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[An improved association analysis pipeline for tumor susceptibility variant in haplotype amplification area]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2020; 40:1493-1499. [PMID: 33118521 PMCID: PMC7606235 DOI: 10.12122/j.issn.1673-4254.2020.10.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
OBJECTIVE Haplotype amplification on germline variants is suggested to imply potential selective advantages and clonal expansion susceptibility and has become an important signature for seeking cancer susceptibility gene.Here we propose an improved association method that fully considers the haplotype amplification status. METHODS The haplotype amplification status was estimated by the variant allelic frequencies.We adopted a permutation test on variant allelic frequencies to divide the candidate variants into multiple groups.A likelihood clustering method was then applied to establish the neighborhood system of the hidden Markov random field framework.A filtering pipeline was introduced into the proposed method to further refine the candidate variants, including a Wilson's interval filter and a false discovery rate controller.The final candidate set along with the haplotype amplification status was collapsed into the weighted virtual sites for association tests. RESULTS Through simulated tests on a series of datasets, we compared the type Ⅰ error rates of different minor allele frequencies, which stably fell within 2%, suggesting good robustness of the algorithm.In addition, we compared another 5 published association approaches for Type-Ⅰ and Type-Ⅱ error rates with the proposed method, which resulted in the error rates all within 2%, demonstrating significant advantages and a good statistical ability of the proposed method. CONCLUSIONS The proposed method can accurately identify tumor susceptibility variants in haplotype amplification area with good robustness and stability.
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203
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Nazarian A, Kulminski AM. Evaluation of the Genetic Variance of Alzheimer's Disease Explained by the Disease-Associated Chromosomal Regions. J Alzheimers Dis 2020; 70:907-915. [PMID: 31282417 DOI: 10.3233/jad-190168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Heritability analysis of complex traits/diseases is commonly performed to obtain illustrative information about the potential contribution of the genetic factors to their phenotypic variances. In this study, we investigated the narrow-sense heritability (h2) of Alzheimer's disease (AD) using genome-wide single-nucleotide polymorphisms (SNPs) data from three independent studies in the linear mixed models framework. Our meta-analyses demonstrated that the estimated h2 values (and their standard errors) of AD in liability scale were 0.280 (0.091), 0.348 (0.113), and 0.389 (0.126) assuming AD prevalence rates of 10%, 20%, or 30% at ages of 65+, 75+, and 85+ years, respectively. We also found that chromosomal regions containing two or more AD-associated SNPs at p < 5E-08 could collectively explain 37% of the additive genetic variance of AD in our samples. AD-associated regions in which at least one SNP had attained p < 5E-08 explained 56% of the additive genetic variance of AD. These regions harbored 3% and 11% of SNPs in our analyses. Also, the chromosomal regions containing two or more and one or more AD-associated SNPs at p < 5E-06 accounted for 72% and 94% of the additive genetic variance of AD, respectively. These regions harbored 27% and 44% of SNPs in our analyses. Our findings showed that the overall contribution of the additive genetic effects to the AD liability was moderate and age-dependent. Also, they supported the importance of focusing on known AD-associated chromosomal regions to investigate the genetic basis of AD, e.g., through haplotype analysis, analysis of heterogeneity, and functional studies.
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Affiliation(s)
- Alireza Nazarian
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA
| | - Alexander M Kulminski
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA
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204
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Poulsen BG, Ask B, Nielsen HM, Ostersen T, Christensen OF. Prediction of genetic merit for growth rate in pigs using animal models with indirect genetic effects and genomic information. Genet Sel Evol 2020; 52:58. [PMID: 33028188 PMCID: PMC7541226 DOI: 10.1186/s12711-020-00578-y] [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: 01/29/2020] [Accepted: 09/21/2020] [Indexed: 01/12/2023] Open
Abstract
Background Several studies have found that the growth rate of a pig is influenced by the genetics of the group members (indirect genetic effects). Accounting for these indirect genetic effects in a selection program may increase genetic progress for growth rate. However, indirect genetic effects are small and difficult to predict accurately. Genomic information may increase the ability to predict indirect genetic effects. Thus, the objective of this study was to test whether including indirect genetic effects in the animal model increases the predictive performance when genetic effects are predicted with genomic relationships. In total, 11,255 pigs were phenotyped for average daily gain between 30 and 94 kg, and 10,995 of these pigs were genotyped. Two relationship matrices were used: a numerator relationship matrix (\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{A}}$$\end{document}A) and a combined pedigree and genomic relationship matrix (\documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{H}}$$\end{document}H); and two different animal models were used: an animal model with only direct genetic effects and an animal model with both direct and indirect genetic effects. The predictive performance of the models was defined as the Pearson correlation between corrected phenotypes and predicted genetic levels. The predicted genetic level of a pig was either its direct genetic effect or the sum of its direct genetic effect and the indirect genetic effects of its group members (total genetic effect). Results The highest predictive performance was achieved when total genetic effects were predicted with genomic information (21.2 vs. 14.7%). In general, the predictive performance was greater for total genetic effects than for direct genetic effects (0.1 to 0.5% greater; not statistically significant). Both types of genetic effects had greater predictive performance when they were predicted with \documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{A}}$$\end{document}A (5.9 to 6.3%). The difference between predictive performances of total genetic effects and direct genetic effects was smaller when \documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{A}}$$\end{document}A. Conclusions This study provides evidence that: (1) corrected phenotypes are better predicted with total genetic effects than with direct genetic effects only; (2) both direct genetic effects and indirect genetic effects are better predicted with \documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{A}}$$\end{document}A; (3) using \documentclass[12pt]{minimal}
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\begin{document}$${\mathbf{A}}$$\end{document}A primarily improves the predictive performance of direct genetic effects.
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Affiliation(s)
- Bjarke G Poulsen
- Center for Quantitative Genetics and Genomics, Blichers Allé 20, 8830, Tjele, Denmark. .,SEGES, Danish Pig Research Centre, Danish Agriculture and Food Council F.m.b.A., Axelborg, Axeltorv 3, 1609, Copenhagen V, Denmark.
| | - Birgitte Ask
- SEGES, Danish Pig Research Centre, Danish Agriculture and Food Council F.m.b.A., Axelborg, Axeltorv 3, 1609, Copenhagen V, Denmark
| | - Hanne M Nielsen
- Center for Quantitative Genetics and Genomics, Blichers Allé 20, 8830, Tjele, Denmark.,SEGES, Danish Pig Research Centre, Danish Agriculture and Food Council F.m.b.A., Axelborg, Axeltorv 3, 1609, Copenhagen V, Denmark
| | - Tage Ostersen
- SEGES, Danish Pig Research Centre, Danish Agriculture and Food Council F.m.b.A., Axelborg, Axeltorv 3, 1609, Copenhagen V, Denmark
| | - Ole F Christensen
- Center for Quantitative Genetics and Genomics, Blichers Allé 20, 8830, Tjele, Denmark
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205
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Kaiser AJE, Funkhouser CJ, Mittal VA, Walther S, Shankman SA. Test-retest & familial concordance of MDD symptoms. Psychiatry Res 2020; 292:113313. [PMID: 32738552 PMCID: PMC7529979 DOI: 10.1016/j.psychres.2020.113313] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 07/15/2020] [Accepted: 07/19/2020] [Indexed: 11/23/2022]
Abstract
Psychopathology research has increasingly sought to study the etiology and treatment of individual symptoms, rather than categorical diagnoses. However, it is unclear whether commonly used measures have adequate psychometric properties for assessing individual symptoms. This study examined the test-retest reliability and familial concordance (an indicator of validity) of the symptoms of Major Depressive Disorder (MDD), a disorder consisting of nine core symptoms, most of which are aggregated (e.g., symptom 7 of the DSM criteria for MDD is worthlessness or guilt). Lifetime MDD symptoms were measured in 504 young adults (237 sibling pairs) using the Structured Clinical Interview for DSM-5 (SCID). Fifty-one people completed a second SCID within three weeks of their first SCID. Results indicated that aggregated and unaggregated symptoms demonstrated moderate to substantial test-retest reliability and generally significant, but slight to fair familial concordance (with the highest familial concordance being for markedly diminished interest or pleasure and its unaggregated components - decreased interest and decreased pleasure). Given the increasing focus on the differential validity of individual MDD symptoms, the present study suggests that interview-based assessments of depression can assess most individual symptoms with adequate levels of reliability and validity.
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Affiliation(s)
- Ariela J E Kaiser
- University of Illinois at Chicago, Department of Psychology, United States; Northwestern University, Department of Psychiatry and Behavioral Sciences, United States
| | - Carter J Funkhouser
- University of Illinois at Chicago, Department of Psychology, United States; Northwestern University, Department of Psychiatry and Behavioral Sciences, United States
| | - Vijay A Mittal
- Northwestern University, Department of Psychiatry and Behavioral Sciences, United States; Northwestern University, Departments of Psychology, Medical Social Sciences.. Institutes for Policy Research, Innovations in Developmental Sciences (DevSci), United States
| | - Sebastian Walther
- University of Bern, University Hospital of Psychiatry, Translational Research Center, Bern, Switzerland
| | - Stewart A Shankman
- University of Illinois at Chicago, Department of Psychology, United States; Northwestern University, Department of Psychiatry and Behavioral Sciences, United States.
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206
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Johnson LM, Smith OJ, Hahn DA, Baer CF. Short-term heritable variation overwhelms 200 generations of mutational variance for metabolic traits in Caenorhabditis elegans. Evolution 2020; 74:2451-2464. [PMID: 32989734 DOI: 10.1111/evo.14104] [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: 05/11/2020] [Revised: 08/05/2020] [Accepted: 09/21/2020] [Indexed: 10/23/2022]
Abstract
Metabolic disorders have a large heritable component, and have increased markedly in human populations over the past few generations. Genome-wide association studies of metabolic traits typically find a substantial unexplained fraction of total heritability, suggesting an important role of spontaneous mutation. An alternative explanation is that epigenetic effects contribute significantly to the heritable variation. Here, we report a study designed to quantify the cumulative effects of spontaneous mutation on adenosine metabolism in the nematode Caenorhabditis elegans, including both the activity and concentration of two metabolic enzymes and the standing pools of their associated metabolites. The only prior studies on the effects of mutation on metabolic enzyme activity, in Drosophila melanogaster, found that total enzyme activity presents a mutational target similar to that of morphological and life-history traits. However, those studies were not designed to account for short-term heritable effects. We find that the short-term heritable variance for most traits is of similar magnitude as the variance among MA lines. This result suggests that the potential heritable effects of epigenetic variation in metabolic disease warrant additional scrutiny.
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Affiliation(s)
- Lindsay M Johnson
- Department of Biology, University of Florida, Gainesville, Florida, 32611.,Ology Bioservices, Inc., Alachua, Florida, 32615
| | - Olivia J Smith
- Department of Biology, University of Florida, Gainesville, Florida, 32611
| | - Daniel A Hahn
- Department of Entomology and Nematology, University of Florida, Gainesville, Florida, 32611.,University of Florida Genetics Institute, Gainesville, Florida, 32611
| | - Charles F Baer
- Department of Biology, University of Florida, Gainesville, Florida, 32611.,University of Florida Genetics Institute, Gainesville, Florida, 32611
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207
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Genetic nurturing, missing heritability, and causal analysis in genetic statistics. Proc Natl Acad Sci U S A 2020; 117:25646-25654. [PMID: 32989124 PMCID: PMC7568332 DOI: 10.1073/pnas.2015869117] [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] [Indexed: 01/01/2023] Open
Abstract
Correlation between genotypes and phenotypes can be produced by genetic nurturing, namely the effect of parents’ genotypes on their offspring’s phenotypes through the parents’ phenotypes. Population subdivision and assortative mating can give rise to correlations between genotypes and phenotypes similar to those due to genetic nurturing. Variances and correlations may not reveal causal relationships in the presence of these complexities. We analyze mechanistic models of genetic nurturing, population subdivision, and assortative mating and compare these with results obtained within the framework of modern causal analysis. Our results clarify statistical signals emanating from correlations between nontransmitted alleles and offspring phenotypes and reveal difficulties with standard linear models in the interpretation of heritability, in particular, the concept of missing heritability. Genetic nurturing, the effect of parents’ genotypes on offspring phenotypes through parental phenotypic transmission, can be modeled in terms of gene–culture interactions. This paper first uses a simple one-locus, two-phenotype gene–culture cotransmission model to compute the effect of genetic nurturing in terms of regression of children’s phenotypes on transmitted and nontransmitted alleles. With genetic nurturing, interpreting heritability and hence the meaning of “missing heritability” becomes problematic. Other factors, for example, population subdivision and assortative mating, generate similar signals to those of genetic nurturing, namely, correlation between parents’ nontransmitted alleles and children’s phenotypes. Corrections must be made for these to isolate the signal of genetic nurturing. Finally, a unified causal framework is constructed for genetic nurturing, population subdivision, and assortative mating. Causal and noncausal paths from transmitted and nontransmitted alleles to children’s phenotypes are identified and investigated in the presence of genetic nurturing, population subdivision, and assortative mating. Using causal analysis, assumptions made in inferring direct and indirect effects are then clarified and evaluated in a broader causal context.
