# Genome-wide statistical evidence elucidates candidate factors of life expectancy in dogs

Won Hee Ko, Sangil Kim, Alix Catry, Je-Yoel Cho, Seunggwan Shin

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## To cite this version:

Won Hee Ko, Sangil Kim, Alix Catry, Je-Yoel Cho, Seunggwan Shin. Genome-wide statistical evidence elucidates candidate factors of life expectancy in dogs. Molecules and Cells, 2025, 48 (1), pp.100162. 10.1016/j.mocell.2024.100162. hal-05447920

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# Genome-wide statistical evidence elucidates candidate factors of life expectancy in dogs

Won Hee Ko<sup>1,2,†</sup>, Sangil Kim<sup>1,3,†</sup>, Alix Catry<sup>4</sup>, Je-Yoel Cho<sup>2,5,*</sup>, and Seunggwan Shin<sup>1,2,*</sup>

<sup>1</sup>School of Biological Sciences and Institute of Biodiversity, Seoul National University, Seoul 08826, Republic of Korea, <sup>2</sup>Comparative Medicine Disease Research Center, Seoul National University, Seoul 08826, Republic of Korea, <sup>3</sup>Museum of Comparative Zoology and Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA, <sup>4</sup>Université Claude Bernard Lyon 1, Laboratoire d'Ecologie Microbienne, UMR CNRS 5557, UMR INRAE 1418, VetAgro Sup, 69622, Villeurbanne, France, <sup>5</sup>Department of Biochemistry, BK21 Plus and Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, Seoul 08826, Republic of Korea

*Correspondence: jeycho@snu.ac.kr (J.-Y. Cho), sk83@snu.ac.kr (S. Shin).

https://doi.org/10.1016/j.mocell.2024.100162

## ABSTRACT

It is well-established that large and heavy dogs tend to live shorter lives. In this study, we aimed to determine whether traits other than body size are associated with the life expectancy of dogs. We compiled a dataset of 20 phenotypes, including body size, lifespan, snout ratio, and shedding, into a single matrix for 149 dog breeds using data from the American Kennel Club and other peer-reviewed sources. The analysis revealed that drooling might be associated with both the lifespan and body mass index of dogs. Furthermore, a genome-wide association study with adjusted phenotypes and statistical verification methods, such as Mendelian randomization. Additionally, conducting differential gene expression analysis with the salivary gland for the 2 cases, hypersalivation/less drooling vs various body sizes, we could observe the hypersalivation-related proteins. This genetic analysis suggests that body size and drooling might be candidate factors influencing lifespan. Consequently, we identified several candidate genes, including *IGSF1*, *PACSIN2*, *PIK3R1*, and *MCCC2*, as potential genetic factors influencing longevity-related phenotypes.

© 2024 The Authors. Published by Elsevier Inc. on behalf of Korean Society for Molecular and Cellular Biology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

**Keywords:** *Canis lupus familiaris*, Comparative genomics, Dog breeds, Genome-wide association study, Lifespan

## INTRODUCTION

Domestic dogs, *Canis lupus familiaris*, are the most phenotypically variable mammals, exhibiting significant variation in size, shape, color, behavior, disease risk, and lifespan. Larger breeds typically live shorter lives (6-7 years) compared to smaller breeds, which can live up to 15 years, with aging increasing disease and mortality risk (Kraus et al., 2013). This diversity among breeds provides a valuable model for studying genetic and environmental factors influencing aging, offering insights applicable to all mammals, including humans (Fleming et al., 2011; Lee and Lee, 2022; Ruple et al., 2022; Wayne and Ostrander, 2007). Studies with dogs enhance our understanding of aging and age-related diseases, complementing laboratory models by investigating aging in real-world conditions (O'Neill et al., 2014).

Genetic studies have identified insulin-like growth factor 1 (*IGF1*) as a key gene related to body size and lifespan in dogs, with parallels in humans (Berryman et al., 2008; Greer et al., 2011; Sutter, 2007; Ziv and Hu, 2011). Experimental evidence shows that *IGF1* manipulation in animal models affects both

body size and lifespan, underscoring its role in these processes (Lorenzini et al., 2014; Yakar and Adamo, 2012). However, longevity is a polygenic and complex trait influenced by multiple genetic and environmental factors (López-Otin et al., 2023). Breeds, such as French Bulldogs and Boxers, affected by breed-specific health issues due to artificial selection, often have reduced life expectancy, particularly in brachycephalic breeds prone to disorders such as ptyalism and kidney failure (Bannasch et al., 2021; Lodato and Hedlund, 2012; Mitze et al., 2022; O'Neill et al., 2018). Thus, factors beyond body size also contribute to dog's lifespan.

