1
|
Aponte JD, Katz DC, Roth DM, Vidal-García M, Liu W, Andrade F, Roseman CC, Murray SA, Cheverud J, Graf D, Marcucio RS, Hallgrímsson B. Relating multivariate shapes to genescapes using phenotype-biological process associations for craniofacial shape. eLife 2021; 10:68623. [PMID: 34779766 PMCID: PMC8631940 DOI: 10.7554/elife.68623] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 11/12/2021] [Indexed: 12/20/2022] Open
Abstract
Realistic mappings of genes to morphology are inherently multivariate on both sides of the equation. The importance of coordinated gene effects on morphological phenotypes is clear from the intertwining of gene actions in signaling pathways, gene regulatory networks, and developmental processes underlying the development of shape and size. Yet, current approaches tend to focus on identifying and localizing the effects of individual genes and rarely leverage the information content of high-dimensional phenotypes. Here, we explicitly model the joint effects of biologically coherent collections of genes on a multivariate trait – craniofacial shape – in a sample of n = 1145 mice from the Diversity Outbred (DO) experimental line. We use biological process Gene Ontology (GO) annotations to select skeletal and facial development gene sets and solve for the axis of shape variation that maximally covaries with gene set marker variation. We use our process-centered, multivariate genotype-phenotype (process MGP) approach to determine the overall contributions to craniofacial variation of genes involved in relevant processes and how variation in different processes corresponds to multivariate axes of shape variation. Further, we compare the directions of effect in phenotype space of mutations to the primary axis of shape variation associated with broader pathways within which they are thought to function. Finally, we leverage the relationship between mutational and pathway-level effects to predict phenotypic effects beyond craniofacial shape in specific mutants. We also introduce an online application that provides users the means to customize their own process-centered craniofacial shape analyses in the DO. The process-centered approach is generally applicable to any continuously varying phenotype and thus has wide-reaching implications for complex trait genetics.
Collapse
Affiliation(s)
- Jose D Aponte
- Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - David C Katz
- Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Daniela M Roth
- School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Marta Vidal-García
- Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Wei Liu
- Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Fernando Andrade
- Department of Biology, Loyola University Chicago, Chicago, United States
| | - Charles C Roseman
- Department of Biology, Loyola University Chicago, Chicago, United States
| | | | - James Cheverud
- Department of Biology, Loyola University Chicago, Chicago, United States
| | - Daniel Graf
- School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada.,Department of Medical Genetics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Canada
| | - Ralph S Marcucio
- Department of Orthopaedic Surgery, School of Medicine, University of California, San Francisco, San Francisco, United States
| | - Benedikt Hallgrímsson
- Department of Cell Biology & Anatomy, Alberta Children's Hospital Research Institute and McCaig Bone and Joint Institute, Cumming School of Medicine, University of Calgary, Calgary, Canada.,Department of Animal Biology, University of Illinois Urbana Champaign, Urbana, United States
| |
Collapse
|
2
|
Chen J, Zhang S. Matrix Integrative Analysis (MIA) of Multiple Genomic Data for Modular Patterns. Front Genet 2018; 9:194. [PMID: 29910825 PMCID: PMC5992392 DOI: 10.3389/fgene.2018.00194] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 05/11/2018] [Indexed: 11/13/2022] Open
Abstract
The increasing availability of high-throughput biological data, especially multi-dimensional genomic data across the same samples, has created an urgent need for modular and integrative analysis tools that can reveal the relationships among different layers of cellular activities. To this end, we present a MATLAB package, Matrix Integration Analysis (MIA), implementing and extending four published methods, designed based on two classical techniques, non-negative matrix factorization (NMF), and partial least squares (PLS). This package can integrate diverse types of genomic data (e.g., copy number variation, DNA methylation, gene expression, microRNA expression profiles, and/or gene network data) to identify the underlying modular patterns by each method. Particularly, we demonstrate the differences between these two classes of methods, which give users some suggestions about how to select a suitable method in the MIA package. MIA is a flexible tool which could handle a wide range of biological problems and data types. Besides, we also provide an executable version for users without a MATLAB license.
Collapse
Affiliation(s)
- Jinyu Chen
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, CAS, Beijing, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Shihua Zhang
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, CAS, Beijing, China.,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China
| |
Collapse
|