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208
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Jakobson CM, Jarosz DF. What Has a Century of Quantitative Genetics Taught Us About Nature's Genetic Tool Kit? Annu Rev Genet 2020; 54:439-464. [PMID: 32897739 DOI: 10.1146/annurev-genet-021920-102037] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The complexity of heredity has been appreciated for decades: Many traits are controlled not by a single genetic locus but instead by polymorphisms throughout the genome. The importance of complex traits in biology and medicine has motivated diverse approaches to understanding their detailed genetic bases. Here, we focus on recent systematic studies, many in budding yeast, which have revealed that large numbers of all kinds of molecular variation, from noncoding to synonymous variants, can make significant contributions to phenotype. Variants can affect different traits in opposing directions, and their contributions can be modified by both the environment and the epigenetic state of the cell. The integration of prospective (synthesizing and analyzing variants) and retrospective (examining standing variation) approaches promises to reveal how natural selection shapes quantitative traits. Only by comprehensively understanding nature's genetic tool kit can we predict how phenotypes arise from the complex ensembles of genetic variants in living organisms.
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Affiliation(s)
- Christopher M Jakobson
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California 94305, USA;
| | - Daniel F Jarosz
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, California 94305, USA; .,Department of Developmental Biology, Stanford University School of Medicine, Stanford, California 94305, USA
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209
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Xiang X, Wang S, Liu T, Wang M, Li J, Jiang J, Wu T, Hu Y. Exploring gene-gene interaction in family-based data with an unsupervised machine learning method: EPISFA. Genet Epidemiol 2020; 44:811-824. [PMID: 32869348 DOI: 10.1002/gepi.22342] [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] [Received: 11/12/2019] [Revised: 06/06/2020] [Accepted: 06/21/2020] [Indexed: 11/06/2022]
Abstract
Gene-gene interaction (G × G) is thought to fill the gap between the estimated heritability of complex diseases and the limited genetic proportion explained by identified single-nucleotide polymorphisms. The current tools for exploring G × G were often developed for case-control designs with less considerations for their applications in families. Family-based studies are robust against bias led from population stratification in genetic studies and helpful in understanding G × G. We proposed a new algorithm epistasis sparse factor analysis (EPISFA) and epistasis sparse factor analysis for linkage disequilibrium (EPISFA-LD) based on unsupervised machine learning to screen G × G. Extensive simulations were performed to compare EPISFA/EPISFA-LD with a classical family-based algorithm FAM-MDR (family-based multifactor dimensionality reduction). The results showed that EPISFA/EPISFA-LD is a tool of both high power and computational efficiency that could be applied in family designs and is applicable within high-dimensionality datasets. Finally, we applied EPISFA/EPISFA-LD to a real dataset drawn from the Fangshan/family-based Ischemic Stroke Study in China. Five pairs of G × G were discovered by EPISFA/EPISFA-LD, including three pairs verified by other algorithms (FAM-MDR and logistic), and an additional two pairs uniquely identified by EPISFA/EPISFA-LD only. The results from EPISFA might offer new insights for understanding the genetic etiology of complex diseases. EPISFA/EPISFA-LD was implemented in R. All relevant source code as well as simulated data could be freely downloaded from https://github.com/doublexism/episfa.
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Affiliation(s)
- Xiao Xiang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Siyue Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Tianyi Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing, China
| | - Mengying Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jiawen Li
- Department of Clinical Medicine, School of Medicine, Peking University, Beijing, China
| | - Jin Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Tao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yonghua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
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210
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Ahun MN, Lauzon B, Sylvestre MP, Bergeron-Caron C, Eltonsy S, O'Loughlin J. A systematic review of cigarette smoking trajectories in adolescents. THE INTERNATIONAL JOURNAL OF DRUG POLICY 2020; 83:102838. [PMID: 32683174 DOI: 10.1016/j.drugpo.2020.102838] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 06/06/2020] [Accepted: 06/11/2020] [Indexed: 01/24/2023]
Abstract
Trajectory analyses differentiate subgroups of smokers based on early patterns of cigarette use, but no study has summarized this literature. We systematically reviewed the literature on adolescent cigarette smoking trajectories to document the number and shapes of trajectories identified, assess if certain study characteristics influence the number or shapes of trajectories identified, summarize factors associated with and outcomes of trajectory group membership, and assess whether the results of trajectory analyses help identify windows of opportunity for intervention. We searched PubMed and EMBASE (1/1/1980 to 1/11/2018) and identified 1695 articles. Forty-three articles with data from 37 unique datasets were retained. Each trajectory was categorized into one of three groups (i.e., low-stable, increasing, other). Number of trajectories ranged from 2 to 6 (mode = 4); 44-76% of participants were low-stable cigarette consumers, 11-21% increased consumption, and 3-11% were categorized as "other." Number of data points, smoking indicator used, and time axis influenced the number of trajectories identified. Only two articles depicted the natural course of smoking since onset. Factors associated with trajectory membership included age, sex/gender, race/ethnicity, parental education, behavior problems, depression, academic performance, baseline cigarette use, parental and friends smoking, alcohol use, and cannabis use. Outcomes included illicit drug and alcohol use. Beyond parsimoniously describing cigarette smoking patterns, it is not clear whether trajectory analyses offer increased insight into the natural course, determinants or outcomes of cigarette smoking in ways that inform the development of intervention.
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Affiliation(s)
- Marilyn N Ahun
- Université de Montréal Hospital Research Centre Tour Saint-Antoine, 850 rue Saint-Denis, Montréal, Québec, Canada H2X0A9; Université de Montréal School of Public Health, 7101 Avenue du Parc, Montréal, Québec, Canada H3N1X9
| | - Béatrice Lauzon
- Université de Montréal Hospital Research Centre Tour Saint-Antoine, 850 rue Saint-Denis, Montréal, Québec, Canada H2X0A9; Université de Montréal School of Public Health, 7101 Avenue du Parc, Montréal, Québec, Canada H3N1X9
| | - Marie-Pierre Sylvestre
- Université de Montréal Hospital Research Centre Tour Saint-Antoine, 850 rue Saint-Denis, Montréal, Québec, Canada H2X0A9; Université de Montréal School of Public Health, 7101 Avenue du Parc, Montréal, Québec, Canada H3N1X9.
| | - Cassi Bergeron-Caron
- Université de Montréal Hospital Research Centre Tour Saint-Antoine, 850 rue Saint-Denis, Montréal, Québec, Canada H2X0A9; Université de Montréal School of Public Health, 7101 Avenue du Parc, Montréal, Québec, Canada H3N1X9
| | - Sherif Eltonsy
- University of Moncton, J.-Raymond Frenette Building. 18 rue Antonine Maillet, Moncton, New Brunswick, Canada E1A3E9; College of Pharmacy, Rady Faculty of Health Sciences, University of Manitoba, 750 McDermot Avenue, Winnipeg, Manitoba, Canada R3E0T5
| | - Jennifer O'Loughlin
- Université de Montréal Hospital Research Centre Tour Saint-Antoine, 850 rue Saint-Denis, Montréal, Québec, Canada H2X0A9; Université de Montréal School of Public Health, 7101 Avenue du Parc, Montréal, Québec, Canada H3N1X9
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211
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Bertram L, Tanzi RE. Genomic mechanisms in Alzheimer's disease. Brain Pathol 2020; 30:966-977. [PMID: 32657454 PMCID: PMC8018017 DOI: 10.1111/bpa.12882] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 04/27/2020] [Indexed: 12/20/2022] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease and, owing to its increasing prevalence, represents one of the leading public health problems in aging populations. The molecular causes underlying the onset and progression of AD are manifold and hitherto still incompletely understood. Research over nearly four decades has clearly delineated genetics to play a crucial role in AD susceptibility, likely in concert with non-genetic factors. The field has gained considerable momentum and novel insights over the past 10 years owing to the advent and application of high-throughput genomics technologies in datasets of increasing size. In this contribution to the Mini-Symposium on the Molecular Etiology of Alzheimer's Disease, we review the current status of genomics research in AD. To this end, we scrutinize and discuss the main findings from the two largest and most current genome-wide association studies (GWAS) in the field, that is, the papers published by Jansen et al (Nat Genet 51:404-413) and Kunkle et al (Nat Genet 51:414-430). Particular focus is laid on genomics findings overlapping across both studies and on the novel insights they provide in terms of improving our understanding of the "genomic mechanisms" underlying this devastating disease.
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Affiliation(s)
- Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA)Institutes of Neurogenetics and CardiogeneticsUniversity of LübeckLübeckGermany
- Centre for Lifespan Changes in Brain and CognitionDepartment of PsychologyUniversity of OsloOsloNorway
| | - Rudolph E. Tanzi
- McCance Center for Brain Health and Genetics and Aging Research UnitDepartment of NeurologyMassachusetts General Hospital and Harvard Medical SchoolCharlestownMA
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212
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Awany D, Chimusa ER. Heritability jointly explained by host genotype and microbiome: will improve traits prediction? Brief Bioinform 2020; 22:5893981. [PMID: 32810866 DOI: 10.1093/bib/bbaa175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/09/2020] [Accepted: 07/10/2020] [Indexed: 11/14/2022] Open
Abstract
As we observe the $70$th anniversary of the publication by Robertson that formalized the notion of 'heritability', geneticists remain puzzled by the problem of missing/hidden heritability, where heritability estimates from genome-wide association studies (GWASs) fall short of that from twin-based studies. Many possible explanations have been offered for this discrepancy, including existence of genetic variants poorly captured by existing arrays, dominance, epistasis and unaccounted-for environmental factors; albeit these remain controversial. We believe a substantial part of this problem could be solved or better understood by incorporating the host's microbiota information in the GWAS model for heritability estimation and may also increase human traits prediction for clinical utility. This is because, despite empirical observations such as (i) the intimate role of the microbiome in many complex human phenotypes, (ii) the overlap between genetic variants associated with both microbiome attributes and complex diseases and (iii) the existence of heritable bacterial taxa, current GWAS models for heritability estimate do not take into account the contributory role of the microbiome. Furthermore, heritability estimate from twin-based studies does not discern microbiome component of the observed total phenotypic variance. Here, we summarize the concept of heritability in GWAS and microbiome-wide association studies, focusing on its estimation, from a statistical genetics perspective. We then discuss a possible statistical method to incorporate the microbiome in the estimation of heritability in host GWAS.
