Blvd., Modular Building #100, Brownsville, TX 78250 USA
- Menzies Institute for Medical Research, University of Tasmania, Liverpool St, Hobart, TAS 17 Australia
| | - John Blangero
- South Texas Diabetes and Obesity Institute, Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, One University Blvd., Modular Building #100, Brownsville, TX 78250 USA
- Menzies Institute for Medical Research, University of Tasmania, Liverpool St, Hobart, TAS 17 Australia
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19
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Almeida M, Peralta J, Garcia J, Diego V, Goring H, Williams-Blangero S, Blangero J. Modeling methylation data as an additional genetic variance component. BMC Proc 2018; 12:29. [PMID: 30263043 PMCID: PMC6157027 DOI: 10.1186/s12919-018-0128-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
High-throughput platforms allow the characterization of thousands of previously known methylation sites. These platforms have great potential for investigating the epigenetic effects that are partially responsible for gene expression control. Methylation sites provide a bridge for the investigation of real-time environmental contributions on genomic events by the alteration of methylation status of those sites. Using the data provided by GAW20's organization committee, we calculated the heritability estimates of each cytosine-phosphate-guanine (CpG) island before and after the use of fenofibrate, a lipid-control drug. Surprisingly, we detected substantially high heritability estimates before drug usage. This somewhat unexpected high sample correlation was corrected by the use of principal components and the distributions of heritability estimates before and after fenofibrate treatment, which made the distributions comparable. The methylation sites located near a gene were collected and a genetic relationship matrix estimated to represent the overall correlation between samples. We implemented a random-effect association test to screen genes whose methylation patterns partially explain the observable high-density lipoprotein (HDL) heritability. Our leading association was observed for the TMEM52 gene that encodes a transmembrane protein, and is largely expressed in the liver, had not been previously associated with HDL until this manuscript. Using a variance component decomposition framework with the linear mixed model allows the integration of data from different sources, such as methylation, gene expression, metabolomics, and proteomics. The decomposition of the genetic variance component decomposition provides a flexible analytical approach for the challenges of this new omics era.
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Affiliation(s)
- Marcio Almeida
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, One West University Blvd., STDOI Modular Building #100, Brownsville, TX 78520 USA
| | - Juan Peralta
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, One West University Blvd., STDOI Modular Building #100, Brownsville, TX 78520 USA
- Menzies Institute for Medical Research, University of Tasmania, 17 Liverpool Street, Hobart, TAS Australia
| | - Jose Garcia
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, One West University Blvd., STDOI Modular Building #100, Brownsville, TX 78520 USA
| | - Vincent Diego
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, One West University Blvd., STDOI Modular Building #100, Brownsville, TX 78520 USA
| | - Harald Goring
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, One West University Blvd., STDOI Modular Building #100, Brownsville, TX 78520 USA
| | - Sarah Williams-Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, One West University Blvd., STDOI Modular Building #100, Brownsville, TX 78520 USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, One West University Blvd., STDOI Modular Building #100, Brownsville, TX 78520 USA
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20
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Ganjgahi H, Winkler AM, Glahn DC, Blangero J, Donohue B, Kochunov P, Nichols TE. Fast and powerful genome wide association of dense genetic data with high dimensional imaging phenotypes. Nat Commun 2018; 9:3254. [PMID: 30108209 PMCID: PMC6092439 DOI: 10.1038/s41467-018-05444-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Accepted: 07/09/2018] [Indexed: 01/05/2023] Open
Abstract
Genome wide association (GWA) analysis of brain imaging phenotypes can advance our understanding of the genetic basis of normal and disorder-related variation in the brain. GWA approaches typically use linear mixed effect models to account for non-independence amongst subjects due to factors, such as family relatedness and population structure. The use of these models with high-dimensional imaging phenotypes presents enormous challenges in terms of computational intensity and the need to account multiple testing in both the imaging and genetic domain. Here we present a method that makes mixed models practical with high-dimensional traits by a combination of a transformation applied to the data and model, and the use of a non-iterative variance component estimator. With such speed enhancements permutation tests are feasible, which allows inference on powerful spatial tests like the cluster size statistic.
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Affiliation(s)
- Habib Ganjgahi
- Department of Statistics, University of Oxford, Oxford, UK
- Medical Research Council Harwell Institute, Harwell, UK
| | - Anderson M Winkler
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Big Data Analytics Group, Hospital Israelita Albert Einstein, São Paulo, SP, Brazil
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Brian Donohue
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Thomas E Nichols
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Department of Statistics, University of Warwick, Coventry, UK.
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21
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lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals. BMC Bioinformatics 2018; 19:68. [PMID: 29486711 PMCID: PMC5830078 DOI: 10.1186/s12859-018-2057-x] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 02/13/2018] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. RESULTS To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. CONCLUSIONS Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .
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22
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Arya R, Farook VS, Fowler SP, Puppala S, Chittoor G, Resendez RG, Mummidi S, Vanamala J, Almasy L, Curran JE, Comuzzie AG, Lehman DM, Jenkinson CP, Lynch JL, DeFronzo RA, Blangero J, Hale DE, Duggirala R, Diego VP. Genetic and environmental (physical fitness and sedentary activity) interaction effects on cardiometabolic risk factors in Mexican American children and adolescents. Genet Epidemiol 2018; 42:378-393. [PMID: 29460292 DOI: 10.1002/gepi.22114] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 12/05/2017] [Accepted: 12/11/2017] [Indexed: 12/28/2022]
Abstract
Knowledge on genetic and environmental (G × E) interaction effects on cardiometabolic risk factors (CMRFs) in children is limited. The purpose of this study was to examine the impact of G × E interaction effects on CMRFs in Mexican American (MA) children (n = 617, ages 6-17 years). The environments examined were sedentary activity (SA), assessed by recalls from "yesterday" (SAy) and "usually" (SAu) and physical fitness (PF) assessed by Harvard PF scores (HPFS). CMRF data included body mass index (BMI), waist circumference (WC), fat mass (FM), fasting insulin (FI), homeostasis model of assessment-insulin resistance (HOMA-IR), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), systolic (SBP) and diastolic (DBP) blood pressure, and number of metabolic syndrome components (MSC). We examined potential G × E interaction in the phenotypic expression of CMRFs using variance component models and likelihood-based statistical inference. Significant G × SA interactions were identified for six CMRFs: BMI, WC, FI, HOMA-IR, MSC, and HDL, and significant G × HPFS interactions were observed for four CMRFs: BMI, WC, FM, and HOMA-IR. However, after correcting for multiple hypothesis testing, only WC × SAy, FM × SAy, and FI × SAu interactions became marginally significant. After correcting for multiple testing, most of CMRFs exhibited significant G × E interactions (Reduced G × E model vs. Constrained model). These findings provide evidence that genetic factors interact with SA and PF to influence variation in CMRFs, and underscore the need for better understanding of these relationships to develop strategies and interventions to effectively reduce or prevent cardiometabolic risk in children.
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Affiliation(s)
- Rector Arya
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.,South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America
| | - Vidya S Farook
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.,South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America
| | - Sharon P Fowler
- Department of Medicine, Division of Nephrology, University of Texas Health Science Center, San Antonio, Texas, United States of America
| | - Sobha Puppala
- Department of Internal Medicine, Section on Molecular Medicine Wake Forest Baptist Health Medical University, Winston-Salem, NC, United States of America
| | - Geetha Chittoor
- Biomedical and Translational Informatics Institute, Geisinger, Weis Center for Research, Danville, PA, United States of America
| | - Roy G Resendez
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.,South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America
| | - Srinivas Mummidi
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.,South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America
| | - Jairam Vanamala
- Department of Food Science, Penn State University, University Park, PA, United States of America
| | - Laura Almasy
- Department of Biomedical and Health Informatics, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Joanne E Curran
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.,South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America
| | - Anthony G Comuzzie
- The Obesity Society, 1110 Bonifant St. Silver Spring, Maryland, United States of America
| | - Donna M Lehman
- Department of Cellular & Structural Biology, University of Texas Health Science Center, San Antonio, Texas, United States of America
| | - Christopher P Jenkinson
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.,South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America
| | - Jane L Lynch
- Department of Pediatrics, University of Texas Health Science Center, San Antonio, Texas, United States of America
| | - Ralph A DeFronzo
- Department of Medicine, Division of Diabetes, University of Texas Health Science Center, San Antonio, Texas, United States of America
| | - John Blangero
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.,South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America
| | - Daniel E Hale
- Penn State Hershey Pediatric Endocrinology, Penn State University, Hershey, PA, United States of America
| | - Ravindranath Duggirala
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.,South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America
| | - Vincent P Diego
- Department of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.,South Texas Diabetes and Obesity Institute, School of Medicine, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America
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23
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Abstract
Relatedness within a sample can be of ancient (population stratification) or recent (familial structure) origin, and can either be known (pedigree data) or unknown (cryptic relatedness). All of these forms of familial relatedness have the potential to confound the results of genome-wide association studies. This chapter reviews the major methods available to researchers to adjust for the biases introduced by relatedness and maximize power to detect associations. The advantages and disadvantages of different methods are presented with reference to elements of study design, population characteristics, and computational requirements.
