Testing association and linkage using affected-sib-parent study designs.
We have developed a method for jointly testing linkage and association using data from affected sib pairs and their parents. We specify a conditional logistic regression model with two covariates, one that quantifies association (either direct association or indirect association via linkage disequilibrium), and a second that quantifies linkage. The latter covariate is computed based on expected identity-by-descend (ibd) sharing of marker alleles between siblings. In addition to a joint test of linkage and association, our general framework can be used to obtain a linkage test comparable to the mean test (Blackwelder and Elston [1985] Genet. Epidemiol. 2:85-97), and an association test comparable to the Family-Based Association Test (FBAT; Rabinowitz and Laird [2000] Hum. Hered. 50:211-223). We present simulation results demonstrating that our joint test can be more powerful than some standard tests of linkage or association. For example, with a relative risk of 2.7 per variant allele at a disease locus, the estimated power to detect a nearby marker with a modest level of LD was 58.1% by the mean test (linkage only), 69.8% by FBAT, and 82.5% by our joint test of linkage and association. Our model can also be used to obtain tests of linkage conditional on association and association conditional on linkage, which can be helpful in fine mapping.
Millstein
Joshua
J
National Oceanic and Atmospheric Administration/National Marine Fisheries Service, Alaska Fisheries Science Center, Seattle, Washington 98115, USA. josh.millstein@noaa.gov
Siegmund
Kimberly D
KD
Conti
David V
DV
Gauderman
W James
WJ
D000483:Alleles; D008040:Genetic Linkage; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium; D016015:Logistic Models; D008957:Models, Genetic; D010290:Parents; D012107:Research Design; D035781:Siblings