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Carmichael J, Hicks AJ, Spitz G, Gould KR, Ponsford J. Moderators of gene-outcome associations following traumatic brain injury. Neurosci Biobehav Rev 2021; 130:107-124. [PMID: 34411558 DOI: 10.1016/j.neubiorev.2021.08.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 07/04/2021] [Accepted: 08/13/2021] [Indexed: 12/14/2022]
Abstract
The field of genomics is the principal avenue in the ongoing development of precision/personalised medicine for a variety of health conditions. However, relating genes to outcomes is notoriously complex, especially when considering that other variables can change, or moderate, gene-outcome associations. Here, we comprehensively discuss moderation of gene-outcome associations in the context of traumatic brain injury (TBI), a common, chronically debilitating, and costly neurological condition that is under complex polygenic influence. We focus our narrative review on single nucleotide polymorphisms (SNPs) of three of the most studied genes (apolipoprotein E, brain-derived neurotrophic factor, and catechol-O-methyltransferase) and on three demographic variables believed to moderate associations between these SNPs and TBI outcomes (age, biological sex, and ethnicity). We speculate on the mechanisms which may underlie these moderating effects, drawing widely from biomolecular and behavioural research (n = 175 scientific reports) within the TBI population (n = 72) and other neurological, healthy, ageing, and psychiatric populations (n = 103). We conclude with methodological recommendations for improved exploration of moderators in future genetics research in TBI and other populations.
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Affiliation(s)
- Jai Carmichael
- Monash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia.
| | - Amelia J Hicks
- Monash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
| | - Gershon Spitz
- Monash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
| | - Kate Rachel Gould
- Monash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
| | - Jennie Ponsford
- Monash-Epworth Rehabilitation Research Centre, Epworth HealthCare, Melbourne, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Australia
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152
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Liu Y, Gao Y, Fang R, Cao H, Sa J, Wang J, Liu H, Wang T, Cui Y. Identifying complex gene-gene interactions: a mixed kernel omnibus testing approach. Brief Bioinform 2021; 22:6346804. [PMID: 34373892 DOI: 10.1093/bib/bbab305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 07/01/2021] [Accepted: 07/17/2021] [Indexed: 11/12/2022] Open
Abstract
Genes do not function independently; rather, they interact with each other to fulfill their joint tasks. Identification of gene-gene interactions has been critically important in elucidating the molecular mechanisms responsible for the variation of a phenotype. Regression models are commonly used to model the interaction between two genes with a linear product term. The interaction effect of two genes can be linear or nonlinear, depending on the true nature of the data. When nonlinear interactions exist, the linear interaction model may not be able to detect such interactions; hence, it suffers from substantial power loss. While the true interaction mechanism (linear or nonlinear) is generally unknown in practice, it is critical to develop statistical methods that can be flexible to capture the underlying interaction mechanism without assuming a specific model assumption. In this study, we develop a mixed kernel function which combines both linear and Gaussian kernels with different weights to capture the linear or nonlinear interaction of two genes. Instead of optimizing the weight function, we propose a grid search strategy and use a Cauchy transformation of the P-values obtained under different weights to aggregate the P-values. We further extend the two-gene interaction model to a high-dimensional setup using a de-biased LASSO algorithm. Extensive simulation studies are conducted to verify the performance of the proposed method. Application to two case studies further demonstrates the utility of the model. Our method provides a flexible and computationally efficient tool for disentangling complex gene-gene interactions associated with complex traits.
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Affiliation(s)
- Yan Liu
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Yuzhao Gao
- School of Statistics, Shanxi University of Finance and Economics, Taiyuan, PR China
| | - Ruiling Fang
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Hongyan Cao
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Jian Sa
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Jianrong Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI, USA
| | - Hongqi Liu
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Tong Wang
- Division of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, PR China
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA
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153
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Liu D, Ban HJ, El Sergani AM, Lee MK, Hecht JT, Wehby GL, Moreno LM, Feingold E, Marazita ML, Cha S, Szabo-Rogers HL, Weinberg SM, Shaffer JR. PRICKLE1 × FOCAD Interaction Revealed by Genome-Wide vQTL Analysis of Human Facial Traits. Front Genet 2021; 12:674642. [PMID: 34434215 PMCID: PMC8381734 DOI: 10.3389/fgene.2021.674642] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/03/2021] [Indexed: 12/14/2022] Open
Abstract
The human face is a highly complex and variable structure resulting from the intricate coordination of numerous genetic and non-genetic factors. Hundreds of genomic loci impacting quantitative facial features have been identified. While these associations have been shown to influence morphology by altering the mean size and shape of facial measures, their effect on trait variance remains unclear. We conducted a genome-wide association analysis for the variance of 20 quantitative facial measurements in 2,447 European individuals and identified several suggestive variance quantitative trait loci (vQTLs). These vQTLs guided us to conduct an efficient search for gene-by-gene (G × G) interactions, which uncovered an interaction between PRICKLE1 and FOCAD affecting cranial base width. We replicated this G × G interaction signal at the locus level in an additional 5,128 Korean individuals. We used the hypomorphic Prickle1 Beetlejuice (Prickle1 Bj ) mouse line to directly test the function of Prickle1 on the cranial base and observed wider cranial bases in Prickle1 Bj/Bj . Importantly, we observed that the Prickle1 and Focadhesin proteins co-localize in murine cranial base chondrocytes, and this co-localization is abnormal in the Prickle1 Bj/Bj mutants. Taken together, our findings uncovered a novel G × G interaction effect in humans with strong support from both epidemiological and molecular studies. These results highlight the potential of studying measures of phenotypic variability in gene mapping studies of facial morphology.
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Affiliation(s)
- Dongjing Liu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Hyo-Jeong Ban
- Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Ahmed M. El Sergani
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Myoung Keun Lee
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jacqueline T. Hecht
- Department of Pediatrics, McGovern Medical Center, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - George L. Wehby
- Department of Health Management and Policy, The University of Iowa, Iowa City, IA, United States
| | - Lina M. Moreno
- Department of Orthodontics, The University of Iowa, Iowa City, IA, United States
| | - Eleanor Feingold
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Mary L. Marazita
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Psychiatry, Clinical and Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Seongwon Cha
- Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Heather L. Szabo-Rogers
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Regenerative Medicine at the McGowan Institute, University of Pittsburgh, Pittsburgh, PA, United States
- Center for Craniofacial Regeneration, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - John R. Shaffer
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
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154
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Clarke TK, Adams MJ, Howard DM, Xia C, Davies G, Hayward C, Campbell A, Padmanabhan S, Smith BH, Murray A, Porteous D, Deary IJ, McIntosh AM. Genetic and shared couple environmental contributions to smoking and alcohol use in the UK population. Mol Psychiatry 2021; 26:4344-4354. [PMID: 31767999 PMCID: PMC8550945 DOI: 10.1038/s41380-019-0607-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 11/07/2019] [Accepted: 11/13/2019] [Indexed: 11/22/2022]
Abstract
Alcohol use and smoking are leading causes of death and disability worldwide. Both genetic and environmental factors have been shown to influence individual differences in the use of these substances. In the present study we tested whether genetic factors, modelled alongside common family environment, explained phenotypic variance in alcohol use and smoking behaviour in the Generation Scotland (GS) family sample of up to 19,377 individuals. SNP and pedigree-associated effects combined explained between 18 and 41% of the variance in substance use. Shared couple effects explained a significant amount of variance across all substance use traits, particularly alcohol intake, for which 38% of the phenotypic variance was explained. We tested whether the within-couple substance use associations were due to assortative mating by testing the association between partner polygenic risk scores in 34,987 couple pairs from the UK Biobank (UKB). No significant association between partner polygenic risk scores were observed. Associations between an individual's alcohol PRS (b = 0.05, S.E. = 0.006, p < 2 × 10-16) and smoking status PRS (b = 0.05, S.E. = 0.005, p < 2 × 10-16) were found with their partner's phenotype. In support of this, G carriers of a functional ADH1B polymorphism (rs1229984), known to be associated with greater alcohol intake, were found to consume less alcohol if they had a partner who carried an A allele at this SNP. Together these results show that the shared couple environment contributes significantly to patterns of substance use. It is unclear whether this is due to shared environmental factors, assortative mating, or indirect genetic effects. Future studies would benefit from longitudinal data and larger sample sizes to assess this further.
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Affiliation(s)
- Toni-Kim Clarke
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK.
| | - Mark J Adams
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - David M Howard
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Charley Xia
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Gail Davies
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Usher Institute for Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Sandosh Padmanabhan
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Blair H Smith
- Division of Population and Health Genomics, University of Dundee, Dundee, UK
| | - Alison Murray
- The Institute of Medical Sciences, Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK
| | - David Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
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155
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Maglione A, Zuccalà M, Tosi M, Clerico M, Rolla S. Host Genetics and Gut Microbiome: Perspectives for Multiple Sclerosis. Genes (Basel) 2021; 12:1181. [PMID: 34440354 PMCID: PMC8394267 DOI: 10.3390/genes12081181] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/28/2021] [Accepted: 07/28/2021] [Indexed: 12/12/2022] Open
Abstract
As a complex disease, Multiple Sclerosis (MS)'s etiology is determined by both genetic and environmental factors. In the last decade, the gut microbiome has emerged as an important environmental factor, but its interaction with host genetics is still unknown. In this review, we focus on these dual aspects of MS pathogenesis: we describe the current knowledge on genetic factors related to MS, based on genome-wide association studies, and then illustrate the interactions between the immune system, gut microbiome and central nervous system in MS, summarizing the evidence available from Experimental Autoimmune Encephalomyelitis mouse models and studies in patients. Finally, as the understanding of influence of host genetics on the gut microbiome composition in MS is in its infancy, we explore this issue based on the evidence currently available from other autoimmune diseases that share with MS the interplay of genetic with environmental factors (Inflammatory Bowel Disease, Rheumatoid Arthritis and Systemic Lupus Erythematosus), and discuss avenues for future research.
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Affiliation(s)
- Alessandro Maglione
- Department of Clinical and Biological Sciences, University of Torino, 10100 Torino, Italy; (A.M.); (M.C.)
| | - Miriam Zuccalà
- Department of Health Sciences, Center on Autoimmune and Allergic Diseases (CAAD), Università del Piemonte Orientale, 28100 Novara, Italy; (M.Z.); (M.T.)
| | - Martina Tosi
- Department of Health Sciences, Center on Autoimmune and Allergic Diseases (CAAD), Università del Piemonte Orientale, 28100 Novara, Italy; (M.Z.); (M.T.)
| | - Marinella Clerico
- Department of Clinical and Biological Sciences, University of Torino, 10100 Torino, Italy; (A.M.); (M.C.)
| | - Simona Rolla
- Department of Clinical and Biological Sciences, University of Torino, 10100 Torino, Italy; (A.M.); (M.C.)
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156
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The genetic architecture of primary biliary cholangitis. Eur J Med Genet 2021; 64:104292. [PMID: 34303876 DOI: 10.1016/j.ejmg.2021.104292] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/03/2021] [Accepted: 07/21/2021] [Indexed: 12/12/2022]
Abstract
Primary biliary cholangitis (PBC) is a rare autoimmune disease of the liver affecting the small bile ducts. From a genetic point of view, PBC is a complex trait and several genetic and environmental factors have been called in action to explain its etiopathogenesis. Similarly to other complex traits, PBC has benefited from the introduction of genome-wide association studies (GWAS), which identified many variants predisposing or protecting toward the development of the disease. While a progressive endeavour toward the characterization of candidate loci and downstream pathways is currently ongoing, there is still a relatively large portion of heritability of PBC to be revealed. In addition, genetic variation behind progression of the disease and therapeutic response are mostly to be investigated yet. This review outlines the state-of-the-art regarding the genetic architecture of PBC and provides some hints for future investigations, focusing on the study of gene-gene interactions, the application of whole-genome sequencing techniques, and the investigation of X chromosome that can be helpful to cover the missing heritability gap in PBC.
