Frailty Models for Familial Risk With Application to Breast Cancer

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 article 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. Supplementary materials for this article are available online.

[1]  S. Cummings,et al.  Sequence analysis of BRCA1 and BRCA2: correlation of mutations with family history and ovarian cancer risk. , 1998, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[2]  Hajime Uno,et al.  Calibrating parametric subject-specific risk estimation. , 2010, Biometrika.

[3]  Youngjo Lee,et al.  Hierarchical likelihood approach for frailty models , 2001 .

[4]  Dorota M. Dabrowska,et al.  Uniform Consistency of the Kernel Conditional Kaplan-Meier Estimate , 1989 .

[5]  David R. Cox,et al.  Regression models and life tables (with discussion , 1972 .

[6]  D.,et al.  Regression Models and Life-Tables , 2022 .

[7]  D. Berry,et al.  Determining carrier probabilities for breast cancer-susceptibility genes BRCA1 and BRCA2. , 1998, American journal of human genetics.

[8]  Leif E. Peterson,et al.  Validity of Models for Predicting BRCA1 and BRCA2 Mutations , 2007, Annals of Internal Medicine.

[9]  G. Mills,et al.  Assessing BRCA carrier probabilities in extended families. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  P. Buell Changing incidence of breast cancer in Japanese-American women. , 1973, Journal of the National Cancer Institute.

[11]  S Wacholder,et al.  The prevalence of common BRCA1 and BRCA2 mutations among Ashkenazi Jews. , 1999, American journal of human genetics.

[12]  J. Cuzick Event-based analysis times for randomised clinical trials , 2001 .

[13]  H. Nevanlinna,et al.  A probability model for predicting BRCA1 and BRCA2 mutations in breast and breast-ovarian cancer families , 2001, British Journal of Cancer.

[14]  Alfred A. Boyd,et al.  Ashkenazi Jewish population frequencies for common mutations in BRCA1 and BRCA2 , 1996, Nature Genetics.

[15]  Malka Gorfine,et al.  Prospective survival analysis with a general semiparametric shared frailty model: A pseudo full likelihood approach , 2005 .

[16]  Nilanjan Chatterjee,et al.  Case–Control and Case‐Only Designs with Genotype and Family History Data: Estimating Relative Risk, Residual Familial Aggregation, and Cumulative Risk , 2006, Biometrics.

[17]  D. Berry,et al.  Probability of carrying a mutation of breast-ovarian cancer gene BRCA1 based on family history. , 1997, Journal of the National Cancer Institute.

[18]  Leif E. Peterson,et al.  Characterization of BRCA1 and BRCA2 mutations in a large United States sample. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[19]  Malka Gorfine,et al.  Missing genetic information in case-control family data with general semi-parametric shared frailty model , 2011, Lifetime data analysis.

[20]  C. Begg,et al.  Variation of breast cancer risk among BRCA1/2 carriers. , 2008, JAMA.

[21]  E. Murphy,et al.  The Application of Bayesian Methods in Genetic Counselling , 1969 .

[22]  Malka Gorfine,et al.  Calibrated predictions for multivariate competing risks models , 2014, Lifetime data analysis.

[23]  D. Berry,et al.  Effect of BRCA1 and BRCA2 on the association between breast cancer risk and family history. , 1998, Journal of the National Cancer Institute.

[24]  Hormuzd A Katki,et al.  Effect of Misreported Family History on Mendelian Mutation Prediction Models , 2006, Biometrics.

[25]  F. Harrell,et al.  Evaluating the yield of medical tests. , 1982, JAMA.

[26]  Donglin Zeng,et al.  Maximum likelihood estimation in semiparametric regression models with censored data , 2007, Statistica Sinica.

[27]  Malka Gorfine,et al.  CASE-CONTROL SURVIVAL ANALYSIS WITH A GENERAL SEMIPARAMETRIC SHARED FRAILTY MODEL - A PSEUDO FULL LIKELIHOOD APPROACH. , 2009, Annals of statistics.

[28]  H A Risch,et al.  The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions , 2008, British Journal of Cancer.

[29]  F. Couch,et al.  BRCA1 mutations in women attending clinics that evaluate the risk of breast cancer. , 1997, The New England journal of medicine.

[30]  N E Day,et al.  Evidence for further breast cancer susceptibility genes in addition to BRCA1 and BRCA2 in a population‐based study , 2001, Genetic epidemiology.

[31]  Donald A. Berry,et al.  Modeling Risk of Breast Cancer and Decisions about Genetic Testing , 1999 .

[32]  L. Hsu,et al.  A Frailty‐Model‐Based Approach to Estimating the Age‐Dependent Penetrance Function of Candidate Genes Using Population‐Based Case‐Control Study Designs: An Application to Data on the BRCA1 Gene , 2009, Biometrics.

[33]  M. Gorfine,et al.  Semiparametric Estimation of Marginal Hazard Function from Case–Control Family Studies , 2004, Biometrics.

[34]  Daniel Zelterman,et al.  Modeling Survival Data: Extending the Cox Model , 2002, Technometrics.

[35]  Karl W Broman,et al.  BayesMendel: an R Environment for Mendelian Risk Prediction , 2004, Statistical applications in genetics and molecular biology.

[36]  James R. Kenyon,et al.  Analysis of Multivariate Survival Data , 2002, Technometrics.

[37]  Paul Janssen,et al.  Frailty Model , 2007, International Encyclopedia of Statistical Science.

[38]  N. Chatterjee,et al.  Analysis of Survival Data from Case–Control Family Studies , 2002, Biometrics.

[39]  P. Hartge,et al.  The risk of cancer associated with specific mutations of BRCA1 and BRCA2 among Ashkenazi Jews. , 1997, The New England journal of medicine.