We collected data on 149 dog breeds, covering approximately 75% of all American Kennel Club registered breeds, and compiled it into a data matrix for statistical studies such as comparative genomics. Using this matrix, we conducted statistical analyses to identify phenotypic traits associated with life expectancy. We performed genome-wide association study (GWAS) analyses using a previously published single nucleotide polymorphism (SNP) dataset of 722 individuals, including wild canines and village dogs (Plassais et al., 2019). This

<footnote>† These authors contributed equally to the work.</footnote>
analysis revealed both known loci associated with life ex-
pectancy and new genic regions with potential candidate genes.
Through Mendelian randomization (MR) and differential gene
expression (DEG) analyses, we identified several relevant
genes, including *IGSF1*, protein kinase C and casein kinase
substrate in neurons 2 (*PACSIN2*), *PIK3R1*, and *MCCC2*.

MATERIALS AND METHODS

Phenotype Matrix and Statistical Analysis

We compiled 20 phenotypes for 149 dog breeds, encompassing
722 datasets, using American Kennel Club, Fédération
Cynologique Internationale, and peer-reviewed sources (Hayward
et al., 2016; Jones et al., 2008). Since the dataset lacked individual
phenotype or disease records and adhered to strict breed stan-
dards, morphological traits were defined as breed averages, fol-
lowing methods used in previous studies (Hayward et al., 2016;
MacLean et al., 2019; Spady and Ostrander, 2008). The pheno-
types were organized into a matrix, with each breed assigned a
value (Appendices S1 and S2). Correlation analyses between
lifespan and other phenotypes were conducted using R (R core
team, 2023). We removed non-numeric variables, converted basal
and nonbasal breeds to numeric values, and eliminated missing
data using tidyverse (Wickham, 2019), checking for linearity and
collinearity (Fig. S1). Pearson correlation was used for nominal
datasets (eg, lifespan and body mass index [BMI]), while polyserial
correlation was applied to nominal and ordinal datasets (eg, life-
span and drooling) using the polycor (https://r-forge.r-project.org/
projects/polycor) package in R. Breeds were categorized into
small, medium, and large body sizes for grouped correlation
analysis.

GWAS and MR Analysis

We utilized the 722 datasets by converting the original variant
call format to a PLINK binary file format (Purcell et al., 2007),
removing insertions/deletions and multiallelic variants to focus
on biallelic SNPs. After filtering with a 0.05 minor allele fre-
quency and 0.01 missingness threshold, we obtained approxi-
mately 8.1M SNPs. Phenotype adjustments for drooling, sex,
and body size were conducted using a linear regression model
in R. We then performed a GWAS using GEMMA v0.98.3 (Zhou
and Stephens, 2012) on 3 phenotypes: drooling, BMI, and
lifespan, employing a univariate linear mixed model and the
Wald test for Ps. Significant associations were visualized with a
multitrack Manhattan plot using the CMplot package (Yin et al.,
2021) in R. For MR analysis, we used the TwoSampleMR R
package (Hemani et al., 2017). Linkage disequilibrium clumping
was conducted with PLINK (r² threshold as 0.01), and genome-
wide significant SNPs were selected for instrumental variance.
We harmonized effect sizes for BMI and drooling with lifespan-
associated SNPs and performed MR analysis using 5 regres-
sion models, visualized with forest and funnel plots. Sensitivity
analysis was also conducted using the mr_singlesnp and
mr_forest_plot functions in the TwoSampleMR package.

DEG Analysis

To investigate additional candidate genes, we downloaded
breed-specific salivary gland RNA-seq data from National

Center for Biotechnology Information (NCBI) (PRJNA396033) for 3 breeds: Belgian Malinois (SRR8997026), Yorkshire Terrier (SRR8997053), and Newfoundland (SRR8996972). We used Trinity v2.14.0 (Grabherr et al., 2011) for de novo transcriptome assembly, incorporating Trimmomatic for adapter trimming. Quality was assessed using BUSCO v5.4.2 (Simao et al., 2015) against the carnivora_odb10 database, and redundant sequences were removed with CD-hit-est (Fu et al., 2012). We quantified gene expression with RNA-seq by Expectation-Maximization (RSEM) (Li and Dewey, 2011), aligning reads with Bowtie2 (Langmead and Salzberg, 2012), and conducted DEG analysis with Trinity's run_DE_analysis.pl, visualizing results with R. Significant DEGs were identified with a *P* cutoff of 1E-03 and log(FC) > 1.5. For functional annotation, we used TransDecoder (github.com/TransDecoder/TransDecoder) to predict open reading frames and Trinotate (Bryant et al., 2017) to integrate annotation data, including BLASTP, BLASTX (Camacho et al., 2009), HMMER (Eddy, 2008), and signalP5 (Armenteros et al., 2019) results. We created a comprehensive annotation report in Excel, including KEGG pathway information (Kanehisa and Goto, 2000). Gene ontology term analysis was performed using topGO (Alexa et al., 2006; Ashburner et al., 2000) to visualize breed-specific enriched subsets.

