Multivariate survival models induced by genetic frailties, with application to linkage analysis

Li, Hongzhe; Zhong, Xiaoyun

doi:10.1093/biostatistics/3.1.57

Cited by 24 publications

(40 citation statements)

References 29 publications

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“…We explored the weight function for rare and common disease models. In line with previous papers on ASP [6,9] our results show that age at onset is not informative for rare diseases. In the case of common diseases, discordant parent-sibling families appear to be the most [2] .…”

Section: Discussionsupporting

confidence: 90%

Robust Age at Onset Linkage Analysis in Nuclear Families

Callegaro¹,

Houwelingen²,

Houwing-Duistermaat³

2009

Hum Hered

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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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Section: Discussionsupporting

confidence: 90%

Robust Age at Onset Linkage Analysis in Nuclear Families

Callegaro¹,

Houwelingen²,

Houwing-Duistermaat³

2009

Hum Hered

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“…In order to account for variable age of onset and potential environmental risk factors in genetic linkage analysis, Li (1999), Li and Zhong (2002) and Li (2002) developed the additive genetic gamma frailty model, which utilizes inheritance vectors (Lander and Green, 1987). Within this modelling framework, Li and Zhong (2002) (abbreviated as LZ in the rest of the paper) used a retrospective likelihood formulation for estimating the parameters and for performing linkage analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Within this modelling framework, Li and Zhong (2002) (abbreviated as LZ in the rest of the paper) used a retrospective likelihood formulation for estimating the parameters and for performing linkage analysis. While this approach is free of potential ascertainment biases, it may result in loss of efficiency due to conditioning.…”

Section: Introductionmentioning

confidence: 99%

Ascertainment-Adjusted Maximum Likelihood Estimation for the Additive Genetic Gamma Frailty Model

Sun

2004

Lifetime Data Anal

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The additive genetic gamma frailty model has been proposed for genetic linkage analysis for complex diseases to account for variable age of onset and possible covariates effects. To avoid ascertainment biases in parameter estimates, retrospective likelihood ratio tests are often used, which may result in loss of efficiency due to conditioning. This paper considers when the sibships are ascertained by having at least two affected sibs with the disease before a given age and provides two approaches for estimating the parameters in the additive gamma frailty model. One approach is based on the likelihood function conditioning on the ascertainment event, the other is based on maximizing a full ascertainment-adjusted likelihood. Explicit forms for these likelihood functions are derived. Simulation studies indicate that when the baseline hazard function can be correctly pre-specified, both approaches give accurate estimates of the model parameters. However, when the baseline hazard function has to be estimated simultaneously, only the ascertainment-adjusted likelihood method gives an unbiased estimate of the parameters. These results imply that the ascertainment-adjusted likelihood ratio test in the context of the additive genetic gamma frailty may be used for genetic linkage analysis.

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“…For these adult onset cancers, carriers of high-risk alleles were estimated to have an earlier onset of cancer than noncarriers (sporadic cases). Taking into account age of onset information has been shown to be important in studying disease correlation and aggregation Li and Thompson, 1997), in parametric linkage analysis (Morton and Kidd, 1980;Haynes et al, 1986), in segregation analysis (Li and Thompson, 1997;, and in allele-sharing based linkage analysis Zhong and Li, 2004;Li et al, 2002).…”

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confidence: 99%

Survival Analysis Methods in Genetic Epidemiology

Li¹

2007

Current Topics in Human Genetics

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Mapping genes for complex human diseases is a challenging problem due to the fact that many such diseases are due to both genetic and enviromental risk factors and many also exhibit phenotypic heterogeneity, such as variable age of onset. Information on variable age of disease onset is often a good indicator for disease heterogeneity and incorporation of such information together with enviromental risk factors into genetic analysis should lead to more powerful tests for genetic analysis. Due to the problem of censoring, survival analysis methods have proved to be very useful for genetic analysis. In this paper, I review some recent methodological developments on integrating modern survival analysis methods and human genetics in order to rigorously incorporate both age of onset and enviromental covariates data into aggregation analysis, segregation analysis, linkage analysis, association analysis and gene risk characterization. I also briefly discuss the issue of ascertainment correction and survival analysis methods for high-dimensional genomic data. Finally, I outline several areas that need further methodological developments. Survival Analysis Methods in Genetic Epidemiology AbstractMapping genes for complex human diseases is a challenging problem due to the fact that many such diseases are due to both genetic and environmental risk factors and many also exhibit phenotypic heterogeneity, such as variable age of onset. Information on variable age of disease onset is often a good indicator for disease heterogeneity and incorporation of such information together with environmental risk factors into genetic analysis should lead to more powerful tests for genetic analysis. Due to the problem of censoring, survival analysis methods have proved to be very useful for genetic analysis. In this paper, I review some recent methodological developments on integrating modern survival analysis methods and human genetics in order to rigorously incorporate both age of onset and environmental covariates data into aggregation analysis, segregation analysis, linkage analysis, association analysis and gene risk characterization. I also briefly discuss the issue of ascertainment correction and survival analysis methods for high-dimensional genomic data. Finally, I outline several areas that need further methodological developments.

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Multivariate survival models induced by genetic frailties, with application to linkage analysis

Cited by 24 publications

References 29 publications

Robust Age at Onset Linkage Analysis in Nuclear Families

Robust Age at Onset Linkage Analysis in Nuclear Families

Ascertainment-Adjusted Maximum Likelihood Estimation for the Additive Genetic Gamma Frailty Model

Survival Analysis Methods in Genetic Epidemiology

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