1
|
Moon J, Posada-Quintero HF, Chon KH. Genetic data visualization using literature text-based neural networks: Examples associated with myocardial infarction. Neural Netw 2023; 165:562-595. [PMID: 37364469 DOI: 10.1016/j.neunet.2023.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 04/11/2023] [Accepted: 05/09/2023] [Indexed: 06/28/2023]
Abstract
Data visualization is critical to unraveling hidden information from complex and high-dimensional data. Interpretable visualization methods are critical, especially in the biology and medical fields, however, there are limited effective visualization methods for large genetic data. Current visualization methods are limited to lower-dimensional data and their performance suffers if there is missing data. In this study, we propose a literature-based visualization method to reduce high-dimensional data without compromising the dynamics of the single nucleotide polymorphisms (SNP) and textual interpretability. Our method is innovative because it is shown to (1) preserves both global and local structures of SNP while reducing the dimension of the data using literature text representations, and (2) enables interpretable visualizations using textual information. For performance evaluations, we examined the proposed approach to classify various classification categories including race, myocardial infarction event age groups, and sex using several machine learning models on the literature-derived SNP data. We used visualization approaches to examine clustering of data as well as quantitative performance metrics for the classification of the risk factors examined above. Our method outperformed all popular dimensionality reduction and visualization methods for both classification and visualization, and it is robust against missing and higher-dimensional data. Moreover, we found it feasible to incorporate both genetic and other risk information obtained from literature with our method.
Collapse
Affiliation(s)
- Jihye Moon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
| | | | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.
| |
Collapse
|
2
|
Curtis A, Yu Y, Carey M, Parfrey P, Yilmaz YE, Savas S. Examining SNP-SNP interactions and risk of clinical outcomes in colorectal cancer using multifactor dimensionality reduction based methods. Front Genet 2022; 13:902217. [PMID: 35991579 PMCID: PMC9385108 DOI: 10.3389/fgene.2022.902217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/30/2022] [Indexed: 12/24/2022] Open
Abstract
Background: SNP interactions may explain the variable outcome risk among colorectal cancer patients. Examining SNP interactions is challenging, especially with large datasets. Multifactor Dimensionality Reduction (MDR)-based programs may address this problem.Objectives: 1) To compare two MDR-based programs for their utility; and 2) to apply these programs to sets of MMP and VEGF-family gene SNPs in order to examine their interactions in relation to colorectal cancer survival outcomes.Methods: This study applied two data reduction methods, Cox-MDR and GMDR 0.9, to study one to three way SNP interactions. Both programs were run using a 5-fold cross validation step and the top models were verified by permutation testing. Prognostic associations of the SNP interactions were verified using multivariable regression methods. Eight datasets, including SNPs from MMP family genes (n = 201) and seven sets of VEGF-family interaction networks (n = 1,517 SNPs) were examined.Results: ∼90 million potential interactions were examined. Analyses in the MMP and VEGF gene family datasets found several novel 1- to 3-way SNP interactions. These interactions were able to distinguish between the patients with different outcome risks (regression p-values 0.03–2.2E-09). The strongest association was detected for a 3-way interaction including CHRM3.rs665159_EPN1.rs6509955_PTGER3.rs1327460 variants.Conclusion: Our work demonstrates the utility of data reduction methods while identifying potential prognostic markers in colorectal cancer.
