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Hayeck TJ, Li Y, Mosbruger TL, Bradfield JP, Gleason AG, Damianos G, Shaw GTW, Duke JL, Conlin LK, Turner TN, Fernández-Viña MA, Sarmady M, Monos DS. The Impact of Patterns in Linkage Disequilibrium and Sequencing Quality on the Imprint of Balancing Selection. Genome Biol Evol 2024; 16:evae009. [PMID: 38302106 PMCID: PMC10853003 DOI: 10.1093/gbe/evae009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 02/03/2024] Open
Abstract
Regions under balancing selection are characterized by dense polymorphisms and multiple persistent haplotypes, along with other sequence complexities. Successful identification of these patterns depends on both the statistical approach and the quality of sequencing. To address this challenge, at first, a new statistical method called LD-ABF was developed, employing efficient Bayesian techniques to effectively test for balancing selection. LD-ABF demonstrated the most robust detection of selection in a variety of simulation scenarios, compared against a range of existing tests/tools (Tajima's D, HKA, Dng, BetaScan, and BalLerMix). Furthermore, the impact of the quality of sequencing on detection of balancing selection was explored, as well, using: (i) SNP genotyping and exome data, (ii) targeted high-resolution HLA genotyping (IHIW), and (iii) whole-genome long-read sequencing data (Pangenome). In the analysis of SNP genotyping and exome data, we identified known targets and 38 new selection signatures in genes not previously linked to balancing selection. To further investigate the impact of sequencing quality on detection of balancing selection, a detailed investigation of the MHC was performed with high-resolution HLA typing data. Higher quality sequencing revealed the HLA-DQ genes consistently demonstrated strong selection signatures otherwise not observed from the sparser SNP array and exome data. The HLA-DQ selection signature was also replicated in the Pangenome samples using considerably less samples but, with high-quality long-read sequence data. The improved statistical method, coupled with higher quality sequencing, leads to more consistent identification of selection and enhanced localization of variants under selection, particularly in complex regions.
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Affiliation(s)
- Tristan J Hayeck
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yang Li
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy L Mosbruger
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Adam G Gleason
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - George Damianos
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Grace Tzun-Wen Shaw
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jamie L Duke
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Laura K Conlin
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tychele N Turner
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Marcelo A Fernández-Viña
- Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
- Histocompatibility and Immunogenetics Laboratory, Stanford Blood Center, Palo Alto, CA, USA
| | - Mahdi Sarmady
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dimitri S Monos
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Hayeck TJ, Busby GB, Chun S, Lewis AC, Roberts MC, Vilhjálmsson BJ. Polygenic Risk Scores: Genomes to Risk Prediction. Clin Chem 2023; 69:hvad049. [PMID: 37232050 PMCID: PMC10681370 DOI: 10.1093/clinchem/hvad049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 04/03/2023] [Indexed: 05/27/2023]
Affiliation(s)
- Tristan J. Hayeck
- Assistant Professor, Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Assistant Professor, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - George B. Busby
- Chief Scientific Officer, Allelica Inc., New York, NY, United States
| | - Sung Chun
- Scientist, Division of Pulmonary Medicine, Boston Children’s Hospital, Boston, MA, United States
- Instructor of Pediatrics, Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Anna C.F. Lewis
- Research Associate, Edmond and Lily Safra Center for Ethics, Harvard University, and Division of Genetics, Brigham and Women’s Hospital, Boston, MA, United States
| | - Megan C. Roberts
- Assistant Professor and Director Implementation Science, in Precision Health & Society, Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, United States
| | - Bjarni J. Vilhjálmsson
- Professor, National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
- Professor, Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
- Professor, Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, United States
