A major project of this section is the development of new statistical genetics methodology as prompted by the needs of our applied studies and the testing and comparison of novel and existing statistical methods. This year, we have continued our collaboration with Dr. Silvano Presciuttini and Dr. Fabio Marroni from the University of Pisa, on development of better methods for predicting mutation carrier status for known cancer genes in Italian families. We have extended the model development that we did for BRCA1 carrier status to develop models that allow improved prediction of MSH2 and MLH1 carriers. A paper is in preparation presenting these results. The project to develop propensity scores in linkage analyses as a method for inclusion of covariate effects has been continued in conjunction with Betty Doan and Yin Yao. This method appears promising in that it is generally more powerful than including the covariates directly into the model, and does not have strongly inflated Type I error rates. One manuscript is in press and another manuscript has been submitted. Dr. Doan used this work as part of her PhD dissertation work, which she successfully defended this summer at Johns Hopkins Bloomberg School of Public Health. In addition, we have pursued another project designed to examine the effects of important environmental covariates on power and Type I error in linkage studies. We are simulating traits for which moderate to strong environmentsl risk factors play a role in risk of a disease trait (affected vs. unaffected) and then comparing the performance of various analytic methods that ignore covariates to the performance of methods that incorporate these covariates into the analysis. A manuscript is in preparation describing these results. In this fiscal year we also devoted a large amount of time to evaluating existing methods for linkage, association and haplotype analysis using both STRP and dense SNP maps. We examined the effect of low to moderate LD on the Type I error rates of linkage statistics, and compared various methods of picking tagSNPs and building haplotypes in family data. Three papers are in press as a result of this work. We also used computer simulation techniques to determine the power and robustness of several types of linkage analysis with and without the inclusion of laboratory-determined (direct) haplotypes on some or all family members. We found that large increases in power can be obtained by including such information in the analysis, particularly when some family members are ungenotyped. A paper is in preparation.
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