title: Estimating gene penetrance from family data.
abstract: Family data are useful for estimating disease risk in carriers of specific genotypes of a given gene (penetrance). Penetrance is frequently estimated assuming that relatives' phenotypes are independent, given their genotypes for the gene of interest. This assumption is unrealistic when multiple shared risk factors contribute to disease risk. In this setting, the phenotypes of relatives are correlated even after adjustment for the genotypes of any one gene (residual correlation). Many methods have been proposed to address this problem, but their performance has not been evaluated systematically. In simulations we generated genotypes for a rare (frequency 0.35%) allele of moderate penetrance, and a common (frequency 15%) allele of low penetrance, and then generated correlated disease survival times using the Clayton-Oakes copula model. We ascertained families using both population and clinic designs. We then compared the estimates of several methods to the optimal ones obtained from the model used to generate the data. We found that penetrance estimates for common low-risk genotypes were more robust to model misspecification than those for rare, moderate-risk genotypes. For the latter, penetrance estimates obtained ignoring residual disease correlation had large biases. Also biased were estimates based only on families that segregate the risk allele. In contrast, a method for accommodating phenotype correlation by assuming the presence of genetic heterogeneity performed nearly optimally, even when the survival data were coded as binary outcomes. We conclude that penetrance estimates that accommodate residual phenotype correlation (even only approximately) outperform those that ignore it, and that coding censored survival outcomes as binary does not substantially increase the mean-square error of the estimates, provided the censoring is not extensive.
authors.affiliation: Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA.
authors.forename: Gail
authors.initials: G
authors.lastname: Gong
authors.forename: Nathan
authors.initials: N
authors.lastname: Hannon
authors.forename: Alice S
authors.initials: AS
authors.lastname: Whittemore
mesh_terms: D000328:Adult; D000368:Aged; D000483:Alleles; D004194:Disease / Q000235:genetics*; D005192:Family Health; D005260:Female; D005838:Genotype; D006801:Humans; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D015233:Models, Statistical; D019683:Penetrance; D010641:Phenotype; D012306:Risk; D012307:Risk Factors; D016896:Treatment Outcome
references.citation: Am J Hum Genet. 1984 Mar;36(2):363-86
references.pmid: 6608876
references.citation: Genet Epidemiol. 1999;16(1):15-39
references.pmid: 9915565
references.citation: Genet Epidemiol. 2003 Apr;24(3):173-80
references.pmid: 12652521
references.citation: Nat Rev Genet. 2006 May;7(5):385-94
references.pmid: 16619052
references.citation: Biometrics. 2006 Mar;62(1):36-48
references.pmid: 16542227
references.citation: Genet Epidemiol. 1994;11(4):329-42
references.pmid: 7813895
references.citation: Am J Hum Genet. 2000 Mar;66(3):1119-31
references.pmid: 10712222
references.citation: Genet Epidemiol. 2003 Apr;24(3):220-9
references.pmid: 12652526
references.citation: Nat Genet. 2002 May;31(1):55-9
references.pmid: 11967536
references.citation: Stat Med. 1997 Jan 15-Feb 15;16(1-3):153-67
references.pmid: 9004389
references.citation: J Natl Cancer Inst. 2002 Aug 21;94(16):1221-6
references.pmid: 12189225
references.citation: Biometrics. 1988 Dec;44(4):1033-48
references.pmid: 3233244
references.citation: Am J Hum Genet. 2007 Aug;81(2):321-37
references.pmid: 17668381
references.citation: Biometrics. 2006 Dec;62(4):1081-8
references.pmid: 17156282
references.citation: Genome Res. 1999 Mar;9(3):234-41
references.pmid: 10077529
references.citation: Genet Epidemiol. 2003 Nov;25(3):190-202
references.pmid: 14557987
references.citation: Biometrics. 2001 Mar;57(1):245-52
references.pmid: 11252606
references.citation: Genet Epidemiol. 2003 Nov;25(3):177-89
references.pmid: 14557986
references.citation: Biometrics. 1995 Dec;51(4):1384-99
references.pmid: 8589230
references.citation: Genet Epidemiol. 2004 Sep;27(2):109-17
references.pmid: 15305327
references.citation: Stat Med. 1988 Jan-Feb;7(1-2):149-60
references.pmid: 3353602
references.citation: Am J Hum Genet. 2001 Jun;68(6):1457-62
references.pmid: 11353403
references.citation: Hum Hered. 2008;66(4):238-51
references.pmid: 18612208
references.citation: Biometrics. 2007 Sep;63(3):964-5; author reply 965-6
references.pmid: 17825027
references.citation: Am J Hum Genet. 1998 Mar;62(3):676-89
references.pmid: 9497246
references.citation: Cancer Res. 2007 Dec 1;67(23):11128-32
references.pmid: 18056436
references.citation: Am J Hum Genet. 2003 May;72(5):1117-30
references.pmid: 12677558