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Margolis DJ, Duke JL, Mitra N, Berna RA, Hoffstad OJ, Wasserman JR, Dinou A, Damianos G, Kotsopoulou I, Tairis N, Ferriola DA, Mosbruger TL, Hayeck TJ, Yan AC, Monos DS. A combination of HLA-DP α and β chain polymorphisms paired with a SNP in the DPB1 3' UTR region, denoting expression levels, are associated with atopic dermatitis. Front Genet 2023; 14:1004138. [PMID: 36911412 PMCID: PMC9995861 DOI: 10.3389/fgene.2023.1004138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/13/2023] [Indexed: 02/25/2023] Open
Abstract
Introduction: Components of the immune response have previously been associated with the pathophysiology of atopic dermatitis (AD), specifically the Human Leukocyte Antigen (HLA) Class II region via genome-wide association studies, however the exact elements have not been identified. Methods: This study examines the genetic variation of HLA Class II genes using next generation sequencing (NGS) and evaluates the resultant amino acids, with particular attention on binding site residues, for associations with AD. The Genetics of AD cohort was used to evaluate HLA Class II allelic variation on 464 subjects with AD and 384 controls. Results: Statistically significant associations with HLA-DP α and β alleles and specific amino acids were found, some conferring susceptibility to AD and others with a protective effect. Evaluation of polymorphic residues in DP binding pockets revealed the critical role of P1 and P6 (P1: α31M + (β84G or β84V) [protection]; α31Q + β84D [susceptibility] and P6: α11A + β11G [protection]) and were replicated with a national cohort of children consisting of 424 AD subjects. Independently, AD susceptibility-associated residues were associated with the G polymorphism of SNP rs9277534 in the 3' UTR of the HLA-DPB1 gene, denoting higher expression of these HLA-DP alleles, while protection-associated residues were associated with the A polymorphism, denoting lower expression. Discussion: These findings lay the foundation for evaluating non-self-antigens suspected to be associated with AD as they potentially interact with particular HLA Class II subcomponents, forming a complex involved in the pathophysiology of AD. It is possible that a combination of structural HLA-DP components and levels of expression of these components contribute to AD pathophysiology.
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Affiliation(s)
- David J. Margolis
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jamie L. Duke
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Nandita Mitra
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Ronald A. Berna
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Ole J. Hoffstad
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jenna R. Wasserman
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Amalia Dinou
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Georgios Damianos
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Ioanna Kotsopoulou
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Nikolaos Tairis
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Deborah A. Ferriola
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Timothy L. Mosbruger
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Tristan J. Hayeck
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman Schools of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Albert C. Yan
- Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Section of Dermatology, Division of General Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Dimitri S. Monos
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Pathology and Laboratory Medicine, Perelman Schools of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Hayeck TJ, Stong N, Baugh E, Dhindsa R, Turner TN, Malakar A, Mosbruger TL, Shaw GTW, Duan Y, Ionita-Laza I, Goldstein D, Allen AS. Ancestry adjustment improves genome-wide estimates of regional intolerance. Genetics 2022; 221:iyac050. [PMID: 35385101 PMCID: PMC9157129 DOI: 10.1093/genetics/iyac050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 02/24/2022] [Indexed: 11/12/2022] Open
Abstract
Genomic regions subject to purifying selection are more likely to carry disease-causing mutations than regions not under selection. Cross species conservation is often used to identify such regions but with limited resolution to detect selection on short evolutionary timescales such as that occurring in only one species. In contrast, genetic intolerance looks for depletion of variation relative to expectation within a species, allowing species-specific features to be identified. When estimating the intolerance of noncoding sequence, methods strongly leverage variant frequency distributions. As the expected distributions depend on ancestry, if not properly controlled for, ancestral population source may obfuscate signals of selection. We demonstrate that properly incorporating ancestry in intolerance estimation greatly improved variant classification. We provide a genome-wide intolerance map that is conditional on ancestry and likely to be particularly valuable for variant prioritization.
