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Breeze CE, Lin BM, Winkler CA, Franceschini N. African ancestry-derived APOL1 risk genotypes show proximal epigenetic associations. BMC Genomics 2024; 25:452. [PMID: 38714935 PMCID: PMC11077761 DOI: 10.1186/s12864-024-10226-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 03/14/2024] [Indexed: 05/12/2024] Open
Abstract
Apolipoprotein L1 (APOL1) coding variants, termed G1 and G2, are established genetic risk factors for a growing spectrum of diseases, including kidney disease, in individuals of African ancestry. Evidence suggests that the risk variants, which show a recessive mode of inheritance, lead to toxic gain-of-function changes of the APOL1 protein. Disease occurrence and presentation vary, likely due to modifiers or second hits. To understand the role of the epigenetic landscape in relation to APOL1 risk variants, we performed methylation quantitative trait locus (meQTL) analysis to identify differentially methylated CpGs influenced by APOL1 risk variants in 611 African American individuals. We identified five CpGs that were significantly associated with APOL1 risk alleles in discovery and replication studies, and one CpG-APOL1 association was independent of other genomic variants. Our study highlights proximal DNA methylation alterations that may help explain the variable disease risk and clinical manifestation of APOL1 variants.
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Affiliation(s)
- Charles E Breeze
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Bridget M Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Cheryl A Winkler
- Cancer Innovation Laboratory, National Cancer Institute, National Institutes of Health, Basic Research Program, Frederick National Laboratory, Frederick, MD, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
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Tsitkov S, Valentine K, Kozareva V, Donde A, Frank A, Lei S, E Van Eyk J, Finkbeiner S, Rothstein JD, Thompson LM, Sareen D, Svendsen CN, Fraenkel E. Disease related changes in ATAC-seq of iPSC-derived motor neuron lines from ALS patients and controls. Nat Commun 2024; 15:3606. [PMID: 38697975 PMCID: PMC11066062 DOI: 10.1038/s41467-024-47758-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 04/09/2024] [Indexed: 05/05/2024] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS), like many other neurodegenerative diseases, is highly heritable, but with only a small fraction of cases explained by monogenic disease alleles. To better understand sporadic ALS, we report epigenomic profiles, as measured by ATAC-seq, of motor neuron cultures derived from a diverse group of 380 ALS patients and 80 healthy controls. We find that chromatin accessibility is heavily influenced by sex, the iPSC cell type of origin, ancestry, and the inherent variance arising from sequencing. Once these covariates are corrected for, we are able to identify ALS-specific signals in the data. Additionally, we find that the ATAC-seq data is able to predict ALS disease progression rates with similar accuracy to methods based on biomarkers and clinical status. These results suggest that iPSC-derived motor neurons recapitulate important disease-relevant epigenomic changes.
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Affiliation(s)
- Stanislav Tsitkov
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kelsey Valentine
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Velina Kozareva
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aneesh Donde
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aaron Frank
- Cedars-Sinai Biomanufacturing Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan Lei
- Cedars-Sinai Biomanufacturing Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jennifer E Van Eyk
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Steve Finkbeiner
- Center for Systems and Therapeutics, Gladstone Institutes, San Francisco, CA, USA
- Taube/Koret Center for Neurodegenerative Disease, Gladstone Institutes, San Francisco, CA, USA
- Departments of Neurology and Physiology, University of California, San Francisco, San Francisco, CA, USA
| | - Jeffrey D Rothstein
- Brain Science Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Leslie M Thompson
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Sue and Bill Gross Stem Cell Center, University of California, Irvine, CA, USA
| | - Dhruv Sareen
- Cedars-Sinai Biomanufacturing Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- The Board of Governors Regenerative Medicine Institute and Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Clive N Svendsen
- The Board of Governors Regenerative Medicine Institute and Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Ernest Fraenkel
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Mishra M, Nahlawi L, Zhong Y, De T, Yang G, Alarcon C, Perera MA. LA-GEM: imputation of gene expression with incorporation of Local Ancestry. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2024; 29:341-358. [PMID: 38160291 PMCID: PMC10764069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Gene imputation and TWAS have become a staple in the genomics medicine discovery space; helping to identify genes whose regulation effects may contribute to disease susceptibility. However, the cohorts on which these methods are built are overwhelmingly of European Ancestry. This means that the unique regulatory variation that exist in non-European populations, specifically African Ancestry populations, may not be included in the current models. Moreover, African Americans are an admixed population, with a mix of European and African segments within their genome. No gene imputation model thus far has incorporated the effect of local ancestry (LA) on gene expression imputation. As such, we created LA-GEM which was trained and tested on a cohort of 60 African American hepatocyte primary cultures. Uniquely, LA-GEM include local ancestry inference in its prediction of gene expression. We compared the performance of LA-GEM to PrediXcan trained the same dataset (with no inclusion of local ancestry) We were able to reliably predict the expression of 2559 genes (1326 in LA-GEM and 1236 in PrediXcan). Of these, 546 genes were unique to LA-GEM, including the CYP3A5 gene which is critical to drug metabolism. We conducted TWAS analysis on two African American clinical cohorts with pharmacogenomics phenotypic information to identity novel gene associations. In our IWPC warfarin cohort, we identified 17 transcriptome-wide significant hits. No gene reached are prespecified significance level in the clopidogrel cohort. We did see suggestive association with RAS3A to P2RY12 Reactivity Units (PRU), a clinical measure of response to anti-platelet therapy. This method demonstrated the need for the incorporation of LA into study in admixed populations.