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157
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Mieth B, Rozier A, Rodriguez JA, Höhne MMC, Görnitz N, Müller KR. DeepCOMBI: explainable artificial intelligence for the analysis and discovery in genome-wide association studies. NAR Genom Bioinform 2021; 3:lqab065. [PMID: 34296082 PMCID: PMC8291080 DOI: 10.1093/nargab/lqab065] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 05/27/2021] [Accepted: 07/08/2021] [Indexed: 02/06/2023] Open
Abstract
Deep learning has revolutionized data science in many fields by greatly improving prediction performances in comparison to conventional approaches. Recently, explainable artificial intelligence has emerged as an area of research that goes beyond pure prediction improvement by extracting knowledge from deep learning methodologies through the interpretation of their results. We investigate such explanations to explore the genetic architectures of phenotypes in genome-wide association studies. Instead of testing each position in the genome individually, the novel three-step algorithm, called DeepCOMBI, first trains a neural network for the classification of subjects into their respective phenotypes. Second, it explains the classifiers’ decisions by applying layer-wise relevance propagation as one example from the pool of explanation techniques. The resulting importance scores are eventually used to determine a subset of the most relevant locations for multiple hypothesis testing in the third step. The performance of DeepCOMBI in terms of power and precision is investigated on generated datasets and a 2007 study. Verification of the latter is achieved by validating all findings with independent studies published up until 2020. DeepCOMBI is shown to outperform ordinary raw P-value thresholding and other baseline methods. Two novel disease associations (rs10889923 for hypertension, rs4769283 for type 1 diabetes) were identified.
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Affiliation(s)
- Bettina Mieth
- Machine Learning Group, Technische Universität Berlin, Berlin 10587, Germany
| | - Alexandre Rozier
- Machine Learning Group, Technische Universität Berlin, Berlin 10587, Germany
| | - Juan Antonio Rodriguez
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona 08003, Spain
| | - Marina M C Höhne
- Machine Learning Group, Technische Universität Berlin, Berlin 10587, Germany
| | | | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, Berlin 10587, Germany
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158
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Heng J, Heng HH. Karyotype coding: The creation and maintenance of system information for complexity and biodiversity. Biosystems 2021; 208:104476. [PMID: 34237348 DOI: 10.1016/j.biosystems.2021.104476] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/02/2021] [Accepted: 07/03/2021] [Indexed: 12/22/2022]
Abstract
The mechanism of biological information flow is of vital importance. However, traditional research surrounding the genetic code that follows the central dogma to a phenotype faces challengers, including missing heritability and two-phased evolution. Here, we propose the karyotype code, which by organizing genes along chromosomes at once preserves species genome information and provides a platform for other genetic and nongenetic information to develop and accumulate. This specific genome-level code, which exists in all living systems, is compared to the genetic code and other organic codes in the context of information management, leading to the concept of hierarchical biological codes and an 'extended' definition of adaptor where the adaptors of a code can be not only molecular structures but also, more commonly, biological processes. Notably, different levels of a biosystem have their own mechanisms of information management, and gene-coded parts inheritance preserves "parts information" while karyotype-coded system inheritance preserves the "system information" which organizes parts information. The karyotype code prompts many questions regarding the flow of biological information, including the distinction between information creation, maintenance, modification, and usage, along with differences between living and non-living systems. How do biological systems exist, reproduce, and self-evolve for increased complexity and diversity? Inheritance is mediated by organic codes which function as informational tools to organize chemical reactions, create new information, and preserve frozen accidents, transforming historical miracles into biological routines.
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Affiliation(s)
- Julie Heng
- Harvard College, 86 Brattle Street Cambridge, MA, 02138, USA
| | - Henry H Heng
- Molecular Medicine and Genomics, Wayne State University School of Medicine, Detroit, MI, 48201, USA; Department of Pathology, Wayne State University School of Medicine, Detroit, MI, 48201, USA.
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159
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Corrado A, Aceto R, Silvestri R, Dell'Anno I, Ricci B, Miglietta S, Romei C, Giovannoni R, Poliseno L, Evangelista M, Vitiello M, Cipollini M, Garritano S, Giusti L, Zallocco L, Elisei R, Landi S, Gemignani F. Pro64His (rs4644) Polymorphism Within Galectin-3 Is a Risk Factor of Differentiated Thyroid Carcinoma and Affects the Transcriptome of Thyrocytes Engineered via CRISPR/Cas9 System. Thyroid 2021; 31:1056-1066. [PMID: 33308024 DOI: 10.1089/thy.2020.0366] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background: Galectin-3 (LGALS3) is an important glycoprotein involved in the malignant transformation of thyrocytes acting in the extracellular matrix, cytoplasm, and nucleus where it regulates TTF-1 and TCF4 transcription factors. Within LGALS3 gene, a common single-nucleotide polymorphism (SNP) (c.191C>A, p.Pro64His; rs4644) encoding for the variant Proline to Histidine at codon 64 has been extensively studied. However, data on rs4644 in the context of thyroid cancer are lacking. Thus, the aim of the present work was to evaluate the role of the rs4644 SNP as risk factor for differentiated thyroid cancer (DTC) and to determine the effect on the transcriptome in thyrocytes. Methods: A case/control association study in 1223 controls and 1142 unrelated consecutive DTC patients was carried out to evaluate the association between rs4644-P64H and the risk of DTC. We used the nonmalignant cell line Nthy-Ori (rs4644-C/A) and the CRISPR/Cas9 technique to generate isogenic cells carrying either the rs4644-A/A or rs4644-C/C homozygosis. Then, the transcriptome of the derivative and unmodified parental cells was analyzed by RNA-seq. Genes differentially expressed were validated by quantitative reverse transcription PCR and further tested in the parental Nthy-Ori cells after LGALS3 gene silencing, to investigate whether the expression of target genes was dependent on galectin-3 levels. Results: rs4644 AA genotype was associated with a reduced risk of DTC (compared with CC, ORadj = 0.66; 95% confidence interval = 0.46-0.93; Pass = 0.02). We found that rs4644 affects galectin-3 as a transcriptional coregulator. Among 34 genes affected by rs4644, HES1, HSPA6, SPC24, and NHS were of particular interest since their expression was rs4644-dependent (CC>AA for the first and AA>CC for the others), also in 574 thyroid tissues of Genotype-Tissue Expression (GTEx) biobank. Moreover, the expression of these genes was regulated by LGALS3-silencing. Using the proximity ligation assay in Nthy-Ori cells, we found that the TTF-1 interaction was genotype dependent. Conclusions: Our data show that in thyroid, rs4644 is a trans-expression quantitative trait locus that can modify the transcriptional expression of downstream genes, through the modulation of TTF-1.
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Affiliation(s)
- Alda Corrado
- Genetic Unit, Department of Biology, University of Pisa, Pisa, Italy
| | - Romina Aceto
- Genetic Unit, Department of Biology, University of Pisa, Pisa, Italy
- Humanitas Clinical and Research Centre-IRCCS, Milan, Italy
| | - Roberto Silvestri
- Genetic Unit, Department of Biology, University of Pisa, Pisa, Italy
| | - Irene Dell'Anno
- Genetic Unit, Department of Biology, University of Pisa, Pisa, Italy
| | - Benedetta Ricci
- Fondazione I.R.C.C.S., Istituto Neurologico Carlo Besta, Milan, Italy
| | - Simona Miglietta
- San Raffaele Telethon Institute for Gene Therapy (SR-Tiget), IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Cristina Romei
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | | - Laura Poliseno
- Institute of Clinical Physiology (IFC), CNR, Pisa, Italy
| | | | | | - Monica Cipollini
- Genetic Unit, Department of Biology, University of Pisa, Pisa, Italy
| | - Sonia Garritano
- Centre for Integrative Biology, University of Trento, Trento, Italy
| | - Laura Giusti
- School of Pharmacy, University of Camerino, Camerino, Italy
| | - Lorenzo Zallocco
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Siena, Italy
- Department of Pharmacy, University of Pisa, Pisa, Italy
| | - Rossella Elisei
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Stefano Landi
- Genetic Unit, Department of Biology, University of Pisa, Pisa, Italy
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160
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Mahmood A, Shah AA, Umair M, Wu Y, Khan A. Recalling the pathology of Parkinson's disease; lacking exact figure of prevalence and genetic evidence in Asia with an alarming outcome: A time to step-up. Clin Genet 2021; 100:659-677. [PMID: 34195994 DOI: 10.1111/cge.14019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/14/2021] [Accepted: 06/23/2021] [Indexed: 11/26/2022]
Abstract
Parkinson's disease (PD) is the second most common and progressive neurodegenerative disease globally, with major symptoms like bradykinesia, impaired posture, and tremor. Several genetic and environmental factors have been identified but elucidating the main factors have been challenging due to the disease's complex nature. Diagnosis, prognosis, and management of such diseases are challenging and require effective targeted attention in developing countries. Recently, PD is growing rapidly in many crowded Asian countries as an alarming threat with inadequate knowledge of its prevalence, genetic architecture, and geographic distribution. This study gave an in-depth overview of the prevalence, incidence and genomic/genetics studies published so far in the Asian population. To the best of our knowledge, PD has increased significantly in several Asian countries, including China, South Korea, Japan, Thailand, and Israel over the past few years, requiring a greater level of care and attention. Genetic screening of families with PD at national levels and establishing an official database of PD cases are essential to get a comprehensive and conclusive view of the exact prevalence and genetic diversity of PD in the Asian population to properly manage and treat the disease.
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Affiliation(s)
- Arif Mahmood
- Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China.,Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Abid Ali Shah
- Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Muhammad Umair
- Medical Genomics Research Department, King Abdullah International Medical Research Center (KAIMRC), King Saud bin Abdul-Aziz University for Health Sciences, Ministry of National Guard-Health Affairs (MNGHA), Riyadh, Saudi Arabia
| | - Yiming Wu
- Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Amjad Khan
- Faculty of Science, Department of Biological Sciences, University of Lakki Marwat, Lakki Marwat, Pakistan
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161
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Abstract
Disease classification, or nosology, was historically driven by careful examination of clinical features of patients. As technologies to measure and understand human phenotypes advanced, so too did classifications of disease, and the advent of genetic data has led to a surge in genetic subtyping in the past decades. Although the fundamental process of refining disease definitions and subtypes is shared across diverse fields, each field is driven by its own goals and technological expertise, leading to inconsistent and conflicting definitions of disease subtypes. Here, we review several classical and recent subtypes and subtyping approaches and provide concrete definitions to delineate subtypes. In particular, we focus on subtypes with distinct causal disease biology, which are of primary interest to scientists, and subtypes with pragmatic medical benefits, which are of primary interest to physicians. We propose genetic heterogeneity as a gold standard for establishing biologically distinct subtypes of complex polygenic disease. We focus especially on methods to find and validate genetic subtypes, emphasizing common pitfalls and how to avoid them.
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Affiliation(s)
- Andy Dahl
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois 60637, USA; .,Department of Neurology, University of California, Los Angeles, California 90024, USA; .,Department of Computational Medicine, University of California, Los Angeles, California 90095, USA
| | - Noah Zaitlen
- Department of Neurology, University of California, Los Angeles, California 90024, USA; .,Department of Computational Medicine, University of California, Los Angeles, California 90095, USA
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162
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Pedruzzi G, Rouzine IM. An evolution-based high-fidelity method of epistasis measurement: Theory and application to influenza. PLoS Pathog 2021; 17:e1009669. [PMID: 34153082 PMCID: PMC8248644 DOI: 10.1371/journal.ppat.1009669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 07/01/2021] [Accepted: 05/25/2021] [Indexed: 12/18/2022] Open
Abstract
Linkage effects in a multi-locus population strongly influence its evolution. The models based on the traveling wave approach enable us to predict the average speed of evolution and the statistics of phylogeny. However, predicting statistically the evolution of specific sites and pairs of sites in the multi-locus context remains a mathematical challenge. In particular, the effects of epistasis, the interaction of gene regions contributing to phenotype, is difficult to predict theoretically and detect experimentally in sequence data. A large number of false-positive interactions arises from stochastic linkage effects and indirect interactions, which mask true epistatic interactions. Here we develop a proof-of-principle method to filter out false-positive interactions. We start by demonstrating that the averaging of haplotype frequencies over multiple independent populations is necessary but not sufficient for epistatic detection, because it still leaves high numbers of false-positive interactions. To compensate for the residual stochastic noise, we develop a three-way haplotype method isolating true interactions. The fidelity of the method is confirmed analytically and on simulated genetic sequences evolved with a known epistatic network. The method is then applied to a large sequence database of neurominidase protein of influenza A H1N1 obtained from various geographic locations to infer the epistatic network responsible for the difference between the pre-pandemic virus and the pandemic strain of 2009. These results present a simple and reliable technique to measure epistatic interactions of any sign from sequence data. Interactions between genomic sites create a fitness landscape. The knowledge of topology and strength of interactions is vital for predicting the escape of viruses from drugs and immune response and their passing through fitness valleys. Many efforts have been invested into measuring these interactions from DNA sequence sets. Unfortunately, reproducibility of the results remains low due partly to a very small fraction of interaction pairs and partly to stochastic linkage noise masking true interactions. Here we propose a method to separate stochastic linkage and indirect interactions from epistatic interactions and apply it to influenza virus sequence data.