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Affiliation(s)
- Denis Awany
- Division of Human Genetics, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Emile R Chimusa
- Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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213
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Berg EL, Pedersen LR, Pride MC, Petkova SP, Patten KT, Valenzuela AE, Wallis C, Bein KJ, Wexler A, Lein PJ, Silverman JL. Developmental exposure to near roadway pollution produces behavioral phenotypes relevant to neurodevelopmental disorders in juvenile rats. Transl Psychiatry 2020; 10:289. [PMID: 32807767 PMCID: PMC7431542 DOI: 10.1038/s41398-020-00978-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 07/07/2020] [Accepted: 07/15/2020] [Indexed: 01/09/2023] Open
Abstract
Epidemiological studies consistently implicate traffic-related air pollution (TRAP) and/or proximity to heavily trafficked roads as risk factors for developmental delays and neurodevelopmental disorders (NDDs); however, there are limited preclinical data demonstrating a causal relationship. To test the effects of TRAP, pregnant rat dams were transported to a vivarium adjacent to a major freeway tunnel system in northern California where they were exposed to TRAP drawn directly from the face of the tunnel or filtered air (FA). Offspring remained housed under the exposure condition into which they were born and were tested in a variety of behavioral assays between postnatal day 4 and 50. To assess the effects of near roadway exposure, offspring of dams housed in a standard research vivarium were tested at the laboratory. An additional group of dams was transported halfway to the facility and then back to the laboratory to control for the effect of potential transport stress. Near roadway exposure delayed growth and development of psychomotor reflexes and elicited abnormal activity in open field locomotion. Near roadway exposure also reduced isolation-induced 40-kHz pup ultrasonic vocalizations, with the TRAP group having the lowest number of call emissions. TRAP affected some components of social communication, evidenced by reduced neonatal pup ultrasonic calling and altered juvenile reciprocal social interactions. These findings confirm that living in close proximity to highly trafficked roadways during early life alters neurodevelopment.
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Affiliation(s)
- Elizabeth L. Berg
- grid.27860.3b0000 0004 1936 9684MIND Institute and Department of Psychiatry and Behavioral Sciences, University of California Davis School of Medicine, Sacramento, CA USA
| | - Lauren R. Pedersen
- grid.27860.3b0000 0004 1936 9684MIND Institute and Department of Psychiatry and Behavioral Sciences, University of California Davis School of Medicine, Sacramento, CA USA
| | - Michael C. Pride
- grid.27860.3b0000 0004 1936 9684MIND Institute and Department of Psychiatry and Behavioral Sciences, University of California Davis School of Medicine, Sacramento, CA USA
| | - Stela P. Petkova
- grid.27860.3b0000 0004 1936 9684MIND Institute and Department of Psychiatry and Behavioral Sciences, University of California Davis School of Medicine, Sacramento, CA USA
| | - Kelley T. Patten
- grid.27860.3b0000 0004 1936 9684Department of Molecular Biosciences, University of California Davis School of Veterinary Medicine, Davis, CA USA
| | - Anthony E. Valenzuela
- grid.27860.3b0000 0004 1936 9684Department of Molecular Biosciences, University of California Davis School of Veterinary Medicine, Davis, CA USA
| | - Christopher Wallis
- grid.27860.3b0000 0004 1936 9684Air Quality Research Center, University of California Davis, Davis, CA USA
| | - Keith J. Bein
- grid.27860.3b0000 0004 1936 9684Air Quality Research Center, University of California Davis, Davis, CA USA
| | - Anthony Wexler
- grid.27860.3b0000 0004 1936 9684Air Quality Research Center, University of California Davis, Davis, CA USA
| | - Pamela J. Lein
- grid.27860.3b0000 0004 1936 9684Department of Molecular Biosciences, University of California Davis School of Veterinary Medicine, Davis, CA USA
| | - Jill L. Silverman
- grid.27860.3b0000 0004 1936 9684MIND Institute and Department of Psychiatry and Behavioral Sciences, University of California Davis School of Medicine, Sacramento, CA USA
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214
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Tada H, Fujino N, Nomura A, Nakanishi C, Hayashi K, Takamura M, Kawashiri MA. Personalized medicine for cardiovascular diseases. J Hum Genet 2020; 66:67-74. [PMID: 32772049 DOI: 10.1038/s10038-020-0818-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 05/28/2020] [Accepted: 07/19/2020] [Indexed: 12/24/2022]
Abstract
Personalized medicine is an emerging concept involving managing the health of patients based on their individual characteristics, including particular genotypes. Cardiovascular diseases are heritable traits, and family history information is useful for risk prediction. As such, determining genetic information (germline genetic mutations) may also be applied to risk prediction. Furthermore, accumulating evidence suggests that genetic background can provide guidance for selecting effective treatments and preventive strategies in individuals with particular genotypes. These concepts may be applicable both to rare Mendelian diseases and to common complex traits. In this review, we define the concept and provide examples of personalized medicine based on human genetics for cardiovascular diseases, including coronary artery disease, arrhythmia, and cardiomyopathies. We also provide a particular focus on Mendelian randomization studies, especially those examining loss-of function genetic variations, for identifying high-risk individuals, as well as signaling pathways that may be useful targets for improving healthy living without cardiovascular events.
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Affiliation(s)
- Hayato Tada
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan.
| | - Noboru Fujino
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Akihiro Nomura
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Chiaki Nakanishi
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Kenshi Hayashi
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Masayuki Takamura
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
| | - Masa-Aki Kawashiri
- Department of Cardiovascular Medicine, Kanazawa University Graduate School of Medical Sciences, Kanazawa, Japan
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215
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Schober AF, Mathis AD, Ingle C, Park JO, Chen L, Rabinowitz JD, Junier I, Rivoire O, Reynolds KA. A Two-Enzyme Adaptive Unit within Bacterial Folate Metabolism. Cell Rep 2020; 27:3359-3370.e7. [PMID: 31189117 DOI: 10.1016/j.celrep.2019.05.030] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 04/05/2019] [Accepted: 05/09/2019] [Indexed: 11/29/2022] Open
Abstract
Enzyme function and evolution are influenced by the larger context of a metabolic pathway. Deleterious mutations or perturbations in one enzyme can often be compensated by mutations to others. We used comparative genomics and experiments to examine evolutionary interactions with the essential metabolic enzyme dihydrofolate reductase (DHFR). Analyses of synteny and co-occurrence across bacterial species indicate that DHFR is coupled to thymidylate synthase (TYMS) but relatively independent from the rest of folate metabolism. Using quantitative growth rate measurements and forward evolution in Escherichia coli, we demonstrate that the two enzymes adapt as a relatively independent unit in response to antibiotic stress. Metabolomic profiling revealed that TYMS activity must not exceed DHFR activity to prevent the depletion of reduced folates and the accumulation of the intermediate dihydrofolate. Comparative genomics analyses identified >200 gene pairs with similar statistical signatures of modular co-evolution, suggesting that cellular pathways may be decomposable into small adaptive units.
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Affiliation(s)
- Andrew F Schober
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Andrew D Mathis
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Christine Ingle
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Junyoung O Park
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
| | - Li Chen
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
| | - Joshua D Rabinowitz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
| | - Ivan Junier
- Centre National de la Recherche Scientifique, Université Grenoble Alpes, TIMC-IMAG, F-38000 Grenoble, France
| | - Olivier Rivoire
- Center for Interdisciplinary Research in Biology (CIRB), Collège de France, CNRS, INSERM, PSL Research University, F-75005 Paris, France
| | - Kimberly A Reynolds
- The Green Center for Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
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216
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Aleknonytė-Resch M, Freitag-Wolf S, Schreiber S, Krawczak M, Dempfle A. Case-only analysis of gene-gene interactions in inflammatory bowel disease. Scand J Gastroenterol 2020; 55:897-906. [PMID: 32649238 DOI: 10.1080/00365521.2020.1790646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Gene-gene interactions (G × G) potentially play a role in the etiology of complex human diseases, including inflammatory bowel disease (IBD), and may partially explain their 'missing heritability'. METHODS Using the largest genotype dataset available for IBD (16,636 Crohn's disease (CD) and 12,888 ulcerative colitis (UC) cases) we analyzed G × G with the powerful case-only (CO) design. We studied 169 single nucleotide polymorphisms (SNPs) for CD (156 for UC), previously shown to be associated with the respective diseases. To ensure the validity of the CO design, we confined our analysis to pairs of unlinked SNPs. We used principal component analysis at the center level to adjust for possible causes of genotypic association other than G × G, such as population stratification and genotyping batch effects. Results from center-wise logistic regression analyses were combined by a random effects meta-analysis. RESULTS A number of nominally significant (p < .05) G × G interactions were observed, but none of these withstood the Bonferroni multiple testing correction. However, one SNP pair, comprising rs26528 in the IL27 gene and rs9297145 in the KPNA7 gene region was characterized by an interaction odds ratio of 1.18 (95% CI: 1.10-1.27) for CD and a p-value of 7.75 × 10-6. Owing to the concurrent role of the IL27 and KPNA7 genes in NF-κB signaling, a master regulator of pro- and anti-inflammatory processes in IBD, the observed interaction also has biological plausibility. CONCLUSIONS We were able to exemplify the utility of the CO design for analyzing G × G, but had to recognize that such interactions are probably scarce for IBD.
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Affiliation(s)
| | - Sandra Freitag-Wolf
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
| | | | - Stefan Schreiber
- Department of Internal Medicine I, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Michael Krawczak
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
| | - Astrid Dempfle
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
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217
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Allum F, Grundberg E. Capturing functional epigenomes for insight into metabolic diseases. Mol Metab 2020; 38:100936. [PMID: 32199819 PMCID: PMC7300388 DOI: 10.1016/j.molmet.2019.12.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 12/23/2019] [Accepted: 12/30/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Metabolic diseases such as obesity are known to be driven by both environmental and genetic factors. Although genome-wide association studies of common variants and their impact on complex traits have provided some biological insight into disease etiology, identified genetic variants have been found to contribute only a small proportion to disease heritability, and to map mainly to non-coding regions of the genome. To link variants to function, association studies of cellular traits, such as epigenetic marks, in disease-relevant tissues are commonly applied. SCOPE OF THE REVIEW We review large-scale efforts to generate genome-wide maps of coordinated epigenetic marks and their utility in complex disease dissection with a focus on DNA methylation. We contrast DNA methylation profiling methods and discuss the advantages of using targeted methods for single-base resolution assessments of methylation levels across tissue-specific regulatory regions to deepen our understanding of contributing factors leading to complex diseases. MAJOR CONCLUSIONS Large-scale assessments of DNA methylation patterns in metabolic disease-linked study cohorts have provided insight into the impact of variable epigenetic variants in disease etiology. In-depth profiling of epigenetic marks at regulatory regions, particularly at tissue-specific elements, will be key to dissect the genetic and environmental components contributing to metabolic disease onset and progression.
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Affiliation(s)
- Fiona Allum
- Department of Human Genetics, McGill University, Montréal, Québec, H3A 0C7, Canada; McGill University and Genome Quebec Innovation Centre, Montréal, Québec, H3A 0G1, Canada
| | - Elin Grundberg
- Children's Mercy Kansas City, Kansas City, MO, 64108, United States.
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218
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Early environmental risk factors for neurodevelopmental disorders - a systematic review of twin and sibling studies. Dev Psychopathol 2020; 33:1448-1495. [PMID: 32703331 PMCID: PMC8564717 DOI: 10.1017/s0954579420000620] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
While neurodevelopmental disorders (NDDs) are highly heritable, several environmental risk factors have also been suggested. However, the role of familial confounding is unclear. To shed more light on this, we reviewed the evidence from twin and sibling studies. A systematic review was performed on case control and cohort studies including a twin or sibling within-pair comparison of neurodevelopmental outcomes, with environmental exposures until the sixth birthday. From 7,315 screened abstracts, 140 eligible articles were identified. After adjustment for familial confounding advanced paternal age, low birth weight, birth defects, and perinatal hypoxia and respiratory stress were associated with autism spectrum disorder (ASD), and low birth weight, gestational age and family income were associated with attention-deficit/hyperactivity disorder (ADHD), categorically and dimensionally. Several previously suspected factors, including pregnancy-related factors, were deemed due to familial confounding. Most studies were conducted in North America and Scandinavia, pointing to a global research bias. Moreover, most studies focused on ASD and ADHD. This genetically informed review showed evidence for a range of environmental factors of potential casual significance in NDDs, but also points to a critical need of more genetically informed studies of good quality in the quest of the environmental causes of NDDs.