RESULTS

Analysis of Correlation Between Phenotype Matrix Using Association Test

We gathered data on 149 dog breeds from the 722 datasets to explore phenotype-genotype associations through GWAS and statistical analyses. This comprehensive dataset, comprising 20 phenotypes (Appendix S1), enabled us to investigate lifespan-related factors. Missing data were minimal for physical traits such as weight and BMI (<1%) but higher for traits such as snout angle and coat colors (> 30%) (Fig. S2). Correlation analysis revealed a strong negative correlation between lifespan and drooling (correlation coefficient = -0.81, *P* < 2.2E-16) and a positive correlation between drooling and BMI (correlation coefficient = 0.91, *P* < 2.2E-16) (Figs. S2 and S3). To address potential confounding effects, we analyzed drooling and lifespan correlations across 3 body size subgroups: x-small + small (correlation coefficient = -0.39, *P* = 1.4E-03), medium (correlation coefficient = -0.28, *P* = 1.7E-02), and large + x-large (correlation coefficient = -0.51, *P* = 4.087E-06), finding significant negative correlations in all groups. These results suggest that drooling may influence life expectancy independent of body size (Appendix S1). Before GWAS analysis, we adjusted for potential confounders such as sex and body size to reduce correlation bias (Figs. S2-S4). On the other hand, drooling may also be influenced by breed-specific health traits, such as brachycephalic syndrome, due to the lack of detailed information on the cephalic index for each breed, it was difficult to account for this confounding factor accurately.

Identification of Candidate Genes Associated With
Lifespan by GWAS

The GWAS was applied to the 3 phenotypes, including life-
span and potential candidate factors (BMI and drooling), fit-
ting a linear mixed model to account for population structure.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** The multitrack Manhattan plot based on the GWAS results. (A) Manhattan plot displaying the GWAS results for body mass index (BMI) in dogs. The x-axis represents the chromosomal position, while the y-axis represents the -log10(*P*) of the SNPs. Significant loci identified include *MSRB3* and *HMGA2* on chromosome 10, *LCORL* on chromosome 3, and *IGSF1* on the X chromosome. (B) Manhattan plot displaying the GWAS results for drooling in dogs. The x-axis represents the chromosomal position, while the y-axis represents the -log10(*P*) of the SNPs. Significant loci identified include *CDK7* on chromosome 2, *PACSIN2* on chromosome 10, and *IGSF1* on the X chromosome. (C) Manhattan plot displaying the GWAS results for lifespan in dogs. The x-axis represents the chromosomal position, while the y-axis represents the -log10(*P*) of the SNPs. The significant locus identified is *IGSF1* on the X chromosome. Dashed horizontal lines indicate the genome-wide significance threshold. SNPs above this threshold are considered to be significantly associated with the trait of interest.</figcaption></figure>

To avoid detecting false positives, we used the cutoff with the
genome-wide significant threshold (5E-08) to identify sig-
nificant associations in GWAS (Kaler and Purcell, 2019)
(Fig. 1 and Table S1). All quantile-quantile plots and the in-
flation factors are represented in the Supplementary
Information (Fig. S5 and Table S1). For BMI, our results align
with previous research findings, specifically identifying me-
thionine sulfoxide reductase B3 (*MSRB3*), high mobility group
AT-Hook 2 (*HMGA2*), *IGF1*, and immunoglobulin superfamily
member 1 (*IGSF1*) (Table S2). Regarding drooling, several
genes were detected (Table S3), including cyclin-dependent
kinase 7 (*CDK7*), with the highest peak observed on chro-
mosome 2. Additionally, *PACSIN2* and aspartate beta-hy-
droxylase (*ASPH*) showed other peaks and are involved in
critical cellular processes related to protein modification and
membrane dynamics, respectively. Also, *IGSF1* in chromo-
some X was detected. Last, for lifespan, we detected *IGSF1*
and rho GTPase activating protein 36 (*ARHGAP36*) on the X
chromosome, which has been mentioned in previous studies.
In addition, SNP rs24859997, was detected in the GWAS of
the 3 phenotypes located upstream of *IGSF1*.

