A novel genomic region on chromosome 11 associated with fearfulness in dogs

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Abstract

The complex phenotypic and genetic nature of anxieties hampers progress in unravelling their molecular etiologies. Dogs present extensive natural variation in fear and anxiety behaviour and could advance the understanding of the molecular background of behaviour due to their unique breeding history and genetic architecture. As dogs live as part of human families under constant care and monitoring, information from their behaviour and experiences are easily available. Here we have studied the genetic background of fearfulness in the Great Dane breed. Dogs were scored and categorised into cases and controls based on the results of the validated owner-completed behavioural survey. A genome-wide association study in a cohort of 124 dogs with and without socialisation as a covariate revealed a genome-wide significant locus on chromosome 11. Whole exome sequencing and whole genome sequencing revealed extensive regions of opposite homozygosity in the same locus on chromosome 11 between the cases and controls with interesting neuronal candidate genes such as MAPK9/JNK2, a known hippocampal regulator of anxiety. Further characterisation of the identified locus will pave the way for molecular understanding of fear in dogs and may provide a natural animal model for human anxieties.

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The endocytic network comprises a series of interconnected tubulo-vesicular membranous compartments that together regulate various sorting and signalling events. Although it is clear that defects in endocytic function underlie a variety of human diseases, our understanding of the molecular entities that regulate these sorting and signalling events remains limited. Here we discuss the sorting nexins family of proteins and propose that they have a fundamental role in orchestrating the formation of protein complexes that are involved in endosomal sorting and signalling.

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Background Feasibility of genotyping of hundreds and thousands of single nucleotide polymorphisms (SNPs) in thousands of study subjects have triggered the need for fast, powerful, and reliable methods for genome-wide association analysis. Here we consider a situation when study participants are genetically related (e.g. due to systematic sampling of families or because a study was performed in a genetically isolated population). Of the available methods that account for relatedness, the Measured Genotype (MG) approach is considered the ‘gold standard’. However, MG is not efficient with respect to time taken for the analysis of genome-wide data. In this context we proposed a fast two-step method called Genome-wide Association using Mixed Model and Regression (GRAMMAR) for the analysis of pedigree-based quantitative traits. This method certainly overcomes the drawback of time limitation of the measured genotype (MG) approach, but pays in power. One of the major drawbacks of both MG and GRAMMAR, is that they crucially depend on the availability of complete and correct pedigree data, which is rarely available. Methodology In this study we first explore type 1 error and relative power of MG, GRAMMAR, and Genomic Control (GC) approaches for genetic association analysis. Secondly, we propose an extension to GRAMMAR i.e. GRAMMAR-GC. Finally, we propose application of GRAMMAR-GC using the kinship matrix estimated through genomic marker data, instead of (possibly missing and/or incorrect) genealogy. Conclusion Through simulations we show that MG approach maintains high power across a range of heritabilities and possible pedigree structures, and always outperforms other contemporary methods. We also show that the power of our proposed GRAMMAR-GC approaches to that of the ‘gold standard’ MG for all models and pedigrees studied. We show that this method is both feasible and powerful and has correct type 1 error in the context of genome-wide association analysis in related individuals.

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Author and article information

Contributors

H. Lohi:

ORCID: http://orcid.org/0000-0003-1087-5532

hannes.lohi@helsinki.fi

Journal

Journal ID (nlm-ta): Transl Psychiatry

Journal ID (iso-abbrev): Transl Psychiatry

Title: Translational Psychiatry

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2158-3188

Publication date (Electronic): 28 May 2020

Publication date PMC-release: 28 May 2020

Publication date Collection: 2020

Volume: 10

Electronic Location Identifier: 169

Affiliations

[1 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department of Veterinary Biosciences, , University of Helsinki, ; 00014 Helsinki, Finland

[2 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Department of Medical and Clinical Genetics, , University of Helsinki, ; 00014 Helsinki, Finland

[3 ]GRID grid.428673.c, ISNI 0000 0004 0409 6302, Folkhälsan Research Center, ; 00290 Helsinki, Finland

[4 ]GRID grid.7737.4, ISNI 0000 0004 0410 2071, Equine and Small Animal Medicine, , University of Helsinki, ; Helsinki, Finland

[5 ]GRID grid.10858.34, ISNI 0000 0001 0941 4873, Department of Mathematical Sciences, Biocenter Oulu and Infotech Oulu, , University of Oulu, ; Oulu, Finland

Author information

K. Tiira http://orcid.org/0000-0002-2253-8755

J. E. Niskanen http://orcid.org/0000-0002-2871-6890

M. K. Hytönen http://orcid.org/0000-0003-1976-5874

M. J. Sillanpää http://orcid.org/0000-0003-2808-2768

H. Lohi http://orcid.org/0000-0003-1087-5532

Article

Publisher ID: 849

DOI: 10.1038/s41398-020-0849-z

PMC ID: 7256038

PubMed ID: 32467585

SO-VID: d43a035d-a556-422d-8e95-37b7b9b71e35

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.