@article {37, title = {An ancestry-based approach for detecting interactions.}, journal = {Genet Epidemiol}, volume = {42}, year = {2018}, month = {2018 Feb}, pages = {49-63}, abstract = {

BACKGROUND: Epistasis and gene-environment interactions are known to contribute significantly to variation of complex phenotypes in model organisms. However, their identification in human association studies remains challenging for myriad reasons. In the case of epistatic interactions, the large number of potential interacting sets of genes presents computational, multiple hypothesis correction, and other statistical power issues. In the case of gene-environment interactions, the lack of consistently measured environmental covariates in most disease studies precludes searching for interactions and creates difficulties for replicating studies.

RESULTS: In this work, we develop a new statistical approach to address these issues that leverages genetic ancestry, defined as the proportion of ancestry derived from each ancestral population (e.g., the fraction of European/African ancestry in African Americans), in admixed populations. We applied our method to gene expression and methylation data from African American and Latino admixed individuals, respectively, identifying nine interactions that were significant at P<5{\texttimes}10-8. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low P-values (P<1.8{\texttimes}10-6).

CONCLUSION: We show that genetic ancestry can be a useful proxy for unknown and unmeasured covariates in the search for interaction effects. These results have important implications for our understanding of the genetic architecture of complex traits.

}, keywords = {Black or African American, Black People, DNA Methylation, Epistasis, Genetic, Gene-Environment Interaction, Hispanic or Latino, Humans, Models, Genetic, Phenotype, White People}, issn = {1098-2272}, doi = {10.1002/gepi.22087}, author = {Park, Danny S and Eskin, Itamar and Kang, Eun Yong and Gamazon, Eric R and Eng, Celeste and Gignoux, Christopher R and Galanter, Joshua M and Burchard, Esteban and Ye, Chun J and Aschard, Hugues and Eskin, Eleazar and Halperin, Eran and Zaitlen, Noah} } @article {26, title = {Improved methods for multi-trait fine mapping of pleiotropic risk loci.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 Jan 15}, pages = {248-255}, abstract = {

MOTIVATION: Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation.

RESULTS: In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution compared to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90\% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data.

AVAILABILITY AND IMPLEMENTATION: The fastPAINTOR framework is implemented in the PAINTOR v3.0 package which is publicly available to the research community http://bogdan.bioinformatics.ucla.edu/software/paintor CONTACT: gkichaev@ucla.eduSupplementary information: Supplementary data are available at Bioinformatics online.

}, keywords = {Chromosome Mapping, Genetic Diseases, Inborn, Genetic Loci, Genetic Pleiotropy, Genome-Wide Association Study, Genomics, Humans, Linkage Disequilibrium, Lipid Metabolism, Models, Genetic, Polymorphism, Single Nucleotide, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw615}, author = {Kichaev, Gleb and Roytman, Megan and Johnson, Ruth and Eskin, Eleazar and Lindstr{\"o}m, Sara and Kraft, Peter and Pasaniuc, Bogdan} } @article {23, title = {Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions.}, journal = {Am J Epidemiol}, volume = {186}, year = {2017}, month = {2017 Oct 01}, pages = {771-777}, abstract = {

A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.

}, keywords = {Animals, Disease, Gene-Environment Interaction, Genome-Wide Association Study, Genomics, Humans, Models, Animal, Sequence Analysis, RNA}, issn = {1476-6256}, doi = {10.1093/aje/kwx229}, author = {Ritchie, Marylyn D and Davis, Joe R and Aschard, Hugues and Battle, Alexis and Conti, David and Du, Mengmeng and Eskin, Eleazar and Fallin, M Daniele and Hsu, Li and Kraft, Peter and Moore, Jason H and Pierce, Brandon L and Bien, Stephanie A and Thomas, Duncan C and Wei, Peng and Montgomery, Stephen B} }