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Affiliation(s)
- Russell Thomson
- Centre for Research in Mathematics, School of Computing, Engineering and Mathematics, Western Sydney University, Parramatta, Australia.
| | - Rebekah McWhirter
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
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24
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Evaluating the contribution of rare variants to type 2 diabetes and related traits using pedigrees. Proc Natl Acad Sci U S A 2017; 115:379-384. [PMID: 29279374 DOI: 10.1073/pnas.1705859115] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
A major challenge in evaluating the contribution of rare variants to complex disease is identifying enough copies of the rare alleles to permit informative statistical analysis. To investigate the contribution of rare variants to the risk of type 2 diabetes (T2D) and related traits, we performed deep whole-genome analysis of 1,034 members of 20 large Mexican-American families with high prevalence of T2D. If rare variants of large effect accounted for much of the diabetes risk in these families, our experiment was powered to detect association. Using gene expression data on 21,677 transcripts for 643 pedigree members, we identified evidence for large-effect rare-variant cis-expression quantitative trait loci that could not be detected in population studies, validating our approach. However, we did not identify any rare variants of large effect associated with T2D, or the related traits of fasting glucose and insulin, suggesting that large-effect rare variants account for only a modest fraction of the genetic risk of these traits in this sample of families. Reliable identification of large-effect rare variants will require larger samples of extended pedigrees or different study designs that further enrich for such variants.
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25
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Population-based familial aggregation of eosinophilic esophagitis suggests a genetic contribution. J Allergy Clin Immunol 2017; 140:1138-1143. [DOI: 10.1016/j.jaci.2016.12.979] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Revised: 11/30/2016] [Accepted: 12/21/2016] [Indexed: 01/07/2023]
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26
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Significant Linkage Evidence for Interstitial Cystitis/Painful Bladder Syndrome on Chromosome 3. J Urol 2017; 199:172-177. [PMID: 28734863 DOI: 10.1016/j.juro.2017.07.068] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2017] [Indexed: 11/23/2022]
Abstract
PURPOSE Interstitial cystitis/painful bladder syndrome is a chronic pelvic pain condition of unknown etiology. We hypothesized that related interstitial cystitis/painful bladder syndrome cases were more likely to have a genetic etiology. The purpose of this study was to perform a genetic linkage analysis. MATERIALS AND METHODS We identified interstitial cystitis/painful bladder syndrome cases using diagnostic codes linked to the Utah Population Database genealogy resource and to electronic medical records. For this analysis we used 13 high risk pedigrees, defined as having a statistical excess number of interstitial cystitis/painful bladder syndrome cases among descendants compared to matched hospital population rates. Case status was confirmed in medical records using natural language processing. DNA was obtained from stored, nonneoplastic, formalin fixed, paraffin embedded tissue blocks. Each pedigree had at least 2 cases with DNA available. Parametric linkage analysis was performed. RESULTS Pedigrees ranged in size from 2 to 12 genotyped cases for a total of 48 cases. Significant genome wide linkage evidence was found under a dominant model on chromosome 3p13-p12.3 (maximum heterogeneity θ logarithm of odds 3.56). Two pedigrees showed at least nominal linkage evidence in this region (logarithm of odds greater than 0.59). The most informative pedigree included 12 interstitial cystitis/painful bladder syndrome cases (pedigree θ logarithm of odds 2.1). Other regions with suggestive linkage evidence included 1p21-q25, 3p21.1-p14.3, 4q12-q13, 9p24-p22 and 14q24-q31, all under a dominant model. CONCLUSIONS While the etiology of interstitial cystitis/painful bladder syndrome is unknown, this study provides evidence that a genetic variant(s) on chromosome 3 and possibly on chromosomes 1, 4, 9 and 14 contribute to an interstitial cystitis/painful bladder syndrome predisposition. Sequence analysis of affected cases in identified pedigrees may provide insight into genes contributing to interstitial cystitis/painful bladder syndrome.
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27
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Bruce HA, Kochunov P, Paciga SA, Hyde CL, Chen X, Xie Z, Zhang B, Xi HS, O'Donnell P, Whelan C, Schubert CR, Bellon A, Ament SA, Shukla DK, Du X, Rowland LM, O'Neill H, Hong LE. Potassium channel gene associations with joint processing speed and white matter impairments in schizophrenia. GENES BRAIN AND BEHAVIOR 2017; 16:515-521. [PMID: 28188958 DOI: 10.1111/gbb.12372] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Revised: 01/14/2017] [Accepted: 02/07/2017] [Indexed: 12/17/2022]
Abstract
Patients with schizophrenia show decreased processing speed on neuropsychological testing and decreased white matter integrity as measured by diffusion tensor imaging, two traits shown to be both heritable and genetically associated indicating that there may be genes that influence both traits as well as schizophrenia disease risk. The potassium channel gene family is a reasonable candidate to harbor such a gene given the prominent role potassium channels play in the central nervous system in signal transduction, particularly in myelinated axons. We genotyped members of the large potassium channel gene family focusing on putatively functional single nucleotide polymorphisms (SNPs) in a population of 363 controls, 194 patients with schizophrenia spectrum disorder (SSD) and 28 patients with affective disorders with psychotic features who completed imaging and neuropsychological testing. We then performed three association analyses using three phenotypes - processing speed, whole-brain white matter fractional anisotropy (FA) and schizophrenia spectrum diagnosis. We extracted SNPs showing an association at a nominal P value of <0.05 with all three phenotypes in the expected direction: decreased processing speed, decreased FA and increased risk of SSD. A single SNP, rs8234, in the 3' untranslated region of voltage-gated potassium channel subfamily Q member 1 (KCNQ1) was identified. Rs8234 has been shown to affect KCNQ1 expression levels, and KCNQ1 levels have been shown to affect neuronal action potentials. This exploratory analysis provides preliminary data suggesting that KCNQ1 may contribute to the shared risk for diminished processing speed, diminished white mater integrity and increased risk of schizophrenia.