Collapse
Affiliation(s)
- Aaron Curtis
- Discipline of Genetics, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
- Division of Biomedical Sciences, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
| | - Yajun Yu
- Discipline of Genetics, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
- Division of Biomedical Sciences, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
| | - Megan Carey
- Discipline of Genetics, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
| | - Patrick Parfrey
- Discipline of Medicine, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
| | - Yildiz E. Yilmaz
- Discipline of Genetics, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
- Discipline of Medicine, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
- Department of Mathematics and Statistics, Faculty of Science, Memorial University, St. John’s, NL, Canada
| | - Sevtap Savas
- Discipline of Genetics, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
- Division of Biomedical Sciences, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
- Discipline of Oncology, Faculty of Medicine, Memorial University, St. John’s, NL, Canada
- *Correspondence: Sevtap Savas,
| |
Collapse
|
3
|
Magaña J, Contreras MG, Keys KL, Risse-Adams O, Goddard PC, Zeiger AM, Mak ACY, Elhawary JR, Samedy-Bates LA, Lee E, Thakur N, Hu D, Eng C, Salazar S, Huntsman S, Hu T, Burchard EG, White MJ. An epistatic interaction between pre-natal smoke exposure and socioeconomic status has a significant impact on bronchodilator drug response in African American youth with asthma. BioData Min 2020; 13:7. [PMID: 32636926 PMCID: PMC7333373 DOI: 10.1186/s13040-020-00218-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/23/2020] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Asthma is one of the leading chronic illnesses among children in the United States. Asthma prevalence is higher among African Americans (11.2%) compared to European Americans (7.7%). Bronchodilator medications are part of the first-line therapy, and the rescue medication, for acute asthma symptoms. Bronchodilator drug response (BDR) varies substantially among different racial/ethnic groups. Asthma prevalence in African Americans is only 3.5% higher than that of European Americans, however, asthma mortality among African Americans is four times that of European Americans; variation in BDR may play an important role in explaining this health disparity. To improve our understanding of disparate health outcomes in complex phenotypes such as BDR, it is important to consider interactions between environmental and biological variables. RESULTS We evaluated the impact of pairwise and three-variable interactions between environmental, social, and biological variables on BDR in 233 African American youth with asthma using Visualization of Statistical Epistasis Networks (ViSEN). ViSEN is a non-parametric entropy-based approach able to quantify interaction effects using an information-theory metric known as Information Gain (IG). We performed analyses in the full dataset and in sex-stratified subsets. Our analyses identified several interaction models significantly, and suggestively, associated with BDR. The strongest interaction significantly associated with BDR was a pairwise interaction between pre-natal smoke exposure and socioeconomic status (full dataset IG: 2.78%, p = 0.001; female IG: 7.27%, p = 0.004)). Sex-stratified analyses yielded divergent results for females and males, indicating the presence of sex-specific effects. CONCLUSIONS Our study identified novel interaction effects significantly, and suggestively, associated with BDR in African American children with asthma. Notably, we found that all of the interactions identified by ViSEN were "pure" interaction effects, in that they were not the result of strong main effects on BDR, highlighting the complexity of the network of biological and environmental factors impacting this phenotype. Several associations uncovered by ViSEN would not have been detected using regression-based methods, thus emphasizing the importance of employing statistical methods optimized to detect both additive and non-additive interaction effects when studying complex phenotypes such as BDR. The information gained in this study increases our understanding and appreciation of the complex nature of the interactions between environmental and health-related factors that influence BDR and will be invaluable to biomedical researchers designing future studies.
Collapse
Affiliation(s)
- J. Magaña
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - M. G. Contreras
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Department of Biology, San Francisco State University, San Francisco, CA USA
| | - K. L. Keys
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Berkeley Institute for Data Science, University of California, Berkeley, CA USA
| | - O. Risse-Adams
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Lowell Science Research Program, Lowell High School, San Francisco, CA USA
- Department of Biology, University of California, Santa Cruz, CA USA
| | - P. C. Goddard
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Department of Genetics, Stanford University, Stanford, CA USA
| | - A. M. Zeiger
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA USA
| | - A. C. Y. Mak
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - J. R. Elhawary
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - L. A. Samedy-Bates
