The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases

Exome array genotyping (Huyghe et al., 2013)

WGS (Yin et al., 2022; Ganel et al., 2021)

WES (Locke et al., 2019)

Adipose tissue DNA methylation (Orozco et al., 2018)

Adipose tissue transcriptomics (Gusev et al., 2016)

Plasma metabolomics (Yin et al., 2022)

Medical history

Clinical and metabolic traits

Cardiovascular disease risk factors

Medication usage

Oral glucose tolerance test (Stancáková et al., 2009)

DNA genotyping (Lin et al., 2014)

WGS

CHIP-Seq (Tan et al., 2020)

Single-nuclear RNA-Seq (Tucker et al., 2020)

ATAC-Seq

Cardiac transcriptomics (Roselli et al., 2018; Lin et al., 2014; Liu et al., 2015)

Cardiomyopathy classification

DNA genotyping (Hägg et al., 2009)

Transcriptomics (Hägg et al., 2009; Franzén et al., 2016)

Clinical and biomedical traits

Medical history

Medication usage

Preoperative angiographic assessment of CAD

History of CAD and stroke

DNA genotyping (GTEx Consortium, 2015)

WGS (GTEx Consortium, 2020)

WES

Transcriptomics (GTEx Consortium, 2015; GTEx Consortium, 2020)

Proteomics (29 tissues) (Jiang et al., 2020)

Medical history

Disease risk factors

Cause of death

DNA genotyping (Bycroft et al., 2018)

WGS (Halldorsson et al., 2022)

WES (Van Hout et al., 2020; Szustakowski et al., 2021)

Metabolomics (Julkunen et al., 2021)

Plasma proteomics (Sun et al., 2022)

Medical history

Health records

Clinical biomarkers

Physical activity monitors (Khurshid et al., 2021)

Online questionnaires

Several imaging modalities (Littlejohns et al., 2020)

Psychosocial factors and environmental exposures (Mutz et al., 2021)

Meta-analysis of numerous published and unpublished GWAS in individuals with European ancestry

Case/control status for CAD and MI (Deloukas et al., 2013; Preuss et al., 2010; Schunkert et al., 2011; The Coronary Artery Disease (C4D) Genetics Consortium, 2011)

DNA genotyping (Cadby et al., 2018)

Lipidomics (Cadby et al., 2020; Cadby et al., 2022)

Clinical and biomedical traits

Self-reported medical history

DNA Genotyping (Giri et al., 2019)

WGS

WES

Self-reported medical history

Electronic health records

Most inbred strains are readily available (i.e. JAX labs)

Data available for several thousand phenotypes and >100 omics datasets

Large quantity of strains enables enormous mapping power

Lower mapping precision and genetic diversity compared to multi-parent populations

Less genetic diversity than outbred mice due to homozygosity at each loci

Lipid metabolism (Jha et al., 2018b; Jha et al., 2018a)

Body weight (Roy et al., 2021)

Atherosclerosis (Colinayo et al., 2003)

Blood pressure (Koutnikova et al., 2009)

NAFLD progression (Zhu et al., 2020)

Cardiac hypertrophy/pathology (Chen et al., 2020)

Liver mitochondrial function (Williams et al., 2016)

Captures a relatively large proportion of the genetic diversity in mice due to being a multi-parent population

High genetic diversity; ~45 million segregating SNPs

Strains fully genotyped

Less genetic diversity than outbred mice due to homozygosity at each locus

Body weight (Yam et al., 2022)

NASH/NAFLD (Abu-Toamih Atamni et al., 2016a; de Conti et al., 2020)

Diabetes/IR (Abu-Toamih Atamni et al., 2016b; Abu-Toamih Atamni et al., 2017; Abu-Toamih Atamni et al., 2019)

Cardiac pathology (Zeiss et al., 2019)

Response to exercise (McMullan et al., 2018; Mathes et al., 2011)

Captures a relatively large proportion of the genetic diversity in mice due to multi-parent ancestry and inclusion of wild-derived strains

Less genetic diversity than outbred mice due to homozygosity at each loci

Lipid metabolism (Bennett et al., 2010; Parker et al., 2019; Norheim et al., 2021)

Body weight (Parks et al., 2013)

NASH/NAFLD (Hui et al., 2018; Hui et al., 2015; Chella Krishnan et al., 2018; Norheim et al., 2021; Kurt et al., 2018; Norheim et al., 2017; Chella Krishnan et al., 2021a)

Diabetes/IR (Parks et al., 2015; Norheim et al., 2018)

Atherosclerosis (Talukdar et al., 2016; Koplev et al., 2022; von Scheidt et al., 2017; Bennett et al., 2015; Cohain et al., 2021; Kessler et al., 2017)

Cardiac hypertrophy/pathology (Wang et al., 2016; Rau et al., 2015; Santolini et al., 2018; Rau et al., 2017; Wang et al., 2019; Seldin et al., 2017; Lin et al., 2018; Cao et al., 2022; Krishnan et al., 2022)

Exercise metabolism (Moore et al., 2019)

Multiple cardiometabolic-related traits (Civelek et al., 2017; Chella Krishnan et al., 2019; Chella Krishnan et al., 2021b; Norheim et al., 2019; Seldin et al., 2018)

Alternative inbred cross that provides differing foundational strains and therefore diversity.

Less genetic diversity than outbred mice due to homozygosity at each loci

Body weight/adiposity (Liao et al., 2011; Rikke et al., 2006; Bennett et al., 2005)

Metabolic response to dietary challenge (Mulvey et al., 2021; Yau et al., 2021)

IR (Stöckli et al., 2017; Nelson et al., 2022)

F₂ offspring are derived from parents with known linkage phase, allowing for the study of parent-of-origin effects

Each mouse is genetically unique

High allelic variation between F₂ offspring

The population is reproducible, allowing comparison of genetic and phenotypic data across generations, provided sample sizes are sufficiently large

Genotyping is required for each mouse for genetic association studies

Compounds with effects on cardiometabolic-related traits (Zhu et al., 2022; Miller et al., 2014; Herrera et al., 2020; Miller et al., 2020; Snyder et al., 2023)

Impact of dietary conditions on cardiometabolic-related traits (Miller et al., 2014; Zheng et al., 2022; Green et al., 2022)

High mapping precision due to extensive allelic diversity

Captures ~90% of the genetic diversity in laboratory mice

Requires more mice to achieve comparable statistical power to inbred designs

Cannot measure intra-strain response to intervention

Each mouse requires genotyping for GWA analysis

Lipid metabolism (Coffey et al., 2017; Linke et al., 2020)

Body weight (Wright et al., 2022)

Atherosclerosis (Smallwood et al., 2014)

Diabetes/IR (Keller et al., 2018; Keller et al., 2019)

Cardiac hypertrophy (Starcher et al., 2021)

Hepatic mRNA and miRNA expression (Coffey et al., 2017; Que et al., 2021)

Multiple cardiometabolic-related traits (Svenson et al., 2012; Tyler et al., 2017)