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A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure.
Sung YJ, Winkler TW, de Las Fuentes L, Bentley AR, Brown MR, Kraja AT, Schwander K, Ntalla I, Guo X, Franceschini N, Lu Y, Cheng CY, Sim X, Vojinovic D, Marten J, Musani SK, Li C, Feitosa MF, Kilpeläinen TO, Richard MA, Noordam R, Aslibekyan S, Aschard H, Bartz TM, Dorajoo R, Liu Y, Manning AK, Rankinen T, Smith AV, Tajuddin SM, Tayo BO, Warren HR, Zhao W, Zhou Y, Matoba N, Sofer T, Alver M, Amini M, Boissel M, Chai JF, Chen X, Divers J, Gandin I, Gao C, Giulianini F, Goel A, Harris SE, Hartwig FP, Horimoto ARVR, Hsu FC, Jackson AU, Kähönen M, Kasturiratne A, Kühnel B, Leander K, Lee WJ, Lin KH, 'an Luan J, McKenzie CA, Meian H, Nelson CP, Rauramaa R, Schupf N, Scott RA, Sheu WHH, Stančáková A, Takeuchi F, van der Most PJ, Varga TV, Wang H, Wang Y, Ware EB, Weiss S, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Alfred T, Amin N, Arking D, Aung T, Barr RG, Bielak LF, Boerwinkle E, Bottinger EP, Braund PS, Brody JA, Broeckel U, Cabrera CP, Cade B, Caizheng Y, Campbell A, Canouil M, Chakravarti A, CHARGE Neurology Working Group, Chauhan G, Christensen K, Cocca M, COGENT-Kidney Consortium, Collins FS, Connell JM, de Mutsert R, de Silva HJ, Debette S, Dörr M, Duan Q, Eaton CB, Ehret G, Evangelou E, Faul JD, Fisher VA, Forouhi NG, Franco OH, Friedlander Y, Gao H, GIANT Consortium, Gigante B, Graff M, Gu CC, Gu D, Gupta P, Hagenaars SP, Harris TB, He J, Heikkinen S, Heng CK, Hirata M, Hofman A, Howard BV, Hunt S, Irvin MR, Jia Y, Joehanes R, Justice AE, Katsuya T, Kaufman J, Kerrison ND, Khor CC, Koh WP, Koistinen HA, Komulainen P, Kooperberg C, Krieger JE, Kubo M, Kuusisto J, Langefeld CD, Langenberg C, Launer LJ, Lehne B, Lewis CE, Li Y, Lifelines Cohort Study, Lim SH, Lin S, Liu CT, Liu J, Liu J, Liu K, Liu Y, Loh M, Lohman KK, Long J, Louie T, Mägi R, Mahajan A, Meitinger T, Metspalu A, Milani L, Momozawa Y, Morris AP, Mosley TH, Munson P, Murray AD, Nalls MA, Nasri U, Norris JM, North K, Ogunniyi A, Padmanabhan S, Palmas WR, Palmer ND, Pankow JS, Pedersen NL, Peters A, Peyser PA, Polasek O, Raitakari OT, Renström F, Rice TK, Ridker PM, Robino A, Robinson JG, Rose LM, Rudan I, Sabanayagam C, Salako BL, Sandow K, Schmidt CO, Schreiner PJ, Scott WR, Seshadri S, Sever P, Sitlani CM, Smith JA, Snieder H, Starr JM, Strauch K, Tang H, Taylor KD, Teo YY, Tham YC, Uitterlinden AG, Waldenberger M, Wang L, Wang YX, Wei WB, Williams C, Wilson G, Wojczynski MK, Yao J, Yuan JM, Zonderman AB, Becker DM, Boehnke M, Bowden DW, Chambers JC, Chen YI, de Faire U, Deary IJ, Esko T, Farrall M, Forrester T, Franks PW, Freedman BI, Froguel P, Gasparini P, Gieger C, Horta BL, Hung YJ, Jonas JB, Kato N, Kooner JS, Laakso M, Lehtimäki T, Liang KW, Magnusson PKE, Newman AB, Oldehinkel AJ, Pereira AC, Redline S, Rettig R, Samani NJ, Scott J, Shu XO, van der Harst P, Wagenknecht LE, Wareham NJ, Watkins H, Weir DR, Wickremasinghe AR, Wu T, Zheng W, Kamatani Y, Laurie CC, Bouchard C, Cooper RS, Evans MK, Gudnason V, Kardia SLR, Kritchevsky SB, Levy D, O'Connell JR, Psaty BM, van Dam RM, Sims M, Arnett DK, Mook-Kanamori DO, Kelly TN, Fox ER, Hayward C, Fornage M, Rotimi CN, Province MA, van Duijn CM, Tai ES, Wong TY, Loos RJF, Reiner AP, Rotter JI, Zhu X, Bierut LJ, Gauderman WJ, Caulfield MJ, Elliott P, Rice K, Munroe PB, Morrison AC, Cupples LA, Rao DC, Chasman DI
(2018) Am J Hum Genet 102: 375-400
MeSH Terms: Blood Pressure, Cohort Studies, Continental Population Groups, Diastole, Epistasis, Genetic, Female, Genetic Loci, Genome-Wide Association Study, Humans, Male, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Reproducibility of Results, Smoking, Systole
Show Abstract · Added April 10, 2018
Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactions in 610,091 individuals. Stage 1 analysis examined ∼18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-up analysis of promising variants in 480,178 additional individuals from five ancestries. We identified 15 loci that were genome-wide significant (p < 5 × 10) in stage 1 and formally replicated in stage 2. A combined stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci (13, 35, and 18 loci in European, African, and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (p < 5 × 10). Of the newly identified loci, ten showed significant interaction with smoking status, but none of them were replicated in stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies (SDCCAG8, RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2).
Copyright © 2018 American Society of Human Genetics. All rights reserved.
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15 MeSH Terms
Local ancestry transitions modify snp-trait associations.
Fish AE, Crawford DC, Capra JA, Bush WS
(2018) Pac Symp Biocomput 23: 424-435
MeSH Terms: Adult, African Continental Ancestry Group, Chromosomes, Human, Computational Biology, Epistasis, Genetic, European Continental Ancestry Group, Evolution, Molecular, Gene Frequency, Genetics, Population, Genome-Wide Association Study, Haplotypes, Humans, Linear Models, Models, Genetic, Polymorphism, Single Nucleotide, Recombination, Genetic
Show Abstract · Added March 14, 2018
Genomic maps of local ancestry identify ancestry transitions - points on a chromosome where recent recombination events in admixed individuals have joined two different ancestral haplotypes. These events bring together alleles that evolved within separate continential populations, providing a unique opportunity to evaluate the joint effect of these alleles on health outcomes. In this work, we evaluate the impact of genetic variants in the context of nearby local ancestry transitions within a sample of nearly 10,000 adults of African ancestry with traits derived from electronic health records. Genetic data was located using the Metabochip, and used to derive local ancestry. We develop a model that captures the effect of both single variants and local ancestry, and use it to identify examples where local ancestry transitions significantly interact with nearby variants to influence metabolic traits. In our most compelling example, we find that the minor allele of rs16890640 occuring on a European background with a downstream local ancestry transition to African ancestry results in significantly lower mean corpuscular hemoglobin and volume. This finding represents a new way of discovering genetic interactions, and is supported by molecular data that suggest changes to local ancestry may impact local chromatin looping.
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16 MeSH Terms
An ancestry-based approach for detecting interactions.
Park DS, Eskin I, Kang EY, Gamazon ER, Eng C, Gignoux CR, Galanter JM, Burchard E, Ye CJ, Aschard H, Eskin E, Halperin E, Zaitlen N
(2018) Genet Epidemiol 42: 49-63
MeSH Terms: African Americans, African Continental Ancestry Group, DNA Methylation, Epistasis, Genetic, European Continental Ancestry Group, Gene-Environment Interaction, Hispanic Americans, Humans, Models, Genetic, Phenotype
Show Abstract · Added November 29, 2017
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<5×10-8. We show that two of the interactions in methylation data replicate, and the remaining six are significantly enriched for low P-values (P<1.8×10-6).
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.
© 2017 WILEY PERIODICALS, INC.
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10 MeSH Terms
Genetic Interactions with Age, Sex, Body Mass Index, and Hypertension in Relation to Atrial Fibrillation: The AFGen Consortium.
