We had previously developed methods for qualitative traits using multiple-SNP genotypes for affected individuals and their parents in a method called TRIMM (triad multi-marker). The testing approach is robust against bias due to population stratification. We further extended the approach to allow testing for haplotype-by-environment interaction, via a method we call GEI-TRIMM. The paper describing this approach and characterizing its performance through simulations was published this year. In another project, we are estimating the asymmetry that would exist in family history data secondary to the existence of a maternally-mediated genetic effect. We applied this strategy to family history data from the Sister Study, and found evidence that maternal grandmothers of young-onset (under 50) cases of breast cancer were more likely to have had breast cancer than were paternal grandmothers. This suggests there may be maternally-mediated genetic risk factors for breast cancer, that there may be imprinted genes related to risk or that mitochondrial variants play a role. Epigenetics could also be important for breast cancer. A particularly important design we are now considering involves a """"""""tetrad"""""""" structure, with one affected and one unaffected offspring, in addition to the two parents. This design has been implemented in the Two Sister Study, which is assessing the joint role of genetic and environmental risk factors in young-onset (under age 50) breast cancer. The discordant sib pair allows estimation of effects of exposures, while the embedded case-parent triad allows detection of haplotypes that confer either protection or risk. The tetrad analyzed together should provide a powerful design for assessing gene-by-environment interaction. We have been working on developing and evaluating methods for use with the tetrad design. The Two Sister Study is continuing to enroll nuclear families where one daughter developed breast cancer before age 50 and the other daughter is unaffected. We currently have enrolled over 1,300 such families. This is described under a separate project. Inherited genotypes, together with tumor characteristics, will need to be explored to investigate factors that predict the clinical course following treatment, and improved statistical methods will also need to be developed in that context. We have developed a method for studying gene-by-environment interaction using the tetrad structure and we carried out extensive simulations to document its performance under a range of scenarios, some with and some without exposure-involved population structure. We learned to our surprise that all of the existing gene-by-environment interaction methods are subject to bias if the population has exposured-involved population structure. This happens when there are subpopulations that differ both in their frequency of the marker allele under study and in their exposure prevalence. The resulting bias can best be understood as reflecting the fact that with that kind of structure the exposure can serve as a surrogate for the degree of linkage disequilibrium (which also varies across subpopulations) between the marker under study and a causative SNP/haplotype. This bias can be extreme. We have now developed some remedies for avoiding it, while preserving good statistical power, in work that was recently published. In another project, we considered the problem of carrying out genome-wide association study analyses where one wishes to combine information from a number of studies that have used different platforms for doing the genotyping. Existing software called MACH is used to model missing genotypes based on observed genotype data, but the output generates a score, rather than a discrete allele count. With family data the existing methods are not amenable to that format and the quality of the assigned score has not been well studied. We developed methods to incorporate the imputed genotype scores into a case-parent triad analysis and we assessed the agreement between results based on imputed genotypes and results based on actual genotypes for some 7000 SNPs where we had both, using data (Stephanie London, PI) from the Mexico City Childhood Asthma Study. This paper was recently published.
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