Showing papers by "Nancy L. Saccone published in 1999"
TL;DR: A novel technique to detect significant covariates in linkage analysis using a logistic regression approach, which has the advantages of using all sib pairs and of providing a formal test for heterogeneity across populations.
Abstract: We apply a novel technique to detect significant covariates in linkage analysis using a logistic regression approach. An overall test of linkage is first performed to determine whether there is significant perturbation from the expected 50% sharing under the hypothesis of no linkage; if the overall test is significant, the importance of the individual covariate is assessed. In addition, association analyses were performed. These methods were applied to simulated data from multiple populations, and detected correct marker linkages and associations. No population heterogeneity was detected. These methods have the advantages of using all sib pairs and of providing a formal test for heterogeneity across populations.
41 citations
TL;DR: To identify loci involved in the control of platelet monoamine oxidase B (MAO-B) activity, a genomewide linkage screen was performed using 291 markers in 148 nuclear families containing a total of 1,008 nonindependent sib-pairs to provide consistency with linkage.
Abstract: To identify loci involved in the control of platelet monoamine oxidase B (MAO-B) activity, a genomewide linkage screen was performed using 291 markers in 148 nuclear families containing a total of 1,008 nonindependent sib-pairs. Participants were genotyped and their platelet MAO-B activity levels were measured as part of the Collaborative Study on the Genetics of Alcoholism (COGA). Sib-pair analysis using Haseman-Elston regression was carried out with two programs. Two-point analysis on all pairs with SIBPAL indicated three markers with p-values below 0.01: D6S1018 (p = 0.0004), D2S1328 (p = 0.008), and D2S408 (p = 0.003). MAPMAKER/SIBS multipoint analyses using independent pairs(N = 409) gave maximal lod scores of 2. 0 on chromosome 6 and 1.1 and 1.4 for the two regions on chromosome 2. These results are consistent with linkage, but do not provide definitive evidence. We are currently creating a denser map in these regions and have begun genotyping a second sample in COGA.
17 citations
TL;DR: Using simulated data from GAW11, categorical classification methods and neural network analysis are applied to model functions that use genetic information as input and trait information as output to understand genetic linkage in complex diseases.
Abstract: We model functions that use genetic information as input and trait information as output to understand genetic linkage in complex diseases. Using simulated data from GAW11, we have applied categorical classification methods and neural network analysis. We use sharing at selected markers as input, and the classification of the sib pair (for example, affected-affected or affected-unaffected) as output. In addition, our methods include environmental risk factors as predictors of phenotype. Categorical and neural network methods each led to results consistent with findings from other methods such as the logistic regression method of Rice et al. [this issue]. Post-analysis comparison with the GAW11 answers showed that these methods are capable of detecting correct signals in a single replicate. One advantage of our methods is that they allow analysis of the entire genome at once, so that interactions among multiple trait-influencing loci may be detected. Furthermore, these methods can use a variety of sib pairs rather than affected pairs only.
16 citations