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Affiliation(s)
- H A Bruce
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - P Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - S A Paciga
- Pfizer Inc., Worldwide Research and Development, Cambridge, MA
| | - C L Hyde
- Pfizer Inc., Worldwide Research and Development, Cambridge, MA
| | - X Chen
- Pfizer Inc., Worldwide Research and Development, Cambridge, MA
| | - Z Xie
- Pfizer Inc., Worldwide Research and Development, Cambridge, MA
| | - B Zhang
- Pfizer Inc., Worldwide Research and Development, Cambridge, MA
| | - H S Xi
- Pfizer Inc., Worldwide Research and Development, Cambridge, MA
| | - P O'Donnell
- Pfizer Inc., Worldwide Research and Development, Cambridge, MA
| | - C Whelan
- Pfizer Inc., Worldwide Research and Development, Cambridge, MA
| | | | - A Bellon
- Department of Psychiatry, Penn State Hershey Medical Center, Hershey, PA, USA
| | - S A Ament
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - D K Shukla
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - X Du
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - L M Rowland
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - H O'Neill
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
| | - L E Hong
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD
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28
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Poveda A, Chen Y, Brändström A, Engberg E, Hallmans G, Johansson I, Renström F, Kurbasic A, Franks PW. The heritable basis of gene-environment interactions in cardiometabolic traits. Diabetologia 2017; 60:442-452. [PMID: 28004149 PMCID: PMC6518092 DOI: 10.1007/s00125-016-4184-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 11/24/2016] [Indexed: 11/17/2022]
Abstract
AIMS/HYPOTHESIS Little is known about the heritable basis of gene-environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype-environment interactions. METHODS Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software. RESULTS All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities (h 2) ranging from 24% to 47%. Genotype-environment interactions were detected for age and sex (for the majority of traits), physical activity (for triacylglycerols, 2 h glucose and diastolic BP), smoking (for weight), alcohol intake (for weight, BMI and 2 h glucose) and diet pattern (for weight, BMI, glycaemic traits and systolic BP). Genotype-age interactions for weight and systolic BP, genotype-sex interactions for BMI and triacylglycerols and genotype-alcohol intake interactions for weight remained significant after multiple test correction. CONCLUSIONS/INTERPRETATION Age, sex and alcohol intake are likely to be major modifiers of genetic effects for a range of cardiometabolic traits. This information may prove valuable for studies that seek to identify specific loci that modify the effects of lifestyle in cardiometabolic disease.
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Affiliation(s)
- Alaitz Poveda
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Building 91, Skåne University Hospital, SE-20502, Malmö, Sweden
- Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and Technology, University of the Basque Country (UPV/EHU), Bilbao, Spain
| | - Yan Chen
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Building 91, Skåne University Hospital, SE-20502, Malmö, Sweden
| | - Anders Brändström
- Centre for Demographic and Ageing Research, Umeå University, Umeå, Sweden
| | - Elisabeth Engberg
- Centre for Demographic and Ageing Research, Umeå University, Umeå, Sweden
| | - Göran Hallmans
- Department of Biobank Research, Umeå University, Umeå, Sweden
| | | | - Frida Renström
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Building 91, Skåne University Hospital, SE-20502, Malmö, Sweden
- Department of Biobank Research, Umeå University, Umeå, Sweden
| | - Azra Kurbasic
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Building 91, Skåne University Hospital, SE-20502, Malmö, Sweden
| | - Paul W Franks
- Genetic and Molecular Epidemiology Unit, Department of Clinical Sciences, Clinical Research Centre, Lund University, Jan Waldenströms gata 35, Building 91, Skåne University Hospital, SE-20502, Malmö, Sweden.
- Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University, Umeå, Sweden.
- Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
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29
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Clark MM, Chazara O, Sobel EM, Gjessing HK, Magnus P, Moffett A, Sinsheimer JS. Human Birth Weight and Reproductive Immunology: Testing for Interactions between Maternal and Offspring KIR and HLA-C Genes. Hum Hered 2017; 81:181-193. [PMID: 28214848 DOI: 10.1159/000456033] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 01/11/2017] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND/AIMS Maternal and offspring cell contact at the site of placentation presents a plausible setting for maternal-fetal genotype (MFG) interactions affecting fetal growth. We test hypotheses regarding killer cell immunoglobulin-like receptor (KIR) and HLA-C MFG effects on human birth weight by extending the quantitative MFG (QMFG) test. METHODS Until recently, association testing for MFG interactions had limited applications. To improve the ability to test for these interactions, we developed the extended QMFG test, a linear mixed-effect model that can use multi-locus genotype data from families. RESULTS We demonstrate the extended QMFG test's statistical properties. We also show that if an offspring-only model is fit when MFG effects exist, associations can be missed or misattributed. Furthermore, imprecisely modeling the effects of both KIR and HLA-C could result in a failure to replicate if these loci's allele frequencies differ among populations. To further illustrate the extended QMFG test's advantages, we apply the extended QMFG test to a UK cohort study and the Norwegian Mother and Child Cohort (MoBa) study. CONCLUSION We find a significant KIR-HLA-C interaction effect on birth weight. More generally, the QMFG test can detect genetic associations that may be missed by standard genome-wide association studies for quantitative traits.
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Affiliation(s)
- Michelle M Clark
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA
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30
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van der Lee SJ, Roshchupkin GV, Adams HHH, Schmidt H, Hofer E, Saba Y, Schmidt R, Hofman A, Amin N, van Duijn CM, Vernooij MW, Ikram MA, Niessen WJ. Gray matter heritability in family-based and population-based studies using voxel-based morphometry. Hum Brain Mapp 2017; 38:2408-2423. [PMID: 28145022 DOI: 10.1002/hbm.23528] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Revised: 10/27/2016] [Accepted: 01/12/2017] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The combination of genetics and imaging has improved their understanding of the brain through studies of aggregate measures obtained from high-resolution structural imaging. Voxel-wise analyses have the potential to provide more detailed information of genetic influences on the brain. Here they report a large-scale study of the heritability of gray matter at voxel resolution (1 × 1 × 1 mm). METHODS Validated voxel-based morphometry (VBM) protocols were applied to process magnetic resonance imaging data of 3,239 unrelated subjects from a population-based study and 491 subjects from two family-based studies. Genome-wide genetic data was used to estimate voxel-wise gray matter heritability of the unrelated subjects and pedigree-structure was used to estimate heritability in families. They subsequently associated two genetic variants, known to be linked with subcortical brain volume, with most heritable voxels to determine if this would enhance their association signals. RESULTS Voxels significantly heritable in both estimates mapped to subcortical structures, but also voxels in the language areas of the left hemisphere were found significantly heritable. When comparing regional patterns of heritability, family-based estimates were higher than population-based estimates. However, regional consistency of the heritability measures across study designs was high (Pearson's correlation coefficient = 0.73, P = 2.6 × 10-13 ). They further show enhancement of the association signal of two previously discovered genetic loci with subcortical volume by using only the most heritable voxels. CONCLUSION Gray matter voxel-wise heritability can be reliably estimated with different methods. Combining heritability estimates from multiple studies is feasible to construct reliable heritability maps of gray matter voxels. Hum Brain Mapp 38:2408-2423, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Sven J van der Lee
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Gennady V Roshchupkin
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Hieab H H Adams
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Helena Schmidt
- Institute of Molecular Biology and Biochemistry, Centre for Molecular Medicine, Medical University of Graz, Graz, Austria.,Department of Neurology, Medical University Graz, Graz, Austria
| | - Edith Hofer
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University Graz, Graz, Austria.,Institute of Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria
| | - Yasaman Saba
- Institute of Molecular Biology and Biochemistry, Centre for Molecular Medicine, Medical University of Graz, Graz, Austria
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University Graz, Graz, Austria
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Najaf Amin
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Meike W Vernooij
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands.,Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
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Abstract
BACKGROUND Identifying variants that regulate gene expression and delineating their genetic architecture is a critical next step in our endeavors to better understand the genetic etiology of complex diseases. The appropriate genomic tools are in place, and preliminary analytic strategies have been developed. METHODS Here we used Genetic Analysis Workshop (GAW) 19 data to investigate the genetic complexity of expression quantitative trait loci (eQTL), chromosomal regions likely to harbor regulatory elements responsible for gene expression. For this investigation, we analyzed the lymphocyte expression profiles of 653 individuals in 20 pedigrees who were also genotyped by single nucleotide polymorphism (SNP) arrays, followed by sequencing and imputation. We used these data to examine the degree of allelic heterogeneity, a contributor to genetic complexity at eQTL, by sequentially conditioning on the most significantly associated SNPs. RESULT SOLAR (Sequential Oligogenic Linkage Analysis Routines)-MGA (measured genotype approach) and FaST-LMM (Factored Spectrally Transformed Linear Mixed Model) software allowed us to analyze pedigree data. The power and Type 1 error rates for single SNP association testing and multiple SNP sequential association testing were consistent for these programs. Sequential conditioning of the real expression data revealed substantial levels of allelic heterogeneity at the 2 eQTL examined, illustrating this feature of genetic complexity. CONCLUSIONS eQTL exhibit substantial genetic complexity among and within pedigrees.
