This project will develop and evaluate genetic and gene-environment risk profiles for breast cancer and type 2 diabetes using data from the Nurses'Health Study (NHS) and Health Professionals'Follow-Up Study (HPFS), based on validated risk markers from recent genome-wide association studies. Because the NHS and HPFS are cohorts with extensive prospectively-collected data on known, modifiable environmental, lifestyle and anthropometric risk factors, this project will be the first to empirically examine how much the inclusion of genetic factors and gene-environment interactions improves clinical validity relative to standard risk prediction models which only use non-genetic factors. In particular, the project will calculate empirical absolute risks (5-, 10-year and lifetime risks) of breast cancer and type 2 diabetes for individuals with different profiles. These absolute risk estimates are needed to evaluate the risk-benefit balance for """"""""personalized prevention"""""""" strategies, such as recommending that women with a particular gene-environment profile start mammography screening at earlier ages. This project will also develop new statistical methods to infer absolute disease risks for genetic and gene-environment risk profiles by combining data on genetic markers and environmental exposures collected in a nested case-control with data on environmental exposures collected on the entire underlying cohort. These methods will be broadly applicable to other cohort studies and other common diseases and traits of public health relevance.
This project will develop and evaluate genetic and gene-environment risk profiles for breast cancer and type 2 diabetes using data from the Nurses'Health Study and Health Professionals'Follow-Up Study, based on validated risk markers from recent genome-wide association studies. This work will provide estimates of the net benefits of """"""""personalized prevention"""""""" strategies based on these profiles. The statistical methods developed for this project will be broadly applicable to other cohort studies and other common diseases and traits of public health relevance.
|Aschard, Hugues; Zaitlen, Noah; Lindström, Sara et al. (2015) Variation in predictive ability of common genetic variants by established strata: the example of breast cancer and age. Epidemiology 26:51-8|
|Aschard, Hugues; Zaitlen, Noah; Tamimi, Rulla M et al. (2013) A nonparametric test to detect quantitative trait loci where the phenotypic distribution differs by genotypes. Genet Epidemiol 37:323-33|
|Hancock, Dana B; Soler Artigas, María; Gharib, Sina A et al. (2012) Genome-wide joint meta-analysis of SNP and SNP-by-smoking interaction identifies novel loci for pulmonary function. PLoS Genet 8:e1003098|
|Aschard, Hugues; Lutz, Sharon; Maus, Bärbel et al. (2012) Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. Hum Genet 131:1591-613|
|Aschard, Hugues; Chen, Jinbo; Cornelis, Marilyn C et al. (2012) Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases. Am J Hum Genet 90:962-72|
|Zaitlen, Noah; Kraft, Peter (2012) Heritability in the genome-wide association era. Hum Genet 131:1655-64|
|Tintle, Nathan; Aschard, Hugues; Hu, Inchi et al. (2011) Inflated type I error rates when using aggregation methods to analyze rare variants in the 1000 Genomes Project exon sequencing data in unrelated individuals: summary results from Group 7 at Genetic Analysis Workshop 17. Genet Epidemiol 35 Suppl 1:S56-60|
|Aschard, Hugues; Hancock, Dana B; London, Stephanie J et al. (2010) Genome-wide meta-analysis of joint tests for genetic and gene-environment interaction effects. Hum Hered 70:292-300|