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA USA
| | - E. Lee
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - N. Thakur
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - D. Hu
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - C. Eng
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - S. Salazar
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - S. Huntsman
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - T. Hu
- School of Computing, Queen’s University, Kingston, ON Canada
| | - E. G. Burchard
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA USA
| | - M. J. White
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| |
Collapse
|
4
|
Habic A, Mattick JS, Calin GA, Krese R, Konc J, Kunej T. Genetic Variations of Ultraconserved Elements in the Human Genome. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2020; 23:549-559. [PMID: 31689173 DOI: 10.1089/omi.2019.0156] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Ultraconserved elements (UCEs) are among the most popular DNA markers for phylogenomic analysis. In at least three of five placental mammalian genomes (human, dog, cow, mouse, and rat), 2189 UCEs of at least 200 bp in length that are identical have been identified. Most of these regions have not yet been functionally annotated, and their associations with diseases remain largely unknown. This is an important knowledge gap in human genomics with regard to UCE roles in physiologically critical functions, and by extension, their relevance for shared susceptibilities to common complex diseases across several mammalian organisms in the event of their polymorphic variations. In the present study, we remapped the genomic locations of these UCEs to the latest human genome assembly, and examined them for documented polymorphisms in sequenced human genomes. We identified 29,983 polymorphisms within analyzed UCEs, but revealed that a vast majority exhibits very low minor allele frequencies. Notably, only 112 of the identified polymorphisms are associated with a phenotype in the Ensembl genome browser. Through literature analyses, we confirmed associations of 37 (i.e., out of the 112) polymorphisms within 23 UCEs with 25 diseases and phenotypic traits, including, muscular dystrophies, eye diseases, and cancers (e.g., familial adenomatous polyposis). Most reports of UCE polymorphism-disease associations appeared to be not cognizant that their candidate polymorphisms were actually within UCEs. The present study offers strategic directions and knowledge gaps for future computational and experimental work so as to better understand the thus far intriguing and puzzling role(s) of UCEs in mammalian genomes.
Collapse
Affiliation(s)
- Anamarija Habic
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia
| | - John S Mattick
- School of Biotechnology and Biomolecular Science, University of New South Wales, Sydney, Australia.,Green Templeton College, University of Oxford, Oxford, United Kingdom
| | - George Adrian Calin
- Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas.,The Center for RNA Interference and Noncoding RNAs, The University of Texas M.D. Anderson Cancer Center, Houston, Texas
| | - Rok Krese
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia
| | - Janez Konc
- National Institute of Chemistry, Ljubljana, Slovenia
| | - Tanja Kunej
- Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Domzale, Slovenia
| |
Collapse
|
5
|
Stanaway IB, Hall TO, Rosenthal EA, Palmer M, Naranbhai V, Knevel R, Namjou-Khales B, Carroll RJ, Kiryluk K, Gordon AS, Linder J, Howell KM, Mapes BM, Lin FTJ, Joo YY, Hayes MG, Gharavi AG, Pendergrass SA, Ritchie MD, de Andrade M, Croteau-Chonka DC, Raychaudhuri S, Weiss ST, Lebo M, Amr SS, Carrell D, Larson EB, Chute CG, Rasmussen-Torvik LJ, Roy-Puckelwartz MJ, Sleiman P, Hakonarson H, Li R, Karlson EW, Peterson JF, Kullo IJ, Chisholm R, Denny JC, Jarvik GP, Crosslin DR. The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype. Genet Epidemiol 2018; 43:63-81. [PMID: 30298529 PMCID: PMC6375696 DOI: 10.1002/gepi.22167] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 08/10/2018] [Accepted: 08/28/2018] [Indexed: 12/30/2022]
Abstract
The Electronic Medical Records and Genomics (eMERGE) network is a network of medical centers with electronic medical records linked to existing biorepository samples for genomic discovery and genomic medicine research. The network sought to unify the genetic results from 78 Illumina and Affymetrix genotype array batches from 12 contributing medical centers for joint association analysis of 83,717 human participants. In this report, we describe the imputation of eMERGE results and methods to create the unified imputed merged set of genome‐wide variant genotype data. We imputed the data using the Michigan Imputation Server, which provides a missing single‐nucleotide variant genotype imputation service using the minimac3 imputation algorithm with the Haplotype Reference Consortium genotype reference set. We describe the quality control and filtering steps used in the generation of this data set and suggest generalizable quality thresholds for imputation and phenotype association studies. To test the merged imputed genotype set, we replicated a previously reported chromosome 6 HLA‐B herpes zoster (shingles) association and discovered a novel zoster‐associated loci in an epigenetic binding site near the terminus of chromosome 3 (3p29).