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Affiliation(s)
- Rita M. Cantor
- Department of Human Genetics, David Geffen School of Medicine at UCLA, 695 Charles E. Young Dr, South, Los Angeles, CA 90024-7088 USA
| | - Calvin Pan
- Department of Human Genetics, David Geffen School of Medicine at UCLA, 695 Charles E. Young Dr, South, Los Angeles, CA 90024-7088 USA
| | - Kimberly Siegmund
- Department of Preventive Medicine, Keck School of Medicine of USC, SSB 202WHealth Sciences Campus, Los Angeles, CA 90089-9234 USA
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Mundra PA, Shaw JE, Meikle PJ. Lipidomic analyses in epidemiology. Int J Epidemiol 2016; 45:1329-1338. [PMID: 27286762 DOI: 10.1093/ije/dyw112] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/08/2016] [Indexed: 12/31/2022] Open
Abstract
Clinical lipid measurements have been the mainstay of risk assessment for chronic disease since the Framingham study commenced over 60 years ago. Thousands of subsequent epidemiological studies have provided much insight into the relationship between plasma lipid profiles, health and disease. However, the human lipidome consists of thousands of individual lipid species, and current lipidomic technology presents us with an unprecedented opportunity to measure lipid phenotypes, representing genomic, metabolic, diet and lifestyle-related exposures, in large epidemiological studies. The number of epidemiological studies using lipidomic profiling is increasing and has the potential to provide improved biological and clinical insight into human disease. In this review, we discuss current lipidomic technologies, epidemiological studies using these technologies and the statistical approaches used in the analysis of the resulting data. We highlight the potential of integrating genomic and lipidomic datasets and discuss the future opportunities and challenges in this emerging field.
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Affiliation(s)
| | - Jonathan E Shaw
- Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia and
| | - Peter J Meikle
- Baker IDI Heart and Diabetes Institute, Melbourne, VIC, Australia and
- Department of Biochemistry and Molecular Biology, University of Melbourne, Parkville, VIC, Australia
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33
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Ho TC, Sanders SJ, Gotlib IH, Hoeft F. Intergenerational Neuroimaging of Human Brain Circuitry. Trends Neurosci 2016; 39:644-648. [PMID: 27623194 DOI: 10.1016/j.tins.2016.08.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Revised: 07/16/2016] [Accepted: 08/19/2016] [Indexed: 12/13/2022]
Abstract
Neuroscientists are increasingly using advanced neuroimaging methods to elucidate the intergenerational transmission of human brain circuitry. This new line of work promises to shed light on the ontogeny of complex behavioral traits, including psychiatric disorders, and possible mechanisms of transmission. Here we highlight recent intergenerational neuroimaging studies and provide recommendations for future work.
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Affiliation(s)
- Tiffany C Ho
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA; Department of Psychology, Stanford University, Stanford, CA, USA
| | - Stephan J Sanders
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA; Neurosciences Program, Stanford University, Stanford, CA, USA
| | - Fumiko Hoeft
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, USA; Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
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34
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Schrodi SJ. Reflections on the Field of Human Genetics: A Call for Increased Disease Genetics Theory. Front Genet 2016; 7:106. [PMID: 27375680 PMCID: PMC4896932 DOI: 10.3389/fgene.2016.00106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 05/25/2016] [Indexed: 12/29/2022] Open
Abstract
Development of human genetics theoretical models and the integration of those models with experiment and statistical evaluation are critical for scientific progress. This perspective argues that increased effort in disease genetics theory, complementing experimental, and statistical efforts, will escalate the unraveling of molecular etiologies of complex diseases. In particular, the development of new, realistic disease genetics models will help elucidate complex disease pathogenesis, and the predicted patterns in genetic data made by these models will enable the concurrent, more comprehensive statistical testing of multiple aspects of disease genetics predictions, thereby better identifying disease loci. By theoretical human genetics, I intend to encompass all investigations devoted to modeling the heritable architecture underlying disease traits and studies of the resulting principles and dynamics of such models. Hence, the scope of theoretical disease genetics work includes construction and analysis of models describing how disease-predisposing alleles (1) arise, (2) are transmitted across families and populations, and (3) interact with other risk and protective alleles across both the genome and environmental factors to produce disease states. Theoretical work improves insight into viable genetic models of diseases consistent with empirical results from linkage, transmission, and association studies as well as population genetics. Furthermore, understanding the patterns of genetic data expected under realistic disease models will enable more powerful approaches to discover disease-predisposing alleles and additional heritable factors important in common diseases. In spite of the pivotal role of disease genetics theory, such investigation is not particularly vibrant.
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Affiliation(s)
- Steven J Schrodi
- Marshfield Clinic Research Foundation, Center for Human GeneticsMarshfield, WI, USA; Computation and Informatics in Biology and Medicine, University of Wisconsin-MadisonMadison, WI, USA
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35
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Abstract
Correlation between study units in quantitative genetics studies often makes it difficult to compare important inferential aspects of studies. Describing the relatedness between study units is critical to capture features of pedigree studies involving heritability, including power and precision of heritability estimates. Blangero et al (2012) showed that in pedigree studies the power to detect heritability is a function of the true heritability and the eigenvalues of the kinship matrix. We extend this to a more general setting which allows statements about expected precision of heritability estimates. Using two different Taylor series approximations, we summarize the relatedness in a study design by one or two parameters. These relatedness summary parameters (RSPs) are functions of the eigenvalues or log-eigenvalues of the kinship matrix. Using the RSPs based on the log-eigenvalues, we accurately approximate the expectation of the likelihood ratio test and expected confidence interval widths. We define an effective sample size of a target study as one which has the equivalent power and precision to a reference design. Using unrelated sibpairs as the reference design provides very accurate assessments of power. RSPs and effective sample sizes provide new tools for comparing studies and communicating information about relatedness in heritability studies.
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Affiliation(s)
- Jesse D Raffa
- University of Washington, Department of Statistics, Box 354322, Seattle, WA, 98195-4322, U.S.A
| | - Elizabeth A Thompson
- University of Washington, Department of Statistics, Box 354322, Seattle, WA, 98195-4322, U.S.A
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36
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Diego VP, de Chaves RN, Blangero J, de Souza MC, Santos D, Gomes TN, dos Santos FK, Garganta R, Katzmarzyk PT, Maia JAR. Sex-specific genetic effects in physical activity: results from a quantitative genetic analysis. BMC MEDICAL GENETICS 2015; 16:58. [PMID: 26231751 PMCID: PMC4557754 DOI: 10.1186/s12881-015-0207-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2013] [Accepted: 07/22/2015] [Indexed: 11/10/2022]
Abstract
BACKGROUND The objective of this study is to present a model to estimate sex-specific genetic effects on physical activity (PA) levels and sedentary behaviour (SB) using three generation families. METHODS The sample consisted of 100 families covering three generations from Portugal. PA and SB were assessed via the International Physical Activity Questionnaire short form (IPAQ-SF). Sex-specific effects were assessed by genotype-by-sex interaction (GSI) models and sex-specific heritabilities. GSI effects and heterogeneity were tested in the residual environmental variance. SPSS 17 and SOLAR v. 4.1 were used in all computations. RESULTS The genetic component for PA and SB domains varied from low to moderate (11% to 46%), when analyzing both genders combined. We found GSI effects for vigorous PA (p = 0.02) and time spent watching television (WT) (p < 0.001) that showed significantly higher additive genetic variance estimates in males. The heterogeneity in the residual environmental variance was significant for moderate PA (p = 0.02), vigorous PA (p = 0.006) and total PA (p = 0.001). Sex-specific heritability estimates were significantly higher in males only for WT, with a male-to-female difference in heritability of 42.5 (95% confidence interval: 6.4, 70.4). CONCLUSIONS Low to moderate genetic effects on PA and SB traits were found. Results from the GSI model show that there are sex-specific effects in two phenotypes, VPA and WT with a stronger genetic influence in males.
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Affiliation(s)
- Vincent P Diego
- University of Texas Rio Grande Valley, School of Medicine, South Texas Diabetes and Obesity Institute, Brownsville, Texas.
| | - Raquel Nichele de Chaves
- Academic Department of Physical Education, Federal University of Technology - Parana, Curitiba - PR, Brazil.
