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Kuiper LM, Polinder-Bos HA, Bizzarri D, Vojinovic D, Vallerga CL, Beekman M, Dollé MET, Ghanbari M, Voortman T, Reinders MJT, Verschuren WMM, Slagboom PE, van den Akker EB, van Meurs JBJ. Epigenetic and Metabolomic Biomarkers for Biological Age: A Comparative Analysis of Mortality and Frailty Risk. J Gerontol A Biol Sci Med Sci 2023; 78:1753-1762. [PMID: 37303208 PMCID: PMC10562890 DOI: 10.1093/gerona/glad137] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Indexed: 06/13/2023] Open
Abstract
Biological age captures a person's age-related risk of unfavorable outcomes using biophysiological information. Multivariate biological age measures include frailty scores and molecular biomarkers. These measures are often studied in isolation, but here we present a large-scale study comparing them. In 2 prospective cohorts (n = 3 222), we compared epigenetic (DNAm Horvath, DNAm Hannum, DNAm Lin, DNAm epiTOC, DNAm PhenoAge, DNAm DunedinPoAm, DNAm GrimAge, and DNAm Zhang) and metabolomic-based (MetaboAge and MetaboHealth) biomarkers in reflection of biological age, as represented by 5 frailty measures and overall mortality. Biomarkers trained on outcomes with biophysiological and/or mortality information outperformed age-trained biomarkers in frailty reflection and mortality prediction. DNAm GrimAge and MetaboHealth, trained on mortality, showed the strongest association with these outcomes. The associations of DNAm GrimAge and MetaboHealth with frailty and mortality were independent of each other and of the frailty score mimicking clinical geriatric assessment. Epigenetic, metabolomic, and clinical biological age markers seem to capture different aspects of aging. These findings suggest that mortality-trained molecular markers may provide novel phenotype reflecting biological age and strengthen current clinical geriatric health and well-being assessment.
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Affiliation(s)
- Lieke M Kuiper
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- Center for Nutrition, Prevention and Health Services, Bilthoven, The Netherlands
| | | | - Daniele Bizzarri
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Dina Vojinovic
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | | | - Marian Beekman
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
| | - Martijn E T Dollé
- Center for Health Protection, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen, The Netherlands
| | - Marcel J T Reinders
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - W M Monique Verschuren
- Center for Nutrition, Prevention and Health Services, Bilthoven, The Netherlands
- Julius Center for Health Sciences and Primary Care Utrecht, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Max Planck Institute for the Biology of Ageing, Cologne, Germany
| | - Erik B van den Akker
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, The Netherlands
| | - Joyce B J van Meurs
- Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Orthopaedics and Sports Medicine, Erasmus MC, Rotterdam, The Netherlands
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2
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Raz Y, Guerrero-Ros I, Maier A, Slagboom PE, Atzmon G, Barzilai N, Macian F. Activation-Induced Autophagy Is Preserved in CD4+ T-Cells in Familial Longevity. J Gerontol A Biol Sci Med Sci 2017; 72:1201-1206. [PMID: 28486590 DOI: 10.1093/gerona/glx020] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Indexed: 12/16/2022] Open
Abstract
As with many other tissues and organs, the immune system is also affected by age. Immunosenescence is characterized by a decreased ability of immune cells to mount a productive response upon exposure to new antigens. Several studies have reported that members of families with exceptional longevity show improved immune function, which might contribute to the increased life- and health-span observed in those families. Autophagy is a catabolic process that delivers cytoplasmic material to the lysosomes for degradation. Defective autophagy is known to be associated with age in several cell types and tissues and its dysregulation is related to age-associated diseases. In T-cells, autophagy has an essential role in modulating protein and organelle homeostasis and in the regulation of activation-induced responses. In this study, using two different cohorts of individuals belonging to families with exceptional longevity, we show that CD4+ T-cells isolated from the offspring of parents with exceptional longevity show improved activation-induced autophagic activity compared with age-matched controls. Interestingly, increased autophagy is positively correlated with increased interferon-γ production. In conclusion, autophagy appears to be better maintained in members of families with extended longevity and positively correlates with improved T-cell function.