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Affiliation(s)
- Tristan J Hayeck
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nicholas Stong
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY 10032, USA
| | - Evan Baugh
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY 10032, USA
| | - Ryan Dhindsa
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY 10032, USA
| | - Tychele N Turner
- Department of Genetics, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Ayan Malakar
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY 10032, USA
| | - Timothy L Mosbruger
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Grace Tzun-Wen Shaw
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Yuncheng Duan
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA
| | | | - David Goldstein
- Institute for Genomic Medicine, Columbia University Medical Center, New York, NY 10032, USA
| | - Andrew S Allen
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27710, USA
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Padhi EM, Hayeck TJ, Cheng Z, Chatterjee S, Mannion BJ, Byrska-Bishop M, Willems M, Pinson L, Redon S, Benech C, Uguen K, Audebert-Bellanger S, Le Marechal C, Férec C, Efthymiou S, Rahman F, Maqbool S, Maroofian R, Houlden H, Musunuri R, Narzisi G, Abhyankar A, Hunter RD, Akiyama J, Fries LE, Ng JK, Mehinovic E, Stong N, Allen AS, Dickel DE, Bernier RA, Gorkin DU, Pennacchio LA, Zody MC, Turner TN. Coding and noncoding variants in EBF3 are involved in HADDS and simplex autism. Hum Genomics 2021; 15:44. [PMID: 34256850 PMCID: PMC8278787 DOI: 10.1186/s40246-021-00342-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 06/17/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Previous research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of protein-coding (coding) de novo variants (DNVs) within specific genes. The role of de novo noncoding variation has been observable as a general increase in genetic burden but has yet to be resolved to individual functional elements. In this study, we assessed whole-genome sequencing data in 2671 families with autism (discovery cohort of 516 families, replication cohort of 2155 families). We focused on DNVs in enhancers with characterized in vivo activity in the brain and identified an excess of DNVs in an enhancer named hs737. RESULTS We adapted the fitDNM statistical model to work in noncoding regions and tested enhancers for excess of DNVs in families with autism. We found only one enhancer (hs737) with nominal significance in the discovery (p = 0.0172), replication (p = 2.5 × 10-3), and combined dataset (p = 1.1 × 10-4). Each individual with a DNV in hs737 had shared phenotypes including being male, intact cognitive function, and hypotonia or motor delay. Our in vitro assessment of the DNVs showed they all reduce enhancer activity in a neuronal cell line. By epigenomic analyses, we found that hs737 is brain-specific and targets the transcription factor gene EBF3 in human fetal brain. EBF3 is genome-wide significant for coding DNVs in NDDs (missense p = 8.12 × 10-35, loss-of-function p = 2.26 × 10-13) and is widely expressed in the body. Through characterization of promoters bound by EBF3 in neuronal cells, we saw enrichment for binding to NDD genes (p = 7.43 × 10-6, OR = 1.87) involved in gene regulation. Individuals with coding DNVs have greater phenotypic severity (hypotonia, ataxia, and delayed development syndrome [HADDS]) in comparison to individuals with noncoding DNVs that have autism and hypotonia. CONCLUSIONS In this study, we identify DNVs in the hs737 enhancer in individuals with autism. Through multiple approaches, we find hs737 targets the gene EBF3 that is genome-wide significant in NDDs. By assessment of noncoding variation and the genes they affect, we are beginning to understand their impact on gene regulatory networks in NDDs.
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Affiliation(s)
- Evin M Padhi
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA
| | - Tristan J Hayeck
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Zhang Cheng
- Center for Epigenomics, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Sumantra Chatterjee
- Center for Human Genetics and Genomics, NYU School of Medicine, New York, NY, 10016, USA
| | - Brandon J Mannion
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Marjolaine Willems
- University of Montpellier, département de Génétique, maladies rares médecine personnalisée, U 1298, CHU Montpellier, University of Montpellier, Montpellier, France
| | - Lucile Pinson
- University of Montpellier, département de Génétique, maladies rares médecine personnalisée, U 1298, CHU Montpellier, University of Montpellier, Montpellier, France
| | - Sylvia Redon
- CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
| | - Caroline Benech
- CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
| | - Kevin Uguen
- CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
| | | | - Cédric Le Marechal
- CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
| | - Claude Férec
- CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
| | - Stephanie Efthymiou
- Department of Neuromuscular Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Fatima Rahman
- Development and Behavioral Pediatrics Department, Institute of Child Health and Children Hospital, Lahore, Pakistan
| | - Shazia Maqbool
- Department of Neuromuscular Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- Development and Behavioral Pediatrics Department, Institute of Child Health and Children Hospital, Lahore, Pakistan
| | - Reza Maroofian
- Department of Neuromuscular Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Henry Houlden
- Department of Neuromuscular Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | | | | | | | - Riana D Hunter
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jennifer Akiyama
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Lauren E Fries
- Center for Human Genetics and Genomics, NYU School of Medicine, New York, NY, 10016, USA
| | - Jeffrey K Ng
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA
| | - Elvisa Mehinovic
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA
| | - Nick Stong
- Institute for Genomic Medicine, Columbia University, New York, NY, 10027, USA
| | - Andrew S Allen
- Center for Statistical Genetics and Genomics, Duke University, Durham, NC, 27708, USA
- Division of Integrative Genomics, Duke University, Durham, NC, 27708, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, 27708, USA
| | - Diane E Dickel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Raphael A Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, 98195, USA
| | - David U Gorkin
- Center for Epigenomics, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- Department of Biology, Emory University, Atlanta, GA, 30322, USA
| | - Len A Pennacchio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA, 94598, USA
| | | | - Tychele N Turner
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA.
