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1.  Complex System Approaches to Genetic Analysis: Bayesian Approaches 
Advances in genetics  2010;72:47-71.
Genetic epidemiology is increasingly focused on complex diseases involving multiple genes and environmental factors, often interacting in complex ways. Although standard frequentist methods still have a role in hypothesis generation and testing for discovery of novel main effects and interactions, Bayesian methods are particularly well suited to modeling the relationships in an integrated “systems biology” manner. In this chapter, we provide an overview of the principles of Bayesian analysis and their advantages in this context and describe various approaches to applying them for both model building and discovery in a genome-wide setting. In particular, we highlight the ability of Bayesian methods to construct complex probability models via a hierarchical structure and to account for uncertainty in model specification by averaging over large spaces of alternative models.
PMCID: PMC4190044  PMID: 21029848
2.  A scalable, knowledge-based analysis framework for genetic association studies 
BMC Bioinformatics  2013;14:312.
Testing for marginal associations between numerous genetic variants and disease may miss complex relationships among variables (e.g., gene-gene interactions). Bayesian approaches can model multiple variables together and offer advantages over conventional model building strategies, including using existing biological evidence as modeling priors and acknowledging that many models may fit the data well. With many candidate variables, Bayesian approaches to variable selection rely on algorithms to approximate the posterior distribution of models, such as Markov-Chain Monte Carlo (MCMC). Unfortunately, MCMC is difficult to parallelize and requires many iterations to adequately sample the posterior. We introduce a scalable algorithm called PEAK that improves the efficiency of MCMC by dividing a large set of variables into related groups using a rooted graph that resembles a mountain peak. Our algorithm takes advantage of parallel computing and existing biological databases when available.
By using graphs to manage a model space with more than 500,000 candidate variables, we were able to improve MCMC efficiency and uncover the true simulated causal variables, including a gene-gene interaction. We applied PEAK to a case-control study of childhood asthma with 2,521 genetic variants. We used an informative graph for oxidative stress derived from Gene Ontology and identified several variants in ERBB4, OXR1, and BCL2 with strong evidence for associations with childhood asthma.
We introduced an extremely flexible analysis framework capable of efficiently performing Bayesian variable selection on many candidate variables. The PEAK algorithm can be provided with an informative graph, which can be advantageous when considering gene-gene interactions, or a symmetric graph, which simply divides the model space into manageable regions. The PEAK framework is compatible with various model forms, allowing for the algorithm to be configured for different study designs and applications, such as pathway or rare-variant analyses, by simple modifications to the model likelihood and proposal functions.
PMCID: PMC4015032  PMID: 24152222
3.  Meta-analysis of Genome-wide Association Studies of Asthma In Ethnically Diverse North American Populations 
Torgerson, Dara G. | Ampleford, Elizabeth J. | Chiu, Grace Y. | Gauderman, W. James | Gignoux, Christopher R. | Graves, Penelope E. | Himes, Blanca E. | Levin, Albert M. | Mathias, Rasika A. | Hancock, Dana B. | Baurley, James W. | Eng, Celeste | Stern, Debra A. | Celedón, Juan C. | Rafaels, Nicholas | Capurso, Daniel | Conti, David V. | Roth, Lindsey A. | Soto-Quiros, Manuel | Togias, Alkis | Li, Xingnan | Myers, Rachel A. | Romieu, Isabelle | Van Den Berg, David J. | Hu, Donglei | Hansel, Nadia N. | Hernandez, Ryan D. | Israel, Elliott | Salam, Muhammad T. | Galanter, Joshua | Avila, Pedro C. | Avila, Lydiana | Rodriquez-Santana, Jose R. | Chapela, Rocio | Rodriguez-Cintron, William | Diette, Gregory B. | Adkinson, N. Franklin | Abel, Rebekah A. | Ross, Kevin D. | Shi, Min | Faruque, Mezbah U. | Dunston, Georgia M. | Watson, Harold R. | Mantese, Vito J. | Ezurum, Serpil C. | Liang, Liming | Ruczinski, Ingo | Ford, Jean G. | Huntsman, Scott | Chung, Kian Fan | Vora, Hita | Li, Xia | Calhoun, William J. | Castro, Mario | Sienra-Monge, Juan J. | del Rio-Navarro, Blanca | Deichmann, Klaus A. | Heinzmann, Andrea | Wenzel, Sally E. | Busse, William W. | Gern, James E. | Lemanske, Robert F. | Beaty, Terri H. | Bleecker, Eugene R. | Raby, Benjamin A. | Meyers, Deborah A. | London, Stephanie J. | Gilliland, Frank D. | Burchard, Esteban G. | Martinez, Fernando D. | Weiss, Scott T. | Williams, L. Keoki | Barnes, Kathleen C. | Ober, Carole | Nicolae, Dan L.