Weng LC, Lunetta KL, Müller-Nurasyid M, Smith AV, Thériault S, Weeke PE, Barnard J, Bis JC, Lyytikäinen LP, Kleber ME, Martinsson A, Lin HJ, Rienstra M, Trompet S, Krijthe BP, Dörr M, Klarin D, Chasman DI, Sinner MF, Waldenberger M, Launer LJ, Harris TB, Soliman EZ, Alonso A, Paré G, Teixeira PL, Denny JC, Shoemaker MB, Van Wagoner DR, Smith JD, Psaty BM, Sotoodehnia N, Taylor KD, Kähönen M, Nikus K, Delgado GE, Melander O, Engström G, Yao J, Guo X, Christophersen IE, Ellinor PT, Geelhoed B, Verweij N, Macfarlane P, Ford I, Heeringa J, Franco OH, Uitterlinden AG, Völker U, Teumer A, Rose LM, Kääb S, Gudnason V, Arking DE, Conen D, Roden DM, Chung MK, Heckbert SR, Benjamin EJ, Lehtimäki T, März W, Smith JG, Rotter JI, van der Harst P, Jukema JW, Stricker BH, Felix SB, Albert CM, Lubitz SA
(2017) Sci Rep 7: 11303
MeSH Terms: Age Factors, Aged, Atrial Fibrillation, Body Mass Index, Chromosomes, Human, Pair 4, Epistasis, Genetic, Female, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Hypertension, Male, Middle Aged, Odds Ratio, Polymorphism, Single Nucleotide, Reproducibility of Results, Risk Factors, Sex Characteristics
Show Abstract · Added March 14, 2018
It is unclear whether genetic markers interact with risk factors to influence atrial fibrillation (AF) risk. We performed genome-wide interaction analyses between genetic variants and age, sex, hypertension, and body mass index in the AFGen Consortium. Study-specific results were combined using meta-analysis (88,383 individuals of European descent, including 7,292 with AF). Variants with nominal interaction associations in the discovery analysis were tested for association in four independent studies (131,441 individuals, including 5,722 with AF). In the discovery analysis, the AF risk associated with the minor rs6817105 allele (at the PITX2 locus) was greater among subjects ≤ 65 years of age than among those > 65 years (interaction p-value = 4.0 × 10). The interaction p-value exceeded genome-wide significance in combined discovery and replication analyses (interaction p-value = 1.7 × 10). We observed one genome-wide significant interaction with body mass index and several suggestive interactions with age, sex, and body mass index in the discovery analysis. However, none was replicated in the independent sample. Our findings suggest that the pathogenesis of AF may differ according to age in individuals of European descent, but we did not observe evidence of statistically significant genetic interactions with sex, body mass index, or hypertension on AF risk.
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19 MeSH Terms
Genome-wide association study to identify variants associated with acute severe vaso-occlusive pain in sickle cell anemia.
Chaturvedi S, Bhatnagar P, Bean CJ, Steinberg MH, Milton JN, Casella JF, Barron-Casella E, Arking DE, DeBaun MR
(2017) Blood 130: 686-688
MeSH Terms: Acute Pain, Adolescent, African Continental Ancestry Group, Anemia, Sickle Cell, Arterial Occlusive Diseases, Child, Child, Preschool, Chromosomes, Human, Pair 4, Epistasis, Genetic, Female, Fetal Hemoglobin, Genetic Variation, Genome-Wide Association Study, Genotyping Techniques, Humans, Male, Multicenter Studies as Topic, Polymorphism, Single Nucleotide, Prospective Studies
Added June 7, 2017
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19 MeSH Terms
Are Interactions between cis-Regulatory Variants Evidence for Biological Epistasis or Statistical Artifacts?
Fish AE, Capra JA, Bush WS
(2016) Am J Hum Genet 99: 817-830
MeSH Terms: Artifacts, Binding Sites, Datasets as Topic, Epistasis, Genetic, Ethnic Groups, Gene Expression Regulation, Genetic Variation, Haplotypes, Humans, Linkage Disequilibrium, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide, Quantitative Trait Loci, RNA-Binding Proteins, Regulatory Sequences, Nucleic Acid
Show Abstract · Added April 18, 2017
The importance of epistasis-or statistical interactions between genetic variants-to the development of complex disease in humans has been controversial. Genome-wide association studies of statistical interactions influencing human traits have recently become computationally feasible and have identified many putative interactions. However, statistical models used to detect interactions can be confounded, which makes it difficult to be certain that observed statistical interactions are evidence for true molecular epistasis. In this study, we investigate whether there is evidence for epistatic interactions between genetic variants within the cis-regulatory region that influence gene expression after accounting for technical, statistical, and biological confounding factors. We identified 1,119 (FDR = 5%) interactions that appear to regulate gene expression in human lymphoblastoid cell lines, a tightly controlled, largely genetically determined phenotype. Many of these interactions replicated in an independent dataset (90 of 803 tested, Bonferroni threshold). We then performed an exhaustive analysis of both known and novel confounders, including ceiling/floor effects, missing genotype combinations, haplotype effects, single variants tagged through linkage disequilibrium, and population stratification. Every interaction could be explained by at least one of these confounders, and replication in independent datasets did not protect against some confounders. Assuming that the confounding factors provide a more parsimonious explanation for each interaction, we find it unlikely that cis-regulatory interactions contribute strongly to human gene expression, which calls into question the relevance of cis-regulatory interactions for other human phenotypes. We additionally propose several best practices for epistasis testing to protect future studies from confounding.
