Mice as a Mammalian Model for Research on the Genetics of Aging (2024)

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ILAR J. Author manuscript; available in PMC 2011 May 8.

Published in final edited form as:

ILAR J. 2011 Feb 8; 52(1): 4–15.

doi:10.1093/ilar.52.1.4

PMCID: PMC3074346

NIHMSID: NIHMS279168

PMID: 21411853

Rong Yuan, Ph.D., M.D., Luanne L. Peters, Ph.D., and Beverly Paigen, Ph.D.

Author information Copyright and License information PMC Disclaimer

The publisher's final edited version of this article is available at ILAR J

Abstract

Mice are an ideal mammalian model for studying the genetics of aging: considerable resources are available, the generation time is short, and the environment can be easily controlled, an important consideration when performing mapping studies to identify genes that influence lifespan and age-related diseases. In this review we highlight some salient contributions of the mouse in aging research: lifespan intervention studies in the Interventions Testing Program of the National Institute on Aging; identification of the genetic underpinnings of the effects of calorie restriction on lifespan; the Aging Phenome Project at the Jackson Laboratory, which has submitted multiple large, freely available phenotyping datasets to the Mouse Phenome Database; insights from spontaneous and engineered mouse mutants; and complex traits analyses identifying quantitative trait loci that affect lifespan. We also show that genomewide association peaks for lifespan in humans and lifespan quantitative loci for mice map to hom*ologous locations in the genome. Thus, the vast bioinformatic and genetic resources of the mouse can be used to screen candidate genes identified in both mouse and human mapping studies, followed by functional testing, often not possible in humans, to determine their influence on aging.

Keywords: aging, calorie restriction (CR), gene mutation, genetics, lifespan, longevity, mouse genome, quantitative trait locus (QTL)

Introduction

Much has been learned from the study of aging in worms and flies, but it is important to test the knowledge derived from these lower organisms in a mammalian species. For this, the mouse is ideal. Not only does it have a relatively short lifespan but, as a mammalian research model that shares 99% of its genes with humans (Boguski 2002), outstanding genetic resources and sophisticated genetic engineering technology are available for manipulating its genome (Paigen 1995). The many genetic resources of the mouse have been reviewed recently (Peters et al. 2007), and new resources, such as the Collaborative Cross (Churchill et al. 2004; Threadgill et al. 2011), are being developed at a steady pace.

Interventions Testing Program of the National Institute on Aging

One practical use of the mouse is to test diets and compounds for their ability to slow aging and extend longevity in a mammalian model. The Interventions Testing Program (ITP) of the National Institute on Aging is a three-site project with simultaneous identical lifespan studies at the Jackson Laboratory, University of Michigan, and University of Texas Health Science Center at San Antonio (Miller et al. 2007).² The diets and compounds tested are selected from proposals by the extramural research community (Nadon et al. 2008). The ITP mice are generated by breeding two hybrids, (BALB/cByJ × C57BL/6J) F1 × (C3H/HeJ × DBA/2J) F1, so that all mice are genetically heterogeneous but the genetic variation of the population is reproducible. The use of these mice avoids genotype-specific effects on disease susceptibility while ensuring the replicability of the study.

Although all three sites follow the same standardized protocols, both control and drug-exposed mice at the University of Michigan site were significantly smaller throughout adult life than those at the other two sites, and researchers observed significant differences in survival of male (but not female) mice in the control groups (Harrison et al. 2009; Strong et al. 2008). The researchers hypothesized that such differences could be due to the sources and formulations of food. At the start of the program, the diets used for breeders and weanlings (before drug exposure) differed in fat content (4.5–6.5%), supplemental levels of thiamine and other heat-sensitive vitamins, and protein source and content (18–24%). Starting with Cohort 4 (born in 2007), however, the three ITP sites adopted a uniform protocol for diet composition at all stages of the test process, including diets for breeder mice and for test mice before drug administration. It is also possible that other site-specific factors, such as minor differences in water quality, noise level, ventilation, extraneous odors, or cage-changing frequency contribute to site-specific differences.

The ITP website provides the list of compounds in testing. So far, one of the major findings of the study is that rapamycin, an inhibitor of mTOR (mammalian target of rapamycin) signaling, significantly increased lifespan in both males and females even though treatment did not start until mice were 600 days old (Harrison et al. 2009). However, the rapamycin-treated mice did not differ from control mice in the pattern of diseases as shown by pathology. Two other compounds—NDGA (p = 0.0006) and aspirin (p = 0.01), as assessed using the log rank test, which evaluates survivorship of the entire cohort—extended the median lifespan in male mice but not maximum lifespan as shown by comparisons of the proportion of mice alive at the age of 90% mortality (Strong et al. 2008). This suggests that the drugs may delay the onset or reduce the severity of specific diseases but that they do not affect the rate of aging.

Calorie Restriction

One of the interventions most reliably associated with an extension of lifespan and a reduced rate of aging is calorie restriction (CR¹), the reduction of food intake without malnutrition. CR has been shown to extend the lifespan of yeast, flies, worms, fish, rodents, and rhesus monkeys (Fontana et al. 2010) and, in mammals, decrease the risk of age-related diseases such as diabetes, cardiovascular diseases, and cancers (Fontana and Klein 2007; Morley et al. 2010).