Collapse
Affiliation(s)
- Ian B Stanaway
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| | - Taryn O Hall
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| | - Elisabeth A Rosenthal
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Melody Palmer
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Vivek Naranbhai
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington.,Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Rachel Knevel
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Bahram Namjou-Khales
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Robert J Carroll
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Krzysztof Kiryluk
- Department of Medicine, Columbia University, New York City, New York
| | - Adam S Gordon
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | - Jodell Linder
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Kayla Marie Howell
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Brandy M Mapes
- Vanderbilt Institute for Clinical and Translational Research, School of Medicine, Vanderbilt University, Nashville, Tennessee
| | - Frederick T J Lin
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | | | - M Geoffrey Hayes
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Ali G Gharavi
- Department of Medicine, Columbia University, New York City, New York
| | | | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | | | - Soumya Raychaudhuri
- Harvard Medical School, Harvard University, Cambridge, Massachusetts.,Program in Medical and Population Genetics, Broad Institute of Massachusetts Technical Institute and Harvard University, Cambridge, Massachusetts
| | - Scott T Weiss
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Matt Lebo
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - Sami S Amr
- Harvard Medical School, Harvard University, Cambridge, Massachusetts
| | - David Carrell
- Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, Washington
| | - Eric B Larson
- Kaiser Permanente Washington Health Research Institute (Formerly Group Health Cooperative-Seattle), Kaiser Permanente, Seattle, Washington
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland
| | | | | | - Patrick Sleiman
- Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | | | - Rongling Li
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - Elizabeth W Karlson
- Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Josh F Peterson
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | | | - Rex Chisholm
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Joshua Charles Denny
- Departments of Biomedical Informatics and Medicine, Vanderbilt University, Nashville, Tennessee
| | - Gail P Jarvik
- Division of Medical Genetics, School of Medicine, University of Washington, Seattle, Washington
| | -
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
| | - David R Crosslin
- Department of Biomedical Informatics Medical Education, School of Medicine, University of Washington, Seattle, Washington
| |
Collapse
|
6
|
Verma SS, Lucas A, Zhang X, Veturi Y, Dudek S, Li B, Li R, Urbanowicz R, Moore JH, Kim D, Ritchie MD. Collective feature selection to identify crucial epistatic variants. BioData Min 2018; 11:5. [PMID: 29713383 PMCID: PMC5907720 DOI: 10.1186/s13040-018-0168-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 04/04/2018] [Indexed: 01/17/2023] Open
Abstract
Background Machine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called "short fat data" problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach. Results Through our simulation study we propose a collective feature selection approach to select features that are in the "union" of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~ 44,000 samples obtained from Geisinger's MyCode Community Health Initiative (on behalf of DiscovEHR collaboration). Conclusions In this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.
Collapse
Affiliation(s)
- Shefali S Verma
- 1Biomedical and Translational Bioinformatics Institute, Geisinger Health System, 100 N Academy Avenue, Danville, PA 17822 USA.,2Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA USA.,3Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 USA
| | - Anastasia Lucas
- 1Biomedical and Translational Bioinformatics Institute, Geisinger Health System, 100 N Academy Avenue, Danville, PA 17822 USA.,3Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 USA
| | - Xinyuan Zhang
- 2Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA USA.,3Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 USA
| | - Yogasudha Veturi
- 1Biomedical and Translational Bioinformatics Institute, Geisinger Health System, 100 N Academy Avenue, Danville, PA 17822 USA.,3Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 USA
| | - Scott Dudek
- 1Biomedical and Translational Bioinformatics Institute, Geisinger Health System, 100 N Academy Avenue, Danville, PA 17822 USA.,3Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 USA
| | - Binglan Li