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Affiliation(s)
- Gabriele Pedruzzi
- Sorbonne Université, Institute de Biologie Paris-Seine, Laboratoire de Biologie Computationelle et Quantitative LCQB, Paris, France
| | - Igor M. Rouzine
- Sorbonne Université, Institute de Biologie Paris-Seine, Laboratoire de Biologie Computationelle et Quantitative LCQB, Paris, France
- * E-mail:
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163
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Moldovan A, Waldman YY, Brandes N, Linial M. Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes. J Pers Med 2021; 11:582. [PMID: 34205563 PMCID: PMC8233887 DOI: 10.3390/jpm11060582] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/10/2021] [Accepted: 06/17/2021] [Indexed: 12/25/2022] Open
Abstract
One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight (n = 172,239) and comparative body size at age ten (n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.
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Affiliation(s)
- Avigail Moldovan
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel;
| | | | - Nadav Brandes
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel;
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel;
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164
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Byun J, Han Y, Ostrom QT, Edelson J, Walsh KM, Pettit RW, Bondy ML, Hung RJ, McKay JD, Amos CI. The Shared Genetic Architectures Between Lung Cancer and Multiple Polygenic Phenotypes in Genome-Wide Association Studies. Cancer Epidemiol Biomarkers Prev 2021; 30:1156-1164. [PMID: 33771847 PMCID: PMC9108090 DOI: 10.1158/1055-9965.epi-20-1635] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 01/19/2021] [Accepted: 03/23/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Prior genome-wide association studies have identified numerous lung cancer risk loci and reveal substantial etiologic heterogeneity across histologic subtypes. Analyzing the shared genetic architecture underlying variation in complex traits can elucidate common genetic etiologies across phenotypes. Exploring pairwise genetic correlations between lung cancer and other polygenic traits can reveal the common genetic etiology of correlated phenotypes. METHODS Using cross-trait linkage disequilibrium score regression, we estimated the pairwise genetic correlation and heritability between lung cancer and multiple traits using publicly available summary statistics. Identified genetic relationships were also examined after excluding genomic regions known to be associated with smoking behaviors, a major risk factor for lung cancer. RESULTS We observed several traits showing moderate single nucleotide polymorphism-based heritability and significant genetic correlations with lung cancer. We observed highly significant correlations between the genetic architectures of lung cancer and emphysema/chronic bronchitis across all histologic subtypes, as well as among lung cancer occurring among smokers. Our analyses revealed highly significant positive correlations between lung cancer and paternal history of lung cancer. We also observed a strong negative correlation with parental longevity. We observed consistent directions in genetic patterns after excluding genomic regions associated with smoking behaviors. CONCLUSIONS This study identifies numerous phenotypic traits that share genomic architecture with lung carcinogenesis and are not fully accounted for by known smoking-associated genomic loci. IMPACT These findings provide new insights into the etiology of lung cancer by identifying traits that are genetically correlated with increased risk of lung cancer.
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Affiliation(s)
- Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Quinn T Ostrom
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Jacob Edelson
- Department of Medicine, Center for Biomedical Informatics Research, Stanford University, Stanford, California
| | - Kyle M Walsh
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina
| | - Rowland W Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
| | - Melissa L Bondy
- Department of Epidemiology and Population Health, School of Medicine, Stanford University, Stanford, California
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Canada
| | - James D McKay
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
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165
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Costanzo M, Hou J, Messier V, Nelson J, Rahman M, VanderSluis B, Wang W, Pons C, Ross C, Ušaj M, San Luis BJ, Shuteriqi E, Koch EN, Aloy P, Myers CL, Boone C, Andrews B. Environmental robustness of the global yeast genetic interaction network. Science 2021; 372:372/6542/eabf8424. [PMID: 33958448 DOI: 10.1126/science.abf8424] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 03/30/2021] [Indexed: 12/18/2022]
Abstract
Phenotypes associated with genetic variants can be altered by interactions with other genetic variants (GxG), with the environment (GxE), or both (GxGxE). Yeast genetic interactions have been mapped on a global scale, but the environmental influence on the plasticity of genetic networks has not been examined systematically. To assess environmental rewiring of genetic networks, we examined 14 diverse conditions and scored 30,000 functionally representative yeast gene pairs for dynamic, differential interactions. Different conditions revealed novel differential interactions, which often uncovered functional connections between distantly related gene pairs. However, the majority of observed genetic interactions remained unchanged in different conditions, suggesting that the global yeast genetic interaction network is robust to environmental perturbation and captures the fundamental functional architecture of a eukaryotic cell.
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Affiliation(s)
- Michael Costanzo
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Jing Hou
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Vincent Messier
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Justin Nelson
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.,Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Mahfuzur Rahman
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA.,Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Benjamin VanderSluis
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Wen Wang
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Carles Pons
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute for Science and Technology, Barcelona, Spain
| | - Catherine Ross
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Matej Ušaj
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Bryan-Joseph San Luis
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Emira Shuteriqi
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
| | - Elizabeth N Koch
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute for Science and Technology, Barcelona, Spain.,Institució Catalana de Recerca I Estudis Avaçats (ICREA), Barcelona, Spain
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA. .,Program in Biomedical Informatics and Computational Biology, University of Minnesota-Twin Cities, 200 Union Street, Minneapolis, MN 55455, USA
| | - Charles Boone
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada. .,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada.,RIKEN Center for Sustainable Resource Science, Wako, Saitama, Japan
| | - Brenda Andrews
- The Donnelly Centre, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada. .,Department of Molecular Genetics, University of Toronto, 160 College Street, Toronto, ON M5S 3E1, Canada
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166
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Heng J, Heng HH. Two-phased evolution: Genome chaos-mediated information creation and maintenance. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 165:29-42. [PMID: 33992670 DOI: 10.1016/j.pbiomolbio.2021.04.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 04/16/2021] [Accepted: 04/22/2021] [Indexed: 12/11/2022]
Abstract
Cancer is traditionally labeled a "cellular growth problem." However, it is fundamentally an issue of macroevolution where new systems emerge from tissue by breaking various constraints. To study this process, we used experimental platforms to "watch evolution in action" by comparing the profiles of karyotypes, transcriptomes, and cellular phenotypes longitudinally before, during, and after key phase transitions. This effort, alongside critical rethinking of current gene-based genomic and evolutionary theory, led to the development of the Genome Architecture Theory. Following a brief historical review, we present four case studies and their takeaways to describe the pattern of genome-based cancer evolution. Our discoveries include 1. The importance of non-clonal chromosome aberrations or NCCAs; 2. Two-phased cancer evolution, comprising a punctuated phase and a gradual phase, dominated by karyotype changes and gene mutation/epigenetic alterations, respectively; 3. How the karyotype codes system inheritance, which organizes gene interactions and provides the genomic basis for physiological regulatory networks; and 4. Stress-induced genome chaos, which creates genomic information by reorganizing chromosomes for macroevolution. Together, these case studies redefine the relationship between cellular macro- and microevolution: macroevolution does not equal microevolution + time. Furthermore, we incorporate genome chaos and gene mutation in a general model: genome reorganization creates new karyotype coding, then diverse cancer gene mutations can promote the dominance of tumor cell populations. Finally, we call for validation of the Genome Architecture Theory of cancer and organismal evolution, as well as the systematic study of genomic information flow in evolutionary processes.
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Affiliation(s)
- Julie Heng
- Harvard College, 86 Brattle Street Cambridge, MA, 02138, USA
| | - Henry H Heng
- Molecular Medicine and Genomics, Wayne State University School of Medicine, Detroit, MI, 48201, USA; Department of Pathology, Wayne State University School of Medicine, Detroit, MI, 48201, USA.
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167
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Hivert V, Sidorenko J, Rohart F, Goddard ME, Yang J, Wray NR, Yengo L, Visscher PM. Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals. Am J Hum Genet 2021; 108:786-798. [PMID: 33811805 DOI: 10.1016/j.ajhg.2021.02.014] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 02/22/2021] [Indexed: 12/20/2022] Open
Abstract
Non-additive genetic variance for complex traits is traditionally estimated from data on relatives. It is notoriously difficult to estimate without bias in non-laboratory species, including humans, because of possible confounding with environmental covariance among relatives. In principle, non-additive variance attributable to common DNA variants can be estimated from a random sample of unrelated individuals with genome-wide SNP data. Here, we jointly estimate the proportion of variance explained by additive (hSNP2), dominance (δSNP2) and additive-by-additive (ηSNP2) genetic variance in a single analysis model. We first show by simulations that our model leads to unbiased estimates and provide a new theory to predict standard errors estimated using either least-squares or maximum likelihood. We then apply the model to 70 complex traits using 254,679 unrelated individuals from the UK Biobank and 1.1 M genotyped and imputed SNPs. We found strong evidence for additive variance (average across traits h¯SNP2=0.208). In contrast, the average estimate of δ¯SNP2 across traits was 0.001, implying negligible dominance variance at causal variants tagged by common SNPs. The average epistatic variance η¯SNP2 across the traits was 0.055, not significantly different from zero because of the large sampling variance. Our results provide new evidence that genetic variance for complex traits is predominantly additive and that sample sizes of many millions of unrelated individuals are needed to estimate epistatic variance with sufficient precision.
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Affiliation(s)
- Valentin Hivert
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Florian Rohart
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Michael E Goddard
- Agriculture Victoria Research, AgriBio, 5 Ring Road, Bundoora, VIC 3083, Australia; Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC 3010, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia.
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168
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Wang H, Ye M, Fu Y, Dong A, Zhang M, Feng L, Zhu X, Bo W, Jiang L, Griffin CH, Liang D, Wu R. Modeling genome-wide by environment interactions through omnigenic interactome networks. Cell Rep 2021; 35:109114. [PMID: 33979624 DOI: 10.1016/j.celrep.2021.109114] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/11/2021] [Accepted: 04/21/2021] [Indexed: 10/21/2022] Open
Abstract
How genes interact with the environment to shape phenotypic variation and evolution is a fundamental question intriguing to biologists from various fields. Existing linear models built on single genes are inadequate to reveal the complexity of genotype-environment (G-E) interactions. Here, we develop a conceptual model for mechanistically dissecting G-E interplay by integrating previously disconnected theories and methods. Under this integration, evolutionary game theory, developmental modularity theory, and a variable selection method allow us to reconstruct environment-induced, maximally informative, sparse, and casual multilayer genetic networks. We design and conduct two mapping experiments by using a desert-adapted tree species to validate the biological application of the model proposed. The model identifies previously uncharacterized molecular mechanisms that mediate trees' response to saline stress. Our model provides a tool to comprehend the genetic architecture of trait variation and evolution and trace the information flow of each gene toward phenotypes within omnigenic networks.
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Affiliation(s)
- Haojie Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Meixia Ye
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Yaru Fu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Ang Dong
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Miaomiao Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Li Feng
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Xuli Zhu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Wenhao Bo
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Libo Jiang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Christopher H Griffin
- Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA
| | - Dan Liang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Rongling Wu
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China; Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA.
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169
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Abstract
Fisher's partitioning of genotypic values and genetic variance is highly relevant in the current era of genome-wide association studies (GWASs). However, despite being more than a century old, a number of persistent misconceptions related to nonadditive genetic effects remain. We developed a user-friendly web tool, the Falconer ShinyApp, to show how the combination of gene action and allele frequencies at causal loci translate to genetic variance and genetic variance components for a complex trait. The app can be used to demonstrate the relationship between a SNP effect size estimated from GWAS and the variation the SNP generates in the population, i.e., how locus-specific effects lead to individual differences in traits. In addition, it can also be used to demonstrate how within and between locus interactions (dominance and epistasis, respectively) usually do not lead to a large amount of nonadditive variance relative to additive variance, and therefore, that these interactions usually do not explain individual differences in a population.