Investigation of the Relationship Between Lifespan and
Other Factors

To investigate the relationship between lifespan (outcome) and
drooling/BMI (exposures), we performed a 1-sample univariate
MR analysis using genetic variants as instrumental variables
(Reay and Cairns, 2021). The analysis utilized GWAS statistics
for BMI and drooling against lifespan, focusing on SNPs with
genome-wide significance and using linkage disequilibrium
clumping for independence. We tested 5 MR models: MR
Egger, weighted median, inverse variance weighted, simple
mode, and weighted mode. Scatter and forest plots of SNP
effects on BMI-lifespan and drooling-lifespan revealed sig-
nificant relationships (Fig. 2). In the MR test, we observed a
symmetric pattern—for the IVW method, the scatter of points
formed a symmetrical inverted funnel shape—indicating that the
instrumental variable estimates are unbiased. In the forest plots
obtained from the single SNP analysis (Fig. S6), for BMI-life-
span and drooling-lifespan, rs24859997 (located upstream of
*IGSF1*) showed a high MR effect size value (Tables S4 and S5).
In addition, the pleiotropy test results indicate that for BMI-life-
span, the *P* is .3, and for drooling-lifespan, the *P* is .95. These
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** One-sample MR test results for BMI, drooling, and lifespan. (A) Scatter plot shows the effect of SNPs on BMI against their effect on lifespan. Each point represents an SNP, with error bars indicating standard errors. The plot includes lines from different MR methods: inverse variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode. The symmetric pattern and the consistency across methods suggest a robust relationship. (B) Funnel plot assessing the presence of bias for the effect of BMI on lifespan. Each point represents an SNP. The symmetrical inverted funnel shape, with the MR Egger intercept near 0, indicates unbiased estimates and no significant directional pleiotropy. (C) Scatter plot shows the effect of SNPs on drooling against their effect on lifespan. Each point represents an SNP, with error bars indicating standard errors. The plot includes lines from different MR methods: IVW, MR Egger, weighted median, simple mode, and weighted mode. The symmetry and consistency across methods support the robustness of the relationship inference. (D) Funnel plot assessing the presence of bias for the effect of drooling on lifespan. Each point represents an SNP. The symmetrical inverted funnel shape, with the MR Egger intercept near 0, suggests unbiased estimates and no significant directional pleiotropy.</figcaption></figure>

indicate that the intercepts are not significantly different from
zero, suggesting that there is no significant pleiotropy in either
case. We detected a valid signal from the MR test using 2

genotype datasets, confirming a significant relationship not only
between BMI and lifespan but also, between drooling and
lifespan.
Investigation of Gene Expression Differences in Salivary Glands Among Various Breeds

We conducted a DEG analysis using salivary gland RNA-Seq
datasets to explore the relationship between drooling, BMI, and
lifespan across dog breeds. We categorized our analysis into 3
groups: (1) different body size with differing drooling levels
(Newfoundland vs Yorkshire Terrier), (2) similar body size with
differing drooling levels (Newfoundland vs Belgian Malinois),
and (3) different body size with the same drooling level (Belgian
Malinois vs Yorkshire Terrier) (Appendix S3). BUSCO scores for
the RNA-Seq data were 69.4%, 67.2%, and 70.1% for these
comparisons, respectively (Table S6). RSEM results revealed
that larger body size breeds commonly expressed higher levels
of immunoglobulin proteins (Fig. S7, Tables S7, S8). In the
Newfoundland vs Belgian Malinois comparison, Newfoundland,
a hypersalivation breed, showed high SCGB1A1 expression
associated with hypersalivation (Côté et al., 2012). However, no
hypersalivation-related genes were detected in comparing
breeds with the same drooling level but different body sizes
(Table S7). Gene ontology enrichment analysis showed that
larger breeds upregulated genes related to immune responses,
antigen binding, and the immunoglobulin complex (Table S8).
Additionally, Newfoundland upregulated pathways related to
metabolism and secretory granules, while Belgian Malinois
upregulated pathways related to sensory perception of smell in
both comparisons (Table S8).

