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Heritability of blood pressure through latent curve trajectories in families from the Gubbio population study. J Hypertens 2014; 32:2179-87. [DOI: 10.1097/hjh.0000000000000311] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Campbell DD, Sham PC, Knight J, Wickham H, Landau S. Software for generating liability distributions for pedigrees conditional on their observed disease states and covariates. Genet Epidemiol 2010; 34:159-70. [PMID: 19771574 DOI: 10.1002/gepi.20446] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
For many multifactorial diseases, aetiology is poorly understood. A major research aim is the identification of disease predictors (environmental, biological, and genetic markers). In order to achieve this, a two-stage approach is proposed. The initial or synthesis stage combines observed pedigree data with previous genetic epidemiological research findings, to produce estimates of pedigree members' disease risk and predictions of their disease liability. A further analysis stage uses the latter as inputs to look for associations with potential disease markers. The incorporation of previous research findings into an analysis should lead to power gains. It also allows separate predictions for environmental and genetic liabilities to be generated. This should increase power for detecting disease predictors that are environmental or genetic in nature. Finally, the approach brings pragmatic benefits in terms of data reduction and synthesis, improving comprehensibility, and facilitating the use of existing statistical genetics tools. In this article we present a statistical model and Gibbs sampling approach to generate liability predictions for multifactorial disease for the synthesis stage. We have implemented the approach in a software program. We apply this program to a specimen disease pedigree, and discuss the results produced, comparing its results with those generated under a more naïve model. We also detail simulation studies that validate the software's operation.
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Affiliation(s)
- Desmond D Campbell
- Department of Biostatistics, Institute of Psychiatry, King's College London, United Kingdom
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Pitkäniemi J, Moltchanova E, Haapala L, Harjutsalo V, Tuomilehto J, Hakulinen T. Genetic random effects model for family data with long-term survivors: analysis of diabetic nephropathy in type 1 diabetes. Genet Epidemiol 2008; 31:697-708. [PMID: 17487884 DOI: 10.1002/gepi.20234] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A shared and additive genetic variance component-long-term survivor (LTS) model for familial aggregation studies of complex diseases with variable age-at-onset phenotype and non-susceptible subjects in the study cohort is proposed. LTS has been used from the early 1970s, especially in epidemiological studies of cancer. The LTS model utilizes information on the age at onset (survival) distribution to make inference on partially latent susceptibility. Bayesian modeling with uninformative priors is used and estimates of the posterior distribution of age at onset and susceptibility parameters of interest have been obtained using Bayesian Markov chain Monte Carlo (MCMC) methods with OpenBugs program. A simulation study confirms that we obtain posterior estimates of the model parameters on shared and genetic variance components of age at onset and susceptibility with good coverage rates. Further, we analyze familial aggregation of diabetic nephropathy (DN) in large Finnish cohort of 528 sibships with type 1 diabetes (T1D). According to the variance components estimated a substantial familial variation in the susceptibility to DN exist among families, while time to DN is less influenced by shared familial factors.
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Affiliation(s)
- Janne Pitkäniemi
- Department of Public Health, University of Helsinki, Helsinki, Finland.
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Scurrah K, Gurrin L, Palmer L, Burton P. Estimation of genetic and environmental factors for binary traits using family data by Y. Pawitan, M. Reilly, E. Nilsson, S. Cnattingius and P. Lichtenstein,Statistics in Medicine 2004;23:449–465. Stat Med 2005; 24:1613-7; author reply 1617-8. [PMID: 15880579 DOI: 10.1002/sim.2066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Gauderman WJ, Macgregor S, Briollais L, Scurrah K, Tobin M, Park T, Wang D, Rao S, John S, Bull S. Longitudinal data analysis in pedigree studies. Genet Epidemiol 2004; 25 Suppl 1:S18-28. [PMID: 14635165 DOI: 10.1002/gepi.10280] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Longitudinal family studies provide a valuable resource for investigating genetic and environmental factors that influence long-term averages and changes over time in a complex trait. This paper summarizes 13 contributions to Genetic Analysis Workshop 13, which include a wide range of methods for genetic analysis of longitudinal data in families. The methods can be grouped into two basic approaches: 1) two-step modeling, in which repeated observations are first reduced to one summary statistic per subject (e.g., a mean or slope), after which this statistic is used in a standard genetic analysis, or 2) joint modeling, in which genetic and longitudinal model parameters are estimated simultaneously in a single analysis. In applications to Framingham Heart Study data, contributors collectively reported evidence for genes that affected trait mean on chromosomes 1, 2, 3, 5, 8, 9, 10, 13, and 17, but most did not find genes affecting slope. Applications to simulated data suggested that even for a gene that only affected slope, use of a mean-type statistic could provide greater power than a slope-type statistic for detecting that gene. We report on the results of a small experiment that sheds some light on this apparently paradoxical finding, and indicate how one might form a more powerful test for finding a slope-affecting gene. Several areas for future research are discussed.
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Affiliation(s)
- W James Gauderman
- Department of Preventive Medicine, University of Southern California, Los Angeles, 90089, USA.