- CIFI²D, Kinanthropometry Lab, Faculty of Sport, University of Porto, Porto, Portugal Faculty of Sports, University of Porto, Porto, Portugal.
| | - John Blangero
- University of Texas Rio Grande Valley, School of Medicine, South Texas Diabetes and Obesity Institute, Brownsville, Texas.
| | - Michele Caroline de Souza
- CIFI²D, Kinanthropometry Lab, Faculty of Sport, University of Porto, Porto, Portugal Faculty of Sports, University of Porto, Porto, Portugal.
| | - Daniel Santos
- CIFI²D, Kinanthropometry Lab, Faculty of Sport, University of Porto, Porto, Portugal Faculty of Sports, University of Porto, Porto, Portugal.
| | - Thayse Natacha Gomes
- CIFI²D, Kinanthropometry Lab, Faculty of Sport, University of Porto, Porto, Portugal Faculty of Sports, University of Porto, Porto, Portugal.
| | - Fernanda Karina dos Santos
- CIFI²D, Kinanthropometry Lab, Faculty of Sport, University of Porto, Porto, Portugal Faculty of Sports, University of Porto, Porto, Portugal.
- Department of Physical Education and Sports Science, CAV, Federal University of Pernambuco, Vitória de Santo Antão, Brazil.
| | - Rui Garganta
- CIFI²D, Kinanthropometry Lab, Faculty of Sport, University of Porto, Porto, Portugal Faculty of Sports, University of Porto, Porto, Portugal.
| | | | - José A R Maia
- CIFI²D, Kinanthropometry Lab, Faculty of Sport, University of Porto, Porto, Portugal Faculty of Sports, University of Porto, Porto, Portugal.
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37
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Ganjgahi H, Winkler AM, Glahn DC, Blangero J, Kochunov P, Nichols TE. Fast and powerful heritability inference for family-based neuroimaging studies. Neuroimage 2015; 115:256-68. [PMID: 25812717 PMCID: PMC4463976 DOI: 10.1016/j.neuroimage.2015.03.005] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Accepted: 03/03/2015] [Indexed: 11/29/2022] Open
Abstract
Heritability estimation has become an important tool for imaging genetics studies. The large number of voxel- and vertex-wise measurements in imaging genetics studies presents a challenge both in terms of computational intensity and the need to account for elevated false positive risk because of the multiple testing problem. There is a gap in existing tools, as standard neuroimaging software cannot estimate heritability, and yet standard quantitative genetics tools cannot provide essential neuroimaging inferences, like family-wise error corrected voxel-wise or cluster-wise P-values. Moreover, available heritability tools rely on P-values that can be inaccurate with usual parametric inference methods. In this work we develop fast estimation and inference procedures for voxel-wise heritability, drawing on recent methodological results that simplify heritability likelihood computations (Blangero et al., 2013). We review the family of score and Wald tests and propose novel inference methods based on explained sum of squares of an auxiliary linear model. To address problems with inaccuracies with the standard results used to find P-values, we propose four different permutation schemes to allow semi-parametric inference (parametric likelihood-based estimation, non-parametric sampling distribution). In total, we evaluate 5 different significance tests for heritability, with either asymptotic parametric or permutation-based P-value computations. We identify a number of tests that are both computationally efficient and powerful, making them ideal candidates for heritability studies in the massive data setting. We illustrate our method on fractional anisotropy measures in 859 subjects from the Genetics of Brain Structure study.
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Affiliation(s)
- Habib Ganjgahi
- Department of Statistics, The University of Warwick, Coventry, UK
| | - Anderson M Winkler
- Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK; Department of Psychiatry, Yale University School of Medicine, New Haven, USA
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - John Blangero
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Thomas E Nichols
- Department of Statistics, The University of Warwick, Coventry, UK; Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK; WMG, The University of Warwick, Coventry, UK.
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38
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A general unified framework to assess the sampling variance of heritability estimates using pedigree or marker-based relationships. Genetics 2014; 199:223-32. [PMID: 25361897 PMCID: PMC4286686 DOI: 10.1534/genetics.114.171017] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Heritability is a population parameter of importance in evolution, plant and animal breeding, and human medical genetics. It can be estimated using pedigree designs and, more recently, using relationships estimated from markers. We derive the sampling variance of the estimate of heritability for a wide range of experimental designs, assuming that estimation is by maximum likelihood and that the resemblance between relatives is solely due to additive genetic variation. We show that well-known results for balanced designs are special cases of a more general unified framework. For pedigree designs, the sampling variance is inversely proportional to the variance of relationship in the pedigree and it is proportional to 1/N, whereas for population samples it is approximately proportional to 1/N(2), where N is the sample size. Variation in relatedness is a key parameter in the quantification of the sampling variance of heritability. Consequently, the sampling variance is high for populations with large recent effective population size (e.g., humans) because this causes low variation in relationship. However, even using human population samples, low sampling variance is possible with high N.
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39
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Santos DMV, Katzmarzyk PT, Diego VP, Blangero J, Souza MC, Freitas DL, Chaves RN, Gomes TN, Santos FK, Maia JAR. Genotype by sex and genotype by age interactions with sedentary behavior: the Portuguese Healthy Family Study. PLoS One 2014; 9:e110025. [PMID: 25302714 PMCID: PMC4193845 DOI: 10.1371/journal.pone.0110025] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Accepted: 09/12/2014] [Indexed: 01/08/2023] Open
Abstract
Sedentary behavior (SB) expression and its underlying causal factors have been progressively studied, as it is a major determinant of decreased health quality. In the present study we applied Genotype x Age (GxAge) and Genotype x Sex (GxSex) interaction methods to determine if the phenotypic expression of different SB traits is influenced by an interaction between genetic architecture and both age and sex. A total of 1345 subjects, comprising 249 fathers, 327 mothers, 334 sons and 325 daughters, from 339 families of The Portuguese Healthy Family Study were included in the analysis. SB traits were assessed by means of a 3-d physical activity recall, the Baecke and IPAQ questionnaires. GxAge and GxSex interactions were analyzed using SOLAR 4.0 software. Sedentary behaviour heritability estimates were not always statistically significant (p>0.05) and ranged from 3% to 27%. The GxSex and GxAge interaction models were significantly better than the single polygenic models for TV (min/day), EEsed (kcal/day), personal computer (PC) usage and physical activty (PA) tertiles. The GxAge model is also significantly better than the polygenic model for Sed (min/day). For EEsed, PA tertiles, PC and Sed, the GxAge interaction was significant because the genetic correlation between SB environments was significantly different from 1. Further, PC and Sed variance heterogeneity among distinct ages were observed. The GxSex interaction was significant for EEsed due to genetic variance heterogeneity between genders and for PC due to a genetic correlation less than 1 across both sexes. Our results suggest that SB expression may be influenced by the interactions between genotype with both sex and age. Further, different sedentary behaviors seem to have distinct genetic architectures and are differentially affected by age and sex.