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Shieh M, Hayeck TJ, Dinh A, Duke JL, Chitnis N, Mosbruger T, Morlen RP, Ferriola D, Kneib C, Hu T, Huang Y, Monos DS. Complex Linkage Disequilibrium Effects in HLA-DPB1 Expression and Molecular Mismatch Analyses of Transplantation Outcomes. Transplantation 2021; 105:637-647. [PMID: 32301906 PMCID: PMC8628253 DOI: 10.1097/tp.0000000000003272] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND HLA molecular mismatch (MM) is a risk factor for de novo donor-specific antibody (dnDSA) development in solid organ transplantation. HLA expression differences have also been associated with adverse outcomes in hematopoietic cell transplantation. We sought to study both MM and expression in assessing dnDSA risk. METHODS One hundred three HLA-DP-mismatched solid organ transplantation pairs were retrospectively analyzed. MM was computed using amino acids (aa), eplets, and, supplementarily, Grantham/Epstein scores. DPB1 alleles were classified as rs9277534-A (low-expression) or rs9277534-G (high-expression) linked. To determine the associations between risk factors and dnDSA, logistic regression, linkage disequilibrium (LD), and population-based analyses were performed. RESULTS A high-risk AA:GX (recipient:donor) expression combination (X = A or G) demonstrated strong association with HLA-DP dnDSA (P = 0.001). MM was also associated with HLA-DP dnDSA when evaluated by itself (eplet P = 0.007, aa P = 0.003, Grantham P = 0.005, Epstein P = 0.004). When attempting to determine the relative individual effects of the risk factors in multivariable analysis, only AA:GX expression status retained a strong association (relative risk = 18.6, P = 0.007 with eplet; relative risk = 15.8, P = 0.02 with aa), while MM was no longer significant (eplet P = 0.56, aa P = 0.51). Importantly, these risk factors are correlated, due to LD between the expression-tagging single-nucleotide polymorphism and polymorphisms along HLA-DPB1. CONCLUSIONS The MM and expression risk factors each appear to be strong predictors of HLA-DP dnDSA and to possess clinical utility; however, these two risk factors are closely correlated. These metrics may represent distinct ways of characterizing a common overlapping dnDSA risk profile, but they are not independent. Further, we demonstrate the importance and detailed implications of LD effects in dnDSA risk assessment and possibly transplantation overall.