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Palmer LJ, Scurrah KJ, Tobin M, Patel SR, Celedon JC, Burton PR, Weiss ST. Genome-wide linkage analysis of longitudinal phenotypes using sigma2A random effects (SSARs) fitted by Gibbs sampling. BMC Genet 2003; 4 Suppl 1:S12. [PMID: 14975080 PMCID: PMC1866446 DOI: 10.1186/1471-2156-4-s1-s12] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
The study of change in intermediate phenotypes over time is important in genetics. In this paper we explore a new approach to phenotype definition in the genetic analysis of longitudinal phenotypes. We utilized data from the longitudinal Framingham Heart Study Family Cohort to investigate the familial aggregation and evidence for linkage to change in systolic blood pressure (SBP) over time. We used Gibbs sampling to derive sigma-squared-A-random-effects (SSARs) for the longitudinal phenotype, and then used these as a new phenotype in subsequent genome-wide linkage analyses. Additive genetic effects (σ2A.time) were estimated to account for ~9.2% of the variance in the rate of change of SBP with age, while additive genetic effects (σ2A) were estimated to account for ~43.9% of the variance in SBP at the mean age. The linkage results suggested that one or more major loci regulating change in SBP over time may localize to chromosomes 2, 3, 4, 6, 10, 11, 17, and 19. The results also suggested that one or more major loci regulating level of SBP may localize to chromosomes 3, 8, and 14. Our results support a genetic component to both SBP and change in SBP with age, and are consistent with a complex, multifactorial susceptibility to the development of hypertension. The use of SSARs derived from quantitative traits as input to a conventional linkage analysis appears to be valuable in the linkage analysis of genetically complex traits. We have now demonstrated in this paper the use of SSARs in the context of longitudinal family data.
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Affiliation(s)
- Lyle J Palmer
- Channing Laboratory, Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA
| | - Katrina J Scurrah
- Department of Epidemiology and Public Health and Institute of Genetics, University of Leicester, United Kingdom
| | - Martin Tobin
- Department of Epidemiology and Public Health and Institute of Genetics, University of Leicester, United Kingdom
| | - Sanjay R Patel
- Channing Laboratory, Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Juan C Celedon
- Channing Laboratory, Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Paul R Burton
- Department of Epidemiology and Public Health and Institute of Genetics, University of Leicester, United Kingdom
| | - Scott T Weiss
- Channing Laboratory, Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Briollais L, Tzontcheva A, Bull S. Multilevel modeling for the analysis of longitudinal blood pressure data in the Framingham Heart Study pedigrees. BMC Genet 2003; 4 Suppl 1:S19. [PMID: 14975087 PMCID: PMC1866453 DOI: 10.1186/1471-2156-4-s1-s19] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background The data arising from a longitudinal familial study have a complex correlation structure that cannot be modeled using classical methods for the analysis of familial data at a single time point. Methods To fit the longitudinal systolic blood pressure (SBP) pedigree data arising from the Framingham Heart Study, we proposed to use multilevel modeling. That approach was used to distinguish multiple levels of information with individual repeated measurements (Level 1) being made within individuals (Level 2), and individuals clustered within pedigrees (Level 3). Residuals from the subject-specific and pedigree-specific regression models were summed both for the mean SBP and slope of SBP change over time, in order to define two new outcomes that were then used in a genome-wide linkage analysis. Results Evidence for linkage for the two outcomes (mean SBP and slope) was found in several chromosomal regions with a maximum LOD score of 3.6 on chromosome 8 and 3.5 on chromosome 17 for the mean SBP, and 2.5 on chromosome 1 for SBP slope. However, the linkage on chromosome 8 was only detected when the sample was restricted to subjects between age 25 and 75 and with at least four exams (Cohort 1) or 3 exams (Cohort 2). Discussion Multilevel modeling is a powerful approach to detect genes involved in complex traits when longitudinal data are available. It allows for complex hierarchical data structure to be taken into account and therefore, a better partitioning of random within-individual variation from other sources of variability (genetic or nongenetic).
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Affiliation(s)
- Laurent Briollais
- Division of Epidemiology and Biostatistics, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario, Canada, M5G 1X5
- Department of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada, M5G 1X5
| | - Anjela Tzontcheva
- Division of Epidemiology and Biostatistics, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario, Canada, M5G 1X5
- Department of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada, M5G 1X5
| | - Shelley Bull
- Division of Epidemiology and Biostatistics, Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario, Canada, M5G 1X5
- Department of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada, M5G 1X5
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Gee C, Morrison JL, Thomas DC, Gauderman WJ. Segregation and linkage analysis for longitudinal measurements of a quantitative trait. BMC Genet 2003; 4 Suppl 1:S21. [PMID: 14975089 PMCID: PMC1866456 DOI: 10.1186/1471-2156-4-s1-s21] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
We present a method for using slopes and intercepts from a linear regression of a quantitative trait as outcomes in segregation and linkage analyses. We apply the method to the analysis of longitudinal systolic blood pressure (SBP) data from the Framingham Heart Study. A first-stage linear model was fit to each subject's SBP measurements to estimate both their slope over time and an intercept, the latter scaled to represent the mean SBP at the average observed age (53.7 years). The subject-specific intercepts and slopes were then analyzed using segregation and linkage analysis. We describe a method for using the standard errors of the first-stage intercepts and slopes as weights in the genetic analyses. For the intercepts, we found significant evidence of a Mendelian gene in segregation analysis and suggestive linkage results (with LOD scores ≥ 1.5) for specific markers on chromosomes 1, 3, 5, 9, 10, and 17. For the slopes, however, the data did not support a Mendelian model, and thus no formal linkage analyses were conducted.
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Affiliation(s)
- Conway Gee
- Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, Los Angeles, California, USA
| | - John L Morrison
- Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, Los Angeles, California, USA
| | - Duncan C Thomas
- Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, Los Angeles, California, USA
| | - W James Gauderman
- Department of Preventive Medicine, University of Southern California, 1540 Alcazar Street, Los Angeles, California, USA
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