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Affiliation(s)
- Daniel M. V. Santos
- Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sports, University of Porto, Porto, Portugal
- * E-mail:
| | - Peter T. Katzmarzyk
- Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, Louisiana, United States of America
| | - Vincent P. Diego
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - John Blangero
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Michele C. Souza
- Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sports, University of Porto, Porto, Portugal
| | - Duarte L. Freitas
- Sport and Physical Education Department, University of Madeira, Funchal, Portugal
| | - Raquel N. Chaves
- Physical Education Department, Federal University of Technology - Parana, Campus Curitiba, Curitiba/PR, Brasil
| | - Thayse N. Gomes
- Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sports, University of Porto, Porto, Portugal
| | - Fernanda K. Santos
- Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sports, University of Porto, Porto, Portugal
| | - José A. R. Maia
- Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sports, University of Porto, Porto, Portugal
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40
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Al-Sinani S, Hassan MO, Zadjali F, Al-Yahyaee S, Albarwani S, Rizvi S, Jaju D, Comuzzie A, Voruganti VS, Bayoumi R. Utility of large consanguineous family-based model for investigating the genetics of type 2 diabetes mellitus. Gene 2014; 548:22-8. [DOI: 10.1016/j.gene.2014.06.053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2013] [Revised: 06/16/2014] [Accepted: 06/23/2014] [Indexed: 12/24/2022]
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41
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Ziegler A, Bohossian N, Diego VP, Yao C. Genetic prediction in the Genetic Analysis Workshop 18 sequencing data. Genet Epidemiol 2014; 38 Suppl 1:S57-62. [PMID: 25112190 DOI: 10.1002/gepi.21826] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
High-throughput sequencing data can be used to predict phenotypes from genotypes, and this corresponds to establishing a prognostic model. In extended pedigrees the relatedness of subjects provides additional information so that genetic values, fixed or random genetic components, and heritability can be estimated. At the Genetic Analysis Workshop 18, the working group on genetic prediction dealt with both establishing a prognostic model and, in one contribution, comparing standard logistic regression with robust logistic regression in a sample of unrelated affected or unaffected individuals. Results of both logistic regression approaches were similar. All other contributions to this group used extended family data, in general using the quantitative trait blood pressure. The individual contributions varied in several important aspects, such as the estimation of the kinship matrix and the estimation method. Contributors chose various approaches for model validation, including different versions of cross-validation or within-family validation. Within-family validation included model building in the upper generations and validation in later generations. The choice of the statistical model and the computational algorithm had substantial effects on computation time. If decorrelation approaches were applied, the computational burden was substantially reduced. Some software packages estimated negative eigenvalues, although eigenvalues of correlation matrices should be non-negative. Most statistical models and software packages have been developed for experimental crosses and planned breeding programs. With their specialized pedigree structures, they are not sufficiently flexible to accommodate the variability of human pedigrees in general, and improved implementations are required.
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Affiliation(s)
- Andreas Ziegler
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany; Center for Clinical Trials, University of Lübeck, Lübeck, Germany
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Kochunov P, Jahanshad N, Sprooten E, Nichols TE, Mandl RC, Almasy L, Booth T, Brouwer RM, Curran JE, de Zubicaray GI, Dimitrova R, Duggirala R, Fox PT, Hong LE, Landman BA, Lemaitre H, Lopez LM, Martin NG, McMahon KL, Mitchell BD, Olvera RL, Peterson CP, Starr JM, Sussmann JE, Toga AW, Wardlaw JM, Wright MJ, Wright SN, Bastin ME, McIntosh AM, Boomsma DI, Kahn RS, den Braber A, de Geus EJC, Deary IJ, Hulshoff Pol HE, Williamson DE, Blangero J, van 't Ent D, Thompson PM, Glahn DC. Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter: Comparing meta and megaanalytical approaches for data pooling. Neuroimage 2014; 95:136-50. [PMID: 24657781 PMCID: PMC4043878 DOI: 10.1016/j.neuroimage.2014.03.033] [Citation(s) in RCA: 102] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2013] [Revised: 02/21/2014] [Accepted: 03/04/2014] [Indexed: 01/25/2023] Open
Abstract
Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.
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Affiliation(s)
- Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Neda Jahanshad
- Imaging Genetics Center, Institute of Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Emma Sprooten
- Olin Neuropsychiatry Research Center in the Institute of Living, Yale University School of Medicine, New Haven, CT, USA
| | - Thomas E Nichols
- Department of Statistics & Warwick Manufacturing Group, The University of Warwick, Coventry, UK; Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Oxford University, UK
| | - René C Mandl
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Laura Almasy
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Tom Booth
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Rachel M Brouwer
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Joanne E Curran
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | | | - Rali Dimitrova
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Ravi Duggirala
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center San Antonio, San Antonio, TX, USA
| | - L Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bennett A Landman
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Hervé Lemaitre
- U1000 Research Unit Neuroimaging and Psychiatry, INSERM-CEA-Faculté de Médecine Paris-Sud, Orsay, France
| | - Lorna M Lopez
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | | | - Katie L McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rene L Olvera
- Department of Psychiatry, University of Texas Health Science Center San Antonio, San Antonio, TX, USA
| | - Charles P Peterson
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK; Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Jessika E Sussmann
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Arthur W Toga
- Imaging Genetics Center, Institute of Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | | | - Susan N Wright
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK; Brain Research Imaging Centre, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Dorret I Boomsma
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - René S Kahn
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anouk den Braber
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Hilleke E Hulshoff Pol
- Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Douglas E Williamson
- Department of Psychiatry, University of Texas Health Science Center San Antonio, San Antonio, TX, USA
| | - John Blangero
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Dennis van 't Ent
- Department of Biological Psychology, VU University, Amsterdam, The Netherlands
| | - Paul M Thompson
- Imaging Genetics Center, Institute of Neuroimaging and Informatics, Department of Neurology, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Neurology, Pediatrics, Engineering, Psychiatry, Radiology, & Ophthalmology, University of Southern California, Los Angeles, CA, USA
| | - David C Glahn
- Olin Neuropsychiatry Research Center in the Institute of Living, Yale University School of Medicine, New Haven, CT, USA
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Almeida M, Peralta JM, Farook V, Puppala S, Kent JW, Duggirala R, Blangero J. Pedigree-based random effect tests to screen gene pathways. BMC Proc 2014; 8:S100. [PMID: 25519354 PMCID: PMC4143680 DOI: 10.1186/1753-6561-8-s1-s100] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
The new generation of sequencing platforms opens new horizons in the genetics field. It is possible to exhaustively assay all genetic variants in an individual and search for phenotypic associations. The whole genome sequencing approach, when applied to a large human sample like the San Antonio Family Study, detects a very large number (>25 million) of single nucleotide variants along with other more complex variants. The analytical challenges imposed by this number of variants are formidable, suggesting that methods are needed to reduce the overall number of statistical tests. In this study, we develop a single degree-of-freedom test of variants in a gene pathway employing a random effect model that uses an empirical pathway-specific genetic relationship matrix as the focal covariance kernel. The empirical pathway-specific genetic relationship uses all variants (or a chosen subset) from gene members of a given biological pathway. Using SOLAR's pedigree-based variance components modeling, which also allows for arbitrary fixed effects, such as principal components, to deal with latent population structure, we employ a likelihood ratio test of the pathway-specific genetic relationship matrix model. We examine all gene pathways in KEGG database gene pathways using our method in the first replicate of the Genetic Analysis Workshop 18 simulation of systolic blood pressure. Our random effect approach was able to detect true association signals in causal gene pathways. Those pathways could be easily be further dissected by the independent analysis of all markers.
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Affiliation(s)
- Marcio Almeida
- Department of Genetics, Texas Biomedical Research Institute. 7620 NW Loop 410, San Antonio, TX 78245, USA
| | - Juan M Peralta
- Department of Genetics, Texas Biomedical Research Institute. 7620 NW Loop 410, San Antonio, TX 78245, USA.,Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, WA, Australia
| | - Vidya Farook
- Department of Genetics, Texas Biomedical Research Institute. 7620 NW Loop 410, San Antonio, TX 78245, USA
| | - Sobha Puppala
- Department of Genetics, Texas Biomedical Research Institute. 7620 NW Loop 410, San Antonio, TX 78245, USA
| | - John W Kent
- Department of Genetics, Texas Biomedical Research Institute. 7620 NW Loop 410, San Antonio, TX 78245, USA
| | - Ravindranath Duggirala
- Department of Genetics, Texas Biomedical Research Institute. 7620 NW Loop 410, San Antonio, TX 78245, USA
| | - John Blangero
- Department of Genetics, Texas Biomedical Research Institute. 7620 NW Loop 410, San Antonio, TX 78245, USA
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Mamtani M, Kulkarni H, Dyer TD, Almasy L, Mahaney MC, Duggirala R, Comuzzie AG, Samollow PB, Blangero J, Curran JE. Increased waist circumference is independently associated with hypothyroidism in Mexican Americans: replicative evidence from two large, population-based studies. BMC Endocr Disord 2014; 14:46. [PMID: 24913450 PMCID: PMC4057819 DOI: 10.1186/1472-6823-14-46] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2014] [Accepted: 06/04/2014] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Mexican Americans are at an increased risk of both thyroid dysfunction and metabolic syndrome (MS). Thus it is conceivable that some components of the MS may be associated with the risk of thyroid dysfunction in these individuals. Our objective was to investigate and replicate the potential association of MS traits with thyroid dysfunction in Mexican Americans. METHODS We conducted association testing for 18 MS traits in two large studies on Mexican Americans - the San Antonio Family Heart Study (SAFHS) and the National Health and Nutrition Examination Survey (NHANES) 2007-10. A total of 907 participants from 42 families in SAFHS and 1633 unrelated participants from NHANES 2007-10 were included in this study. The outcome measures were prevalence of clinical and subclinical hypothyroidism and thyroid function index (TFI) - a measure of thyroid function. For the SAFHS, we used polygenic regression analyses with multiple covariates to test associations in setting of family studies. For the NHANES 2007-10, we corrected for the survey design variables as needed for association analyses in survey data. In both datasets, we corrected for age, sex and their linear and quadratic interactions. RESULTS TFI was an accurate indicator of clinical thyroid status (area under the receiver-operating-characteristic curve to detect clinical hypothyroidism, 0.98) in both SAFHS and NHANES 2007-10. Of the 18 MS traits, waist circumference (WC) showed the most consistent association with TFI in both studies independently of age, sex and body mass index (BMI). In the SAFHS and NHANES 2007-10 datasets, each standard deviation increase in WC was associated with 0.13 (p < 0.001) and 0.11 (p < 0.001) unit increase in the TFI, respectively. In a series of polygenic and linear regression models, central obesity (defined as WC ≥ 102 cm in men and ≥88 cm in women) was associated with clinical and subclinical hypothyroidism independent of age, sex, BMI and type 2 diabetes in both datasets. Estimated prevalence of hypothyroidism was consistently high in those with central obesity, especially below 45y of age. CONCLUSIONS WC independently associates with increased risk of thyroid dysfunction. Use of WC to identify Mexican American subjects at high risk of thyroid dysfunction should be investigated in future studies.
