Case–Control and Case‐Only Designs with Genotype and Family History Data: Estimating Relative Risk, Residual Familial Aggregation, and Cumulative Risk

Chatterjee, Nilanjan; Kalaylıoğlu, Zeynep; Shih, Joanna H.; Gail, Mitchell H.

doi:10.1111/j.1541-0420.2005.00442.x

Cited by 34 publications

(54 citation statements)

References 41 publications

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“…To our knowledge, no previous studies have thoroughly exploited these advantages. Chatterjee et al (2006) developed a combined approach of kin-cohort and case-control analysis with application to breast cancer to estimate residual familial aggregation, defined as risk conferred by a positive family history excluding risk caused by known variants, but do not partition this estimate into environmental and unknown genetic factors. Bajdik et al (2001) created a model to simulate the incidence of breast/ovarian cancer in the family of a mutation carrier, but do not incorporate environmental factors.…”

Section: Discussionmentioning

confidence: 99%

Genetic and environmental components of family history in type 2 diabetes

Cornelis

Zaitlen

et al. 2014

Hum Genet

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Family history of diabetes is a major risk factor for type 2 diabetes (T2D), but whether this association derives from shared genetic or environmental factors is unclear. To address this question, we developed a statistical framework that models four components of variance, including known and unknown genetic and environmental factors, using a liability threshold model. Focusing on parental history, we simulated case-control studies with two first-degree relatives for each individual, assuming 50% genetic similarity and a range of values of environmental similarity. By comparing the association of parental history with T2D in our simulations to case-control studies of T2D nested in the Nurses’ Health Study and Health Professionals Follow-up Study, we estimate that first-degree relatives have a correlation of 23% (95%CI: 15-27%) in their environmental contribution to T2D liability and that this shared environment is responsible for 32% (95%CI: 24-36%) of the association between parental history and T2D, with the remainder due to shared genetics. Estimates are robust to varying model parameter values and our framework can be extended to different definitions of family history. In conclusion, we find that the association between parental history and T2D derives from predominately genetic but also environmental effects.

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Section: Discussionmentioning

confidence: 99%

Genetic and environmental components of family history in type 2 diabetes

Cornelis

Zaitlen

et al. 2014

Hum Genet

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“…Several authors have considered this problem [2,4,[20][21][22] and have developed methods which allow for residual familial correlation in the analysis [21,22] . However, a general methodology that could be applied to various designs and to various patterns of residual correlations is still lacking.…”

Section: Discussionmentioning

confidence: 99%

Estimating Disease Risk Associated with Mutated Genes in Family-Based Designs

Choi

Kopciuk²,

Briollais³

2008

Hum Hered

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Objective: Many clinical decisions require accurate estimates of disease risk associated with inherited gene mutations. While several family-based designs have been proposed, their relative advantages remain unclear. Methods: We considered four commonly-used family-based designs and evaluated their performance in terms of accuracy and efficiency under several genetic models via simulation studies. We also derived and assessed several ascertainment-corrected likelihood methods for analyzing the simulated data and real data from 12 HNPCC pedigrees from Newfoundland. Results: We found that the design efficiency depends on the question of interest: the clinic-based family design with random probands yields the most efficient estimate of genetic relative risks, whereas the population-based family design with mutation carrier probands provides the most efficient penetrance estimates. For a particular question, an ascertainment correction seems possible using regular likelihood methods but the presence of genetic heterogeneity due to a strong second gene effect can lead to some bias in the risk estimation. Conclusions: This work gives a general methodological framework for analyzing family-based designs in gene characterization studies and provides more rationale for the choice of an efficient design and an appropriate likelihood method to estimate the risk associated with an inherited gene mutation.

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“…For clarity of presentation of the main idea, we assume that the true value of the population parameters ψ (1) = (π, β,θ ,Λ 0 ) or ψ (2) = (π, θ , Λ 00 , Λ 01 ) are available. In practice, these can be estimated from family studies by well-established statistical methods (e.g., Gorfine et al, 2006; Zeng and Lin, 2007 for cohort family study, and Shih and Chatterjee, 2002; Hsu et al, 2004; Chatterjee et al, 2006; Chen et al, 2009; Gorfine et al, 2009; Graber-Naidich et al, 2011 for case-control family study).…”

Section: Notation and Model Definitionmentioning

confidence: 99%

Frailty Models for Familial Risk With Application to Breast Cancer

Gorfine

Hsu

Parmigiani

2013

Journal of the American Statistical Association

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In evaluating familial risk for disease we have two main statistical tasks: assessing the probability of carrying an inherited genetic mutation conferring higher risk; and predicting the absolute risk of developing diseases over time, for those individuals whose mutation status is known. Despite substantial progress, much remains unknown about the role of genetic and environmental risk factors, about the sources of variation in risk among families that carry high-risk mutations, and about the sources of familial aggregation beyond major Mendelian effects. These sources of heterogeneity contribute substantial variation in risk across families. In this paper we present simple and efficient methods for accounting for this variation in familial risk assessment. Our methods are based on frailty models. We implemented them in the context of generalizing Mendelian models of cancer risk, and compared our approaches to others that do not consider heterogeneity across families. Our extensive simulation study demonstrates that when predicting the risk of developing a disease over time conditional on carrier status, accounting for heterogeneity results in a substantial improvement in the area under the curve of the receiver operating characteristic. On the other hand, the improvement for carriership probability estimation is more limited. We illustrate the utility of the proposed approach through the analysis of BRCA1 and BRCA2 mutation carriers in the Washington Ashkenazi Kin-Cohort Study of Breast Cancer.

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Case–Control and Case‐Only Designs with Genotype and Family History Data: Estimating Relative Risk, Residual Familial Aggregation, and Cumulative Risk

Cited by 34 publications

References 41 publications

Genetic and environmental components of family history in type 2 diabetes

Genetic and environmental components of family history in type 2 diabetes

Estimating Disease Risk Associated with Mutated Genes in Family-Based Designs

Frailty Models for Familial Risk With Application to Breast Cancer

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