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Affiliation(s)
- Valentin Hivert
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Naomi R. Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Peter M. Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
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170
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Misra G, Badoni S, Parween S, Singh RK, Leung H, Ladejobi O, Mott R, Sreenivasulu N. Genome-wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:910-925. [PMID: 33220119 PMCID: PMC8131057 DOI: 10.1111/pbi.13516] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 11/07/2020] [Accepted: 11/12/2020] [Indexed: 05/11/2023]
Abstract
Rice varieties whose quality is graded as excellent have a lower percent grain chalkiness (PGC) of two per cent and below with higher whole grain yields upon milling, leading to higher economic returns for farmers. We have conducted a genome-wide association study (GWAS) using a combined population panel of indica and japonica rice varieties, and identified a total of 746 single nucleotide polymorphisms (SNPs) that were strongly associated with the chalk phenotype, covered 78 Quantitative Trait Loci (QTL) regions. Among them, 21 were high-value QTLs, as they explained at least 10 % of the phenotypic variance for PGC. A combined epistasis and GWAS was applied to dissect the genetics of the complex chalkiness trait, and its regulatory cascades were validated using gene regulatory networks. Promising novel epistatic interactions were found between the loci of chromosomes 6 (PGC6.1) and 7 (PGC7.8) that contributed to lower PGC. Based on haplotype mining only a few modern rice varieties confounded with a lower chalkiness, and they possess several PGC QTLs. The importance of PGC6.1 was validated through multi-parent advanced generation intercrosses and several low-chalk lines possessing superior haplotypes were identified. The results of this investigation have deciphered the underlying genetic networks that can reduce PGC to 2%, and will thus support future breeding programs to improve the grain quality of elite genetic material with high-yielding potentials.
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Affiliation(s)
- Gopal Misra
- International Rice Research InstituteLos BañosPhilippines
| | - Saurabh Badoni
- International Rice Research InstituteLos BañosPhilippines
| | - Sabiha Parween
- International Rice Research InstituteLos BañosPhilippines
| | - Rakesh Kumar Singh
- International Rice Research InstituteLos BañosPhilippines
- Present address:
International Center for Biosaline AgricultureAcademic CityDubaiUnited Arab Emirates
| | - Hei Leung
- International Rice Research InstituteLos BañosPhilippines
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171
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Gumpinger AC, Rieck B, Grimm DG, Borgwardt K. Network-guided search for genetic heterogeneity between gene pairs. Bioinformatics 2021; 37:57-65. [PMID: 32573681 PMCID: PMC8034561 DOI: 10.1093/bioinformatics/btaa581] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 05/19/2020] [Accepted: 06/15/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Correlating genetic loci with a disease phenotype is a common approach to improve our understanding of the genetics underlying complex diseases. Standard analyses mostly ignore two aspects, namely genetic heterogeneity and interactions between loci. Genetic heterogeneity, the phenomenon that genetic variants at different loci lead to the same phenotype, promises to increase statistical power by aggregating low-signal variants. Incorporating interactions between loci results in a computational and statistical bottleneck due to the vast amount of candidate interactions. RESULTS We propose a novel method SiNIMin that addresses these two aspects by finding pairs of interacting genes that are, upon combination, associated with a phenotype of interest under a model of genetic heterogeneity. We guide the interaction search using biological prior knowledge in the form of protein-protein interaction networks. Our method controls type I error and outperforms state-of-the-art methods with respect to statistical power. Additionally, we find novel associations for multiple Arabidopsis thaliana phenotypes, and, with an adapted variant of SiNIMin, for a study of rare variants in migraine patients. AVAILABILITY AND IMPLEMENTATION Code available at https://github.com/BorgwardtLab/SiNIMin. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Anja C Gumpinger
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Bastian Rieck
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
| | - Dominik G Grimm
- Technical University of Munich, TUM Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing 94315, Germany.,Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing 94315, Germany
| | | | - Karsten Borgwardt
- Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
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172
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Diaz-Gallo LM, Brynedal B, Westerlind H, Sandberg R, Ramsköld D. Understanding interactions between risk factors, and assessing the utility of the additive and multiplicative models through simulations. PLoS One 2021; 16:e0250282. [PMID: 33901204 PMCID: PMC8075235 DOI: 10.1371/journal.pone.0250282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 04/02/2021] [Indexed: 01/04/2023] Open
Abstract
Understanding the genetic background of complex diseases requires the expansion of studies beyond univariate associations. Therefore, it is important to use interaction assessments of risk factors in order to discover whether, and how genetic risk variants act together on disease development. The principle of interaction analysis is to explore the magnitude of the combined effect of risk factors on disease causation. In this study, we use simulations to investigate different scenarios of causation to show how the magnitude of the effect of two risk factors interact. We mainly focus on the two most commonly used interaction models, the additive and multiplicative risk scales, since there is often confusion regarding their use and interpretation. Our results show that the combined effect is multiplicative when two risk factors are involved in the same chain of events, an interaction called synergism. Synergism is often described as a deviation from additivity, which is a broader term. Our results also confirm that it is often relevant to estimate additive effect relationships, because they correspond to independent risk factors at low disease prevalence. Importantly, we evaluate the threshold of more than two required risk factors for disease causation, called the multifactorial threshold model. We found a simple mathematical relationship (square root) between the threshold and an additive-to-multiplicative linear effect scale (AMLES), where 0 corresponds to an additive effect and 1 to a multiplicative. We propose AMLES as a metric that could be used to test different effects relationships at the same time, given that it can simultaneously reveal additive, multiplicative and intermediate risk effects relationships. Finally, the utility of our simulation study was demonstrated using real data by analyzing and interpreting gene-gene interaction odds ratios from a rheumatoid arthritis case-control cohort.
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Affiliation(s)
- Lina-Marcela Diaz-Gallo
- Division of Rheumatology, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Boel Brynedal
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Helga Westerlind
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Daniel Ramsköld
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
- * E-mail:
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173
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Sheppard B, Rappoport N, Loh PR, Sanders SJ, Zaitlen N, Dahl A. A model and test for coordinated polygenic epistasis in complex traits. Proc Natl Acad Sci U S A 2021; 118:e1922305118. [PMID: 33833052 PMCID: PMC8053945 DOI: 10.1073/pnas.1922305118] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Interactions between genetic variants-epistasis-is pervasive in model systems and can profoundly impact evolutionary adaption, population disease dynamics, genetic mapping, and precision medicine efforts. In this work, we develop a model for structured polygenic epistasis, called coordinated epistasis (CE), and prove that several recent theories of genetic architecture fall under the formal umbrella of CE. Unlike standard epistasis models that assume epistasis and main effects are independent, CE captures systematic correlations between epistasis and main effects that result from pathway-level epistasis, on balance skewing the penetrance of genetic effects. To test for the existence of CE, we propose the even-odd (EO) test and prove it is calibrated in a range of realistic biological models. Applying the EO test in the UK Biobank, we find evidence of CE in 18 of 26 traits spanning disease, anthropometric, and blood categories. Finally, we extend the EO test to tissue-specific enrichment and identify several plausible tissue-trait pairs. Overall, CE is a dimension of genetic architecture that can capture structured, systemic forms of epistasis in complex human traits.
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Affiliation(s)
- Brooke Sheppard
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143
| | - Nadav Rappoport
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA 94143
| | - Po-Ru Loh
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115
| | - Stephan J Sanders
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143
| | - Noah Zaitlen
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94143;
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095
| | - Andy Dahl
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095;
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA 90095
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637
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174
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Kunert-Graf JM, Sakhanenko NA, Galas DJ. Optimized permutation testing for information theoretic measures of multi-gene interactions. BMC Bioinformatics 2021; 22:180. [PMID: 33827420 PMCID: PMC8028212 DOI: 10.1186/s12859-021-04107-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 03/29/2021] [Indexed: 11/17/2022] Open
Abstract
Background Permutation testing is often considered the “gold standard” for multi-test significance analysis, as it is an exact test requiring few assumptions about the distribution being computed. However, it can be computationally very expensive, particularly in its naive form in which the full analysis pipeline is re-run after permuting the phenotype labels. This can become intractable in multi-locus genome-wide association studies (GWAS), in which the number of potential interactions to be tested is combinatorially large. Results In this paper, we develop an approach for permutation testing in multi-locus GWAS, specifically focusing on SNP–SNP-phenotype interactions using multivariable measures that can be computed from frequency count tables, such as those based in Information Theory. We find that the computational bottleneck in this process is the construction of the count tables themselves, and that this step can be eliminated at each iteration of the permutation testing by transforming the count tables directly. This leads to a speed-up by a factor of over 103 for a typical permutation test compared to the naive approach. Additionally, this approach is insensitive to the number of samples making it suitable for datasets with large number of samples. Conclusions The proliferation of large-scale datasets with genotype data for hundreds of thousands of individuals enables new and more powerful approaches for the detection of multi-locus genotype-phenotype interactions. Our approach significantly improves the computational tractability of permutation testing for these studies. Moreover, our approach is insensitive to the large number of samples in these modern datasets. The code for performing these computations and replicating the figures in this paper is freely available at https://github.com/kunert/permute-counts.
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Affiliation(s)
- James M Kunert-Graf
- Pacific Northwest Research Institute, 720 Broadway, Seattle, WA, 98122, USA.
| | | | - David J Galas
- Pacific Northwest Research Institute, 720 Broadway, Seattle, WA, 98122, USA
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175
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Montesinos-López A, Montesinos-López OA, Montesinos-López JC, Flores-Cortes CA, de la Rosa R, Crossa J. A guide for kernel generalized regression methods for genomic-enabled prediction. Heredity (Edinb) 2021; 126:577-596. [PMID: 33649571 PMCID: PMC8115678 DOI: 10.1038/s41437-021-00412-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 01/23/2021] [Accepted: 01/24/2021] [Indexed: 01/30/2023] Open
Abstract
The primary objective of this paper is to provide a guide on implementing Bayesian generalized kernel regression methods for genomic prediction in the statistical software R. Such methods are quite efficient for capturing complex non-linear patterns that conventional linear regression models cannot. Furthermore, these methods are also powerful for leveraging environmental covariates, such as genotype × environment (G×E) prediction, among others. In this study we provide the building process of seven kernel methods: linear, polynomial, sigmoid, Gaussian, Exponential, Arc-cosine 1 and Arc-cosine L. Additionally, we highlight illustrative examples for implementing exact kernel methods for genomic prediction under a single-environment, a multi-environment and multi-trait framework, as well as for the implementation of sparse kernel methods under a multi-environment framework. These examples are followed by a discussion on the strengths and limitations of kernel methods and, subsequently by conclusions about the main contributions of this paper.
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Affiliation(s)
- Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, 44430, Guadalajara, Jalisco, México
| | | | | | | | - Roberto de la Rosa
- Colegio de Postgraduados (CP), Campus Tabasco, Producción Agroalimentaria en el Trópico, H. Cárdenas, Tabasco, México
| | - José Crossa
- Colegio de Postgraduados, Campus Montecillos, CP 56230, Montecillos, Edo. de México, México.
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Km 45, CP 52640, Carretera Mexico-Veracruz, México.
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176
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Neighbor GWAS: incorporating neighbor genotypic identity into genome-wide association studies of field herbivory. Heredity (Edinb) 2021; 126:597-614. [PMID: 33514929 PMCID: PMC8115658 DOI: 10.1038/s41437-020-00401-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 12/25/2020] [Accepted: 12/28/2020] [Indexed: 01/29/2023] Open
Abstract
An increasing number of field studies have shown that the phenotype of an individual plant depends not only on its genotype but also on those of neighboring plants; however, this fact is not taken into consideration in genome-wide association studies (GWAS). Based on the Ising model of ferromagnetism, we incorporated neighbor genotypic identity into a regression model, named "Neighbor GWAS". Our simulations showed that the effective range of neighbor effects could be estimated using an observed phenotype when the proportion of phenotypic variation explained (PVE) by neighbor effects peaked. The spatial scale of the first nearest neighbors gave the maximum power to detect the causal variants responsible for neighbor effects, unless their effective range was too broad. However, if the effective range of the neighbor effects was broad and minor allele frequencies were low, there was collinearity between the self and neighbor effects. To suppress the false positive detection of neighbor effects, the fixed effect and variance components involved in the neighbor effects should be tested in comparison with a standard GWAS model. We applied neighbor GWAS to field herbivory data from 199 accessions of Arabidopsis thaliana and found that neighbor effects explained 8% more of the PVE of the observed damage than standard GWAS. The neighbor GWAS method provides a novel tool that could facilitate the analysis of complex traits in spatially structured environments and is available as an R package at CRAN ( https://cran.rproject.org/package=rNeighborGWAS ).