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Affiliation(s)
- Manju Mamtani
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX, USA
| | - Hemant Kulkarni
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX, USA
| | - Thomas D Dyer
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX, USA
| | - Laura Almasy
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX, USA
| | - Michael C Mahaney
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX, USA
| | - Ravindranath Duggirala
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX, USA
| | - Anthony G Comuzzie
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX, USA
| | - Paul B Samollow
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX, USA
| | - John Blangero
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX, USA
| | - Joanne E Curran
- Department of Genetics, Texas Biomedical Research Institute, 7620 NW Loop 410, San Antonio, TX, USA
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Sprooten E, Knowles EE, McKay DR, Göring HH, Curran JE, Kent JW, Carless MA, Dyer TD, Drigalenko EI, Olvera RL, Fox PT, Almasy L, Duggirala R, Kochunov P, Blangero J, Glahn DC. Common genetic variants and gene expression associated with white matter microstructure in the human brain. Neuroimage 2014; 97:252-61. [PMID: 24736177 DOI: 10.1016/j.neuroimage.2014.04.021] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Revised: 03/29/2014] [Accepted: 04/07/2014] [Indexed: 12/29/2022] Open
Abstract
Identifying genes that contribute to white matter microstructure should provide insights into the neurobiological processes that regulate white matter development, plasticity and pathology. We detected five significant SNPs using genome-wide association analysis on a global measure of fractional anisotropy in 776 individuals from large extended pedigrees. Genetic correlations and genome-wide association results indicated that the genetic signal was largely homogeneous across white matter regions. Using RNA transcripts derived from lymphocytes in the same individuals, we identified two genes (GNA13 and CCDC91) that are likely to be cis-regulated by top SNPs, and whose expression levels were also genetically correlated with fractional anisotropy. A transcript of HTR7 was phenotypically associated with FA, and was associated with an intronic genome-wide significant SNP. These results encourage further research in the mechanisms by which GNA13, HTR7 and CCDC91 influence brain structure, and emphasize a role for g-protein signaling in the development and maintenance of white matter microstructure in health and disease.
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Affiliation(s)
- Emma Sprooten
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, 200 Retreat Avenue, CT, USA.
| | - Emma E Knowles
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, 200 Retreat Avenue, CT, USA
| | - D Reese McKay
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, 200 Retreat Avenue, CT, USA
| | - Harald H Göring
- Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX, USA
| | - Joanne E Curran
- Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX, USA
| | - Jack W Kent
- Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX, USA
| | - Melanie A Carless
- Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX, USA
| | - Thomas D Dyer
- Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX, USA
| | - Eugene I Drigalenko
- Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX, USA
| | - Rene L Olvera
- Department of Psychiatry, University of Texas Health Science Center San Antonio, 7703 Floyd Curl Drive, San Antonio, TX, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center San Antonio, 8403 Floyd Curl Drive, San Antonio, TX, USA; South Texas Veterans Health System, 7400 Merton Minter, San Antonio, TX 78229, USA
| | - Laura Almasy
- Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX, USA
| | - Ravi Duggirala
- Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - John Blangero
- Department of Genetics, Texas Biomedical Research Institute, PO Box 760549, San Antonio, TX, USA
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, 200 Retreat Avenue, CT, USA
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Genotype by energy expenditure interaction and body composition traits: The Portuguese Healthy Family Study. BIOMED RESEARCH INTERNATIONAL 2014; 2014:845207. [PMID: 24791001 PMCID: PMC3984825 DOI: 10.1155/2014/845207] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2013] [Revised: 02/12/2014] [Accepted: 02/25/2014] [Indexed: 01/09/2023]
Abstract
Background and Aims. Energy expenditure has been negatively correlated with fat accumulation. However, this association is highly variable. In the present study we applied a genotype by environment interaction method to examine the presence of Genotype x by Total Daily Energy Expenditure and Genotype x by Daily Energy Expenditure interactions in the expression of different body composition traits. Methods and Results. A total of 958 subjects from 294 families of The Portuguese Healthy Family Study were included in the analysis. TDEE and DEE were assessed using a physical activity recall. Body fat percentages were measured with a bioelectrical impedance scale. GxTDEE and GxDEE examinations were performed using SOLAR 4.0 software. All BC traits were significantly heritable, with heritabilities ranging from 21% to 34%. The GxTDEE and GxDEE interaction models fitted the data better than the polygenic model for all traits. For all traits, a significant GxTDEE and GxDEE interaction was due to variance heterogeneity among distinct levels of TDEE and DEE. For WC, GxTDEE was also significant due to the genetic correlation function. Conclusions. TDEE and DEE are environmental constraints associated with the expression of individuals' BC genotypes, leading to variability in the phenotypic expression of BC traits.
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Kulkarni H, Meikle PJ, Mamtani M, Weir JM, Almeida M, Diego V, Peralta JM, Barlow CK, Bellis C, Dyer TD, Almasy L, Mahaney MC, Comuzzie AG, Göring HHH, Curran JE, Blangero J. Plasma lipidome is independently associated with variability in metabolic syndrome in Mexican American families. J Lipid Res 2014; 55:939-46. [PMID: 24627127 DOI: 10.1194/jlr.m044065] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Plasma lipidome is now increasingly recognized as a potentially important marker of chronic diseases, but the exact extent of its contribution to the interindividual phenotypic variability in family studies is unknown. Here, we used the rich data from the ongoing San Antonio Family Heart Study (SAFHS) and developed a novel statistical approach to quantify the independent and additive value of the plasma lipidome in explaining metabolic syndrome (MS) variability in Mexican American families recruited in the SAFHS. Our analytical approach included two preprocessing steps: principal components analysis of the high-resolution plasma lipidomics data and construction of a subject-subject lipidomic similarity matrix. We then used the Sequential Oligogenic Linkage Analysis Routines software to model the complex family relationships, lipidomic similarities, and other important covariates in a variance components framework. Our results suggested that even after accounting for the shared genetic influences, indicators of lipemic status (total serum cholesterol, TGs, and HDL cholesterol), and obesity, the plasma lipidome independently explained 22% of variability in the homeostatic model of assessment-insulin resistance trait and 16% to 22% variability in glucose, insulin, and waist circumference. Our results demonstrate that plasma lipidomic studies can additively contribute to an understanding of the interindividual variability in MS.