- 2Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA USA.,3Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 USA
| | - Ruowang Li
- 3Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 USA
| | - Ryan Urbanowicz
- 3Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 USA
| | - Jason H Moore
- 3Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 USA
| | - Dokyoon Kim
- 1Biomedical and Translational Bioinformatics Institute, Geisinger Health System, 100 N Academy Avenue, Danville, PA 17822 USA
| | - Marylyn D Ritchie
- 1Biomedical and Translational Bioinformatics Institute, Geisinger Health System, 100 N Academy Avenue, Danville, PA 17822 USA.,2Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA USA.,3Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Richards Building, 3700 Hamilton Walk, Philadelphia, PA 19104 USA
| |
Collapse
|
7
|
Cole BS, Hall MA, Urbanowicz RJ, Gilbert‐Diamond D, Moore JH. Analysis of Gene‐Gene Interactions. ACTA ACUST UNITED AC 2018; 95:1.14.1-1.14.10. [DOI: 10.1002/cphg.45] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Brian S. Cole
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania
| | - Molly A. Hall
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania
- The Center for Systems Genomics, The Pennsylvania State University, University Park Pennsylvania
| | - Ryan J. Urbanowicz
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania
| | - Diane Gilbert‐Diamond
- Institute for Quantitative Biomedical Sciences at Dartmouth Hanover New Hampshire
- Department of Epidemiology, Geisel School of Medicine at Dartmouth Hanover New Hampshire
| | - Jason H. Moore
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania Philadelphia Pennsylvania
| |
Collapse
|
8
|
Verma SS, Ritchie MD. Another Round of "Clue" to Uncover the Mystery of Complex Traits. Genes (Basel) 2018; 9:E61. [PMID: 29370075 PMCID: PMC5852557 DOI: 10.3390/genes9020061] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 12/19/2017] [Accepted: 01/15/2018] [Indexed: 12/13/2022] Open
Abstract
A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that affect the phenotypic variance of complex traits in a population, thus referred to as complex trait architecture. The concept of heritability, or the proportion of phenotypic variance due to genetic inheritance, has been studied for several decades, but its application is mainly in addressing the narrow sense heritability (or additive genetic component) from Genome-Wide Association Studies (GWAS). In this commentary, we reflect on our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues beyond GWAS and an alternative approach, investigating these clues comprehensively to help in elucidating the genetic architecture of complex traits.
Collapse
Affiliation(s)
- Shefali Setia Verma
- The Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Marylyn D Ritchie
- The Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| |
Collapse
|
9
|
Abadi A, Alyass A, Robiou du Pont S, Bolker B, Singh P, Mohan V, Diaz R, Engert JC, Yusuf S, Gerstein HC, Anand SS, Meyre D. Penetrance of Polygenic Obesity Susceptibility Loci across the Body Mass Index Distribution. Am J Hum Genet 2017; 101:925-938. [PMID: 29220676 PMCID: PMC5812888 DOI: 10.1016/j.ajhg.2017.10.007] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2017] [Accepted: 10/12/2017] [Indexed: 12/17/2022] Open
Abstract
A growing number of single-nucleotide polymorphisms (SNPs) have been associated with body mass index (BMI) and obesity, but whether the effects of these obesity-susceptibility loci are uniform across the BMI distribution remains unclear. We studied the effects of 37 BMI-associated SNPs in 75,230 adults of European ancestry across BMI percentiles by using conditional quantile regression (CQR) and meta-regression (MR) models. The effects of nine SNPs (24%)-rs1421085 (FTO; p = 8.69 × 10-15), rs6235 (PCSK1; p = 7.11 × 10-6), rs7903146 (TCF7L2; p = 9.60 × 10-6), rs11873305 (MC4R; p = 5.08 × 10-5), rs12617233 (FANCL; p = 5.30 × 10-5), rs11672660 (GIPR; p = 1.64 × 10-4), rs997295 (MAP2K5; p = 3.25 × 10-4), rs6499653 (FTO; p = 6.23 × 10-4), and rs3824755 (NT5C2; p = 7.90 × 10-4)-increased significantly across the sample BMI distribution. We showed that such increases stemmed from unadjusted gene interactions that enhanced the effects of SNPs in persons with a high BMI. When 125 height-associated SNPs were analyzed for comparison, only one (<1%), rs6219 (IGF1, p = 1.80 × 10-4), showed effects that varied significantly across height percentiles. Cumulative gene scores of these SNPs (GS-BMI and GS-height) showed that only GS-BMI had effects that increased significantly across the sample distribution (BMI: p = 7.03 × 10-37; height: p = 0.499). Overall, these findings underscore the importance of gene-gene and gene-environment interactions in shaping the genetic architecture of BMI and advance a method for detecting such interactions by using only the sample outcome distribution.