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177
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Zhao B, Zou F. On polygenic risk scores for complex traits prediction. Biometrics 2021; 78:499-511. [PMID: 33786811 DOI: 10.1111/biom.13466] [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: 03/04/2021] [Revised: 03/10/2021] [Accepted: 03/15/2021] [Indexed: 12/01/2022]
Abstract
Polygenic risk scores (PRS) have gained substantial attention for complex traits prediction in genome-wide association studies (GWAS). Motivated by the polygenic model of complex traits, we study the statistical properties of PRS under the high-dimensional but sparsity free setting where the triplet ( n , p , m ) → ( ∞ , ∞ , ∞ ) with n , p , m being the sample size, the number of assayed single-nucleotide polymorphisms (SNPs), and the number of assayed causal SNPs, respectively. First, we derive asymptotic results on the out-of-sample (prediction) R-squared for PRS. These results help understand the widespread observed gap between the in-sample heritability (or partial R-squared due to the genetic features) estimate and the out-of-sample R-squared for most complex traits. Next, we investigate how features should be selected (e.g., by a p-value threshold) for constructing optimal PRS. We reveal that the optimal threshold depends largely on the genetic architecture underlying the complex trait and the sample size of the training GWAS, or the m / n ratio. For highly polygenic traits with a large m / n ratio, it is difficult to separate causal and null SNPs and stringent feature selection in principle often leads to poor PRS prediction. We numerically illustrate the theoretical results with intensive simulation studies and real data analysis on 33 complex traits with a wide range of genetic architectures in the UK Biobank database.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
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178
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Chan HW, Schiff ER, Tailor VK, Malka S, Neveu MM, Theodorou M, Moosajee M. Prospective Study of the Phenotypic and Mutational Spectrum of Ocular Albinism and Oculocutaneous Albinism. Genes (Basel) 2021; 12:508. [PMID: 33808351 PMCID: PMC8065601 DOI: 10.3390/genes12040508] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/15/2021] [Accepted: 03/26/2021] [Indexed: 02/07/2023] Open
Abstract
Albinism encompasses a group of hereditary disorders characterized by reduced or absent ocular pigment and variable skin and/or hair involvement, with syndromic forms such as Hermansky-Pudlak syndrome and Chédiak-Higashi syndrome. Autosomal recessive oculocutaneous albinism (OCA) is phenotypically and genetically heterogenous (associated with seven genes). X-linked ocular albinism (OA) is associated with only one gene, GPR143. We report the clinical and genetic outcomes of 44 patients, from 40 unrelated families of diverse ethnicities, with query albinism presenting to the ocular genetics service at Moorfields Eye Hospital NHS Foundation Trust between November 2017 and October 2019. Thirty-six were children (≤ 16 years) with a median age of 31 months (range 2-186), and eight adults with a median age of 33 years (range 17-39); 52.3% (n = 23) were male. Genetic testing using whole genome sequencing (WGS, n = 9) or a targeted gene panel (n = 31) gave an overall diagnostic rate of 42.5% (44.4% (4/9) with WGS and 41.9% (13/31) with panel testing). Seventeen families had confirmed mutations in TYR (n = 9), OCA2, (n = 4), HPS1 (n = 1), HPS3 (n = 1), HPS6 (n = 1), and GPR143 (n = 1). Molecular diagnosis of albinism remains challenging due to factors such as missing heritability. Differential diagnoses must include SLC38A8-associated foveal hypoplasia and syndromic forms of albinism.
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Affiliation(s)
- Hwei Wuen Chan
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK; (H.W.C.); (E.R.S.); (V.K.T.); (S.M.); (M.M.N.); (M.T.)
- Institute of Ophthalmology, University College London, London EC1V 9EL, UK
- Department of Ophthalmology, National University Singapore, Singapore S118177, Singapore
| | - Elena R. Schiff
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK; (H.W.C.); (E.R.S.); (V.K.T.); (S.M.); (M.M.N.); (M.T.)
- Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Vijay K. Tailor
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK; (H.W.C.); (E.R.S.); (V.K.T.); (S.M.); (M.M.N.); (M.T.)
- Experimental Psychology, University College London, London WC1H 0AP, UK
| | - Samantha Malka
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK; (H.W.C.); (E.R.S.); (V.K.T.); (S.M.); (M.M.N.); (M.T.)
| | - Magella M. Neveu
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK; (H.W.C.); (E.R.S.); (V.K.T.); (S.M.); (M.M.N.); (M.T.)
- Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Maria Theodorou
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK; (H.W.C.); (E.R.S.); (V.K.T.); (S.M.); (M.M.N.); (M.T.)
| | - Mariya Moosajee
- Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK; (H.W.C.); (E.R.S.); (V.K.T.); (S.M.); (M.M.N.); (M.T.)
- Institute of Ophthalmology, University College London, London EC1V 9EL, UK
- Great Ormond Street Hospital for Children NHS Foundation Trust, London WC1N 3JH, UK
- The Francis Crick Institute, London NW1 1AT, UK
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179
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Vignogna RC, Buskirk SW, Lang GI. Exploring a Local Genetic Interaction Network Using Evolutionary Replay Experiments. Mol Biol Evol 2021; 38:3144-3152. [PMID: 33749796 PMCID: PMC8321538 DOI: 10.1093/molbev/msab087] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Understanding how genes interact is a central challenge in biology. Experimental evolution provides a useful, but underutilized, tool for identifying genetic interactions, particularly those that involve non-loss-of-function mutations or mutations in essential genes. We previously identified a strong positive genetic interaction between specific mutations in KEL1 (P344T) and HSL7 (A695fs) that arose in an experimentally evolved Saccharomyces cerevisiae population. Because this genetic interaction is not phenocopied by gene deletion, it was previously unknown. Using “evolutionary replay” experiments, we identified additional mutations that have positive genetic interactions with the kel1-P344T mutation. We replayed the evolution of this population 672 times from six timepoints. We identified 30 populations where the kel1-P344T mutation reached high frequency. We performed whole-genome sequencing on these populations to identify genes in which mutations arose specifically in the kel1-P344T background. We reconstructed mutations in the ancestral and kel1-P344T backgrounds to validate positive genetic interactions. We identify several genetic interactors with KEL1, we validate these interactions by reconstruction experiments, and we show these interactions are not recapitulated by loss-of-function mutations. Our results demonstrate the power of experimental evolution to identify genetic interactions that are positive, allele specific, and not readily detected by other methods, shedding light on an underexplored region of the yeast genetic interaction network.
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Affiliation(s)
- Ryan C Vignogna
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
| | - Sean W Buskirk
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
| | - Gregory I Lang
- Department of Biological Sciences, Lehigh University, Bethlehem, PA, USA
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180
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Goodin DS, Khankhanian P, Gourraud PA, Vince N. The nature of genetic and environmental susceptibility to multiple sclerosis. PLoS One 2021; 16:e0246157. [PMID: 33750973 PMCID: PMC7984655 DOI: 10.1371/journal.pone.0246157] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 01/15/2021] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVE To understand the nature of genetic and environmental susceptibility to multiple sclerosis (MS) and, by extension, susceptibility to other complex genetic diseases. BACKGROUND Certain basic epidemiological parameters of MS (e.g., population-prevalence of MS, recurrence-risks for MS in siblings and twins, proportion of women among MS patients, and the time-dependent changes in the sex-ratio) are well-established. In addition, more than 233 genetic-loci have now been identified as being unequivocally MS-associated, including 32 loci within the major histocompatibility complex (MHC), and one locus on the X chromosome. Despite this recent explosion in genetic associations, however, the association of MS with the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 (H+) haplotype has been known for decades. DESIGN/METHODS We define the "genetically-susceptible" subset (G) to include everyone with any non-zero life-time chance of developing MS. Individuals who have no chance of developing MS, regardless of their environmental experiences, belong to the mutually exclusive "non-susceptible" subset (G-). Using these well-established epidemiological parameters, we analyze, mathematically, the implications that these observations have regarding the genetic-susceptibility to MS. In addition, we use the sex-ratio change (observed over a 35-year interval in Canada), to derive the relationship between MS-probability and an increasing likelihood of a sufficient environmental exposure. RESULTS We demonstrate that genetic-susceptibitly is confined to less than 7.3% of populations throughout Europe and North America. Consequently, more than 92.7% of individuals in these populations have no chance whatsoever of developing MS, regardless of their environmental experiences. Even among carriers of the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype, far fewer than 32% can possibly be members the (G) subset. Also, despite the current preponderance of women among MS patients, women are less likely to be in the susceptible (G) subset and have a higher environmental threshold for developing MS compared to men. Nevertheless, the penetrance of MS in susceptible women is considerably greater than it is in men. Moreover, the response-curves for MS-probability in susceptible individuals increases with an increasing likelihood of a sufficient environmental exposure, especially among women. However, these environmental response-curves plateau at under 50% for women and at a significantly lower level for men. CONCLUSIONS The pathogenesis of MS requires both a genetic predisposition and a suitable environmental exposure. Nevertheless, genetic-susceptibility is rare in the population (< 7.3%) and requires specific combinations of non-additive genetic risk-factors. For example, only a minority of carriers of the HLA-DRB1*15:01~HLA-DQB1*06:02~a1 haplotype are even in the (G) subset and, thus, genetic-susceptibility to MS in these carriers must result from the combined effect this haplotype together with the effects of certain other (as yet, unidentified) genetic factors. By itself, this haplotype poses no MS-risk. By contrast, a sufficient environmental exposure (however many events are involved, whenever these events need to act, and whatever these events might be) is common, currently occurring in, at least, 76% of susceptible individuals. In addition, the fact that environmental response-curves plateau well below 50% (especially in men), indicates that disease pathogenesis is partly stochastic. By extension, other diseases, for which monozygotic-twin recurrence-risks greatly exceed the disease-prevalence (e.g., rheumatoid arthritis, diabetes, and celiac disease), must have a similar genetic basis.
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Affiliation(s)
- Douglas S. Goodin
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
| | - Pouya Khankhanian
- Center for Neuro-Engineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Pierre-Antoine Gourraud
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States of America
- Centre de Recherche en Transplantation et Immunologie UMR 1064, INSERM, Université de Nantes, Nantes, France
- Institut de Transplantation Urologie Néphrologie (ITUN), CHU Nantes, Nantes, France
| | - Nicolas Vince
- Centre de Recherche en Transplantation et Immunologie UMR 1064, INSERM, Université de Nantes, Nantes, France
- Institut de Transplantation Urologie Néphrologie (ITUN), CHU Nantes, Nantes, France
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181
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Pang H, Xia Y, Luo S, Huang G, Li X, Xie Z, Zhou Z. Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus. J Med Genet 2021; 58:289-296. [PMID: 33753534 PMCID: PMC8086251 DOI: 10.1136/jmedgenet-2020-107350] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 01/06/2021] [Accepted: 01/10/2021] [Indexed: 12/12/2022]
Abstract
Type 1 diabetes mellitus (T1DM) is defined as an autoimmune disorder and has enormous complexity and heterogeneity. Although its precise pathogenic mechanisms are obscure, this disease is widely acknowledged to be precipitated by environmental factors in individuals with genetic susceptibility. To date, the known susceptibility loci, which have mostly been identified by genome-wide association studies, can explain 80%–85% of the heritability of T1DM. Researchers believe that at least a part of its missing genetic component is caused by undetected rare and low-frequency variants. Most common variants have only small to modest effect sizes, which increases the difficulty of dissecting their functions and restricts their potential clinical application. Intriguingly, many studies have indicated that rare and low-frequency variants have larger effect sizes and play more significant roles in susceptibility to common diseases, including T1DM, than common variants do. Therefore, better recognition of rare and low-frequency variants is beneficial for revealing the genetic architecture of T1DM and for providing new and potent therapeutic targets for this disease. Here, we will discuss existing challenges as well as the great significance of this field and review current knowledge of the contributions of rare and low-frequency variants to T1DM.