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Affiliation(s)
- Yotam Raz
- Department of Pathology, Albert Einstein College of Medicine, Bronx, New York.,Department of Molecular Epidemiology, Leiden University Medical Center, The Netherlands
| | | | - Andrea Maier
- Department of Medicine and Aged Care, Royal Melbourne Hospital, University of Melbourne, Australia.,Department of Human Movement Sciences, MOVE Research Institute Amsterdam, Vrije Universiteit Amsterdam, The Netherlands
| | - P Eline Slagboom
- Department of Molecular Epidemiology, Leiden University Medical Center, The Netherlands
| | - Gil Atzmon
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, New York.,Department of Human Biology, Faculty of Natural Sciences, University of Haifa, Israel
| | - Nir Barzilai
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, New York.,Department of Genetics, Albert Einstein College of Medicine, Bronx, New York
| | - Fernando Macian
- Department of Pathology, Albert Einstein College of Medicine, Bronx, New York.,Institute for Aging Research, Albert Einstein College of Medicine, Bronx, New York
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3
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Tissier R, Tsonaka R, Mooijaart SP, Slagboom E, Houwing-Duistermaat JJ. Secondary phenotype analysis in ascertained family designs: application to the Leiden longevity study. Stat Med 2017; 36:2288-2301. [PMID: 28303589 PMCID: PMC5485037 DOI: 10.1002/sim.7281] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 02/17/2017] [Accepted: 02/20/2017] [Indexed: 01/14/2023]
Abstract
The case-control design is often used to test associations between the case-control status and genetic variants. In addition to this primary phenotype, a number of additional traits, known as secondary phenotypes, are routinely recorded, and typically, associations between genetic factors and these secondary traits are studied too. Analysing secondary phenotypes in case-control studies may lead to biased genetic effect estimates, especially when the marker tested is associated with the primary phenotype and when the primary and secondary phenotypes tested are correlated. Several methods have been proposed in the literature to overcome the problem, but they are limited to case-control studies and not directly applicable to more complex designs, such as the multiple-cases family studies. A proper secondary phenotype analysis, in this case, is complicated by the within families correlations on top of the biased sampling design. We propose a novel approach to accommodate the ascertainment process while explicitly modelling the familial relationships. Our approach pairs existing methods for mixed-effects models with the retrospective likelihood framework and uses a multivariate probit model to capture the association between the mixed type primary and secondary phenotypes. To examine the efficiency and bias of the estimates, we performed simulations under several scenarios for the association between the primary phenotype, secondary phenotype and genetic markers. We will illustrate the method by analysing the association between triglyceride levels and glucose (secondary phenotypes) and genetic markers from the Leiden Longevity Study, a multiple-cases family study that investigates longevity. © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.
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Affiliation(s)
- Renaud Tissier
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Roula Tsonaka
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Simon P Mooijaart
- Department of Gerontology and Geriatrics, Leiden University Medical Centre, Leiden, The Netherlands
| | - Eline Slagboom
- Department of Molecular Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands
| | - Jeanine J Houwing-Duistermaat
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, Leiden, The Netherlands.,Department of Statistics, University of Leeds, U.K
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4
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Association analysis of insulin-like growth factor-1 axis parameters with survival and functional status in nonagenarians of the Leiden Longevity Study. Aging (Albany NY) 2016; 7:956-63. [PMID: 26568155 PMCID: PMC4694065 DOI: 10.18632/aging.100841] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Reduced insulin/insulin-like growth factor 1 (IGF-1) signaling has been associated with longevity in various model organisms. However, the role of insulin/IGF-1 signaling in human survival remains controversial. The aim of this study was to test whether circulating IGF-1 axis parameters associate with old age survival and functional status in nonagenarians from the Leiden Longevity Study. This study examined 858 Dutch nonagenarian (males≥89 years; females≥91 years) siblings from 409 families, without selection on health or demographic characteristics. Nonagenarians were divided over sex-specific strata according to their levels of IGF-1, IGF binding protein 3 and IGF-1/IGFBP3 molar ratio. We found that lower IGF-1/IGFBP3 ratios were associated with improved survival: nonagenarians in the quartile of the lowest ratio had a lower estimated hazard ratio (95% confidence interval) of 0.73 (0.59 – 0.91) compared to the quartile with the highest ratio (ptrend=0.002). Functional status was assessed by (Instrumental) Activities of Daily Living ((I)ADL) scales. Compared to those in the quartile with the highest IGF-1/IGFBP3 ratio, nonagenarians in the lowest quartile had higher scores for ADL (ptrend=0.001) and IADL (ptrend=0.003). These findings suggest that IGF-1 axis parameters are associated with increased old age survival and better functional status in nonagenarians from the Leiden Longevity Study.