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Affiliation(s)
- Mengkai Shieh
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Tristan J. Hayeck
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anh Dinh
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jamie L. Duke
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Nilesh Chitnis
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Timothy Mosbruger
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Ryan P. Morlen
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Deborah Ferriola
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Carolina Kneib
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Taishan Hu
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Yanping Huang
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
| | - Dimitri S. Monos
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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7
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Hayeck TJ, Stong N, Wolock CJ, Copeland B, Kamalakaran S, Goldstein DB, Allen AS. Improved Pathogenic Variant Localization via a Hierarchical Model of Sub-regional Intolerance. Am J Hum Genet 2019; 104:299-309. [PMID: 30686509 DOI: 10.1016/j.ajhg.2018.12.020] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 12/21/2018] [Indexed: 02/04/2023] Open
Abstract
Different parts of a gene can be of differential importance to development and health. This regional heterogeneity is also apparent in the distribution of disease-associated mutations, which often cluster in particular regions of disease-associated genes. The ability to precisely estimate functionally important sub-regions of genes will be key in correctly deciphering relationships between genetic variation and disease. Previous methods have had some success using standing human variation to characterize this variability in importance by measuring sub-regional intolerance, i.e., the depletion in functional variation from expectation within a given region of a gene. However, the ability to precisely estimate local intolerance was restricted by the fact that only information within a given sub-region is used, leading to instability in local estimates, especially for small regions. We show that borrowing information across regions using a Bayesian hierarchical model stabilizes estimates, leading to lower variability and improved predictive utility. Specifically, our approach more effectively identifies regions enriched for ClinVar pathogenic variants. We also identify significant correlations between sub-region intolerance and the distribution of pathogenic variation in disease-associated genes, with AUCs for classifying de novo missense variants in Online Mendelian Inheritance in Man (OMIM) genes of up to 0.86 using exonic sub-regions and 0.91 using sub-regions defined by protein domains. This result immediately suggests that considering the intolerance of regions in which variants are found may improve diagnostic interpretation. We also illustrate the utility of integrating regional intolerance into gene-level disease association tests with a study of known disease-associated genes for epileptic encephalopathy.
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Hayeck TJ, Loh PR, Pollack S, Gusev A, Patterson N, Zaitlen NA, Price AL. Mixed Model Association with Family-Biased Case-Control Ascertainment. Am J Hum Genet 2017; 100:31-39. [PMID: 28017371 DOI: 10.1016/j.ajhg.2016.11.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Accepted: 11/08/2016] [Indexed: 01/06/2023] Open
Abstract
Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average χ2 = 1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average χ2 = 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples.
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Kong CY, Nattinger KJ, Hayeck TJ, Omer ZB, Wang YC, Spechler SJ, McMahon PM, Gazelle GS, Hur C. The impact of obesity on the rise in esophageal adenocarcinoma incidence: estimates from a disease simulation model. Cancer Epidemiol Biomarkers Prev 2011; 20:2450-6. [PMID: 21930957 DOI: 10.1158/1055-9965.epi-11-0547] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The United States has experienced an alarming and unexplained increase in the incidence of esophageal adenocarcinoma (EAC) since the 1970s. A concurrent increase in obesity has led some to suggest a relationship between the two trends. We explore the extent of this relationship. METHODS Using a previously validated disease simulation model of white males in the United States, we estimated EAC incidence 1973 to 2005 given constant obesity prevalence and low population progression rates consistent with the early 1970s. Introducing only the observed, rising obesity prevalence, we calculated the incremental incidence caused by obesity. We compared these with EAC incidence data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) registry to determine obesity's contribution to the rise therein. Incidences were converted to absolute numbers of cases using U.S. population data. RESULTS Using constant obesity prevalence, we projected a total of 30,555 EAC cases cumulatively over 1973 to 2005 and 1,151 in 2005 alone. Incorporating the observed obesity trend resulted in 35,767 cumulative EACs and 1,608 in 2005. Estimates derived from SEER data showed 111,223 cumulative and 7,173 cases in 2005. We conclude that the rise in obesity accounted for 6.5% of the increase in EAC cases that occurred from 1973 to 2005 and 7.6% in the year 2005. CONCLUSION Using published OR for EAC among obese individuals, we found that only a small percentage of the rise in EAC incidence is attributable to secular trends in obesity. IMPACT Other factors, alone and in combination, should be explored as causes of the EAC epidemic.