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219
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Ratnakumar A, Weinhold N, Mar JC, Riaz N. Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks. PLoS Genet 2020; 16:e1008903. [PMID: 32678846 PMCID: PMC7390454 DOI: 10.1371/journal.pgen.1008903] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 07/29/2020] [Accepted: 06/01/2020] [Indexed: 01/09/2023] Open
Abstract
Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named ‘core genes’, while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer’s Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets—consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer–where we identify 109 candidate core genes. A recent theory suggests that only a small number of genes underpin the biology of a disease, these genes are called ‘core genes’, and for most diseases, these core genes remain unknown. The suggested methods for finding them requires complex and expensive experiments. We reasoned that if we merge currently available datasets in smart ways, we may be able to uncover these ‘core genes’. Our method finds “hub” proteins by merging lists of genes previously linked with disease to information on how proteins interact with each other. We found that many of these hub proteins have central roles in disease, such as insulin for both A1C measurement and Type 2 Diabetes, BRCA1 in Breast cancer, and Amyloid Precursor Protein in Alzheimer’s Disease. We think these ‘hub’ proteins are candidate ‘core genes’, and offer our method as a way to find ‘core genes’ by utilizing publicly available reference datasets.
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Affiliation(s)
- Abhirami Ratnakumar
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
- * E-mail:
| | - Nils Weinhold
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Jessica C. Mar
- Australian Institute for Bioengineering and Nanotechnology, University of Queensland, Brisbane, Australia
| | - Nadeem Riaz
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
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220
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Ni X, Zhou M, Wang H, He KY, Broeckel U, Hanis C, Kardia S, Redline S, Cooper RS, Tang H, Zhu X. Detecting fitness epistasis in recently admixed populations with genome-wide data. BMC Genomics 2020; 21:476. [PMID: 32652930 PMCID: PMC7353720 DOI: 10.1186/s12864-020-06874-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 06/30/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Fitness epistasis, the interaction effect of genes at different loci on fitness, makes an important contribution to adaptive evolution. Although fitness interaction evidence has been observed in model organisms, it is more difficult to detect and remains poorly understood in human populations as a result of limited statistical power and experimental constraints. Fitness epistasis is inferred from non-independence between unlinked loci. We previously observed ancestral block correlation between chromosomes 4 and 6 in African Americans. The same approach fails when examining ancestral blocks on the same chromosome due to the strong confounding effect observed in a recently admixed population. RESULTS We developed a novel approach to eliminate the bias caused by admixture linkage disequilibrium when searching for fitness epistasis on the same chromosome. We applied this approach in 16,252 unrelated African Americans and identified significant ancestral correlations in two pairs of genomic regions (P-value< 8.11 × 10- 7) on chromosomes 1 and 10. The ancestral correlations were not explained by population admixture. Historical African-European crossover events are reduced between pairs of epistatic regions. We observed multiple pairs of co-expressed genes shared by the two regions on each chromosome, including ADAR being co-expressed with IFI44 in almost all tissues and DARC being co-expressed with VCAM1, S1PR1 and ELTD1 in multiple tissues in the Genotype-Tissue Expression (GTEx) data. Moreover, the co-expressed gene pairs are associated with the same diseases/traits in the GWAS Catalog, such as white blood cell count, blood pressure, lung function, inflammatory bowel disease and educational attainment. CONCLUSIONS Our analyses revealed two instances of fitness epistasis on chromosomes 1 and 10, and the findings suggest a potential approach to improving our understanding of adaptive evolution.
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Affiliation(s)
- Xumin Ni
- Department of Mathematics, School of Science, Beijing Jiaotong University, Beijing, 100044, China
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Mengshi Zhou
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Karen Y He
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Uli Broeckel
- Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Craig Hanis
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Sharon Kardia
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Richard S Cooper
- Department of Public Health Science, Loyola University Medical Center, Maywood, IL, USA
| | - Hua Tang
- Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA.
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Chinchilla-Vargas J, Kramer LM, Tucker JD, Hubbell DS, Powell JG, Lester TD, Backes EA, Anschutz K, Decker JE, Stalder KJ, Rothschild MF, Koltes JE. Genetic Basis of Blood-Based Traits and Their Relationship With Performance and Environment in Beef Cattle at Weaning. Front Genet 2020; 11:717. [PMID: 32719722 PMCID: PMC7350949 DOI: 10.3389/fgene.2020.00717] [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] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 06/12/2020] [Indexed: 12/16/2022] Open
Abstract
The objectives of this study were to explore the usefulness of blood-based traits as indicators of health and performance in beef cattle at weaning and identify the genetic basis underlying the different blood parameters obtained from complete blood counts (CBCs). Disease costs represent one of the main factors determining profitability in animal production. Previous research has observed associations between blood cell counts and an animal’s health status in some species. CBC were recorded from approximately 570 Angus based, crossbred beef calves at weaning born between 2015 and 2016 and raised on toxic or novel tall fescue. The calves (N = ∼600) were genotyped at a density of 50k SNPs and the genotypes (N = 1160) were imputed to a density of 270k SNPs. Genetic parameters were estimated for 15 blood and 4 production. Finally, with the objective of identifying the genetic basis underlying the different blood-based traits, genome-wide association studies (GWAS) were performed for all traits. Heritability estimates ranged from 0.11 to 0.60, and generally weak phenotypic correlations and strong genetic correlations were observed among blood-based traits only. Genome-wide association study identified ninety-one 1-Mb windows that accounted for 0.5% or more of the estimated genetic variance for at least 1 trait with 21 windows overlapping across two or more traits (explaining more than 0.5% of estimated genetic variance for two or more traits). Five candidate genes have been identified in the most interesting overlapping regions related to blood-based traits. Overall, this study represents one of the first efforts represented in scientific literature to identify the genetic basis of blood cell traits in beef cattle. The results presented in this study allow us to conclude that: (1) blood-based traits have weak phenotypic correlations but strong genetic correlations among themselves. (2) Blood-based traits have moderate to high heritability. (3) There is evidence of an important overlap of genetic control among similar blood-based traits which will allow for their use in improvement programs in beef cattle.
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Affiliation(s)
| | - Luke M Kramer
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - John D Tucker
- Division of Agriculture, Livestock and Forestry Research Station, Batesville, AR, United States
| | - Donald S Hubbell
- Division of Agriculture, Livestock and Forestry Research Station, Batesville, AR, United States
| | - Jeremy G Powell
- Department of Animal Science, University of Arkansas, Fayetteville, AR, United States
| | - Toby D Lester
- Department of Animal Science, University of Arkansas, Fayetteville, AR, United States
| | - Elizabeth A Backes
- Department of Animal Science, University of Arkansas, Fayetteville, AR, United States
| | - Karen Anschutz
- Department of Animal Science, University of Arkansas, Fayetteville, AR, United States
| | - Jared E Decker
- Division of Animal Science, University of Missouri, Columbia, MO, United States
| | - Kenneth J Stalder
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - Max F Rothschild
- Department of Animal Science, Iowa State University, Ames, IA, United States
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA, United States
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222
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Choquet H, Thai KK, Jiang C, Ranatunga DK, Hoffmann TJ, Go AS, Lindsay AC, Ehm MG, Waterworth DM, Risch N, Schaefer C. Meta-Analysis of 26 638 Individuals Identifies Two Genetic Loci Associated With Left Ventricular Ejection Fraction. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2020; 13:e002804. [PMID: 32605384 DOI: 10.1161/circgen.119.002804] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Left ventricular ejection fraction (EF) is an indicator of cardiac function, usually assessed in individuals with heart failure and other cardiac conditions. Although family studies indicate that EF has an important genetic component with heritability estimates up to 0.61, to date only 6 EF-associated loci have been reported. METHODS Here, we conducted a genome-wide association study (GWAS) of EF in 26 638 adults from the Genetic Epidemiology Research on Adult Health and Aging and the UK Biobank cohorts. RESULTS A meta-analysis combining results from Genetic Epidemiology Research on Adult Health and Aging and UK Biobank identified a novel locus: TMEM40 on chromosome 3p25 (rs11719526; β=0.47 and P=3.10×10-8) that replicated in Biobank Japan and confirmed recent findings implicating the BAG3 locus on chromosome 10q26 in EF variation, with the strongest association observed for rs17617337 (β=-0.83 and P=8.24×10-17). Although the minor allele frequencies of TMEM40 rs11719526 were generally common (between 0.13 and 0.44) in different ethnic groups, BAG3 rs17617337 was rare (minor allele frequencies<0.05) in Asian and African ancestry populations. These associations were slightly attenuated, after considering antecedent cardiac conditions (ie, heart failure/cardiomyopathy, hypertension, myocardial infarction, atrial fibrillation, valvular disease, and revascularization procedures). This suggests that the effects of the lead variants at TMEM40 or BAG3 on EF are largely independent of these conditions. CONCLUSIONS In this large and multiethnic study, we identified 2 loci, TMEM40 and BAG3, associated with EF at a genome-wide significance level. Identifying and understanding the genetic determinants of EF is important to better understand the pathophysiology of this strong correlate of cardiac outcomes and to help target the development of future therapies.
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Affiliation(s)
- Hélène Choquet
- Division of Research, Kaiser Permanente Northern California (KPNC), Oakland, CA (H.C., K.K.T., C.J., D.K.R., A.S.G., N.R., C.S.)
| | - Khanh K Thai
- Division of Research, Kaiser Permanente Northern California (KPNC), Oakland, CA (H.C., K.K.T., C.J., D.K.R., A.S.G., N.R., C.S.)
| | - Chen Jiang
- Division of Research, Kaiser Permanente Northern California (KPNC), Oakland, CA (H.C., K.K.T., C.J., D.K.R., A.S.G., N.R., C.S.)
| | - Dilrini K Ranatunga
- Division of Research, Kaiser Permanente Northern California (KPNC), Oakland, CA (H.C., K.K.T., C.J., D.K.R., A.S.G., N.R., C.S.)
| | - Thomas J Hoffmann
- Institute for Human Genetics (T.J.H., N.R.), UCSF, San Francisco, CA.,Department of Epidemiology and Biostatistics (T.J.H., N.R.), UCSF, San Francisco, CA
| | - Alan S Go
- Division of Research, Kaiser Permanente Northern California (KPNC), Oakland, CA (H.C., K.K.T., C.J., D.K.R., A.S.G., N.R., C.S.)
| | | | - Margaret G Ehm
- GlaxoSmithKline, Collegeville, PA (A.C.L., M.G.E., D.M.W.)
| | | | - Neil Risch
- Division of Research, Kaiser Permanente Northern California (KPNC), Oakland, CA (H.C., K.K.T., C.J., D.K.R., A.S.G., N.R., C.S.).,Institute for Human Genetics (T.J.H., N.R.), UCSF, San Francisco, CA.,Department of Epidemiology and Biostatistics (T.J.H., N.R.), UCSF, San Francisco, CA
| | - Catherine Schaefer
- Division of Research, Kaiser Permanente Northern California (KPNC), Oakland, CA (H.C., K.K.T., C.J., D.K.R., A.S.G., N.R., C.S.)