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Affiliation(s)
- Haipeng Pang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Ying Xia
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shuoming Luo
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Gan Huang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xia Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhiguo Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhiguang Zhou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
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182
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Yang Y, Yao S, Ding JM, Chen W, Guo Y. Enhancer-Gene Interaction Analyses Identified the Epidermal Growth Factor Receptor as a Susceptibility Gene for Type 2 Diabetes Mellitus. Diabetes Metab J 2021; 45:241-250. [PMID: 32602275 PMCID: PMC8024152 DOI: 10.4093/dmj.2019.0204] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 01/03/2020] [Indexed: 01/06/2023] Open
Abstract
Background Genetic interactions are known to play an important role in the missing heritability problem for type 2 diabetes mellitus (T2DM). Interactions between enhancers and their target genes play important roles in gene regulation and disease pathogenesis. In the present study, we aimed to identify genetic interactions between enhancers and their target genes associated with T2DM. Methods We performed genetic interaction analyses of enhancers and protein-coding genes for T2DM in 2,696 T2DM patients and 3,548 controls of European ancestry. A linear regression model was used to identify single nucleotide polymorphism (SNP) pairs that could affect the expression of the protein-coding genes. Differential expression analyses were used to identify differentially expressed susceptibility genes in diabetic and nondiabetic subjects. Results We identified one SNP pair, rs4947941×rs7785013, significantly associated with T2DM (combined P=4.84×10-10). The SNP rs4947941 was annotated as an enhancer, and rs7785013 was located in the epidermal growth factor receptor (EGFR) gene. This SNP pair was significantly associated with EGFR expression in the pancreas (P=0.033), and the minor allele "A" of rs7785013 decreased EGFR gene expression and the risk of T2DM with an increase in the dosage of "T" of rs4947941. EGFR expression was significantly upregulated in T2DM patients, which was consistent with the effect of rs4947941×rs7785013 on T2DM and EGFR expression. A functional validation study using the Mouse Genome Informatics (MGI) database showed that EGFR was associated with diabetes-relevant phenotypes. Conclusion Genetic interaction analyses of enhancers and protein-coding genes suggested that EGFR may be a novel susceptibility gene for T2DM.
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Affiliation(s)
- Yang Yang
- Clinical Laboratory, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
- Xi'an Center for Disease Control and Prevention, Xi'an, China
| | - Shi Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Jing-Miao Ding
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Wei Chen
- Clinical Laboratory, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Biomedical Informatics & Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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183
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Abstract
Alzheimer’s disease (AD) is the leading cause of neurodegeneration in the elderly and is clinically characterized by slowly progressing cognitive decline, which most commonly affects episodic memory function. This eventually leads to difficulties in activities of daily living. Biomarker studies show that the underlying pathology of AD begins 20 years before clinical symptoms. This results in the need to define specific targets and preclinical stages in order to address the problems of this disease at an earlier point in time. Genetic studies are indispensable for gaining insight into the etiology of neurodegenerative diseases and can play a major role in the early definition of the individual disease risk. This review provides an overview of the currently known genetic features of AD.
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Affiliation(s)
- Theresa König
- Department of Neurology, Medical University of Vienna, Vienna, Austria
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184
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Kemper KE, Yengo L, Zheng Z, Abdellaoui A, Keller MC, Goddard ME, Wray NR, Yang J, Visscher PM. Phenotypic covariance across the entire spectrum of relatedness for 86 billion pairs of individuals. Nat Commun 2021; 12:1050. [PMID: 33594080 PMCID: PMC7886899 DOI: 10.1038/s41467-021-21283-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 12/10/2020] [Indexed: 01/18/2023] Open
Abstract
Attributing the similarity between individuals to genetic and non-genetic factors is central to genetic analyses. In this paper we use the genomic relationship ([Formula: see text]) among 417,060 individuals to investigate the phenotypic covariance between pairs of individuals for 32 traits across the spectrum of relatedness, from unrelated pairs through to identical twins. We find linear relationships between phenotypic covariance and [Formula: see text] that agree with the SNP-based heritability ([Formula: see text]) in unrelated pairs ([Formula: see text]), and with pedigree-estimated heritability in close relatives ([Formula: see text]). The covariance increases faster than [Formula: see text] in distant relatives ([Formula: see text]), and we attribute this to imperfect linkage disequilibrium between causal variants and the common variants used to construct [Formula: see text]. We also examine the effect of assortative mating on heritability estimates from different experimental designs. We find that full-sib identity-by-descent regression estimates for height (0.66 s.e. 0.07) are consistent with estimates from close relatives (0.82 s.e. 0.04) after accounting for the effect of assortative mating.
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Affiliation(s)
- Kathryn E Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Zhili Zheng
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Abdel Abdellaoui
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Matthew C Keller
- Department of Psychology & Neuroscience, University of Colorado at Boulder, Boulder, CO, USA.,Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, VIC, Australia.,Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources, Bundoora, VIC, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.,Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia. .,Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia.
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185
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Batstra L, Foget L, van Haeringen C, Te Meerman S, Thoutenhoofd ED. What children and young people learn about ADHD from youth information books: A text analysis of nine books on ADHD available in Dutch. Scand J Child Adolesc Psychiatr Psychol 2021; 8:1-9. [PMID: 33520773 PMCID: PMC7685495 DOI: 10.21307/sjcapp-2020-001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is not a singular concept. For the purposes of this study, understandings of ADHD are assumed also to spread along a conceptual dimension that includes some combination of biomedical and psychosocial knowledge. Biomedically, ADHD may be considered a somatic affliction causing inattention and hyperactivity, amenable to pharmaceutical treatment. Psychosocially, ADHD ranks among adverse behaviour patterns that are amenable to psychosocial and pedagogical intervention. Considering both biomedical and psychosocial factors are associated with the ADHD construct, it seems self-evident that young people should be offered information that gives equal consideration to both ways of addressing ADHD, but the question is just how balanced the information available to young people is. This study investigated nine information books on ADHD available in the Netherlands in Dutch, aimed at children and young people up to age 17. Thirteen perspective-dependent text elements were identified in qualitative content analysis. Eight attributes associate with a biomedical view: ADHD as cause, biological factors, clinical diagnosis, brain abnormality, medication, neurofeedback, heritability and persistence. Five text elements associate with a psychosocial view: ADHD as perceived behaviour, environmental factors, descriptive diagnosis, behavioural intervention and normalisation. The most frequent text passages encountered describe ADHD as a brain abnormality, along with medical and behavioural treatment. Providing the main focus for information in eight out of nine books, biomedical information about ADHD predominates in the available youth information books, while psychosocial information about ADHD is far less well covered.
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Affiliation(s)
- Laura Batstra
- Faculty of Behavioral and Social Sciences, The University of Groningen, Groningen, The Netherlands
| | - Linda Foget
- Faculty of Behavioral and Social Sciences, The University of Groningen, Groningen, The Netherlands
| | - Caroline van Haeringen
- Faculty of Behavioral and Social Sciences, The University of Groningen, Groningen, The Netherlands
| | - Sanne Te Meerman
- Youth, Education and Society, Centre of Expertise Healthy Ageing, Hanze University of Applied Sciences, Groningen, The Netherlands
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186
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Kryazhimskiy S. Emergence and propagation of epistasis in metabolic networks. eLife 2021; 10:e60200. [PMID: 33527897 PMCID: PMC7924954 DOI: 10.7554/elife.60200] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 02/01/2021] [Indexed: 12/11/2022] Open
Abstract
Epistasis is often used to probe functional relationships between genes, and it plays an important role in evolution. However, we lack theory to understand how functional relationships at the molecular level translate into epistasis at the level of whole-organism phenotypes, such as fitness. Here, I derive two rules for how epistasis between mutations with small effects propagates from lower- to higher-level phenotypes in a hierarchical metabolic network with first-order kinetics and how such epistasis depends on topology. Most importantly, weak epistasis at a lower level may be distorted as it propagates to higher levels. Computational analyses show that epistasis in more realistic models likely follows similar, albeit more complex, patterns. These results suggest that pairwise inter-gene epistasis should be common, and it should generically depend on the genetic background and environment. Furthermore, the epistasis coefficients measured for high-level phenotypes may not be sufficient to fully infer the underlying functional relationships.
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Affiliation(s)
- Sergey Kryazhimskiy
- Division of Biological Sciences, University of California, San DiegoLa JollaUnited States
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187
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The correlation of salivary telomere length and single nucleotide polymorphisms of the ADIPOQ, SIRT1 and FOXO3A genes with lifestyle-related diseases in a Japanese population. PLoS One 2021; 16:e0243745. [PMID: 33507936 PMCID: PMC7842940 DOI: 10.1371/journal.pone.0243745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 11/26/2020] [Indexed: 02/07/2023] Open
Abstract
Background It has been reported that genetic factors are associated with risk factors and onset of lifestyle-related diseases, but this finding is still the subject of much debate. Objective The aim of the present study was to investigate the correlation of genetic factors, including salivary telomere length and three single nucleotide polymorphisms (SNPs) that may influence lifestyle-related diseases, with lifestyle-related diseases themselves. Methods In one year at a single facility, relative telomere length and SNPs were determined by using monochrome multiplex quantitative polymerase chain reaction and TaqMan SNP Genotyping Assays, respectively, and were compared with lifestyle-related diseases in 120 Japanese individuals near our university. Results In men and all participants, age was inversely correlated with relative telomere length with respective p values of 0.049 and 0.034. In men, the frequency of hypertension was significantly higher in the short relative telomere length group than in the long group with unadjusted p value of 0.039, and the difference in the frequency of hypertension between the two groups was of borderline statistical significance after adjustment for age (p = 0.057). Furthermore, in men and all participants, the sum of the number of affected lifestyle-related diseases, including hypertension, was significantly higher in the short relative telomere length group than in the long group, with p values of 0.004 and 0.029, respectively. For ADIPOQ rs1501299, men’s ankle brachial index was higher in the T/T genotype than in the G/G and G/T genotypes, with p values of 0.001 and 0.000, respectively. For SIRT1 rs7895833, men’s body mass index and waist circumference and all participants’ brachial-ankle pulse wave velocity were higher in the A/G genotype than in the G/G genotype, with respective p values of 0.048, 0.032 and 0.035. For FOXO3A rs2802292, women’s body temperature and all participants’ saturation of peripheral oxygen were lower in the G/T genotype than in the T/T genotype, with respective p values of 0.039 and 0.032. However, relative telomere length was not associated with physiological or anthropometric measurements except for height in men (p = 0.016). ADIPOQ rs1501299 in men, but not the other two SNPs, was significantly associated with the sum of the number of affected lifestyle-related diseases (p = 0.013), by genotype. For each SNPs, there was no significant difference in the frequency of hypertension or relative telomere length by genotype. Conclusion Relative telomere length and the three types of SNPs determined using saliva have been shown to be differentially associated with onset of and measured risk factors for lifestyle-related diseases consisting mainly of cardiovascular diseases and cancer.
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188
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Antrobus MR, Brazier J, Stebbings GK, Day SH, Heffernan SM, Kilduff LP, Erskine RM, Williams AG. Genetic Factors That Could Affect Concussion Risk in Elite Rugby. Sports (Basel) 2021; 9:19. [PMID: 33499151 PMCID: PMC7910946 DOI: 10.3390/sports9020019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/11/2021] [Accepted: 01/18/2021] [Indexed: 11/16/2022] Open
Abstract
Elite rugby league and union have some of the highest reported rates of concussion (mild traumatic brain injury) in professional sport due in part to their full-contact high-velocity collision-based nature. Currently, concussions are the most commonly reported match injury during the tackle for both the ball carrier and the tackler (8-28 concussions per 1000 player match hours) and reports exist of reduced cognitive function and long-term health consequences that can end a playing career and produce continued ill health. Concussion is a complex phenotype, influenced by environmental factors and an individual's genetic predisposition. This article reviews concussion incidence within elite rugby and addresses the biomechanics and pathophysiology of concussion and how genetic predisposition may influence incidence, severity and outcome. Associations have been reported between a variety of genetic variants and traumatic brain injury. However, little effort has been devoted to the study of genetic associations with concussion within elite rugby players. Due to a growing understanding of the molecular characteristics underpinning the pathophysiology of concussion, investigating genetic variation within elite rugby is a viable and worthy proposition. Therefore, we propose from this review that several genetic variants within or near candidate genes of interest, namely APOE, MAPT, IL6R, COMT, SLC6A4, 5-HTTLPR, DRD2, DRD4, ANKK1, BDNF and GRIN2A, warrant further study within elite rugby and other sports involving high-velocity collisions.
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Affiliation(s)
- Mark R. Antrobus
- Sports Genomics Laboratory, Department of Sport and Exercise Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK; (J.B.); (G.K.S.); (A.G.W.)
- Sport and Exercise Science, University of Northampton, Northampton NN1 5PH, UK
| | - Jon Brazier
- Sports Genomics Laboratory, Department of Sport and Exercise Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK; (J.B.); (G.K.S.); (A.G.W.)
