Vladimir Kogan1, Joshua Millstein2, Stephanie J London3, Carole Ober4, Steven R White5, Edward T Naureckas5, W James Gauderman1, Daniel J Jackson6, Albino Barraza-Villarreal7, Isabelle Romieu8, Benjamin A Raby9, Carrie V Breton1. 1. Department of Preventive Medicine, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA. 2. Department of Preventive Medicine, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA, joshua.millstein@usc.edu. 3. Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, RTP, Research Triangle Park, North Carolina, USA. 4. Department of Human Genetics, University of Chicago, Chicago, Illinois, USA. 5. Department of Medicine, University of Chicago, Chicago, Illinois, USA. 6. University of Wisconsin School of Medicine and Public Health, Madison, Illinois, USA. 7. Department of Environmental Health, Population Health Center, National Institute of Public Health of Mexico, Cuernavaca, Mexico. 8. International Agency for Research on Cancer, Section of Nutrition and Metabolism, Lyon, France. 9. Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Abstract
OBJECTIVES: There is evidence to suggest that asthma pathogenesis is affected by both genetic and epigenetic variation independently, and there is some evidence to suggest that genetic-epigenetic interactions affect risk of asthma. However, little research has been done to identify such interactions on a genome-wide scale. The aim of this studies was to identify genes with genetic-epigenetic interactions associated with asthma. METHODS: Using asthma case-control data, we applied a novel nonparametric gene-centric approach to test for interactions between multiple SNPs and CpG sites simultaneously in the vicinities of 18,178 genes across the genome. RESULTS: Twelve genes, PF4, ATF3, TPRA1, HOPX, SCARNA18, STC1, OR10K1, UPK1B, LOC101928523, LHX6, CHMP4B, and LANCL1, exhibited statistically significant SNP-CpG interactions (false discovery rate = 0.05). Of these, three have previously been implicated in asthma risk (PF4, ATF3, and TPRA1). Follow-up analysis revealed statistically significant pairwise SNP-CpG interactions for several of these genes, including SCARNA18, LHX6, and LOC101928523 (p = 1.33E-04, 8.21E-04, 1.11E-03, respectively). CONCLUSIONS: Joint effects of genetic and epigenetic variation may play an important role in asthma pathogenesis. Statistical methods that simultaneously account for multiple variations across chromosomal regions may be needed to detect these types of effects on a genome-wide scale.
OBJECTIVES: There is evidence to suggest that asthma pathogenesis is affected by both genetic and epigenetic variation independently, and there is some evidence to suggest that genetic-epigenetic interactions affect risk of asthma. However, little research has been done to identify such interactions on a genome-wide scale. The aim of this studies was to identify genes with genetic-epigenetic interactions associated with asthma. METHODS: Using asthma case-control data, we applied a novel nonparametric gene-centric approach to test for interactions between multiple SNPs and CpG sites simultaneously in the vicinities of 18,178 genes across the genome. RESULTS: Twelve genes, PF4, ATF3, TPRA1, HOPX, SCARNA18, STC1, OR10K1, UPK1B, LOC101928523, LHX6, CHMP4B, and LANCL1, exhibited statistically significant SNP-CpG interactions (false discovery rate = 0.05). Of these, three have previously been implicated in asthma risk (PF4, ATF3, and TPRA1). Follow-up analysis revealed statistically significant pairwise SNP-CpG interactions for several of these genes, including SCARNA18, LHX6, and LOC101928523 (p = 1.33E-04, 8.21E-04, 1.11E-03, respectively). CONCLUSIONS: Joint effects of genetic and epigenetic variation may play an important role in asthma pathogenesis. Statistical methods that simultaneously account for multiple variations across chromosomal regions may be needed to detect these types of effects on a genome-wide scale.
Authors: Aniruddha Rathod; Jiasong Duan; Hongmei Zhang; John W Holloway; Susan Ewart; S Hasan Arshad; Wilfried Karmaus Journal: Epigenet Insights Date: 2020-07-22