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Affiliation(s)
- Chung Yin Kong
- Institute for Technology Assessment, Harvard Medical School, Boston, MA 02114, USA
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Abstract
Barrett's esophagus (BE) is the precursor and the biggest risk factor for esophageal adenocarcinoma (EAC), the solid cancer with the fastest rising incidence in the US and western world. Current strategies to decrease morbidity and mortality from EAC have focused on identifying and surveying patients with BE using upper endoscopy. An accurate estimate of the number of patients with BE in the population is important to inform public health policy and to prioritize resources for potential screening and management programs. However, the true prevalence of BE is difficult to ascertain because the condition frequently is symptomatically silent, and the numerous clinical studies that have analyzed BE prevalence have produced a wide range of estimates. The aim of this study was to use a computer simulation disease model of EAC to determine the estimates for BE prevalence that best align with US Surveillance Epidemiology and End Results (SEER) cancer registry data. A previously developed mathematical model of EAC was modified to perform this analysis. The model consists of six health states: normal, gastroesophageal reflux disease (GERD), BE, undetected cancer, detected cancer, and death. Published literature regarding the transition rates between these states were used to provide boundaries. During the one million computer simulations that were performed, these transition rates were systematically varied, producing differing prevalences for the numerous health states. Two filters were sequentially applied to select out superior simulations that were most consistent with clinical data. First, among these million simulations, the 1000 that best reproduced SEER cancer incidence data were selected. Next, of those 1000 best simulations, the 100 with an overall calculated BE to Detected Cancer rates closest to published estimates were selected. Finally, the prevalence of BE in the final set of best 100 simulations was analyzed. We present histogram data depicting BE prevalences for all one million simulations, the 1000 simulations that best approximate SEER data, and the final set of 100 simulations. Using the best 100 simulations, we estimate the prevalence of BE to be 5.6% (5.49-5.70%). Using our model, an estimated prevalence for BE in the general population of 5.6% (5.49-5.70%) accurately predicts incidence rates for EAC reported to the US SEER cancer registry. Future clinical studies are needed to confirm our estimate.
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Affiliation(s)
- Tristan J. Hayeck
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA USA, Gastrointestinal Unit, Massachusetts General Hospital, Boston, MA USA
| | - Chung Yin Kong
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA USA, Harvard Medical School, Boston, MA USA
| | | | - G. Scott Gazelle
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA USA, Harvard Medical School, Boston, MA USA
| | - Chin Hur
- Institute for Technology Assessment, Massachusetts General Hospital, Boston, MA USA, Gastrointestinal Unit, Massachusetts General Hospital, Boston, MA USA, Harvard Medical School, Boston, MA USA,Correspondence: Chin Hur, MD, MPH, Massachusetts General Hospital, 101 Merrimac Street, 10 Floor, Boston, MA 02114
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Hur C, Hayeck TJ, Yeh JM, Richards EM, Spechler SJ, Gazelle GS, Kong CY. Development, calibration, and validation of a U.S. white male population-based simulation model of esophageal adenocarcinoma. PLoS One 2010; 5:e9483. [PMID: 20208996 PMCID: PMC2830429 DOI: 10.1371/journal.pone.0009483] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2009] [Accepted: 02/01/2010] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND The incidence of esophageal adenocarcinoma (EAC) has risen rapidly in the U.S. and western world. The aim of the study was to begin the investigation of this rapid rise by developing, calibrating, and validating a mathematical disease simulation model of EAC using available epidemiologic data. METHODS The model represents the natural history of EAC, including the essential biologic health states from normal mucosa to detected cancer. Progression rates between health states were estimated via calibration, which identified distinct parameter sets producing model outputs that fit epidemiologic data; specifically, the prevalence of pre-cancerous lesions and EAC cancer incidence from the published literature and Surveillance, Epidemiology, and End Results (SEER) data. As an illustrative example of a clinical and policy application, the calibrated and validated model retrospectively analyzed the potential benefit of an aspirin chemoprevention program. RESULTS Model outcomes approximated calibration targets; results of the model's fit and validation are presented. Approximately 7,000 cases of EAC could have been prevented over a 30-year period if all white males started aspirin chemoprevention at age 40 in 1965. CONCLUSIONS The model serves as the foundation for future analyses to determine a cost-effective screening and management strategy to prevent EAC morbidity and mortality.
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Affiliation(s)
- Chin Hur
- Massachusetts General Hospital Institute for Technology Assessment, Boston, Massachusetts, United States of America.
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