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Affiliation(s)
- Mrinal Mishra
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA†Contributed equally to the work
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Gao Y, Sharma T, Cui Y. Addressing the Challenge of Biomedical Data Inequality: An Artificial Intelligence Perspective. Annu Rev Biomed Data Sci 2023; 6:153-171. [PMID: 37104653 PMCID: PMC10529864 DOI: 10.1146/annurev-biodatasci-020722-020704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
Artificial intelligence (AI) and other data-driven technologies hold great promise to transform healthcare and confer the predictive power essential to precision medicine. However, the existing biomedical data, which are a vital resource and foundation for developing medical AI models, do not reflect the diversity of the human population. The low representation in biomedical data has become a significant health risk for non-European populations, and the growing application of AI opens a new pathway for this health risk to manifest and amplify. Here we review the current status of biomedical data inequality and present a conceptual framework for understanding its impacts on machine learning. We also discuss the recent advances in algorithmic interventions for mitigating health disparities arising from biomedical data inequality. Finally, we briefly discuss the newly identified disparity in data quality among ethnic groups and its potential impacts on machine learning.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
| | - Teena Sharma
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
| | - Yan Cui
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA;
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Bian J, Zhao J, Zhao Y, Hao X, He S, Li Y, Huang L. Impact of individual factors on DNA methylation of drug metabolism genes: A systematic review. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2023; 64:401-415. [PMID: 37522536 DOI: 10.1002/em.22567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/12/2023] [Accepted: 07/26/2023] [Indexed: 08/01/2023]
Abstract
Individual differences in drug response have always existed in clinical treatment. Many non-genetic factors show non-negligible impacts on personalized medicine. Emerging studies have demonstrated epigenetic could connect non-genetic factors and individual treatment differences. We used systematic retrieval methods and reviewed studies that showed individual factors' impact on DNA methylation of drug metabolism genes. In total, 68 studies were included, and half (n = 36) were cohort studies. Six aspects of individual factors were summarized from the perspective of personalized medicine: parental exposure, environmental pollutants exposure, obesity and diet, drugs, gender and others. The most research (n = 11) focused on ABCG1 methylation. The majority of studies showed non-genetic factors could result in a significant DNA methylation alteration in drug metabolism genes, which subsequently affects the pharmacokinetic processes. However, the underlying mechanism remained unknown. Finally, some viewpoints were presented for future research.