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Rodríguez-Girondo M, Deelen J, Slagboom EP, Houwing-Duistermaat JJ. Survival analysis with delayed entry in selected families with application to human longevity. Stat Methods Med Res 2016; 27:933-954. [PMID: 27177884 DOI: 10.1177/0962280216648356] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
In the field of aging research, family-based sampling study designs are commonly used to study the lifespans of long-lived family members. However, the specific sampling procedure should be carefully taken into account in order to avoid biases. This work is motivated by the Leiden Longevity Study, a family-based cohort of long-lived siblings. Families were invited to participate in the study if at least two siblings were 'long-lived', where 'long-lived' meant being older than 89 years for men or older than 91 years for women. As a result, more than 400 families were included in the study and followed for around 10 years. For estimation of marker-specific survival probabilities and correlations among life times of family members, delayed entry due to outcome-dependent sampling mechanisms has to be taken into account. We consider shared frailty models to model left-truncated correlated survival data. The treatment of left truncation in shared frailty models is still an open issue and the literature on this topic is scarce. We show that the current approaches provide, in general, biased estimates and we propose a new method to tackle this selection problem by applying a correction on the likelihood estimation by means of inverse probability weighting at the family level.
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Affiliation(s)
- Mar Rodríguez-Girondo
- 1 Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands
| | - Joris Deelen
- 2 Department of Molecular Epidemiology, Leiden University Medical Center, The Netherlands
| | - Eline P Slagboom
- 2 Department of Molecular Epidemiology, Leiden University Medical Center, The Netherlands
| | - Jeanine J Houwing-Duistermaat
- 1 Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands.,3 Department of Statistics, University of Leeds, United Kingdom
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6
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Uh HW, Beekman M, Meulenbelt I, Houwing-Duistermaat JJ. Genotype-Based Score Test for Association Testing in Families. STATISTICS IN BIOSCIENCES 2015; 7:394-416. [PMID: 26473021 PMCID: PMC4596911 DOI: 10.1007/s12561-015-9128-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Revised: 11/20/2014] [Accepted: 02/17/2015] [Indexed: 11/29/2022]
Abstract
The multiplex-case and control design in which multiple cases are sampled from the same family is considered. In such studies phenotype information of the un-genotyped relatives might be available. We intend to use additional family information when performing genetic association tests. A score test is revisited to provide a flexible framework to accommodate various genetic models and to improve power of the association test by adding available family information. The proposed test accounts for correlations induced by multiple cases from the same pedigree, directly deals with X-linked SNPs in mixed-sex-related samples, and incorporates additional phenotypic information such as the number of (un-genotyped) siblings and parents with similar symptoms by assigning the weights to (genotyped) multiplex cases. In addition, the score test directly incorporates posterior probabilities of imputed genotypes, which leads to an efficiency measure that reflects imputation uncertainty on the test conducted. The proposed test is applied to real applications for illustration. Its efficiency is demonstrated via simulations.
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Affiliation(s)
- Hae-Won Uh
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, S-5-P, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Marian Beekman
- Department of Molecular Epidemiology, Leiden University Medical Center, S-5-P, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Ingrid Meulenbelt
- Department of Molecular Epidemiology, Leiden University Medical Center, S-5-P, P.O. Box 9600, 2300 RC Leiden, The Netherlands
| | - Jeanine J Houwing-Duistermaat
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, S-5-P, P.O. Box 9600, 2300 RC Leiden, The Netherlands
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Beekman M, Blanché H, Perola M, Hervonen A, Bezrukov V, Sikora E, Flachsbart F, Christiansen L, De Craen AJM, Kirkwood TBL, Rea IM, Poulain M, Robine JM, Valensin S, Stazi MA, Passarino G, Deiana L, Gonos ES, Paternoster L, Sørensen TIA, Tan Q, Helmer Q, van den Akker EB, Deelen J, Martella F, Cordell HJ, Ayers KL, Vaupel JW, Törnwall O, Johnson TE, Schreiber S, Lathrop M, Skytthe A, Westendorp RGJ, Christensen K, Gampe J, Nebel A, Houwing-Duistermaat JJ, Slagboom PE, Franceschi C. Genome-wide linkage analysis for human longevity: Genetics of Healthy Aging Study. Aging Cell 2013; 12:184-93. [PMID: 23286790 DOI: 10.1111/acel.12039] [Citation(s) in RCA: 118] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/27/2012] [Indexed: 01/04/2023] Open
Abstract
Clear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives. To identify such longevity alleles, we performed the largest genome-wide linkage scan thus far reported. Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in 15 study centers of 11 European countries as part of the Genetics of Healthy Aging (GEHA) project. In the joint linkage analyses, we observed four regions that show linkage with longevity; chromosome 14q11.2 (LOD = 3.47), chromosome 17q12-q22 (LOD = 2.95), chromosome 19p13.3-p13.11 (LOD = 3.76), and chromosome 19q13.11-q13.32 (LOD = 3.57). To fine map these regions linked to longevity, we performed association analysis using GWAS data in a subgroup of 1228 unrelated nonagenarian and 1907 geographically matched controls. Using a fixed-effect meta-analysis approach, rs4420638 at the TOMM40/APOE/APOC1 gene locus showed significant association with longevity (P-value = 9.6 × 10(-8) ). By combined modeling of linkage and association, we showed that association of longevity with APOEε4 and APOEε2 alleles explain the linkage at 19q13.11-q13.32 with P-value = 0.02 and P-value = 1.0 × 10(-5) , respectively. In the largest linkage scan thus far performed for human familial longevity, we confirm that the APOE locus is a longevity gene and that additional longevity loci may be identified at 14q11.2, 17q12-q22, and 19p13.3-p13.11. As the latter linkage results are not explained by common variants, we suggest that rare variants play an important role in human familial longevity.