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Leal LG, David A, Jarvelin MR, Sebert S, Männikkö M, Karhunen V, Seaby E, Hoggart C, Sternberg MJE. Identification of disease-associated loci using machine learning for genotype and network data integration. Bioinformatics 2020; 35:5182-5190. [PMID: 31070705 PMCID: PMC6954643 DOI: 10.1093/bioinformatics/btz310] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 03/28/2019] [Accepted: 04/25/2019] [Indexed: 01/19/2023] Open
Abstract
Motivation Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wide Association Studies (GWAS) identify loci with a strong effect size, whereas GWAS meta-analyses are often needed to capture weak loci contributing to the missing heritability. Development of novel machine learning algorithms for merging genotype data with other omics data is highly needed as it could enhance the prioritization of weak loci. Results We developed cNMTF (corrected non-negative matrix tri-factorization), an integrative algorithm based on clustering techniques of biological data. This method assesses the inter-relatedness between genotypes, phenotypes, the damaging effect of the variants and gene networks in order to identify loci-trait associations. cNMTF was used to prioritize genes associated with lipid traits in two population cohorts. We replicated 129 genes reported in GWAS world-wide and provided evidence that supports 85% of our findings (226 out of 265 genes), including recent associations in literature (NLGN1), regulators of lipid metabolism (DAB1) and pleiotropic genes for lipid traits (CARM1). Moreover, cNMTF performed efficiently against strong population structures by accounting for the individuals’ ancestry. As the method is flexible in the incorporation of diverse omics data sources, it can be easily adapted to the user’s research needs. Availability and implementation An R package (cnmtf) is available at https://lgl15.github.io/cnmtf_web/index.html. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Luis G Leal
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK
| | - Alessia David
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK
| | - Marjo-Riita Jarvelin
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu FI-90014, Finland.,Biocenter Oulu, University of Oulu, Oulu 90220, Finland.,Unit of Primary Health Care, Oulu University Hospital, Oulu 90220, Finland.,Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK.,Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Middlesex UB8 3PH, UK
| | - Sylvain Sebert
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu FI-90014, Finland.,Biocenter Oulu, University of Oulu, Oulu 90220, Finland
| | - Minna Männikkö
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu FI-90014, Finland
| | - Ville Karhunen
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu FI-90014, Finland.,Biocenter Oulu, University of Oulu, Oulu 90220, Finland.,Unit of Primary Health Care, Oulu University Hospital, Oulu 90220, Finland.,Department of Epidemiology and Biostatistics, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London W2 1PG, UK.,Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Middlesex UB8 3PH, UK
| | - Eleanor Seaby
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Clive Hoggart
- Department of Medicine, Imperial College London, London W2 1PG, UK
| | - Michael J E Sternberg
- Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK
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224
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Fanfani V, Zatopkova M, Harris AL, Pezzella F, Stracquadanio G. Dissecting the heritable risk of breast cancer: From statistical methods to susceptibility genes. Semin Cancer Biol 2020; 72:175-184. [PMID: 32569822 DOI: 10.1016/j.semcancer.2020.06.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Revised: 06/01/2020] [Accepted: 06/02/2020] [Indexed: 12/24/2022]
Abstract
Decades of research have shown that rare highly penetrant mutations can promote tumorigenesis, but it is still unclear whether variants observed at high-frequency in the broader population could modulate the risk of developing cancer. Genome-wide Association Studies (GWAS) have generated a wealth of data linking single nucleotide polymorphisms (SNPs) to increased cancer risk, but the effect of these mutations are usually subtle, leaving most of cancer heritability unexplained. Understanding the role of high-frequency mutations in cancer can provide new intervention points for early diagnostics, patient stratification and treatment in malignancies with high prevalence, such as breast cancer. Here we review state-of-the-art methods to study cancer heritability using GWAS data and provide an updated map of breast cancer susceptibility loci at the SNP and gene level.
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Affiliation(s)
- Viola Fanfani
- Institute of Quantitative Biology, Biochemistry, and Biotechnology, SynthSys, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Martina Zatopkova
- Department of Clinical Studies, Faculty of Medicine, University of Ostrava, Ostrava, Czech Republic
| | - Adrian L Harris
- Molecular Oncology Laboratories, Department of Oncology, The Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Francesco Pezzella
- Nuffield Department of Clinical Laboratory Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Giovanni Stracquadanio
- Institute of Quantitative Biology, Biochemistry, and Biotechnology, SynthSys, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK.
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225
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Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives. Nat Commun 2020; 11:3074. [PMID: 32555176 PMCID: PMC7299943 DOI: 10.1038/s41467-020-16829-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/25/2020] [Indexed: 01/06/2023] Open
Abstract
Polygenic risk scores are emerging as a potentially powerful tool to predict future phenotypes of target individuals, typically using unrelated individuals, thereby devaluing information from relatives. Here, for 50 traits from the UK Biobank data, we show that a design of 5,000 individuals with first-degree relatives of target individuals can achieve a prediction accuracy similar to that of around 220,000 unrelated individuals (mean prediction accuracy = 0.26 vs. 0.24, mean fold-change = 1.06 (95% CI: 0.99-1.13), P-value = 0.08), despite a 44-fold difference in sample size. For lifestyle traits, the prediction accuracy with 5,000 individuals including first-degree relatives of target individuals is significantly higher than that with 220,000 unrelated individuals (mean prediction accuracy = 0.22 vs. 0.16, mean fold-change = 1.40 (1.17-1.62), P-value = 0.025). Our findings suggest that polygenic prediction integrating family information may help to accelerate precision health and clinical intervention. Genetic data from large cohorts of unrelated individuals can be used to create polygenic risk scores, which could be used to predict individual risk of developing a specific disease. Here the authors show that smaller cohorts of related individuals can provide similarly powerful predictive ability.
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226
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Genetic control of non-genetic inheritance in mammals: state-of-the-art and perspectives. Mamm Genome 2020; 31:146-156. [PMID: 32529318 PMCID: PMC7369129 DOI: 10.1007/s00335-020-09841-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 06/03/2020] [Indexed: 12/12/2022]
Abstract
Thought to be directly and uniquely dependent from genotypes, the ontogeny of individual phenotypes is much more complicated. Individual genetics, environmental exposures, and their interaction are the three main determinants of individual's phenotype. This picture has been further complicated a decade ago when the Lamarckian theory of acquired inheritance has been rekindled with the discovery of epigenetic inheritance, according to which acquired phenotypes can be transmitted through fertilization and affect phenotypes across generations. The results of Genome-Wide Association Studies have also highlighted a big degree of missing heritability in genetics and have provided hints that not only acquired phenotypes, but also individual's genotypes affect phenotypes intergenerationally through indirect genetic effects. Here, we review available examples of indirect genetic effects in mammals, what is known of the underlying molecular mechanisms and their potential impact for our understanding of missing heritability, phenotypic variation. and individual disease risk.
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227
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Wu VC, Chueh JS, Hsieh MY, Hu YH, Huang KH, Lin YH, Yang SY, Chu TS, Kuo CF. Familial Aggregation and Heritability of Aldosteronism with Cardiovascular Events. J Clin Endocrinol Metab 2020; 105:5810354. [PMID: 32193536 DOI: 10.1210/clinem/dgz257] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Accepted: 12/10/2019] [Indexed: 01/13/2023]
Abstract
CONTEXT To date, the effect of positive family history as a risk factor of primary aldosteronism (PA) is largely unknown. Studies have failed to distinguish the heritability of PA as well as the associations between positive family history of PA and clinical outcomes. OBJECTIVES We quantified the prevalence, the extent of familial aggregation, the heritability of PA among family members of patients with PA, and the association between positive PA family history and major cardiovascular events (MACE). DESIGN AND SETTINGS Using the Taiwan National Health Insurance Database, 30 245 077 National Health Insurance beneficiaries (both alive and those deceased between January 1, 1999, and December 31, 2015) were identified. RESULTS We identified 7902 PA patients. Forty-four had PA (0.3%) among 10 234 individuals with affected parents, 2298 with affected offspring, 1924 with affected siblings, and 22 with affected twins. A positive family history was associated with the adjusted relative risk (RR) (95% confidence interval [CI]) of 11.60 (7.63-17.63) for PA in people with an affected first-degree relative. In subgroup analysis, the risk for PA across all relationships (parent, siblings, offspring, and spouse) showed highly significant differences to PA without family history. The accountability for phenotypic variance of PA was 51.0% for genetic factors, 24.9% for shared environmental factors, and 24.1% for nonshared environmental factors. PA patients with an affected first-degree relative were associated with an increased risk for composite major cardiovascular events (RR 1.31; 95% CI 1.24-1.40, P < .001) compared with PA patients without family history. CONCLUSION Familial clustering of PA exists among a population-based study, supporting a genetic susceptibility leading to PA. There is increased coaggregation of MACE in first-degree relatives of PA patients. Our findings suggest a strong genetic component in the susceptibility of PA, involving different kinships.
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Affiliation(s)
- Vin-Cent Wu
- Division of Nephrology and Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Jeff S Chueh
- Cleveland Clinic Lerner College of Medicine and Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio
| | - Mei-Yun Hsieh
- Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Ya-Hui Hu
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Tzu Chi Hospital, The Buddhist Medical Foundation, Taipei, Taiwan
| | - Kuo-How Huang
- Division of Urology, National Taiwan University Hospital, Taipei, Taiwan
| | - Yen-Hung Lin
- Division of Nephrology and Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Shao-Yu Yang
- Division of Nephrology and Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Tzong-Shinn Chu
- Division of Nephrology and Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chang-Fu Kuo
- Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan
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228
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Papageorge NW, Thom K. Genes, Education, and Labor Market Outcomes: Evidence from the Health and Retirement Study. JOURNAL OF THE EUROPEAN ECONOMIC ASSOCIATION 2020; 18:1351-1399. [PMID: 32587483 PMCID: PMC7297142 DOI: 10.1093/jeea/jvz072] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Recent advances have led to the discovery of specific genetic variants that predict educational attainment. We study how these variants, summarized as a linear index-known as a polygenic score-are associated with human capital accumulation and labor market outcomes in the Health and Retirement Study (HRS). We present two main sets of results. First, we find evidence that the genetic factors measured by this score interact strongly with childhood socioeconomic status in determining educational outcomes. In particular, although the polygenic score predicts higher rates of college graduation on average, this relationship is substantially stronger for individuals who grew up in households with higher socioeconomic status relative to those who grew up in poorer households. Second, the polygenic score predicts labor earnings even after adjusting for completed education, with larger returns in more recent decades. These patterns suggest that the genetic traits that promote education might allow workers to better accommodate ongoing skill biased technological change. Consistent with this interpretation, we find a positive association between the polygenic score and nonroutine analytic tasks that have benefited from the introduction of new technologies. Nonetheless, the college premium remains a dominant determinant of earnings differences at all levels of the polygenic score. Given the role of childhood SES in predicting college attainment, this raises concerns about wasted potential arising from limited household resources.
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229
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McGuirl MR, Smith SP, Sandstede B, Ramachandran S. Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics. Genetics 2020; 215:511-529. [PMID: 32245788 PMCID: PMC7268989 DOI: 10.1534/genetics.120.303096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 03/31/2020] [Indexed: 12/31/2022] Open
Abstract
Emerging large-scale biobanks pairing genotype data with phenotype data present new opportunities to prioritize shared genetic associations across multiple phenotypes for molecular validation. Past research, by our group and others, has shown gene-level tests of association produce biologically interpretable characterization of the genetic architecture of a given phenotype. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for identifying shared genetic architecture among multiple phenotypes. The objective of WINGS is to identify groups of phenotypes, or "clusters," sharing a core set of genes enriched for mutations in cases. We validate WINGS using extensive simulation studies and then combine gene-level association tests with WINGS to identify shared genetic architecture among 81 case-control and seven quantitative phenotypes in 349,468 European-ancestry individuals from the UK Biobank. We identify eight prioritized phenotype clusters and recover multiple published gene-level associations within prioritized clusters.
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Affiliation(s)
- Melissa R McGuirl
- Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912
| | - Samuel Pattillo Smith
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island 02912
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island 02912
| | - Björn Sandstede
- Division of Applied Mathematics, Brown University, Providence, Rhode Island 02912
- Data Science Initiative, Brown University, Providence, Rhode Island 02912
| | - Sohini Ramachandran
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island 02912
- Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island 02912
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230
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Wen J, Ford CT, Janies D, Shi X. A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models. Bioinformatics 2020; 36:3803-3810. [PMID: 32227194 DOI: 10.1093/bioinformatics/btaa216] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 03/05/2020] [Accepted: 03/26/2020] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Epistasis reflects the distortion on a particular trait or phenotype resulting from the combinatorial effect of two or more genes or genetic variants. Epistasis is an important genetic foundation underlying quantitative traits in many organisms as well as in complex human diseases. However, there are two major barriers in identifying epistasis using large genomic datasets. One is that epistasis analysis will induce over-fitting of an over-saturated model with the high-dimensionality of a genomic dataset. Therefore, the problem of identifying epistasis demands efficient statistical methods. The second barrier comes from the intensive computing time for epistasis analysis, even when the appropriate model and data are specified. RESULTS In this study, we combine statistical techniques and computational techniques to scale up epistasis analysis using Empirical Bayesian Elastic Net (EBEN) models. Specifically, we first apply a matrix manipulation strategy for pre-computing the correlation matrix and pre-filter to narrow down the search space for epistasis analysis. We then develop a parallelized approach to further accelerate the modeling process. Our experiments on synthetic and empirical genomic data demonstrate that our parallelized methods offer tens of fold speed up in comparison with the classical EBEN method which runs in a sequential manner. We applied our parallelized approach to a yeast dataset, and we were able to identify both main and epistatic effects of genetic variants associated with traits such as fitness. AVAILABILITY AND IMPLEMENTATION The software is available at github.com/shilab/parEBEN.