Collapse
Affiliation(s)
- Arkan Abadi
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Akram Alyass
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Sebastien Robiou du Pont
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Ben Bolker
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Pardeep Singh
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation, Gopalapuram, Chennai 600086, India
| | - Rafael Diaz
- Estudios Clínicos Latino America, Paraguay 160, S2000CVD Rosario, Santa Fe, Argentina
| | | | - Salim Yusuf
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Hertzel C Gerstein
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Sonia S Anand
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton General Hospital, Hamilton, ON L8S 4L8, Canada; Department of Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON L8S 4L8, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada.
| |
Collapse
|
10
|
Mediterranean Diet Adherence and Genetic Background Roles within a Web-Based Nutritional Intervention: The Food4Me Study. Nutrients 2017; 9:nu9101107. [PMID: 29019927 PMCID: PMC5691723 DOI: 10.3390/nu9101107] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Revised: 10/04/2017] [Accepted: 10/09/2017] [Indexed: 01/02/2023] Open
Abstract
Mediterranean Diet (MedDiet) adherence has been proven to produce numerous health benefits. In addition, nutrigenetic studies have explained some individual variations in the response to specific dietary patterns. The present research aimed to explore associations and potential interactions between MedDiet adherence and genetic background throughout the Food4Me web-based nutritional intervention. Dietary, anthropometrical and biochemical data from volunteers of the Food4Me study were collected at baseline and after 6 months. Several genetic variants related to metabolic risk features were also analysed. A Genetic Risk Score (GRS) was derived from risk alleles and a Mediterranean Diet Score (MDS), based on validated food intake data, was estimated. At baseline, there were no interactions between GRS and MDS categories for metabolic traits. Linear mixed model repeated measures analyses showed a significantly greater decrease in total cholesterol in participants with a low GRS after a 6-month period, compared to those with a high GRS. Meanwhile, a high baseline MDS was associated with greater decreases in Body Mass Index (BMI), waist circumference and glucose. There also was a significant interaction between GRS and the MedDiet after the follow-up period. Among subjects with a high GRS, those with a high MDS evidenced a highly significant reduction in total carotenoids, while among those with a low GRS, there was no difference associated with MDS levels. These results suggest that a higher MedDiet adherence induces beneficial effects on metabolic outcomes, which can be affected by the genetic background in some specific markers.
Collapse
|
11
|
Mitra I, Lavillaureix A, Yeh E, Traglia M, Tsang K, Bearden CE, Rauen KA, Weiss LA. Reverse Pathway Genetic Approach Identifies Epistasis in Autism Spectrum Disorders. PLoS Genet 2017; 13:e1006516. [PMID: 28076348 PMCID: PMC5226683 DOI: 10.1371/journal.pgen.1006516] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 12/01/2016] [Indexed: 02/08/2023] Open
Abstract
Although gene-gene interaction, or epistasis, plays a large role in complex traits in model organisms, genome-wide by genome-wide searches for two-way interaction have limited power in human studies. We thus used knowledge of a biological pathway in order to identify a contribution of epistasis to autism spectrum disorders (ASDs) in humans, a reverse-pathway genetic approach. Based on previous observation of increased ASD symptoms in Mendelian disorders of the Ras/MAPK pathway (RASopathies), we showed that common SNPs in RASopathy genes show enrichment for association signal in GWAS (P = 0.02). We then screened genome-wide for interactors with RASopathy gene SNPs and showed strong enrichment in ASD-affected individuals (P < 2.2 x 10-16), with a number of pairwise interactions meeting genome-wide criteria for significance. Finally, we utilized quantitative measures of ASD symptoms in RASopathy-affected individuals to perform modifier mapping via GWAS. One top region overlapped between these independent approaches, and we showed dysregulation of a gene in this region, GPR141, in a RASopathy neural cell line. We thus used orthogonal approaches to provide strong evidence for a contribution of epistasis to ASDs, confirm a role for the Ras/MAPK pathway in idiopathic ASDs, and to identify a convergent candidate gene that may interact with the Ras/MAPK pathway.