- Department of Psychology and Sports Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK
| | - Georgina K. Stebbings
- Sports Genomics Laboratory, Department of Sport and Exercise Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK; (J.B.); (G.K.S.); (A.G.W.)
| | - Stephen H. Day
- Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton WV1 1LY, UK;
| | - Shane M. Heffernan
- Applied Sports, Technology, Exercise and Medicine (A-STEM) Research Centre, College of Engineering, Swansea University, Swansea SA1 8EN, UK; (S.M.H.); (L.P.K.)
| | - Liam P. Kilduff
- Applied Sports, Technology, Exercise and Medicine (A-STEM) Research Centre, College of Engineering, Swansea University, Swansea SA1 8EN, UK; (S.M.H.); (L.P.K.)
| | - Robert M. Erskine
- Research Institute for Sport & Exercise Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK;
- Institute of Sport, Exercise and Health, University College London, London WC1E 6BT, UK
| | - Alun G. Williams
- Sports Genomics Laboratory, Department of Sport and Exercise Sciences, Manchester Metropolitan University, Manchester M1 5GD, UK; (J.B.); (G.K.S.); (A.G.W.)
- Institute of Sport, Exercise and Health, University College London, London WC1E 6BT, UK
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189
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Special Article: Translational Science Update. Pharmacological Implications of Emerging Schizophrenia Genetics: Can the Bridge From 'Genomics' to 'Therapeutics' be Defined and Traversed? J Clin Psychopharmacol 2021; 40:323-329. [PMID: 32433256 DOI: 10.1097/jcp.0000000000001215] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Recent schizophrenia genome-wide association studies (GWAS) have identified genomic variants of common and rare frequency, significantly associated with schizophrenia. While numerous functional genomics efforts are ongoing to elucidate the biological effects of schizophrenia risk variants, a consideration of their therapeutic implications is timely and imperative, for patients as well as for an iterative effect on elucidating the underlying biology and pathophysiology of illness. The current article reviews efforts to translate emerging schizophrenia genomics into novel approaches to target discovery and therapeutic intervention. Though the path from 'genetic risk to therapy' is far from straightforward, there are provocative early possibilities that harbor the promise of treatment based on causation rather than phenomenology, as well as 'precision psychiatry,' a basis for stratifying patients to enable more precise and effective, personalized therapy.
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190
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Ray LA, Grodin EN. Clinical Neuroscience of Addiction: What Clinical Psychologists Need to Know and Why. Annu Rev Clin Psychol 2021; 17:465-493. [PMID: 33472009 DOI: 10.1146/annurev-clinpsy-081219-114309] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The last three decades in psychological research have been marked by interdisciplinary science. Addiction represents a prime example of a disorder marked by a complex interaction among psychosocial and biological factors. This review highlights critical findings in the basic neuroscience of addiction and translates them into clinical language that can inform clinical psychologists in their research, teaching, and practice. From mechanisms of reward processing, learning and memory, allostasis, incentive-sensitization, withdrawal, tolerance, goal-directed decision making, habit learning, genetics, inflammation, and the microbiome, the common theme of this review is to illustrate the clinical utility of basic neuroscience research and to identify opportunities for clinical science. The thoughtful integration of basic and clinical science provides a powerful tool to fulfill the scientific mission of improving health care. Clinical psychologists have a crucial role to play in the translational science of addiction.
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Affiliation(s)
- Lara A Ray
- Department of Psychology, University of California, Los Angeles, California 90095, USA; .,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California 90095, USA.,Brain Research Institute, University of California, Los Angeles, California 90095, USA
| | - Erica N Grodin
- Department of Psychology, University of California, Los Angeles, California 90095, USA;
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191
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Hammond RK, Pahl MC, Su C, Cousminer DL, Leonard ME, Lu S, Doege CA, Wagley Y, Hodge KM, Lasconi C, Johnson ME, Pippin JA, Hankenson KD, Leibel RL, Chesi A, Wells AD, Grant SFA. Biological constraints on GWAS SNPs at suggestive significance thresholds reveal additional BMI loci. eLife 2021; 10:e62206. [PMID: 33459256 PMCID: PMC7815306 DOI: 10.7554/elife.62206] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 01/08/2021] [Indexed: 12/27/2022] Open
Abstract
To uncover novel significant association signals (p<5×10-8), genome-wide association studies (GWAS) requires increasingly larger sample sizes to overcome statistical correction for multiple testing. As an alternative, we aimed to identify associations among suggestive signals (5 × 10-8≤p<5×10-4) in increasingly powered GWAS efforts using chromatin accessibility and direct contact with gene promoters as biological constraints. We conducted retrospective analyses of three GIANT BMI GWAS efforts using ATAC-seq and promoter-focused Capture C data from human adipocytes and embryonic stem cell (ESC)-derived hypothalamic-like neurons. This approach, with its extremely low false-positive rate, identified 15 loci at p<5×10-5 in the 2010 GWAS, of which 13 achieved genome-wide significance by 2018, including at NAV1, MTIF3, and ADCY3. Eighty percent of constrained 2015 loci achieved genome-wide significance in 2018. We observed similar results in waist-to-hip ratio analyses. In conclusion, biological constraints on sub-significant GWAS signals can reveal potentially true-positive loci for further investigation in existing data sets without increasing sample size.
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Affiliation(s)
- Reza K Hammond
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Matthew C Pahl
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Chun Su
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Diana L Cousminer
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Michelle E Leonard
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Sumei Lu
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Claudia A Doege
- Naomi Berrie Diabetes Center, Vagelos College of Physicians and Surgeons, Columbia UniversityNew YorkUnited States
- Department of Pathology and Cell Biology, Vagelos College of Physicians and Surgeons, Columbia UniversityNew YorkUnited States
- Columbia Stem Cell Initiative, Vagelos College of Physicians and Surgeons, Columbia UniversityNew YorkUnited States
| | - Yadav Wagley
- Department of Orthopaedic Surgery, University of Michigan Medical SchoolAnn ArborUnited States
| | - Kenyaita M Hodge
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Chiara Lasconi
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Matthew E Johnson
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - James A Pippin
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Kurt D Hankenson
- Department of Orthopaedic Surgery, University of Michigan Medical SchoolAnn ArborUnited States
| | - Rudolph L Leibel
- Division of Molecular Genetics (Pediatrics) and the Naomi Berrie Diabetes Center, Columbia University Vagelos College of Physicians and SurgeonsNew YorkUnited States
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Department of Pathology, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Struan FA Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Division of Human Genetics, The Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Department of Pediatrics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUnited States
- Department of Genetics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaUnited States
- Division of Diabetes and EndocrinologyThe Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
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192
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Wong ML, Arcos-Burgos M, Liu S, Licinio AW, Yu C, Chin EWM, Yao WD, Lu XY, Bornstein SR, Licinio J. Rare Functional Variants Associated with Antidepressant Remission in Mexican-Americans: Short title: Antidepressant remission and pharmacogenetics in Mexican-Americans. J Affect Disord 2021; 279:491-500. [PMID: 33128939 PMCID: PMC7953425 DOI: 10.1016/j.jad.2020.10.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 08/24/2020] [Accepted: 10/11/2020] [Indexed: 12/14/2022]
Abstract
INTRODUCTION Rare genetic functional variants can contribute to 30-40% of functional variability in genes relevant to drug action. Therefore, we investigated the role of rare functional variants in antidepressant response. METHOD Mexican-American individuals meeting the Diagnostic and Statistical Manual-IV criteria for major depressive disorder (MDD) participated in a prospective randomized, double-blind study with desipramine or fluoxetine. The rare variant analysis was performed using whole-exome genotyping data. Network and pathway analyses were carried out with the list of significant genes. RESULTS The Kernel-Based Adaptive Cluster method identified functional rare variants in 35 genes significantly associated with treatment remission (False discovery rate, FDR <0.01). Pathway analysis of these genes supports the involvement of the following gene ontology processes: olfactory/sensory transduction, regulation of response to cytokine stimulus, and meiotic cell cycleprocess. LIMITATIONS Our study did not have a placebo arm. We were not able to use antidepressant blood level as a covariate. Our study is based on a small sample size of only 65 Mexican-American individuals. Further studies using larger cohorts are warranted. CONCLUSION Our data identified several rare functional variants in antidepressant drug response in MDD patients. These have the potential to serve as genetic markers for predicting drug response. TRIAL REGISTRATION ClinicalTrials.gov NCT00265291.
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Affiliation(s)
- Ma-Li Wong
- Department of Psychiatry and Behavioral Sciences, State University of New York, Upstate Medical University, Syracuse, NY, USA; Department of Neuroscience and Physiology, State University of New York, Upstate Medical University, Syracuse, NY, USA; Mind & Brain Theme, South Australian Health and Medical Research Institute Adelaide, South Australia, Australia; Department of Psychiatry, Flinders University College of Medicine and Public Health, Bedford Park, South Australia, Australia.
| | - Mauricio Arcos-Burgos
- Grupo de Investigación en Psiquiatría, Departamento de Psiquiatría, Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellin, Antioquia, Colombia
| | - Sha Liu
- Mind & Brain Theme, South Australian Health and Medical Research Institute Adelaide, South Australia, Australia
| | - Alice W Licinio
- Mind & Brain Theme, South Australian Health and Medical Research Institute Adelaide, South Australia, Australia
| | - Chenglong Yu
- Mind & Brain Theme, South Australian Health and Medical Research Institute Adelaide, South Australia, Australia; Department of Psychiatry, Flinders University College of Medicine and Public Health, Bedford Park, South Australia, Australia
| | - Eunice W M Chin
- Department of Psychiatry and Behavioral Sciences, State University of New York, Upstate Medical University, Syracuse, NY, USA
| | - Wei-Dong Yao
- Department of Psychiatry and Behavioral Sciences, State University of New York, Upstate Medical University, Syracuse, NY, USA; Department of Neuroscience and Physiology, State University of New York, Upstate Medical University, Syracuse, NY, USA
| | - Xin-Yun Lu
- Department of Neuroscience & Regenerative Medicine, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Stefan R Bornstein
- Medical Clinic III, Carl Gustav Carus University Hospital, Dresden University of Technology, Dresden, Germany
| | - Julio Licinio
- Department of Psychiatry and Behavioral Sciences, State University of New York, Upstate Medical University, Syracuse, NY, USA; Department of Neuroscience and Physiology, State University of New York, Upstate Medical University, Syracuse, NY, USA; Mind & Brain Theme, South Australian Health and Medical Research Institute Adelaide, South Australia, Australia; Department of Psychiatry, Flinders University College of Medicine and Public Health, Bedford Park, South Australia, Australia.
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Marderstein AR, Davenport ER, Kulm S, Van Hout CV, Elemento O, Clark AG. Leveraging phenotypic variability to identify genetic interactions in human phenotypes. Am J Hum Genet 2021; 108:49-67. [PMID: 33326753 PMCID: PMC7820920 DOI: 10.1016/j.ajhg.2020.11.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/23/2020] [Indexed: 12/13/2022] Open
Abstract
Although thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly because of the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to when assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.
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Affiliation(s)
- Andrew R Marderstein
- Tri-Institutional Program in Computational Biology & Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA; Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA
| | - Emily R Davenport
- Department of Biology, Huck Institutes of the Life Sciences, Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA 16802, USA
| | - Scott Kulm
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | | | - Olivier Elemento
- Institute of Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Andrew G Clark
- Department of Computational Biology, Cornell University, Ithaca, NY 14850, USA.