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Affiliation(s)
- Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Colby T Ford
- Department of Bioinformatics and Genomics, College of Computing and Informatics.,School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Daniel Janies
- Department of Bioinformatics and Genomics, College of Computing and Informatics
| | - Xinghua Shi
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
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231
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Johansson Å, Rask-Andersen M, Karlsson T, Ek WE. Genome-wide association analysis of 350 000 Caucasians from the UK Biobank identifies novel loci for asthma, hay fever and eczema. Hum Mol Genet 2020; 28:4022-4041. [PMID: 31361310 PMCID: PMC6969355 DOI: 10.1093/hmg/ddz175] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 05/29/2019] [Accepted: 06/03/2019] [Indexed: 12/19/2022] Open
Abstract
Even though heritability estimates suggest that the risk of asthma, hay fever and eczema is largely due to genetic factors, previous studies have not explained a large part of the genetics behind these diseases. In this genome-wide association study, we include 346 545 Caucasians from the UK Biobank to identify novel loci for asthma, hay fever and eczema and replicate novel loci in three independent cohorts. We further investigate if associated lead single nucleotide polymorphisms (SNPs) have a significantly larger effect for one disease compared to the other diseases, to highlight possible disease-specific effects. We identified 141 loci, of which 41 are novel, to be associated (P ≤ 3 × 10−8) with asthma, hay fever or eczema, analyzed separately or as disease phenotypes that includes the presence of different combinations of these diseases. The largest number of loci was associated with the combined phenotype (asthma/hay fever/eczema). However, as many as 20 loci had a significantly larger effect on hay fever/eczema only compared to their effects on asthma, while 26 loci exhibited larger effects on asthma compared with their effects on hay fever/eczema. At four of the novel loci, TNFRSF8, MYRF, TSPAN8, and BHMG1, the lead SNPs were in Linkage Disequilibrium (LD) (>0.8) with potentially casual missense variants. Our study shows that a large amount of the genetic contribution is shared between the diseases. Nonetheless, a number of SNPs have a significantly larger effect on one of the phenotypes, suggesting that part of the genetic contribution is more phenotype specific.
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Affiliation(s)
- Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Mathias Rask-Andersen
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Torgny Karlsson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Weronica E Ek
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- To whom correspondence should be addressed at: Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, PO Box 815, 75108, Uppsala, Sweden. Tel: +46703519004; Fax: +46184714931;
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232
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Levings D, Shaw KE, Lacher SE. Genomic resources for dissecting the role of non-protein coding variation in gene-environment interactions. Toxicology 2020; 441:152505. [PMID: 32450112 DOI: 10.1016/j.tox.2020.152505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 05/18/2020] [Accepted: 05/18/2020] [Indexed: 12/27/2022]
Abstract
The majority of single nucleotide variants (SNVs) identified in Genome Wide Association Studies (GWAS) fall within non-protein coding DNA and have the potential to alter gene expression. Non-protein coding DNA can control gene expression by acting as transcription factor (TF) binding sites or by regulating the organization of DNA into chromatin. SNVs in non-coding DNA sequences can disrupt TF binding and chromatin structure and this can result in pathology. Further, environmental health studies have shown that exposure to xenobiotics can disrupt the ability of TFs to regulate entire gene networks and result in pathology. However, there is a large amount of interindividual variability in exposure-linked health outcomes. One explanation for this heterogeneity is that genetic variation and exposure combine to disrupt gene regulation, and this eventually manifests in disease. Many resources exist that annotate common variants from GWAS and combine them with conservation, functional genomics, and TF binding data. These annotation tools provide clues regarding the biological implications of an SNV, as well as lead to the generation of hypotheses regarding potentially disrupted target genes, epigenetic markers, pathways, and cell types. Collectively this information can be used to predict how SNVs can alter an individual's response to exposure and disease risk. A basic understanding of the regulatory information contained within non-protein coding DNA is needed to predict the biological consequences of SNVs, and to determine how these SNVs impact exposure-related disease. We hope that this review will aid in the characterization of disease-associated genetic variation in the non-protein coding genome.
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Affiliation(s)
- Daniel Levings
- Department of Biomedical Sciences, University of Minnesota Medical School, Duluth Campus, 1035 University Drive, Duluth, MN, 55812, USA
| | - Kirsten E Shaw
- Department of Biomedical Sciences, University of Minnesota Medical School, Duluth Campus, 1035 University Drive, Duluth, MN, 55812, USA
| | - Sarah E Lacher
- Department of Biomedical Sciences, University of Minnesota Medical School, Duluth Campus, 1035 University Drive, Duluth, MN, 55812, USA.
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233
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Kim BH, Choi YH, Yang JJ, Kim S, Nho K, Lee JM. Identification of Novel Genes Associated with Cortical Thickness in Alzheimer’s Disease: Systems Biology Approach to Neuroimaging Endophenotype. J Alzheimers Dis 2020; 75:531-545. [DOI: 10.3233/jad-191175] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Affiliation(s)
- Bo-Hyun Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Yong-Ho Choi
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Jin-Ju Yang
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - SangYun Kim
- Department of Neurology, Seoul National University College of Medicine and Clinical Neuroscience Center of Seoul National University Bundang Hospital, Seongnam, Korea
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
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234
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Eichten SR, Srivastava A, Reddiex AJ, Ganguly DR, Heussler A, Streich JC, Wilson PB, Borevitz JO. Extending the Genotype in Brachypodium by Including DNA Methylation Reveals a Joint Contribution with Genetics on Adaptive Traits. G3 (BETHESDA, MD.) 2020; 10:1629-1637. [PMID: 32132166 PMCID: PMC7202021 DOI: 10.1534/g3.120.401189] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Epigenomic changes have been considered a potential missing link underlying phenotypic variation in quantitative traits but is potentially confounded with the underlying DNA sequence variation. Although the concept of epigenetic inheritance has been discussed in depth, there have been few studies attempting to directly dissect the amount of epigenomic variation within inbred natural populations while also accounting for genetic diversity. By using known genetic relationships between Brachypodium lines, multiple sets of nearly identical accession families were selected for phenotypic studies and DNA methylome profiling to investigate the dual role of (epi)genetics under simulated natural seasonal climate conditions. Despite reduced genetic diversity, appreciable phenotypic variation was still observable in the measured traits (height, leaf width and length, tiller count, flowering time, ear count) between as well as within the inbred accessions. However, with reduced genetic diversity there was diminished variation in DNA methylation within families. Mixed-effects linear modeling revealed large genetic differences between families and a minor contribution of DNA methylation variation on phenotypic variation in select traits. Taken together, this analysis suggests a limited but significant contribution of DNA methylation toward heritable phenotypic variation relative to genetic differences.
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Affiliation(s)
- Steven R Eichten
- Australian Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, Australian National University, Canberra, Acton, Australian Capital Territory 2601, Australia
| | - Akanksha Srivastava
- Australian Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, Australian National University, Canberra, Acton, Australian Capital Territory 2601, Australia
| | - Adam J Reddiex
- Australian Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, Australian National University, Canberra, Acton, Australian Capital Territory 2601, Australia
| | - Diep R Ganguly
- Australian Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, Australian National University, Canberra, Acton, Australian Capital Territory 2601, Australia
| | - Alison Heussler
- Australian Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, Australian National University, Canberra, Acton, Australian Capital Territory 2601, Australia
| | - Jared C Streich
- Australian Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, Australian National University, Canberra, Acton, Australian Capital Territory 2601, Australia
| | - Pip B Wilson
- Australian Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, Australian National University, Canberra, Acton, Australian Capital Territory 2601, Australia
| | - Justin O Borevitz
- Australian Research Council Centre of Excellence in Plant Energy Biology, Research School of Biology, Australian National University, Canberra, Acton, Australian Capital Territory 2601, Australia
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235
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Seidman DN, Shenoy SA, Kim M, Babu R, Woods IG, Dyer TD, Lehman DM, Curran JE, Duggirala R, Blangero J, Williams AL. Rapid, Phase-free Detection of Long Identity-by-Descent Segments Enables Effective Relationship Classification. Am J Hum Genet 2020; 106:453-466. [PMID: 32197076 PMCID: PMC7118564 DOI: 10.1016/j.ajhg.2020.02.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 02/18/2020] [Indexed: 01/29/2023] Open
Abstract
Identity-by-descent (IBD) segments are a useful tool for applications ranging from demographic inference to relationship classification, but most detection methods rely on phasing information and therefore require substantial computation time. As genetic datasets grow, methods for inferring IBD segments that scale well will be critical. We developed IBIS, an IBD detector that locates long regions of allele sharing between unphased individuals, and benchmarked it with Refined IBD, GERMLINE, and TRUFFLE on 3,000 simulated individuals. Phasing these with Beagle 5 takes 4.3 CPU days, followed by either Refined IBD or GERMLINE segment detection in 2.9 or 1.1 h, respectively. By comparison, IBIS finishes in 6.8 min or 7.8 min with IBD2 functionality enabled: speedups of 805-946× including phasing time. TRUFFLE takes 2.6 h, corresponding to IBIS speedups of 20.2-23.3×. IBIS is also accurate, inferring ≥7 cM IBD segments at quality comparable to Refined IBD and GERMLINE. With these segments, IBIS classifies first through third degree relatives in real Mexican American samples at rates meeting or exceeding other methods tested and identifies fourth through sixth degree pairs at rates within 0.0%-2.0% of the top method. While allele frequency-based approaches that do not detect segments can infer relationship degrees faster than IBIS, the fastest are biased in admixed samples, with KING inferring 30.8% fewer fifth degree Mexican American relatives correctly compared with IBIS. Finally, we ran IBIS on chromosome 2 of the UK Biobank dataset and estimate its runtime on the autosomes to be 3.3 days parallelized across 128 cores.
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Affiliation(s)
- Daniel N Seidman
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA
| | - Sushila A Shenoy
- Department of Genetic Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Minsoo Kim
- Department of Genetic Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Ramya Babu
- Department of Computer Science, Cornell University, Ithaca, NY 14853, USA
| | - Ian G Woods
- Department of Biology, Ithaca College, Ithaca, NY 14850, USA
| | - Thomas D Dyer
- South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA
| | - Donna M Lehman
- Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA
| | - Joanne E Curran
- South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA
| | - Ravindranath Duggirala
- South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute and Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520, USA
| | - Amy L Williams
- Department of Computational Biology, Cornell University, Ithaca, NY 14853, USA.
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236
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Zhou Y, Browning SR, Browning BL. A Fast and Simple Method for Detecting Identity-by-Descent Segments in Large-Scale Data. Am J Hum Genet 2020; 106:426-437. [PMID: 32169169 PMCID: PMC7118582 DOI: 10.1016/j.ajhg.2020.02.010] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 02/12/2020] [Indexed: 12/24/2022] Open
Abstract
Segments of identity by descent (IBD) are used in many genetic analyses. We present a method for detecting identical-by-descent haplotype segments in phased genotype data. Our method, called hap-IBD, combines a compressed representation of haplotype data, the positional Burrows-Wheeler transform, and multi-threaded execution to produce very fast analysis times. An attractive feature of hap-IBD is its simplicity: the input parameters clearly and precisely define the IBD segments that are reported, so that program correctness can be confirmed by users. We evaluate hap-IBD and four state-of-the-art IBD segment detection methods (GERMLINE, iLASH, RaPID, and TRUFFLE) using UK Biobank chromosome 20 data and simulated sequence data. We show that hap-IBD detects IBD segments faster and more accurately than competing methods, and that hap-IBD is the only method that can rapidly and accurately detect short 2-4 centiMorgan (cM) IBD segments in the full UK Biobank data. Analysis of 485,346 UK Biobank samples through the use of hap-IBD with 12 computational threads detects 231.5 billion autosomal IBD segments with length ≥2 cM in 24.4 h.
