Sponsoring Section/Society: ASA-BIOMETRICS
Session Slot: 2:00- 3:50 Monday
Estimated Audience Size: 250-300
AudioVisual Request: Overhead Projector, 2*2 slide projector
Session Title: Estimating the Cumulative Risk (Penetrance) Associated
with a Mutation
Disease-associated mutations have been and are being
identified, often through studies of selected families with many
affected members. To understand the public health importance of these
mutations, and to provide informed counselling for people carrying such
mutations, there is a need to estimate their associated disease risk
(penetrance) in the general population. The speakers will discuss the
concept of penetrance and compare the strengths and weaknesses of
various designs to estimate penetrance, including standard epidemiologic
designs, such as cohort and case-control studies, and designs based on
representative nuclear families.
Theme Session: No
Applied Session: Yes
Session Organizer: Gail, Mitchell National Cancer Institute
Address: Mitchell Gail, M.D., Ph.D. National Cancer Institute 6130 Executive Blvd, EPN431 Rockville, MD 20892
Phone: 301-496-4156
Fax: 301-402-0081
Email: gailm@epndce.nci.nih.gov
Session Timing: 110 minutes total (Sorry about format):
110 minutes total...please allocate Opening Remarks by Chair - 5 or 0 minutes First Speaker - 30 minutes (or 25) Second Speaker - 30 minutes Third Speaker - 30 minutes Discussant - 10 minutes (or none) Floor Discussion - 10 minutes (or 5 or 15)
Session Chair: Olson, Jane M. Case Western Reserve University
Address: Department of Epidemiology and Biostatistics Case Western Reserve University MetroHealth Medical Center 2500 MetroHealth Drive Cleveland, Ohio 44109-1998
Phone: (216) 778-4057
Fax: (216) 778-3280
Email: olson@darwin.cwru.edu
1. Penetrance - What Is It?
Wijsman, Ellen, University of Washington
Address: Div. of Medical Genetics and Dept. Biostatistics BOX 357720, University of Washington Seattle, WA 98195-7720
Phone: (206) 543-8987
Fax: (206) 616-1973
Email: wijsman@u.washington.edu
Abstract: Rapid advances in human genetics are leading to identification of many genes involved in diseases of interest in public health. One use of this new information is to quantify the risks of disease to relatives or to estimate the fraction of cases in the population which are attributable to mutations in these genes. This requires estimation of parameters for the penetrance function, which gives the relationship between the observed phenotype, y, and the underlying genotype, g, possibly also including environmental covariates, c. The penetrance function, P(y|g,c) for a discrete trait, with an anlagous function for a continuous trait, is based on the implicit assumption that it represents an average over a randomly selected set of individuals with genotype g. However, because of the rarity of most disease mutations, studies aimed at estimating parameters for the function rarely use randomly selected individuals; the sampling framework typically involves pedigrees, clinic populations, or other high-density populations of individuals. This, coupled with various biological complexities which we often fail to take into account in defining our functions, can lead to great disparity among penetrance estimates obtained in different studies. I will use a number of real data examples to illustrate some of the issues which influence estimates of penetrance, including effects of modifier loci, different mutations, ascertainment biases, and even how we assay the phenotype of interest.
2. A Comparision of Cohort, Case-Control and Family Study Designs for Estimating Penetrance
Gail, Mitchell, National Cancer Institute
Address: National Cancer Institute 6130 Executive Blvd, EPN431 Rockville, MD 20892
Phone: (301)-496-4156
Fax: (301)-402-0081
Email: gailm@epndce.nci.nih.gov
Abstract: One can obtain population-based estimates of the penetrance of a measurable mutation from cohort studies, from population-based case-control studies, and from ``genotyped-proband designs'' (GPD). In a GPD, representative individuals(probands) agree to be genotyped, and then one obtains information on the phenotypes of first degree relatives. We also consider an extension of the GPD in which a relative is genotyped(the GPDR design). We compare the sample sizes needed for these various designs to yield required precision for penetrance estimates. We also discuss the feasibility and validity of these various designs and mention that the GPD and GPDR designs are more susceptible to a bias that results when the tendency for an individual to volunteer to be a proband or to be a subject in a cohort or case-control study depends on the phenotypes of his or her relatives.
3. Estimating Disease Risk From Family-Based Case-Control Studies and Improving Penetrance Estimates With Hierarchical Modeling
Witte, John, Case Western Reserve University
Address: John S. Witte, PhD Department of Epidemiology and Biostatistics Case Western Reserve University 2500 MetroHealth Drive Cleveland, OH 44109-1998
Phone: (216) 778-8523
Fax: (216) 778-3280
Email: witte@darwin.cwru.edu
Abstract: The case-control design offers an attractive approach for estimating disease risk due to candidate genes and gene-environment interactions. The conventional approach of using population controls allows one to estimate population-based penetrance. When using a population-based design, however, potential problems of population stratification must be addressed. To avoid this issue one can instead select family members as controls. Here, I quantify the efficiency of using various family-based designs to estimate risk. Regardless of the design used, standard analytic techniques will often give unstable and unreasonable penetrance estimates due to the large number of relatively rare mutations in disease genes. Hence, I will also discuss how one can attempt to improve standard estimates of penetrance with hierarchical modeling.
Discussant: Schaid, Daniel J. Mayo Clinic
Address: Daniel J. Schaid, Ph.D. Harwick 7 Mayo Clinic Rochester, MN 55905
Phone: 507-284-0639
Fax: 507-284-9542
Email: schaid@mayo.edu
List of speakers who are nonmembers: None