Nature genetics  2011;43(9):887-892.
Asthma is a common disease with a complex risk architecture including both genetic and environmental factors. We performed a meta-analysis of North American genome-wide association studies (GWAS) of asthma in 5,416 asthma cases representing European Americans, African Americans/African Caribbeans, and Latinos, and replicated five regions among the most significant signals in 12,649 individuals from the same ethnic groups. Four were at previously reported loci on 17q21, and near the IL1RL1, TSLP, and IL33, genes, but we report for the first time that these loci are associated with asthma risk in three ethnic groups. In addition, we identified a novel association with asthma in the PYHIN1, gene that was specific to individuals of African descent (p=3.9×10−9). These results suggest that some asthma susceptibility loci are robust to differences in ancestry when sufficiently large samples sizes are investigated, and that ancestry-specific associations also contribute to the complex genetic architecture of asthma.
PMCID: PMC3445408  PMID: 21804549
5.  TSLP Polymorphisms are Associated with Asthma in a Sex-Specific Fashion 
Allergy  2010;65(12):1566-1575.
Single nucleotide polymorphisms (SNPs) in thymic stromal lymphopoietin (TSLP) have been associated with IgE (in girls) and asthma (in general). We sought to determine whether TSLP SNPs are associated with asthma in a sex-specific fashion.
We conducted regular and sex-stratified analyses of association between SNPs in TSLP and asthma in families of asthmatic children in Costa Rica. Significant findings were replicated in white and African-American participants in the Childhood Asthma Management Program, in African Americans in the Genomic Research on Asthma in the African Diaspora study, in whites and Hispanics in the Children’s Health Study, and in whites in the Framingham Heart Study (FHS).
Main Results
Two SNPs in TSLP (rs1837253 and rs2289276) were significantly associated with a reduced risk of asthma in combined analyses of all cohorts (p values of 2×10−5 and 1×10−5, respectively). In a sex-stratified analysis, the T allele of rs1837253 was significantly associated with a reduced risk of asthma in males only (p= 3×10−6). Alternately, the T allele of rs2289276 was significantly associated with a reduced risk of asthma in females only (p= 2×10−4). Findings for rs2289276 were consistent in all cohorts except the FHS.
TSLP variants are associated with asthma in a sex-specific fashion.
PMCID: PMC2970693  PMID: 20560908
asthma; genetic association; sex-specific; thymic stromal lymphopoietin; TSLP
6.  Detecting Gene-Environment Interactions in Genome-Wide Association Data 
Genetic epidemiology  2009;33(Suppl 1):S68-S73.
Despite the importance of gene-environment (G×E) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in genome-wide association study data. This was the focus of Genetic Analysis Workshop 16 Group 10 contributions, which introduced a variety of new methods for the detection of G×E interactions in both case-control and family-based data using both cross-sectional and longitudinal study designs. Many of these contributions detected significant G×E interactions. Although these interactions have not yet been confirmed, the results suggest the importance of testing for interactions. Issues of sample size, quantifying the environmental exposure, longitudinal data analysis, family-based analysis, selection of the most powerful analysis method, population stratification, and computational expense with respect to testing G×E interactions are discussed.
PMCID: PMC2924567  PMID: 19924704
GAW; case-control; family-based; cross-sectional; longitudinal; rheumatoid arthritis; Framingham Heart Study

Results 1-6 (6)