Copyright © 2016 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
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16 MeSH Terms
Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics Consortium.
Hohman TJ, Bush WS, Jiang L, Brown-Gentry KD, Torstenson ES, Dudek SM, Mukherjee S, Naj A, Kunkle BW, Ritchie MD, Martin ER, Schellenberg GD, Mayeux R, Farrer LA, Pericak-Vance MA, Haines JL, Thornton-Wells TA, Alzheimer's Disease Genetics Consortium
(2016) Neurobiol Aging 38: 141-150
MeSH Terms: ATP Binding Cassette Transporter, Subfamily B, Alzheimer Disease, Cadherins, Calcium Channels, L-Type, Datasets as Topic, Disease Progression, Epistasis, Genetic, Female, Genetic Association Studies, Humans, Male, Models, Genetic, Phosphatidylethanolamine Binding Protein, Polymorphism, Single Nucleotide, Receptors, Adrenergic, alpha-1, Receptors, N-Methyl-D-Aspartate, Risk, Ryanodine Receptor Calcium Release Channel, Saposins, Sirtuin 1
Show Abstract · Added April 10, 2018
Late-onset Alzheimer disease (AD) has a complex genetic etiology, involving locus heterogeneity, polygenic inheritance, and gene-gene interactions; however, the investigation of interactions in recent genome-wide association studies has been limited. We used a biological knowledge-driven approach to evaluate gene-gene interactions for consistency across 13 data sets from the Alzheimer Disease Genetics Consortium. Fifteen single nucleotide polymorphism (SNP)-SNP pairs within 3 gene-gene combinations were identified: SIRT1 × ABCB1, PSAP × PEBP4, and GRIN2B × ADRA1A. In addition, we extend a previously identified interaction from an endophenotype analysis between RYR3 × CACNA1C. Finally, post hoc gene expression analyses of the implicated SNPs further implicate SIRT1 and ABCB1, and implicate CDH23 which was most recently identified as an AD risk locus in an epigenetic analysis of AD. The observed interactions in this article highlight ways in which genotypic variation related to disease may depend on the genetic context in which it occurs. Further, our results highlight the utility of evaluating genetic interactions to explain additional variance in AD risk and identify novel molecular mechanisms of AD pathogenesis.
Copyright © 2016 Elsevier Inc. All rights reserved.
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20 MeSH Terms
Environmental Interactions and Epistasis Are Revealed in the Proteomic Responses to Complex Stimuli.
Samir P, Rahul , Slaughter JC, Link AJ
(2015) PLoS One 10: e0134099
MeSH Terms: Chromatography, High Pressure Liquid, Epistasis, Genetic, Genotype, Glucose, Glycerol, Mass Spectrometry, Proteome, RNA, Messenger, Saccharomyces cerevisiae
Show Abstract · Added February 15, 2016
Ultimately, the genotype of a cell and its interaction with the environment determine the cell's biochemical state. While the cell's response to a single stimulus has been studied extensively, a conceptual framework to model the effect of multiple environmental stimuli applied concurrently is not as well developed. In this study, we developed the concepts of environmental interactions and epistasis to explain the responses of the S. cerevisiae proteome to simultaneous environmental stimuli. We hypothesize that, as an abstraction, environmental stimuli can be treated as analogous to genetic elements. This would allow modeling of the effects of multiple stimuli using the concepts and tools developed for studying gene interactions. Mirroring gene interactions, our results show that environmental interactions play a critical role in determining the state of the proteome. We show that individual and complex environmental stimuli behave similarly to genetic elements in regulating the cellular responses to stimuli, including the phenomena of dominance and suppression. Interestingly, we observed that the effect of a stimulus on a protein is dominant over other stimuli if the response to the stimulus involves the protein. Using publicly available transcriptomic data, we find that environmental interactions and epistasis regulate transcriptomic responses as well.