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Affiliation(s)
| | | | - Markus Perola
- The National Institute for Health and Welfare; THL; Helsinki; FI-00271; Finland
| | - Anti Hervonen
- Tampere School of Public Health; Tampere; FI-33014; Finland
| | | | - Ewa Sikora
- Nencki Istitute for Experimental Biology; NENCKI; Warszawa; 02-093; Poland
| | - Friederike Flachsbart
- Institute of Clinical Molecular Biology; Christian-Albrechts-University Kiel (CAU); Kiel; 24118; Germany
| | - Lene Christiansen
- Danish Aging Research Center; Institute of Public Health; University of Southern Denmark; Odense; DK-5230; Denmark
| | | | - Tom B. L. Kirkwood
- Institute for Ageing and Health; Newcastle University; UNEW; Newcastle; NE1 7RU; UK
| | - Irene Maeve Rea
- Queens University of Belfast; QUB; Belfast; Northern Ireland; BT7 1NN; UK
| | | | | | - Silvana Valensin
- Interdepartmental Centre “Luigi Galvani” CIG; University of Bologna UNIBO; Bologna; 40126; Italy
| | | | | | - Luca Deiana
- UNISS; University of Sassari; 07100; Sassari; Italy
| | | | | | | | | | - Quinta Helmer
- Medical Statistics and Bioinformatics; Leiden University Medical Centre; Leiden; ZC; 2333; The Netherlands
| | | | - Joris Deelen
- Molecular Epidemiology; Leiden University Medical Centre; Leiden; ZC; 2333; The Netherlands
| | | | - Heather J. Cordell
- Institute for Ageing and Health; Newcastle University; UNEW; Newcastle; NE1 7RU; UK
| | - Kristin L. Ayers
- Institute for Ageing and Health; Newcastle University; UNEW; Newcastle; NE1 7RU; UK
| | - James W. Vaupel
- Max Planck Institute for Demographic Research; MPIDR; 18057; Rostock; Germany
| | - Outi Törnwall
- The National Institute for Health and Welfare; THL; Helsinki; FI-00271; Finland
| | - Thomas E. Johnson
- Institute for Behavioral Genetics; University of Colorado at Boulder; Boulder; CO 80309-0447; USA
| | - Stefan Schreiber
- Institute of Clinical Molecular Biology; Christian-Albrechts-University Kiel (CAU); Kiel; 24118; Germany
| | - Mark Lathrop
- Foundation Jean Dausset; CEPH; 75010; Paris; France
| | - Axel Skytthe
- Danish Aging Research Center; Institute of Public Health; University of Southern Denmark; Odense; DK-5230; Denmark
| | - Rudi G. J. Westendorp
- Gerontology and Geriatrics; Leiden University Medical Centre; Leiden; ZA; 2333; The Netherlands
| | | | - Jutta Gampe
- Max Planck Institute for Demographic Research; MPIDR; 18057; Rostock; Germany
| | - Almut Nebel
- Institute of Clinical Molecular Biology; Christian-Albrechts-University Kiel (CAU); Kiel; 24118; Germany
| | | | | | - Claudio Franceschi
- Interdepartmental Centre “Luigi Galvani” CIG; University of Bologna UNIBO; Bologna; 40126; Italy
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8
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Martella F, Vermunt JK, Beekman M, Westendorp RGJ, Slagboom PE, Houwing-Duistermaat JJ. A mixture model with random-effects components for classifying sibling pairs. Stat Med 2011; 30:3252-64. [PMID: 21905068 DOI: 10.1002/sim.4365] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2010] [Accepted: 07/21/2011] [Indexed: 11/09/2022]
Abstract
In healthy aging research, typically multiple health outcomes are measured, representing health status. The aim of this paper was to develop a model-based clustering approach to identify homogeneous sibling pairs according to their health status. Model-based clustering approaches will be considered on the basis of linear mixed effect model for the mixture components. Class memberships of siblings within pairs are allowed to be correlated, and within a class the correlation between siblings is modeled using random sibling pair effects. We propose an expectation-maximization algorithm for maximum likelihood estimation. Model performance is evaluated via simulations in terms of estimating the correct parameters, degree of agreement, and the ability to detect the correct number of clusters. The performance of our model is compared with the performance of standard model-based clustering approaches. The methods are used to classify sibling pairs from the Leiden Longevity Study according to their health status. Our results suggest that homogeneous healthy sibling pairs are associated with a longer life span. Software is available for fitting the new models.
