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Weine E, Smith SP, Knowlton RK, Harpak A. Tradeoffs in Modeling Context Dependency in Complex Trait Genetics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.21.545998. [PMID: 38370664 PMCID: PMC10871201 DOI: 10.1101/2023.06.21.545998] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Genetic effects on complex traits may depend on context, such as age, sex, environmental exposures or social settings. However, it is often unclear if the extent of context dependency, or Gene-by-Environment interaction (GxE), merits more involved models than the additive model typically used to analyze data from genome-wide association studies (GWAS). Here, we suggest considering the utility of GxE models in GWAS as a tradeoff between bias and variance parameters. In particular, We derive a decision rule for choosing between competing models for the estimation of allelic effects. The rule weighs the increased estimation noise when context is considered against the potential bias when context dependency is ignored. In the empirical example of GxSex in human physiology, the increased noise of context-specific estimation often outweighs the bias reduction, rendering GxE models less useful when variants are considered independently. However, we argue that for complex traits, the joint consideration of context dependency across many variants mitigates both noise and bias. As a result, polygenic GxE models can improve both estimation and trait prediction. Finally, we exemplify (using GxDiet effects on longevity in fruit flies) how analyses based on independently ascertained "top hits" alone can be misleading, and that considering polygenic patterns of GxE can improve interpretation.
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Kieler IN, Persson SM, Hagman R, Marinescu VD, Hedhammar Å, Strandberg E, Lindblad-Toh K, Arendt ML. Genome wide association study in Swedish Labrador retrievers identifies genetic loci associated with hip dysplasia and body weight. Sci Rep 2024; 14:6090. [PMID: 38480780 PMCID: PMC10937653 DOI: 10.1038/s41598-024-56060-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/01/2024] [Indexed: 03/17/2024] Open
Abstract
Genome wide association studies (GWAS) have been utilized to identify genetic risk loci associated with both simple and complex inherited disorders. Here, we performed a GWAS in Labrador retrievers to identify genetic loci associated with hip dysplasia and body weight. Hip dysplasia scores were available for 209 genotyped dogs. We identified a significantly associated locus for hip dysplasia on chromosome 24, with three equally associated SNPs (p = 4.3 × 10-7) in complete linkage disequilibrium located within NDRG3, a gene which in humans has been shown to be differentially expressed in osteoarthritic joint cartilage. Body weight, available for 85 female dogs, was used as phenotype for a second analysis. We identified two significantly associated loci on chromosome 10 (p = 4.5 × 10-7) and chromosome 31 (p = 2.5 × 10-6). The most associated SNPs within these loci were located within the introns of the PRKCE and CADM2 genes, respectively. PRKCE has been shown to play a role in regulation of adipogenesis whilst CADM2 has been associated with body weight in multiple human GWAS. In summary, we identified credible candidate loci explaining part of the genetic inheritance for hip dysplasia and body weight in Labrador retrievers with strong candidate genes in each locus previously implicated in the phenotypes investigated.
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Affiliation(s)
- Ida Nordang Kieler
- Department of Veterinary Clinical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sofia Malm Persson
- Department for Breeding and Health, Swedish Kennel Club, Stockholm, Sweden
| | - Ragnvi Hagman
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Voichita D Marinescu
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
- SciLifeLab, Uppsala, Sweden
| | - Åke Hedhammar
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Erling Strandberg
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Kerstin Lindblad-Toh
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
- SciLifeLab, Uppsala, Sweden
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Maja Louise Arendt
- Department of Veterinary Clinical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
- SciLifeLab, Uppsala, Sweden.
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Fernandez ME, Martinez-Romero J, Aon MA, Bernier M, Price NL, de Cabo R. How is Big Data reshaping preclinical aging research? Lab Anim (NY) 2023; 52:289-314. [PMID: 38017182 DOI: 10.1038/s41684-023-01286-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 10/10/2023] [Indexed: 11/30/2023]
Abstract
The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.
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Affiliation(s)
- Maria Emilia Fernandez
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jorge Martinez-Romero
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Epidemiology and Population Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Miguel A Aon
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
- Laboratory of Cardiovascular Science, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Michel Bernier
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Nathan L Price
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Rafael de Cabo
- Experimental Gerontology Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
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Jurrjens AW, Seldin MM, Giles C, Meikle PJ, Drew BG, Calkin AC. The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases. eLife 2023; 12:e86139. [PMID: 37000167 PMCID: PMC10065800 DOI: 10.7554/elife.86139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/15/2023] [Indexed: 04/01/2023] Open
Abstract
Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.
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Affiliation(s)
- Aaron W Jurrjens
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
| | - Marcus M Seldin
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, United States
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Brian G Drew
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| | - Anna C Calkin
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
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Tonnelé H, Baud A. Accounting for diet and age. eLife 2022; 11:80890. [PMID: 35838133 PMCID: PMC9286735 DOI: 10.7554/elife.80890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The diet and age of mice can modulate how different genetic variants impact body weight, demonstrating the need to take context into account when performing genetic studies.
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Affiliation(s)
- Hélène Tonnelé
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Amelie Baud
- Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra, Barcelona, Spain
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