Collapse
Affiliation(s)
- Ileena Mitra
- Department of Psychiatry, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Alinoë Lavillaureix
- Department of Psychiatry, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
- Université Paris Descartes, Sorbonne Paris Cité, Faculty of Medicine, Paris, France
| | - Erika Yeh
- Department of Psychiatry, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Michela Traglia
- Department of Psychiatry, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Kathryn Tsang
- Department of Psychiatry, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Carrie E. Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Psychology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Katherine A. Rauen
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
- Department of Pediatrics, School of Medicine, University of California San Francisco, San Francisco, California, United States of America
| | - Lauren A. Weiss
- Department of Psychiatry, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| |
Collapse
|
12
|
Tragante V, Asselbergs FW, Swerdlow DI, Palmer TM, Moore JH, de Bakker PIW, Keating BJ, Holmes MV. Harnessing publicly available genetic data to prioritize lipid modifying therapeutic targets for prevention of coronary heart disease based on dysglycemic risk. Hum Genet 2016; 135:453-467. [PMID: 26946290 PMCID: PMC4835528 DOI: 10.1007/s00439-016-1647-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Accepted: 01/07/2016] [Indexed: 01/14/2023]
Abstract
Therapeutic interventions that lower LDL-cholesterol effectively reduce the risk of coronary artery disease (CAD). However, statins, the most widely prescribed LDL-cholesterol lowering drugs, increase diabetes risk. We used genome-wide association study (GWAS) data in the public domain to investigate the relationship of LDL-C and diabetes and identify loci encoding potential drug targets for LDL-cholesterol modification without causing dysglycemia. We obtained summary-level GWAS data for LDL-C from GLGC, glycemic traits from MAGIC, diabetes from DIAGRAM and CAD from CARDIoGRAMplusC4D consortia. Mendelian randomization analyses identified a one standard deviation (SD) increase in LDL-C caused an increased risk of CAD (odds ratio [OR] 1.63 (95 % confidence interval [CI] 1.55, 1.71), which was not influenced by removing SNPs associated with diabetes. LDL-C/CAD-associated SNPs showed consistent effect directions (binomial P = 6.85 × 10−5). Conversely, a 1-SD increase in LDL-C was causally protective of diabetes (OR 0.86; 95 % CI 0.81, 0.91), however LDL-cholesterol/diabetes-associated SNPs did not show consistent effect directions (binomial P = 0.15). HMGCR, our positive control, associated with LDL-C, CAD and a glycemic composite (derived from GWAS meta-analysis of four glycemic traits and diabetes). In contrast, PCSK9, APOB, LPA, CETP, PLG, NPC1L1 and ALDH2 were identified as “druggable” loci that alter LDL-C and risk of CAD without displaying associations with dysglycemia. In conclusion, LDL-C increases the risk of CAD and the relationship is independent of any association of LDL-C with diabetes. Loci that encode targets of emerging LDL-C lowering drugs do not associate with dysglycemia, and this provides provisional evidence that new LDL-C lowering drugs (such as PCSK9 inhibitors) may not influence risk of diabetes.
Collapse
Affiliation(s)
- Vinicius Tragante
- Department of Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands
| | - Folkert W Asselbergs
- Department of Heart and Lungs, University Medical Center Utrecht, Heidelberglaan 100, 3584CX, Utrecht, The Netherlands. .,Institute of Cardiovascular Science, University College London, 222 Euston Road, London, NW1 2DA, UK. .,Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands.
| | - Daniel I Swerdlow
- Institute of Cardiovascular Science, University College London, 222 Euston Road, London, NW1 2DA, UK.,Department of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Tom M Palmer
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Jason H Moore
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, 19104-6021, USA
| | - Paul I W de Bakker
- Department of Medical Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Brendan J Keating
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.,Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Michael V Holmes
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA. .,Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA. .,Clinical Trials Services Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Richard Doll Building, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, OX3 7LF, UK.
| |
Collapse
|