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194
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Amatya S, Ye M, Yang L, Gandhi CK, Wu R, Nagourney B, Floros J. Single Nucleotide Polymorphisms Interactions of the Surfactant Protein Genes Associated With Respiratory Distress Syndrome Susceptibility in Preterm Infants. Front Pediatr 2021; 9:682160. [PMID: 34671583 PMCID: PMC8521105 DOI: 10.3389/fped.2021.682160] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 09/06/2021] [Indexed: 11/14/2022] Open
Abstract
Background: Neonatal respiratory distress syndrome (RDS), due to surfactant deficiency in preterm infants, is the most common cause of respiratory morbidity. The surfactant proteins (SFTP) genetic variants have been well-studied in association with RDS; however, the impact of SNP-SNP (single nucleotide polymorphism) interactions on RDS has not been addressed. Therefore, this study utilizes a newer statistical model to determine the association of SFTP single SNP model and SNP-SNP interactions in a two and a three SNP interaction model with RDS susceptibility. Methods: This study used available genotype and clinical data in the Floros biobank at Penn State University. The patients consisted of 848 preterm infants, born <36 weeks of gestation, with 477 infants with RDS and 458 infants without RDS. Seventeen well-studied SFTPA1, SFTPA2, SFTPB, SFTPC, and SFTPD SNPs were investigated. Wang's statistical model was employed to test and identify significant associations in a case-control study. Results: Only the rs17886395 (C allele) of the SFTPA2 was associated with protection for RDS in a single-SNP model (Odd's Ratio 0.16, 95% CI 0.06-0.43, adjusted p = 0.03). The highest number of interactions (n = 27) in the three SNP interactions were among SFTPA1 and SFTPA2. The three SNP models showed intergenic and intragenic interactions among all SFTP SNPs except SFTPC. Conclusion: The single SNP model and SNP interactions using the two and three SNP interactions models identified SFTP-SNP associations with RDS. However, the large number of significant associations containing SFTPA1 and/or SFTPA2 SNPs point to the importance of SFTPA1 and SFTPA2 in RDS susceptibility.
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Affiliation(s)
- Shaili Amatya
- Department of Pediatrics, Center for Host Defense, Inflammation, and Lung Disease (CHILD) Research, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Meixia Ye
- Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Lili Yang
- School of First Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Chintan K Gandhi
- Department of Pediatrics, Center for Host Defense, Inflammation, and Lung Disease (CHILD) Research, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Rongling Wu
- Public Health Science, Pennsylvania State University College of Medicine, Hershey, PA, United States
| | - Beth Nagourney
- Albert Einstein College of Medicine, New York, NY, United States
| | - Joanna Floros
- Department of Pediatrics, Center for Host Defense, Inflammation, and Lung Disease (CHILD) Research, Pennsylvania State University College of Medicine, Hershey, PA, United States.,Obstetrics and Gynecology, Pennsylvania State University College of Medicine, Hershey, PA, United States
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195
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Kuzmin E, Andrews BJ, Boone C. Trigenic Synthetic Genetic Array (τ-SGA) Technique for Complex Interaction Analysis. Methods Mol Biol 2021; 2212:377-400. [PMID: 33733368 DOI: 10.1007/978-1-0716-0947-7_23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Complex genetic interactions occur when mutant alleles of multiple genes combine to elicit an unexpected phenotype, which could not be predicted given the expectation based on the combination of phenotypes associated with individual mutant alleles. Trigenic Synthetic Genetic Array (τ-SGA) methodology was developed for the systematic analysis of complex interactions involving combinations of three gene perturbations. With a series of replica pinning steps of the τ-SGA procedure, haploid triple mutants are constructed through automated mating and meiotic recombination. For example, a double-mutant query strain carrying two mutant alleles of interest, such as a deletion allele of a nonessential gene and a conditional temperature-sensitive allele of an essential gene, is crossed to an input array of yeast mutants, such as the diagnostic array set of ~1200 mutants, to generate an output array of triple mutants. The colony-size measurements of the resulting triple mutants are used to estimate cellular fitness and quantify trigenic interactions by incorporating corresponding single- and double-mutant fitness estimates. Trigenic interaction networks can be further analyzed for functional modules using various clustering and enrichment analysis tools. Complex genetic interactions are rich in functional information and provide insight into the genotype-to-phenotype relationship, genome size, and speciation.
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Affiliation(s)
- Elena Kuzmin
- Goodman Cancer Research Centre, McGill University, Montreal, QC, Canada
| | - Brenda J Andrews
- The Donnelly Centre, Toronto, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
| | - Charles Boone
- The Donnelly Centre, Toronto, ON, Canada. .,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
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196
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Genetic information from discordant sibling pairs points to ESRP2 as a candidate trans-acting regulator of the CF modifier gene SCNN1B. Sci Rep 2020; 10:22447. [PMID: 33384439 PMCID: PMC7775467 DOI: 10.1038/s41598-020-79804-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 12/10/2020] [Indexed: 11/08/2022] Open
Abstract
SCNN1B encodes the beta subunit of the epithelial sodium channel ENaC. Previously, we reported an association between SNP markers of SCNN1B gene and disease severity in cystic fibrosis-affected sibling pairs. We hypothesized that factors interacting with the SCNN1B genomic sequence are responsible for intrapair discordance. Concordant and discordant pairs differed at six SCNN1B markers (Praw = 0.0075, Pcorr = 0.0397 corrected for multiple testing). To identify the factors binding to these six SCNN1B SNPs, we performed an electrophoretic mobility shift assay and captured the DNA-protein complexes. Based on protein mass spectrometry data, the epithelial splicing regulatory protein ESRP2 was identified when using SCNN1B-derived probes and the ESRP2-SCNN1B interaction was independently confirmed by coimmunoprecipitation assays. We observed an alternative SCNN1B transcript and demonstrated in 16HBE14o- cells that levels of this transcript are decreased upon ESRP2 silencing by siRNA. Furthermore, we confirmed that mildly and severely affected siblings have different ESPR2 genetic backgrounds and that ESRP2 markers are linked to the response of CF patients' nasal epithelium to amiloride, indicating ENaC involvement (Pbest = 0.0131, Pcorr = 0.068 for multiple testing). Our findings demonstrate that sibling pairs clinically discordant for CF can be used to identify meaningful DNA regulatory elements and interacting factors.
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197
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Hassan R, Allali I, Agamah FE, Elsheikh SSM, Thomford NE, Dandara C, Chimusa ER. Drug response in association with pharmacogenomics and pharmacomicrobiomics: towards a better personalized medicine. Brief Bioinform 2020; 22:6012864. [PMID: 33253350 DOI: 10.1093/bib/bbaa292] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/19/2020] [Accepted: 10/03/2020] [Indexed: 12/15/2022] Open
Abstract
Researchers have long been presented with the challenge imposed by the role of genetic heterogeneity in drug response. For many years, Pharmacogenomics and pharmacomicrobiomics has been investigating the influence of an individual's genetic background to drug response and disposition. More recently, the human gut microbiome has proven to play a crucial role in the way patients respond to different therapeutic drugs and it has been shown that by understanding the composition of the human microbiome, we can improve the drug efficacy and effectively identify drug targets. However, our knowledge on the effect of host genetics on specific gut microbes related to variation in drug metabolizing enzymes, the drug remains limited and therefore limits the application of joint host-microbiome genome-wide association studies. In this paper, we provide a historical overview of the complex interactions between the host, human microbiome and drugs. While discussing applications, challenges and opportunities of these studies, we draw attention to the critical need for inclusion of diverse populations and the development of an innovative and combined pharmacogenomics and pharmacomicrobiomics approach, that may provide an important basis in personalized medicine.
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Affiliation(s)
- Radia Hassan
- Division of Human Genetics, Department of Pathology, University of Cape Town
| | - Imane Allali
- Department of Biology, Faculty of Sciences, Mohammed V University in Rabat, Morocco
| | - Francis E Agamah
- Division of Human Genetics, Department of Pathology, University of Cape Town
| | | | - Nicholas E Thomford
- Lecturers at the Department of Medical Biochemistry School of Medical Sciences, University of Cape Coast, Ghana
| | - Collet Dandara
- Division of Human Genetics, Department of Pathology, University of Cape Town
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, University of Cape Town
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198
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Arbet J, McGue M, Basu S. A robust and unified framework for estimating heritability in twin studies using generalized estimating equations. Stat Med 2020; 39:3897-3913. [PMID: 32449216 DOI: 10.1002/sim.8564] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 03/13/2020] [Accepted: 04/10/2020] [Indexed: 11/11/2022]
Abstract
The 'heritability' of a phenotype measures the proportion of trait variance due to genetic factors in a population. In the past 50 years, studies with monozygotic and dizygotic twins have estimated heritability for 17,804 traits;1 thus twin studies are popular for estimating heritability. Researchers are often interested in estimating heritability for non-normally distributed outcomes such as binary, counts, skewed or heavy-tailed continuous traits. In these settings, the traditional normal ACE model (NACE) and Falconer's method can produce poor coverage of the true heritability. Therefore, we propose a robust generalized estimating equations (GEE2) framework for estimating the heritability of non-normally distributed outcomes. The traditional NACE and Falconer's method are derived within this unified GEE2 framework, which additionally provides robust standard errors. Although the traditional Falconer's method cannot adjust for covariates, the corresponding 'GEE2-Falconer' can incorporate mean and variance-level covariate effects (e.g. let heritability vary by sex or age). Given a non-normally distributed outcome, the GEE2 models are shown to attain better coverage of the true heritability compared to traditional methods. Finally, a scenario is demonstrated where NACE produces biased estimates of heritability while Falconer remains unbiased. Therefore, we recommend GEE2-Falconer for estimating the heritability of non-normally distributed outcomes in twin studies.
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Affiliation(s)
- Jaron Arbet
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Matt McGue
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Saonli Basu
- Department of Biostatistics, University of Minnesota, Minneapolis, Minnesota, USA
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199
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Vasilopoulou C, Morris AP, Giannakopoulos G, Duguez S, Duddy W. What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis? J Pers Med 2020; 10:E247. [PMID: 33256133 PMCID: PMC7712791 DOI: 10.3390/jpm10040247] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/21/2020] [Accepted: 11/23/2020] [Indexed: 02/07/2023] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.
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Affiliation(s)
- Christina Vasilopoulou
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PT, UK;
| | - George Giannakopoulos
- Institute of Informatics and Telecommunications, NCSR Demokritos, 153 10 Aghia Paraskevi, Greece;
- Science For You (SciFY) PNPC, TEPA Lefkippos-NCSR Demokritos, 27, Neapoleos, 153 41 Ag. Paraskevi, Greece
| | - Stephanie Duguez
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| | - William Duddy
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
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200
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Ditrich F, Blümel S, Biedermann L, Fournier N, Rossel JB, Ellinghaus D, Franke A, Stange EF, Rogler G, Scharl M. Genetic risk factors predict disease progression in Crohn's disease patients of the Swiss inflammatory bowel disease cohort. Therap Adv Gastroenterol 2020; 13:1756284820959252. [PMID: 33281934 PMCID: PMC7686597 DOI: 10.1177/1756284820959252] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 08/24/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Crohn's disease (CD) may progress from an inflammatory to a stricturing or penetrating disease phenotype. The aim of our study was to identify single nucleotide polymorphisms (SNPs) that predict disease progression in patients of the Swiss IBD Cohort Study (SIBDCS). METHODS We applied a multi-state Markov model for progression behavior of CD with three behavioral states according to the Montreal classification. The model considered transition from B1 to B2/B3 or from B2 to B3 stage. Model dynamics were summarized with transition intensities by including the effect of SNPs and calculating transition intensities for each SNP. RESULTS We included 1276 CD patients [669 (52.4%) B1, 248 (19.4%) B2, 359 (28.1%) B3 patients] with a median follow-up of 6.8 (interquartile range = 3.6-9.1; range 0-11.6) years. Probability for a B1 patient to develop a stenosis (B1 to B2, q = 0.033) was twice as much as compared to developing a penetrating complication (B3) during the disease course. In contrast, the probability of entering B3 stage was similar regardless of whether antecedent stricture was present (B2 to B3, q = 0.016) or not (B1 to B3, q = 0.016). We identified SNPs within the gene loci encoding ZMIZ1, LOC105373831 and KSR1 as carrying the highest risk for progression to B3, while the presence of SNPs within gene loci TNFSF15 and CEBPB-PTPN1 protected from progression to B2 or B3. CONCLUSION We identified new genetic risk factors that can predict disease course in CD patients. A closer understanding on the functional impact of these genetic variations might improve our treatment options finally to prevent disease progression in CD patients.
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Affiliation(s)
- Felicitas Ditrich
- Department of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland,Department of Internal Medicine, Hospital Zollikerberg, Zollikerberg, Switzerland
| | - Sena Blümel
- Department of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Luc Biedermann
- Department of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Nicolas Fournier
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, VD, Switzerland
| | - Jean-Benoit Rossel
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, VD, Switzerland
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, University Hospital Schleswig Holstein, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, University Hospital Schleswig Holstein, Kiel, Germany
| | - Eduard F. Stange
- Department of Internal Medicine I, University Hospital Tübingen, Tübingen, Germany
| | - Gerhard Rogler
- Department of Gastroenterology and Hepatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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