DISCUSSION

In this study, we compiled phenotype information for 149 dog
breeds to explore traits associated with drooling and lifespan.
Adjusting for sex and body size was crucial to isolate the ge-
netic influences on these traits (Austad and Fischer, 2016;
Uffelmann et al., 2021). Our GWAS identified key genes linked
to drooling, including CDK7, ASPH, PACSIN2, PIK3R1,
MCCC2, and IGSF1. PACSIN2 is involved in gland function
(Rual et al., 2005), PIK3R1 is part of the PI3K/Akt pathway
associated with longevity, MCCC2 is related to metabolism
(Zolotukhin, 2013), and IGSF1, located on the X chromosome,
influences hormone secretion (Xue et al., 2005). These findings
highlight the complex genetic and metabolic factors influencing
drooling and its potential impact on longevity. Our combined
GWAS and MR analyses suggest that drooling may be a life-
span-related phenotype. One-sample MR tests showed con-
sistent and unbiased SNP effect estimates, with IVW and MR
Egger plots indicating no significant directional pleiotropy
(Burgess and Thompson, 2017; Richmond and Smith, 2022).
SNP 15:41234395 on the IGF1 gene showed a moderate as-
sociation with BMI and lifespan, while SNP rs24859997 had a
more significant effect on BMI, drooling, and lifespan, sug-
gesting IGSF1's key role in these traits (Joustra et al., 2020).

We conducted a DEG analysis of the salivary gland, com-
paring dogs with hypersalivation to those with less drooling
while accounting for body size as a confounding factor, focusing
on 3 breeds to examine groups with varying levels of BMI and
drooling (Chibly et al., 2022). High-drooling breeds showed
higher expression of genes related to salivary secretion,

particularly *DMBT1*, which is upregulated in Newfoundland and linked to immune response, epithelial cell differentiation, and tumor suppression (Mollenhauer et al., 2000). *SLPI*, co-expressed with *DMBT1* (coexpression score 0.078 from the GEO database, STRING), modulates inflammatory responses after bacterial infection, while *OLFMA4* and *SCGB1A1* are also associated with salivary gland function (Xu et al., 2020). Upregulated processes in Newfoundland include the “hypochlorous acid metabolic process” and “secretory granule lumen,” both crucial for saliva secretion (Carvalho et al., 2019; Denny et al., 1997; Melvin et al., 2005; Turner and Sugiya, 2002). In Belgian Malinois, the “sensory perception of smell” pathway is upregulated, though it primarily influences appetite rather than hypersalivation (Carreira et al., 2020; Liu et al., 2021). Larger breeds showed higher expression of immunoglobulin proteins, possibly reflecting the impact of body size on immune response (Ruhs et al., 2020; Saitou et al., 2020). Our analysis highlights genes directly related to hypersalivation and suggests body size may explain some observed differences.

In summary, our study suggests that BMI and drooling could
be the factors influencing the life expectancy of dogs. Our sta-
tistical tests and MR analysis indicate a possible relationship
among these 3 phenotypes, warranting further investigation of
the identified genes. Canine morphological diversity, shaped by
domestication and selective breeding, impacts lifespan. Breeds
known for hypersalivation, such as Newfoundland, Bloodhound,
and Mastiffs, have shorter lifespans (8.8 years) compared to
other large breeds (9.3 years) (Parker et al., 2017). Excessive
drooling can lead to health issues such as kidney failure (Haley
et al., 2011), potentially affecting overall health and longevity
(Freiche and German, 2021). Further research on the link be-
tween hypersalivation and lifespan is recommended, con-
sidering both canine and comparative medicine.

AUTHOR CONTRIBUTIONS

W.H.K., S.K., S.S., and J.Y.C. conceived the project; W.H.K.
performed analyses; W.H.K. wrote the manuscript and prepared
figures; and A.C. compiled a dispersed phenotype dataset and
calculated a correlation coefficient. J.Y.C., S.K., and S.S. re-
vised the manuscript. All authors approved the submission of
the final manuscript. Further information and requests for re-
sources and reagents should be directed to and will be fulfilled
by the lead contact, Seunggwang Shin (sk83 @snu.ac.kr).

DECLARATION OF COMPETING INTERESTS

None.

ACKNOWLEDGMENTS

This research was supported by grants from the Ministry of
Science and ICT and the National Research Foundation of
Korea (NRF) SRC program: Comparative Medicine Disease
Research Center (CDRC) (2021R1A5A103315714) and partly
by NRF grants (2021R1C1C1003452), and Seoul National
University (Creative-Pioneering Researches Program, no.
3344-20240031).
# APPENDIX A. SUPPLEMENTAL MATERIAL

Supplemental material associated with this article can be found online at: doi:10.1016/j.mocell.2024.100162.

## ORCID

Won Hee Ko: 0009-0003-8605-4201
Sangil Kim: 0000-0003-2758-8943
Alix Catry: 0009-0007-5752-7224
Je-Yoel Cho: 0000-0003-1030-3577
Seunggwang Shin: 0000-0002-4258-8661

Received September 10, 2024
Revised October 28, 2024
Accepted November 19, 2024
Available online 22 November 2024.

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