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Affiliation(s)
- Jialu Bian
- Department of Pharmacy, People's Hospital of Peking University, Beijing, China
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Science, Peking University, Beijing, China
| | - Jinxia Zhao
- Department of Pharmacy, People's Hospital of Peking University, Beijing, China
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Science, Peking University, Beijing, China
| | - Yinyu Zhao
- Department of Pharmacy, People's Hospital of Peking University, Beijing, China
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Science, Peking University, Beijing, China
| | - Xu Hao
- Department of Pharmacy, People's Hospital of Peking University, Beijing, China
| | - Shiyu He
- Department of Pharmacy, People's Hospital of Peking University, Beijing, China
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Science, Peking University, Beijing, China
| | - Yuanyuan Li
- Department of Pharmacy, People's Hospital of Peking University, Beijing, China
| | - Lin Huang
- Department of Pharmacy, People's Hospital of Peking University, Beijing, China
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Yang G, Alarcon C, Friedman P, Gong L, Klein T, O’Brien T, Nutescu EA, Tuck M, Meltzer D, Perera MA. The Role of Global and Local Ancestry on Clopidogrel Response in African Americans. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2023; 28:221-232. [PMID: 36540979 PMCID: PMC9782753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Pharmacogenomics has long lacked dedicated studies in African Americans, resulting in a lack of indepth data in this populations. The ACCOuNT consortium has collected a cohort of 167 African American patients on steady state clopidogrel with the goal of discovering population specific variation that may contribute to the response of this anti-platelet agent. Here we analyze the role of both global and local ancestry on the clinical phenotypes of P2Y12 reaction units (PRU) and high on-treatment platelet reactivity (HTPR) in this cohort. We found that local ancestry at the TSS of three genes, IRS-1, ABCB1 and KDR were nominally associated with PRU, and local ancestry-adjusted SNP association identified variants in ITGA2 associated to increased PRU. These finding help to explain the variability in drug response seen in African Americans, especially as few studies on genes outside of CYP2C19 has been conducted in this population.
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Affiliation(s)
- Guang Yang
- Department of Pharmacology, Center for Pharmacogenomics, Feinherg School of Medicine, Northwestern University, Chicago, IL
| | - Cristina Alarcon
- Department of Pharmacology, Center for Pharmacogenomics, Feinherg School of Medicine, Northwestern University, Chicago, IL
| | - Paula Friedman
- Department of Pharmacology, Center for Pharmacogenomics, Feinherg School of Medicine, Northwestern University, Chicago, IL
| | - Li Gong
- Department of Biomedical Data Science, Stanford University, Stanford, CA
| | - Teri Klein
- Department of Biomedical Data Science and Department of Medicine, Stanford University, Stanford, CA
| | - Travis O’Brien
- Department of Pharmacology and Physiology, The George Washington University, School of Medicine and Health Sciences, Washington, DC
| | - Edith A. Nutescu
- Department of Pharmacy Practice and Center for Pharmacoepidemiology and Pharmacoeconomic Research, University of Illinois Chicago, College of Pharmacy, Chicago, IL
| | - Matthew Tuck
- Washington DC VA Medical Center, Washington, DC and The George Washington University, Washington, DC
| | - David Meltzer
- Section of Hospital Medicine, Department of Medicine, University of Chicago, Chicago, IL
| | - Minoli A Perera
- Department of Pharmacology, Center for Pharmacogenomics, Feinherg School of Medicine, Northwestern University, Chicago, IL
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Zhou G, Winn E, Nguyen D, Kasten EP, Petroff MG, Hoffmann HM. Co-alterations of circadian clock gene transcripts in human placenta in preeclampsia. Sci Rep 2022; 12:17856. [PMID: 36284122 PMCID: PMC9596722 DOI: 10.1038/s41598-022-22507-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 10/17/2022] [Indexed: 01/20/2023] Open
Abstract
Pre-eclampsia (PE) is a hypertensive condition that occurs during pregnancy and complicates up to 4% of pregnancies. PE exhibits several circadian-related characteristics, and the placenta possesses a functioning molecular clock. We examined the associations of 17 core circadian gene transcripts in placenta with PE vs. non-PE (a mixture of pregnant women with term, preterm, small-for-gestational-age, or chorioamnionitis) using two independent gene expression datasets: GSE75010-157 (80 PE vs. 77 non-PE) and GSE75010-173 (77 PE and 96 non-PE). We found a robust difference in circadian gene expression between PE and non-PE across the two datasets, where CRY1 mRNA increases and NR1D2 and PER3 transcripts decrease in PE placenta. Gene set variation analysis revealed an interplay between co-alterations of circadian clock genes and PE with altered hypoxia, cell migration/invasion, autophagy, and membrane trafficking pathways. Using human placental trophoblast HTR-8 cells, we show that CRY1/2 and NR1D1/2 regulate trophoblast migration. A subgroup study including only term samples demonstrated that CLOCK, NR1D2, and PER3 transcripts were simultaneously decreased in PE placenta, a finding supported by CLOCK protein downregulation in an independent cohort of human term PE placenta samples. These findings provide novel insights into the roles of the molecular clock in the pathogenesis of PE.