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Affiliation(s)
- Hemant Kulkarni
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX 78227
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Glahn DC, Knowles EE, McKay DR, Sprooten E, Raventós H, Blangero J, Gottesman I, Almasy L. Arguments for the sake of endophenotypes: examining common misconceptions about the use of endophenotypes in psychiatric genetics. Am J Med Genet B Neuropsychiatr Genet 2014; 165B:122-30. [PMID: 24464604 PMCID: PMC4078653 DOI: 10.1002/ajmg.b.32221] [Citation(s) in RCA: 112] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2013] [Accepted: 12/30/2013] [Indexed: 12/31/2022]
Abstract
Endophenotypes are measurable biomarkers that are correlated with an illness, at least in part, because of shared underlying genetic influences. Endophenotypes may improve our power to detect genes influencing risk of illness by being genetically simpler, closer to the level of gene action, and with larger genetic effect sizes or by providing added statistical power through their ability to quantitatively rank people within diagnostic categories. Furthermore, they also provide insight into the mechanisms underlying illness and will be valuable in developing biologically-based nosologies, through efforts such as RDoC, that seek to explain both the heterogeneity within current diagnostic categories and the overlapping clinical features between them. While neuroimaging, electrophysiological, and cognitive measures are currently most used in psychiatric genetic studies, researchers currently are attempting to identify candidate endophenotypes that are less genetically complex and potentially closer to the level of gene action, such as transcriptomic and proteomic phenotypes. Sifting through tens of thousands of such measures requires automated, high-throughput ways of assessing, and ranking potential endophenotypes, such as the Endophenotype Ranking Value. However, despite the potential utility of endophenotypes for gene characterization and discovery, there is considerable resistance to endophenotypic approaches in psychiatry. In this review, we address and clarify some of the common issues associated with the usage of endophenotypes in the psychiatric genetics community.
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Affiliation(s)
- David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Emma E Knowles
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - D Reese McKay
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Emma Sprooten
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Henriette Raventós
- Centro de Investigación en Biología Molecular y Celular, Universidad de Costa Rica, San José, CR
- Escuela de Biología, Universidad de Costa Rica, San José, CR
| | - John Blangero
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
| | - Irving Gottesman
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Laura Almasy
- Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
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Lee JH, Cheng R, Honig LS, Feitosa M, Kammerer CM, Kang MS, Schupf N, Lin SJ, Sanders JL, Bae H, Druley T, Perls T, Christensen K, Province M, Mayeux R. Genome wide association and linkage analyses identified three loci-4q25, 17q23.2, and 10q11.21-associated with variation in leukocyte telomere length: the Long Life Family Study. Front Genet 2014; 4:310. [PMID: 24478790 PMCID: PMC3894567 DOI: 10.3389/fgene.2013.00310] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2013] [Accepted: 12/20/2013] [Indexed: 11/13/2022] Open
Abstract
Leukocyte telomere length is believed to measure cellular aging in humans, and short leukocyte telomere length is associated with increased risks of late onset diseases, including cardiovascular disease, dementia, etc. Many studies have shown that leukocyte telomere length is a heritable trait, and several candidate genes have been identified, including TERT, TERC, OBFC1, and CTC1. Unlike most studies that have focused on genetic causes of chronic diseases such as heart disease and diabetes in relation to leukocyte telomere length, the present study examined the genome to identify variants that may contribute to variation in leukocyte telomere length among families with exceptional longevity. From the genome wide association analysis in 4,289 LLFS participants, we identified a novel intergenic SNP rs7680468 located near PAPSS1 and DKK2 on 4q25 (p = 4.7E-8). From our linkage analysis, we identified two additional novel loci with HLOD scores exceeding three, including 4.77 for 17q23.2, and 4.36 for 10q11.21. These two loci harbor a number of novel candidate genes with SNPs, and our gene-wise association analysis identified multiple genes, including DCAF7, POLG2, CEP95, and SMURF2 at 17q23.2; and RASGEF1A, HNRNPF, ANF487, CSTF2T, and PRKG1 at 10q11.21. Among these genes, multiple SNPs were associated with leukocyte telomere length, but the strongest association was observed with one contiguous haplotype in CEP95 and SMURF2. We also show that three previously reported genes-TERC, MYNN, and OBFC1-were significantly associated with leukocyte telomere length at p empirical < 0.05.
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Affiliation(s)
- Joseph H Lee
- Sergievsky Center, College of Physicians and Surgeons, Columbia University New York, NY, USA ; Taub Institute, College of Physicians and Surgeons, Columbia University New York, NY, USA ; Department of Epidemiology, School of Public Health, Columbia University New York, NY, USA
| | - Rong Cheng
- Sergievsky Center, College of Physicians and Surgeons, Columbia University New York, NY, USA ; Taub Institute, College of Physicians and Surgeons, Columbia University New York, NY, USA
| | - Lawrence S Honig
- Sergievsky Center, College of Physicians and Surgeons, Columbia University New York, NY, USA ; Taub Institute, College of Physicians and Surgeons, Columbia University New York, NY, USA ; Department of Neurology, College of Physicians and Surgeons, Columbia University New York, NY, USA
| | - Mary Feitosa
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine St. Louis, MO, USA
| | - Candace M Kammerer
- Department of Epidemiology, University of Pittsburgh Pittsburgh, PA, USA ; Department of Human Genetics, University of Pittsburgh Pittsburgh, PA, USA ; Center for Aging and Population Health, University of Pittsburgh Pittsburgh, PA, USA
| | - Min S Kang
- Taub Institute, College of Physicians and Surgeons, Columbia University New York, NY, USA
| | - Nicole Schupf
- Sergievsky Center, College of Physicians and Surgeons, Columbia University New York, NY, USA ; Taub Institute, College of Physicians and Surgeons, Columbia University New York, NY, USA ; Department of Epidemiology, School of Public Health, Columbia University New York, NY, USA ; Department of Psychiatry, College of Physicians and Surgeons, Columbia University New York, NY, USA
| | - Shiow J Lin
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine St. Louis, MO, USA
| | - Jason L Sanders
- Department of Epidemiology, University of Pittsburgh Pittsburgh, PA, USA ; Center for Aging and Population Health, University of Pittsburgh Pittsburgh, PA, USA
| | - Harold Bae
- Department of Biostatistics, Boston University Medical Center Boston, MA, USA
| | - Todd Druley
- Department of Pediatrics and Genetics, Washington University School of Medicine St. Louis, MO, USA
| | - Thomas Perls
- Department of Medicine, Boston University Medical Center Boston, MA, USA
| | - Kaare Christensen
- The Danish Aging Research Center, Epidemiology, University of Southern Denmark Odense, Denmark
| | - Michael Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine St. Louis, MO, USA
| | - Richard Mayeux
- Sergievsky Center, College of Physicians and Surgeons, Columbia University New York, NY, USA ; Taub Institute, College of Physicians and Surgeons, Columbia University New York, NY, USA ; Department of Epidemiology, School of Public Health, Columbia University New York, NY, USA ; Department of Neurology, College of Physicians and Surgeons, Columbia University New York, NY, USA ; Department of Psychiatry, College of Physicians and Surgeons, Columbia University New York, NY, USA
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Lange K, Papp JC, Sinsheimer JS, Sobel EM. Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2014; 1:279-300. [PMID: 24955378 PMCID: PMC4062304 DOI: 10.1146/annurev-statistics-022513-115638] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Statistical genetics is undergoing the same transition to big data that all branches of applied statistics are experiencing. With the advent of inexpensive DNA sequencing, the transition is only accelerating. This brief review highlights some modern techniques with recent successes in statistical genetics. These include: (a) lasso penalized regression and association mapping, (b) ethnic admixture estimation, (c) matrix completion for genotype and sequence data, (d) the fused lasso and copy number variation, (e) haplotyping, (f) estimation of relatedness, (g) variance components models, and (h) rare variant testing. For more than a century, genetics has been both a driver and beneficiary of statistical theory and practice. This symbiotic relationship will persist for the foreseeable future.
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Affiliation(s)
- Kenneth Lange
- Depts of Biomathematics, Human Genetics, and Statistics, UCLA
| | | | - Janet S. Sinsheimer
- Depts of Biomathematics, Human Genetics, Statistics, and Biostatistics, UCLA
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