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Affiliation(s)
- F Martella
- Dipartimento di Scienze Statistiche, Facoltá di Ingegneria dell'Informazione, Informatica e Statistica, Sapienza Universitá di Roma, Rome, Italy.
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9
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Callegaro A, Uh HW, Helmer Q, Houwing-Duistermaat JJ. New score tests for age-at-onset linkage analysis in general pedigrees. BMC Proc 2009; 3 Suppl 7:S97. [PMID: 20018094 PMCID: PMC2796001 DOI: 10.1186/1753-6561-3-s7-s97] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Our aim is to develop methods for mapping genes related to age at onset in general pedigrees. We propose two score tests, one derived from a gamma frailty model with pairwise likelihood and one derived from a log-normal frailty model with approximated likelihood around the null random effect. The score statistics are weighted nonparametric linkage statistics, with weights depending on the age at onset. These tests are correct under the null hypothesis irrespective of the weight used. They are simple, robust, computationally fast, and can be applied to large, complex pedigrees. We apply these methods to simulated data and to the Genetic Analysis Workshop 16 Framingham Heart Study data set. We investigate the time to the first of three events: hard coronary heart disease, diabetes, or death from any cause. We use a two-step procedure. In the first step, we estimate the population parameters under the null hypothesis of no linkage. In the second step, we apply the score tests, using the population parameters estimated in the first step.
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Affiliation(s)
- Andrea Callegaro
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands Medical Center, PO Box 9600, 2300RC Leiden, The Netherlands.
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10
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Callegaro A, van Houwelingen JC, Houwing-Duistermaat JJ. Robust age at onset linkage analysis in nuclear families. Hum Hered 2009; 69:80-90. [PMID: 19996606 DOI: 10.1159/000264446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2009] [Accepted: 07/13/2009] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE Standard methods for linkage analysis ignore the phenotype of the parents when they are not genotyped. However, this information can be useful for gene mapping. In this paper we propose methods for age at onset genetic linkage analysis in sibling pairs, taking into account parental age at onset. METHODS Two new score statistics are derived, one from an additive gamma frailty model and one from a log-normal frailty model. The score statistics are classical non-parametric linkage (NPL) statistics weighted by a function of the age at onset of the four family members. The weight depends on information from registries (age-specific incidences) and family studies (sib-sib and father-mother correlation). RESULTS In order to investigate how age at onset of sibs and their parents affect the information for linkage analysis the weight functions were studied for rare and common disease models, realistic models for breast cancer and human lifespan. We studied the performance of the weighted NPL methods by simulations. As illustration, the score statistics were applied to the GAW12 data. The results show that it is useful to include parental age at onset information in genetic linkage analysis.
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Affiliation(s)
- Andrea Callegaro
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, University of Leiden, Leiden, The Netherlands.
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11
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Callegaro A, van Houwelingen HC, Houwing-Duistermaat JJ. Score test for age at onset genetic linkage analysis in selected sibling pairs. Stat Med 2009; 28:1913-26. [PMID: 19402027 DOI: 10.1002/sim.3596] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
A new score statistic is derived, which uses information from registries (age-specific incidences) and family studies (sib-sib marginal correlation) to weight affected sibling pairs according to their age at onset. Age at onset of sibling pairs is modelled by a gamma frailty model. From this model we derive a bivariate survival function, which depends on the marginal survival and on the marginal correlation. The score statistic for linkage is a classical nonparametric linkage (NPL) statistic where the identical by descent sharing is weighted by a particular function of the age at onset data. Since the statistic is based on survival models, it can also be applied to discordant and healthy sibling pairs. Simulation studies show that the proposed method is robust and more powerful than standard NPL methods. As illustration we apply the new score statistic to data from a breast cancer study.
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Affiliation(s)
- A Callegaro
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
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