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Affiliation(s)
- Ying Zhou
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Brian L Browning
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA; Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA 98195, USA.
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237
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Bandres-Ciga S, Diez-Fairen M, Kim JJ, Singleton AB. Genetics of Parkinson's disease: An introspection of its journey towards precision medicine. Neurobiol Dis 2020; 137:104782. [PMID: 31991247 PMCID: PMC7064061 DOI: 10.1016/j.nbd.2020.104782] [Citation(s) in RCA: 207] [Impact Index Per Article: 51.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 01/15/2020] [Accepted: 01/24/2020] [Indexed: 12/15/2022] Open
Abstract
A substantial proportion of risk for Parkinson's disease (PD) is driven by genetics. Progress in understanding the genetic basis of PD has been significant. So far, highly-penetrant rare genetic alterations in SNCA, LRRK2, VPS35, PRKN, PINK1, DJ-1 and GBA have been linked with typical familial PD and common genetic variability at 90 loci have been linked to risk for PD. In this review, we outline the journey thus far of PD genetics, highlighting how significant advances have improved our knowledge of the genetic basis of PD risk, onset and progression. Despite remarkable progress, our field has yet to unravel how genetic risk variants disrupt biological pathways and molecular networks underlying the pathobiology of the disease. We highlight that currently identified genetic risk factors only represent a fraction of the likely genetic risk for PD. Identifying the remaining genetic risk will require us to diversify our efforts, performing genetic studies across different ancestral groups. This work will inform us on the varied genetic basis of disease across populations and also aid in fine mapping discovered loci. If we are able to take this course, we foresee that genetic discoveries in PD will directly influence our ability to predict disease and aid in defining etiological subtypes, critical steps for the implementation of precision medicine for PD.
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Affiliation(s)
- Sara Bandres-Ciga
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA; Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), Granada 18016, Spain.
| | - Monica Diez-Fairen
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA; Fundació Docència i Recerca Mútua Terrassa and Movement Disorders Unit, Department of Neurology, University Hospital Mútua Terrassa, Terrassa 08221, Barcelona, Spain
| | - Jonggeol Jeff Kim
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
| | - Andrew B Singleton
- Molecular Genetics Section, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA.
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238
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Barth D, Papageorge NW, Thom K. Genetic Endowments and Wealth Inequality. THE JOURNAL OF POLITICAL ECONOMY 2020; 128:1474-1522. [PMID: 32863431 PMCID: PMC7448697 DOI: 10.1086/705415] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
We show that genetic endowments linked to educational attainment strongly and robustly predict wealth at retirement. The estimated relationship is not fully explained by flexibly controlling for education and labor income. We therefore investigate a host of additional mechanisms that could account for the gene-wealth gradient, including inheritances, mortality, risk preferences, portfolio decisions, beliefs about the probabilities of macroeconomic events, and planning horizons. We provide evidence that genetic endowments related to human capital accumulation are associated with wealth not only through educational attainment and labor income, but also through a facility with complex financial decision-making.
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239
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Tikhodeyev ON. Heredity determined by the environment: Lamarckian ideas in modern molecular biology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 710:135521. [PMID: 31784162 DOI: 10.1016/j.scitotenv.2019.135521] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 11/12/2019] [Accepted: 11/12/2019] [Indexed: 06/10/2023]
Abstract
Inheritance of acquired characteristics (IAC) is a well-documented phenomenon occurring both in eukaryotes and prokaryotes. However, it is not included in current biological theories, and the risks of IAC induction are not assessed by genetic toxicology. Furthermore, different kinds of IAC (transgenerational and intergenerational inheritance, genotrophic changes, dauermodifications, vernalization, and some others) are traditionally considered in isolation, thus impeding the development of a comprehensive view on IAC as a whole. Herein, we discuss all currently known kinds of IAC as well as their mechanisms, if unraveled. We demonstrate that IAC is a special case of genotype × environment interactions requiring certain genotypes and, as a rule, prolonged exposure to the inducing influence. Most mechanisms of IAC are epigenetic; these include but not limited to DNA methylation, histone modifications, competition of transcription factors, induction of non-coding RNAs, inhibition of plastid translation, and curing of amyloid and non-amyloid prions. In some cases, changes in DNA sequences or host-microbe interactions are involved as well. The only principal difference between IAC and other environmentally inducible hereditary changes such as the effects of radiation is the origin of the changes: in case of IAC they are definite (determined by the environment), while the others are indefinite (arise from environmentally provoked molecular stochasticity). At least some kinds of IAC are adaptive and could be regarded as the elements of natural selection, though non-canonical in their origin and molecular nature. This is a probable way towards synthesis of the Lamarckian and Darwinian evolutionary conceptions. Applied issues of IAC are also discussed.
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Affiliation(s)
- Oleg N Tikhodeyev
- Department of Genetics & Biotechnology, Saint-Petersburg State University, University emb. 7/9, Saint-Petersburg 199034, Russia.
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240
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Abstract
Genome-wide association studies (GWASs) have identified at least 10 single-nucleotide polymorphisms (SNPs) associated with papillary thyroid cancer (PTC) risk. Most of these SNPs are common variants with small to moderate effect sizes. Here we assessed the combined genetic effects of these variants on PTC risk by using summarized GWAS results to build polygenic risk score (PRS) models in three PTC study groups from Ohio (1,544 patients and 1,593 controls), Iceland (723 patients and 129,556 controls), and the United Kingdom (534 patients and 407,945 controls). A PRS based on the 10 established PTC SNPs showed a stronger predictive power compared with the clinical factors model, with a minimum increase of area under the receiver-operating curve of 5.4 percentage points (P ≤ 1.0 × 10-9). Adding an extended PRS based on 592,475 common variants did not significantly improve the prediction power compared with the 10-SNP model, suggesting that most of the remaining undiscovered genetic risk in thyroid cancer is due to rare, moderate- to high-penetrance variants rather than to common low-penetrance variants. Based on the 10-SNP PRS, individuals in the top decile group of PRSs have a close to sevenfold greater risk (95% CI, 5.4-8.8) compared with the bottom decile group. In conclusion, PRSs based on a small number of common germline variants emphasize the importance of heritable low-penetrance markers in PTC.
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241
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Gordovez FJA, McMahon FJ. The genetics of bipolar disorder. Mol Psychiatry 2020; 25:544-559. [PMID: 31907381 DOI: 10.1038/s41380-019-0634-7] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 11/22/2019] [Accepted: 12/11/2019] [Indexed: 12/11/2022]
Abstract
Bipolar disorder (BD) is one of the most heritable mental illnesses, but the elucidation of its genetic basis has proven to be a very challenging endeavor. Genome-Wide Association Studies (GWAS) have transformed our understanding of BD, providing the first reproducible evidence of specific genetic markers and a highly polygenic architecture that overlaps with that of schizophrenia, major depression, and other disorders. Individual GWAS markers appear to confer little risk, but common variants together account for about 25% of the heritability of BD. A few higher-risk associations have also been identified, such as a rare copy number variant on chromosome 16p11.2. Large scale next-generation sequencing studies are actively searching for other alleles that confer substantial risk. As our understanding of the genetics of BD improves, there is growing optimism that some clear biological pathways will emerge, providing a basis for future studies aimed at molecular diagnosis and novel therapeutics.
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Affiliation(s)
- Francis James A Gordovez
- Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Department of Health and Human Services, National Institutes of Health, Bethesda, MD, USA.,College of Medicine, University of the Philippines Manila, 1000, Ermita, Manila, Philippines
| | - Francis J McMahon
- Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Department of Health and Human Services, National Institutes of Health, Bethesda, MD, USA.
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242
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Riahi P, Kazemnejad A, Mostafaei S, Meguro A, Mizuki N, Ashraf-Ganjouei A, Javinani A, Faezi ST, Shahram F, Mahmoudi M. ERAP1 polymorphisms interactions and their association with Behçet's disease susceptibly: Application of Model-Based Multifactor Dimension Reduction Algorithm (MB-MDR). PLoS One 2020; 15:e0227997. [PMID: 32023277 PMCID: PMC7001967 DOI: 10.1371/journal.pone.0227997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 01/03/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Behçet's disease (BD) is a chronic multi-systemic vasculitis with a considerable prevalence in Asian countries. There are many genes associated with a higher risk of developing BD, one of which is endoplasmic reticulum aminopeptidase-1 (ERAP1). In this study, we aimed to investigate the interactions of ERAP1 single nucleotide polymorphisms (SNPs) using a novel data mining method called Model-based multifactor dimensionality reduction (MB-MDR). METHODS We have included 748 BD patients and 776 healthy controls. A peripheral blood sample was collected, and eleven SNPs were assessed. Furthermore, we have applied the MB-MDR method to evaluate the interactions of ERAP1 gene polymorphisms. RESULTS The TT genotype of rs1065407 had a synergistic effect on BD susceptibility, considering the significant main effect. In the second order of interactions, CC genotype of rs2287987 and GG genotype of rs1065407 had the most prominent synergistic effect (β = 12.74). The mentioned genotypes also had significant interactions with CC genotype of rs26653 and TT genotype of rs30187 in the third-order (β = 12.74 and β = 12.73, respectively). CONCLUSION To the best of our knowledge, this is the first study investigating the interaction of a particular gene's SNPs in BD patients by applying a novel data mining method. However, future studies investigating the interactions of various genes could clarify this issue.
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Affiliation(s)
- Parisa Riahi
- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Anoshirvan Kazemnejad
- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
- * E-mail: (MM); (AK)
| | - Shayan Mostafaei
- Medical Biology Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Akira Meguro
- Department of Ophthalmology and Visual Science, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Nobuhisa Mizuki
- Department of Ophthalmology and Visual Science, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Amir Ashraf-Ganjouei
- Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Javinani
- Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Farhad Shahram
- Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Mahmoudi
- Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran
- Inflammation Research Center, Tehran University of Medical Sciences, Tehran, Iran
- * E-mail: (MM); (AK)
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243
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Wells HRR, Newman TA, Williams FMK. Genetics of age-related hearing loss. J Neurosci Res 2020; 98:1698-1704. [PMID: 31989664 DOI: 10.1002/jnr.24549] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 10/15/2019] [Indexed: 12/13/2022]
Abstract
Age-related hearing loss (ARHL) has recently been confirmed as a common complex trait, that is, it is heritable with many genetic variants each contributing a small amount of risk, as well as environmental determinants. Historically, attempts to identify the genetic variants underlying the ARHL have been of limited success, relying on the selection of candidate genes based on the limited knowledge of the pathophysiology of the condition, and linkage studies in samples comprising related individuals. More recently genome-wide association studies have been performed, but these require very large samples having consistent and reliable phenotyping for hearing loss (HL), and early attempts suffered from lack of reliable replication of their findings. Replicated variants shown associated with ARHL include those lying in genes GRM7, ISG20, TRIOBP, ILDR1, and EYA4. The availability of large biobanks and the development of collaborative consortia have led to a breakthrough over the last couple of years, and many new genetic variants associated with ARHL are becoming available, through the analysis publicly available bioresources and electronic health records. These findings along with immunohistochemistry and mouse models of HL look set to help disentangle the genetic architecture of ARHL, and highlight the need for standardization of phenotyping methods to facilitate data sharing and collaboration across research networks.
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Affiliation(s)
| | - Tracey A Newman
- CES, Medicine, B85, M55, Life Sciences, University of Southampton, Southampton, UK
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
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244
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Pinese M, Lacaze P, Rath EM, Stone A, Brion MJ, Ameur A, Nagpal S, Puttick C, Husson S, Degrave D, Cristina TN, Kahl VFS, Statham AL, Woods RL, McNeil JJ, Riaz M, Barr M, Nelson MR, Reid CM, Murray AM, Shah RC, Wolfe R, Atkins JR, Fitzsimmons C, Cairns HM, Green MJ, Carr VJ, Cowley MJ, Pickett HA, James PA, Powell JE, Kaplan W, Gibson G, Gyllensten U, Cairns MJ, McNamara M, Dinger ME, Thomas DM. The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly. Nat Commun 2020; 11:435. [PMID: 31974348 PMCID: PMC6978518 DOI: 10.1038/s41467-019-14079-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 12/13/2019] [Indexed: 01/24/2023] Open
Abstract
Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the first release of the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analyse the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. MGRB individuals have fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK Biobank cohorts, consistent with risk depletion. Age-related somatic changes are correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing. Healthspan and healthy aging are areas of research with potential socioeconomic impact. Here, the authors present the Medical Genome Reference Bank (MGRB) which consist of over 4,000 individuals aged 70 years and older without a history of the major age-related diseases and report on results from whole-genome sequencing and association analyses.