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Affiliation(s)
- Guoli Zhou
- Clinical & Translational Sciences Institute, Michigan State University, 909 Wilson Rd. Suite B500, East Lansing, MI, 48824, USA.
| | - Emily Winn
- Department of Pathobiology and Diagnostic Investigation, Michigan State University, East Lansing, MI, 48824, USA
| | - Duong Nguyen
- Department of Animal Science, Reproductive and Developmental Science Program and Neuroscience Program, College of Agriculture and Natural Resources, Michigan State University, Interdisciplinary Science and Technology Building #3010, 766 Service Road, East Lansing, MI, 48824, USA
| | - Eric P Kasten
- Clinical & Translational Sciences Institute, Michigan State University, 909 Wilson Rd. Suite B500, East Lansing, MI, 48824, USA
- Department of Radiology, Michigan State University, East Lansing, MI, 48824, USA
| | - Margaret G Petroff
- Department of Pathobiology and Diagnostic Investigation, Michigan State University, East Lansing, MI, 48824, USA
- Department of Microbiology and Molecular Genetics, College of Veterinary Medicine, Michigan State University, East Lansing, MI, 48824, USA
| | - Hanne M Hoffmann
- Department of Animal Science, Reproductive and Developmental Science Program and Neuroscience Program, College of Agriculture and Natural Resources, Michigan State University, Interdisciplinary Science and Technology Building #3010, 766 Service Road, East Lansing, MI, 48824, USA.
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Zhu T, Liu J, Beck S, Pan S, Capper D, Lechner M, Thirlwell C, Breeze CE, Teschendorff AE. A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution. Nat Methods 2022; 19:296-306. [PMID: 35277705 PMCID: PMC8916958 DOI: 10.1038/s41592-022-01412-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Accepted: 01/28/2022] [Indexed: 02/07/2023]
Abstract
Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data. This resource presents an in silico generated DNA methylation atlas that can be used for cell-type deconvolution of human tissues.
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Using machine learning to detect the differential usage of novel gene isoforms. BMC Bioinformatics 2022; 23:45. [PMID: 35042461 PMCID: PMC8764765 DOI: 10.1186/s12859-022-04576-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 01/10/2022] [Indexed: 11/24/2022] Open
Abstract
Background Differential isoform usage is an important driver of inter-individual phenotypic diversity and is linked to various diseases and traits. However, accurately detecting the differential usage of different gene transcripts between groups can be difficult, in particular in less well annotated genomes where the spectrum of transcript isoforms is largely unknown. Results We investigated whether machine learning approaches can detect differential isoform usage based purely on the distribution of reads across a gene region. We illustrate that gradient boosting and elastic net approaches can successfully identify large numbers of genes showing potential differential isoform usage between Europeans and Africans, that are enriched among relevant biological pathways and significantly overlap those identified by previous approaches. We demonstrate that diversity at the 3′ and 5′ ends of genes are primary drivers of these differences between populations. Conclusion Machine learning methods can effectively detect differential isoform usage from read fraction data, and can provide novel insights into the biological differences between groups. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04576-3.
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Singh A, Zhong Y, Nahlawi L, Park CS, De T, Alarcon C, Perera MA. Incorporation of DNA methylation into eQTL mapping in African Americans. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2021; 26:244-255. [PMID: 33691021 PMCID: PMC7958994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Epigenetics is a reversible molecular mechanism that plays a critical role in many developmental, adaptive, and disease processes. DNA methylation has been shown to regulate gene expression and the advent of high throughput technologies has made genome-wide DNA methylation analysis possible. We investigated the effect of DNA methylation on eQTL mapping (methylation-adjusted eQTLs), by incorporating DNA methylation as a SNP-based covariate in eQTL mapping in African American derived hepatocytes. We found that the addition of DNA methylation uncovered new eQTLs and eGenes. Previously discovered eQTLs were significantly altered by the addition of DNA methylation data suggesting that methylation may modulate the association of SNPs to gene expression. We found that methylation-adjusted eQTLs that were less significant compared to PC-adjusted eQTLs were enriched in lipoprotein measurements (FDR=0.0040), immune system disorders (FDR = 0.0042), and liver enzyme measurements (FDR=0.047), suggesting that DNA methylation modulates the genetic regulation of these phenotypes. Our methylation-adjusted eQTL analysis also uncovered novel SNP-gene pairs. For example, we found that the SNP, rs1332018, was associated to GSTM3. GSTM3 expression has been linked to Hepatitis B which African Americans suffer from disproportionately. Our methylation-adjusted method adds new understanding to the genetic basis of complex diseases that disproportionally affect African Americans.
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