One of the most important challenges facing biology today is to understand how genetic variation between individuals translates into variation in phenotype, such as disease status. While the recent wave of genome-wide association studies has led to the discovery of a number of variants associated with disease, the vast majority of that variation remains unexplained. This project proposes to develop guidelines for the next step, the sequencing of samples in the associated regions to determine their complete patterns of genetic variation and thereby identify smaller sets of strongly associated variants that are associated with disease. This will be undertaken by comparing a variety of design and analysis approaches on a range of test datasets, thereby assessing the performance and utility of those approaches for future studies.
The genomic era carries the promise of allowing us to uncover the genetic courses of disease. While some progress has been made to date, a large number of the genetic causes of disease are yet to be determined. The next wave of studies to address this issue will use next-generation sequencing technologies. The purpose of our proposal is to provide guidelines for the design and analysis of these studies. Such guidelines will leverage the newer of those studies.
| Salomon, Matthew P; Li, Wai Lok Sibon; Edlund, Christopher K et al. (2016) GWASeq: targeted re-sequencing follow up to GWAS. BMC Genomics 17:176 |
| Li, Dalin; Zhou, Jin; Thomas, Duncan C et al. (2014) Complex pedigrees in the sequencing era: to track transmissions or decorrelate? Genet Epidemiol 38 Suppl 1:S29-36 |
| Gauderman, W James; Zhang, Pingye; Morrison, John L et al. (2013) Finding novel genes by testing G × E interactions in a genome-wide association study. Genet Epidemiol 37:603-13 |
| Kang, Chul Joo; Marjoram, Paul (2012) A sample selection strategy for next-generation sequencing. Genet Epidemiol 36:696-709 |
| Thomas, Duncan C (2012) Some surprising twists on the road to discovering the contribution of rare variants to complex diseases. Hum Hered 74:113-7 |
| Quintana, Melanie A; Berstein, Jonine L; Thomas, Duncan C et al. (2011) Incorporating model uncertainty in detecting rare variants: the Bayesian risk index. Genet Epidemiol 35:638-49 |
| Li, Dalin; Lewinger, Juan Pablo; Gauderman, William J et al. (2011) Using extreme phenotype sampling to identify the rare causal variants of quantitative traits in association studies. Genet Epidemiol 35:790-9 |
| Murcray, Cassandra E; Lewinger, Juan Pablo; Conti, David V et al. (2011) Sample size requirements to detect gene-environment interactions in genome-wide association studies. Genet Epidemiol 35:201-10 |