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9 MeSH Terms
Epiregulin (EREG) and human V-ATPase (TCIRG1): genetic variation, ethnicity and pulmonary tuberculosis susceptibility in Guinea-Bissau and The Gambia.
White MJ, Tacconelli A, Chen JS, Wejse C, Hill PC, Gomes VF, Velez-Edwards DR, Østergaard LJ, Hu T, Moore JH, Novelli G, Scott WK, Williams SM, Sirugo G
(2014) Genes Immun 15: 370-7
MeSH Terms: Adult, African Continental Ancestry Group, Alleles, Epiregulin, Epistasis, Genetic, Gambia, Gene Frequency, Genetic Predisposition to Disease, Genotype, Guinea-Bissau, Humans, Linkage Disequilibrium, Logistic Models, Male, Odds Ratio, Polymorphism, Single Nucleotide, Tuberculosis, Pulmonary, Vacuolar Proton-Translocating ATPases
Show Abstract · Added February 22, 2016
We analyzed two West African samples (Guinea-Bissau: n=289 cases and 322 controls; The Gambia: n=240 cases and 248 controls) to evaluate single-nucleotide polymorphisms (SNPs) in Epiregulin (EREG) and V-ATPase (T-cell immune regulator 1 (TCIRG1)) using single and multilocus analyses to determine whether previously described associations with pulmonary tuberculosis (PTB) in Vietnamese and Italians would replicate in African populations. We did not detect any significant single locus or haplotype associations in either sample. We also performed exploratory pairwise interaction analyses using Visualization of Statistical Epistasis Networks (ViSEN), a novel method to detect only interactions among multiple variables, to elucidate possible interaction effects between SNPs and demographic factors. Although we found no strong evidence of marginal effects, there were several significant pairwise interactions that were identified in either the Guinea-Bissau or the Gambian samples, two of which replicated across populations. Our results indicate that the effects of EREG and TCIRG1 variants on PTB susceptibility, to the extent that they exist, are dependent on gene-gene interactions in West African populations as detected with ViSEN. In addition, epistatic effects are likely to be influenced by inter- and intra-population differences in genetic or environmental context and/or the mycobacterial lineages causing disease.
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18 MeSH Terms
Genetic interaction between mutations in c-Myb and the KIX domains of CBP and p300 affects multiple blood cell lineages and influences both gene activation and repression.
Kasper LH, Fukuyama T, Lerach S, Chang Y, Xu W, Wu S, Boyd KL, Brindle PK
(2013) PLoS One 8: e82684
MeSH Terms: Animals, Blood Cells, CREB-Binding Protein, Cells, Cultured, E1A-Associated p300 Protein, Epistasis, Genetic, Hematopoiesis, Mice, Mice, Knockout, Protein Structure, Tertiary, Proto-Oncogene Proteins c-myb
Show Abstract · Added March 20, 2014
Adult blood cell production or definitive hematopoiesis requires the transcription factor c-Myb. The closely related KAT3 histone acetyltransferases CBP (CREBBP) and p300 (EP300) bind c-Myb through their KIX domains and mice homozygous for a p300 KIX domain mutation exhibit multiple blood defects. Perplexingly, mice homozygous for the same KIX domain mutation in CBP have normal blood. Here we test the hypothesis that the CBP KIX domain contributes subordinately to hematopoiesis via a genetic interaction with c-Myb. We assessed hematopoiesis in mice bearing compound mutations of c-Myb and/or the KIX domains of CBP and p300, and measured the effect of KIX domain mutations on c-Myb-dependent gene expression. We found that in the context of a p300 KIX mutation, the CBP KIX domain mutation affects platelets, B cells, T cells, and red cells. Gene interaction (epistasis) analysis provides mechanistic evidence that blood defects in KIX mutant mice are consistent with reduced c-Myb and KIX interaction. Lastly, we demonstrated that the CBP and p300 KIX domains contribute to both c-Myb-dependent gene activation and repression. Together these results suggest that the KIX domains of CBP, and especially p300, are principal mediators of c-Myb-dependent gene activation and repression that is required for definitive hematopoiesis.
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11 MeSH Terms