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Affiliation(s)
- Mark Pinese
- Garvan Institute of Medical Research, Sydney, NSW, Australia.,Children's Cancer Institute, University of New South Wales, Sydney, NSW, Australia.,School of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Paul Lacaze
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Emma M Rath
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Andrew Stone
- Garvan Institute of Medical Research, Sydney, NSW, Australia.,Children's Cancer Institute, University of New South Wales, Sydney, NSW, Australia.,School of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Marie-Jo Brion
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Adam Ameur
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.,Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Sini Nagpal
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Clare Puttick
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Shane Husson
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Dmitry Degrave
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | | | - Vivian F S Kahl
- Children's Medical Research Institute, Faculty of Medicine and Health, University of Sydney, Westmead, NSW, Australia
| | - Aaron L Statham
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Robyn L Woods
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - John J McNeil
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Moeen Riaz
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Margo Barr
- Centre for Primary Health Care and Equity, University of New South Wales, Sydney, NSW, Australia
| | - Mark R Nelson
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.,Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Christopher M Reid
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.,School of Public Health, Curtin University, Perth, WA, Australia
| | - Anne M Murray
- Berman Center for Outcomes and Clinical Research, Hennepin Healthcare Research Institute, Hennepin Healthcare, Minneapolis, MN, USA.,Division of Geriatrics, Department of Medicine, Hennepin County Medical Center and University of Minnesota, Minneapolis, MN, USA
| | - Raj C Shah
- Department of Family Medicine and Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Rory Wolfe
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Joshua R Atkins
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Chantel Fitzsimmons
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Heath M Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Melissa J Green
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Neuroscience Research Australia, Sydney, NSW, Australia
| | - Vaughan J Carr
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Neuroscience Research Australia, Sydney, NSW, Australia.,Department of Psychiatry, School of Clinical Sciences, Monash University, Melbourne, VIC, Australia
| | - Mark J Cowley
- Garvan Institute of Medical Research, Sydney, NSW, Australia.,Children's Cancer Institute, University of New South Wales, Sydney, NSW, Australia.,School of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Hilda A Pickett
- Children's Medical Research Institute, Faculty of Medicine and Health, University of Sydney, Westmead, NSW, Australia
| | - Paul A James
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Joseph E Powell
- UNSW Cellular Genomics Futures Institute, School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia.,Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Warren Kaplan
- Garvan Institute of Medical Research, Sydney, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Greg Gibson
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ulf Gyllensten
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | | | - Marcel E Dinger
- Garvan Institute of Medical Research, Sydney, NSW, Australia.,School of Biotechnology and Biomolecular Sciences, Faculty of Science, University of New South Wales, Sydney, NSW, Australia
| | - David M Thomas
- Garvan Institute of Medical Research, Sydney, NSW, Australia. .,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
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246
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Hagenbeek FA, Pool R, van Dongen J, Draisma HHM, Jan Hottenga J, Willemsen G, Abdellaoui A, Fedko IO, den Braber A, Visser PJ, de Geus EJCN, Willems van Dijk K, Verhoeven A, Suchiman HE, Beekman M, Slagboom PE, van Duijn CM, Harms AC, Hankemeier T, Bartels M, Nivard MG, Boomsma DI. Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nat Commun 2020; 11:39. [PMID: 31911595 PMCID: PMC6946682 DOI: 10.1038/s41467-019-13770-6] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 11/25/2019] [Indexed: 01/16/2023] Open
Abstract
Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes.
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Affiliation(s)
- Fiona A Hagenbeek
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
| | - René Pool
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Jenny van Dongen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Harmen H M Draisma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Abdel Abdellaoui
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Iryna O Fedko
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anouk den Braber
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, VU Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, VU Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht University, Maastricht, The Netherlands
| | - Eco J C N de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Aswin Verhoeven
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
| | - H Eka Suchiman
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Marian Beekman
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - P Eline Slagboom
- Department of Biomedical Data Sciences, Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Amy C Harms
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University and The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Center for Drug Research, Leiden University and The Netherlands Metabolomics Centre, Leiden, The Netherlands
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Amsterdam, The Netherlands.
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Network Aggregation to Enhance Results Derived from Multiple Analytics. IFIP ADVANCES IN INFORMATION AND COMMUNICATION TECHNOLOGY 2020. [PMCID: PMC7256384 DOI: 10.1007/978-3-030-49161-1_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The more complex data are, the higher the number of possibilities to extract partial information from those data. These possibilities arise by adopting different analytic approaches. The heterogeneity among these approaches and in particular the heterogeneity in results they produce are challenging for follow-up studies, including replication, validation and translational studies. Furthermore, they complicate the interpretation of findings with wide-spread relevance. Here, we take the example of statistical epistasis networks derived from genome-wide association studies with single nucleotide polymorphisms as nodes. Even though we are only dealing with a single data type, the epistasis detection problem suffers from many pitfalls, such as the wide variety of analytic tools to detect them, each highlighting different aspects of epistasis and exhibiting different properties in maintaining false positive control. To reconcile different network views to the same problem, we considered 3 network aggregation methods and discussed their performance in the context of epistasis network aggregation. We furthermore applied a latent class method as best performer to real-life data on inflammatory bowel disease (IBD) and highlighted its benefits to increase our understanding about IBD underlying genetic architectures.
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248
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Zhao M, Chen L, Qiao Z, Zhou J, Zhang T, Zhang W, Ke S, Zhao X, Qiu X, Song X, Zhao E, Pan H, Yang Y, Yang X. Association Between FoxO1, A2M, and TGF-β1, Environmental Factors, and Major Depressive Disorder. Front Psychiatry 2020; 11:675. [PMID: 32792993 PMCID: PMC7394695 DOI: 10.3389/fpsyt.2020.00675] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Accepted: 06/29/2020] [Indexed: 01/14/2023] Open
Abstract
INTRODUCTION Investigations of gene-environment (G×E) interactions in major depressive disorder (MDD) have been limited to hypothesis testing of candidate genes while poly-gene-environmental causation has not been adequately address. To this end, the present study analyzed the association between three candidate genes, two environmental factors, and MDD using a hypothesis-free testing approach. METHODS A logistic regression model was used to analyze interaction effects; a hierarchical regression model was used to evaluate the effects of different genotypes and the dose-response effects of the environment; genetic risk score (GRS) was used to estimate the cumulative contribution of genetic factors to MDD; and protein-protein interaction (PPI) analyses were carried out to evaluate the relationship between candidate genes and top MDD susceptibility genes. RESULTS Allelic association analyses revealed significant effects of the interaction between the candidate genes Forkhead box (Fox)O1, α2-macroglobulin (A2M), and transforming growth factor (TGF)-β1 genes and the environment on MDD. Gene-gene (G×G) and gene-gene-environment (G×G×E) interactions in MDD were also included in the model. Hierarchical regression analysis showed that the effect of environmental factors on MDD was greater in homozygous than in heterozygous mutant genotypes of the FoxO1 and TGF-β1 genes; a dose-response effect between environment and MDD on genotypes was also included in this model. Haplotype analyses revealed significant global and individual effects of haplotypes on MDD in the whole sample as well as in subgroups. There was a significant association between GRS and MDD (P = 0.029) and a GRS and environment interaction effect on MDD (P = 0.009). Candidate and top susceptibility genes were connected in PPI networks. CONCLUSIONS FoxO1, A2M, and TGF-β1 interact with environmental factors and with each other in MDD. Multi-factorial G×E interactions may be responsible for a higher explained variance and may be associated with causal factors and mechanisms that could inform new diagnosis and therapeutic strategies, which can contribute to the personalized medicine of MDD.
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Affiliation(s)
- Mingzhe Zhao
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Lu Chen
- Department of Endocrinology, Peking Union Medical College Hospital, Beijing, China
| | - Zhengxue Qiao
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Jiawei Zhou
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Tianyu Zhang
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Wenxin Zhang
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Siyuan Ke
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Xiaoyun Zhao
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Xiaohui Qiu
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Xuejia Song
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Erying Zhao
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Hui Pan
- Department of Endocrinology, Peking Union Medical College Hospital, Beijing, China
| | - Yanjie Yang
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
| | - Xiuxian Yang
- Psychology Department, Public Health Institute, Harbin Medical University, Harbin, China
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249
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Yao S, Dong SS, Ding JM, Rong Y, Zhang YJ, Chen H, Chen JB, Chen YX, Yan H, Dai Z, Guo Y. Sex-specific SNP-SNP interaction analyses within topologically associated domains reveals ANGPT1 as a novel tumor suppressor gene for lung cancer. Genes Chromosomes Cancer 2020; 59:13-22. [PMID: 31385379 DOI: 10.1002/gcc.22793] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Revised: 07/16/2019] [Accepted: 07/22/2019] [Indexed: 01/24/2023] Open
Abstract
Genetic interaction has been recognized to be an important cause of the missing heritability. The topologically associating domain (TAD) is a self-interacting genomic region, and the DNA sequences within a TAD physically interact with each other more frequently. Sex differences influence cancer susceptibility at the genetic level. Here, we performed both regular and sex-specific genetic interaction analyses within TAD to identify susceptibility genes for lung cancer in 5204 lung cancer patients and 7389 controls. We found that one SNP pair, rs4262299-rs1654701, was associated with lung cancer in women after multiple testing corrections (combined P = 8.52 × 10-9 ). Single-SNP analyses did not detect significant association signals for these two SNPs. Both identified SNPs are located in the intron region of ANGPT1. We further found that 5% of nonsmall cell lung cancer patients have an alteration in ANGPT1, indicated the potential role of ANGPT1 in the neoplastic progression in lung cancer. The expression of ANGPT1 was significantly down-regulated in patients in lung squamous cell carcinoma and lung adenocarcinoma. We checked the interaction effect on the ANGPT1 expression and lung cancer and found that the minor allele "G" of rs1654701 increased ANGPT1 gene expression and decreased lung cancer risk with the increased dosage of "A" of rs4262299, which consistent with the tumor suppressor function of ANGPT1. Survival analyses found that the high expression of ANGPT1 was individually associated with a higher survival probability in lung cancer patients. In summary, our results suggest that ANGPT1 may be a novel tumor suppressor gene for lung cancer.
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Affiliation(s)
- Shi Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Jing-Miao Ding
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Yu Rong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Yu-Jie Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Hao Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Jia-Bin Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Yi-Xiao Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Han Yan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
| | - Zhijun Dai
- Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, P. R. China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, P. R. China
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250
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Finding relationships among biological entities. LOGIC AND CRITICAL THINKING IN THE BIOMEDICAL SCIENCES 2020. [PMCID: PMC7499094 DOI: 10.1016/b978-0-12-821364-3.00005-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Confusion over the concepts of “relationships” and “similarities” lies at the heart of many battles over the direction and intent of research projects. Here is a short story that demonstrates the difference between the two concepts: You look up at the clouds, and you begin to see the shape of a lion. The cloud has a tail, like a lion’s tale, and a fluffy head, like a lion’s mane. With a little imagination the mouth of the lion seems to roar down from the sky. You have succeeded in finding similarities between the cloud and a lion. If you look at a cloud and you imagine a tea kettle producing a head of steam and you recognize that the physical forces that create a cloud and the physical forces that produced steam from a heated kettle are the same, then you have found a relationship. Most popular classification algorithms operate by grouping together data objects that have similar properties or values. In so doing, they may miss finding the true relationships among objects. Traditionally, relationships among data objects are discovered by an intellectual process. In this chapter, we will discuss the scientific gains that come when we classify biological entities by relationships, not by their similarities.
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