Mouse models have been used extensively to investigate the underlying mechanisms of the antiaging effects of CR. One of the most interesting recent studies was an investigation of the effects of CR in different genetic backgrounds. A set of 42 recombinant inbred strains of mice, generated by crossing strains ILS and ISS, was examined for lifespan under ad libitum (AL) or CR conditions (Liao et al. 2010; Rikke et al. 2010). Although CR significantly extended female lifespan in nine strains, it significantly reduced lifespan in four other strains and had no significant effect in 29 strains, suggesting that genetic background affects the ability of CR to alter aging. This gene-environment interaction is not surprising, nor does the finding that CR acts only in certain genetic backgrounds contradict the widespread observation that CR usually extends lifespan in species with mixed genetic background. The mean lifespan under CR showed no significant correlation to lifespan under AL, suggesting that different genes modulate lifespan under each experimental condition. The study by Rikke and colleagues (2010) also found that increased efficiency of food utilization correlated with longer lifespan (R = 0.34, p = 0.026) as measured by the ability to maintain body weight, hair growth, and tail growth during CR.

The Aging Phenome Project

The Aging Center at the Jackson Laboratory characterized the lifespan and aging-related phenotypes of 32 inbred mouse strains, providing a baseline for further use of mouse models to improve understanding of the genetic regulation of aging. The project included both longitudinal and cross-sectional studies. The former not only assessed lifespan (using 96 mice per strain) but also carried out noninvasive clinical assessments of neuromuscular function at 6, 12, 18, and 24 months (Wooley et al. 2009), kidney and heart function (Tsaih et al. 2009; Xing et al. 2009), hematology, hormone levels, and immune system parameters (Petkova et al. 2008). The cross-sectional study euthanized 30 mice of each strain at 6, 12, and 20 months for body composition, bone density, necropsy, and pathology (Sundberg et al. 2008) and for the collection of tissues to evaluate apoptosis, DNA repair, and chromosome fragility. A reproductive study evaluated the age of sexual maturity in females of the same 32 strains (Yuan et al. manuscript in preparation). In addition to individual reports, all of these data are available in the Mouse Phenome Database (MPD; http://phenome.jax.org), where the Aging Center submits all data after quality control, even before publication. The MPD also provides statistical tools to enable the assessment of correlations of lifespan with other parameters in this and other studies (Grubb et al. 2009).

Among the 32 strains, four were recently derived from the wild and represent the major subspecies of laboratory mice: WSB/EiJ for Mus domesticus, PWD/PhJ for M. musculus, CAST/EiJ for M. castaneus, and MOLF/RkJ for M. molossinus. The remaining 28 strains were chosen for genetic diversity and common use. Median lifespan varied dramatically among the inbred strains (Table 1); the shortest was that of AKR/J (251 and 288 days for female and male, respectively), and the longest, female WSB/EiJ (964 days) and male C57BL/6J (901 days). These results confirmed that genetics plays an important role in determining longevity. Median lifespans for females and males were significantly correlated with each other (R = 0.88; p < 0.001). Proportional hazard analysis showed that sex did not significantly affect lifespan for most strains (Yuan et al. 2009).

Table 1

Lifespan characteristics of 32 inbred mouse strains^a

Strain	Female				Male

	Age (in days) of…			20% longest- lived (mean ± SEM)	Age (in days) of…			20% longest- lived (mean ± SEM)^b

	25% death	50% death	75% death		25% death	50% death	75% death
AKR/J	224	254	308	395 ± 24	244	288	336	415 ± 18
PL/J	373	471	596	736 ± 17	365	469	558	674 ± 19
SJL/J	393	515	632	740 ± 30	330	505	555	632 ± 21
MRL/MpJ	455	555	626	681 ± 9	549	645	669	711 ± 10
NZO/H1LtJ	418	575	700	782 ± 18	286	423	637	762 ± 26
CAST/EiJ	219	589	754	n.a.	239	591	754	n.a.
KK/H1J	564	608	653	720 ± 13	545	616	700	826 ± 43
BTBR T⁺tf/J	550	611	668	743 ± 19	444	575	728	822 ± 20
BUB/BnJ	392	621	755	876 ± 23	354	493	873	906 ± 23
SWR/J	499	630	814	n.a.	411	726	904	1020 ± 29
CBA/J	476	637	786	855 ± 11	532	679	808	872 ± 10
A/J	505	639	739	806 ± 19	541	623	708	785 ± 18
P/J	546	660	791	n.a.	439	607	673	n.a.
NOD.B10-H2b	599	667	770	827 ± 13	501	696	878	954 ± 11
C3H/HeJ	532	683	797	833 ± 7	623	728	834	894 ± 15
DBA/2J	443	687	823	872 ± 7	410	701	759	825 ± 17
MOLF/EiJ	590	705	n.a.^c	n.a.	503	686	730	n.a.
C57L/J	700	721	749	800 ± 5	658	736	768	806 ± 9
NZW/LacJ	600	732	866	950 ± 16	607	792	1013	1126 ± 14
SM/J	650	733	817	902 ± 15	730	783	833	873 ± 6
FVB/NJ	518	760	952	1023 ± 13	553	591	708	879 ± 56
129S1/SvImJ	651	791	920	1012 ± 25	798	882	992	1044 ± 12
BALB/cByJ	700	795	877	936 ± 10	512	714	840	927 ± 13
NON/ShiLtJ	631	806	861	887 ± 5	793	847	919	958 ± 11
RIIIS/J	691	813	883	938 ± 5	779	886	940	970 ± 12
LP/J	715	833	966	1047 ± 17	721	822	862	984 ± 28
PWD/PhJ	600	839	929	993 ± 12	575	813	905	956 ± 12
C57BR/CDJ	757	861	917	973 ± 7	737	849	943	993 ± 21
C57BLKS/J	672	867	926	989 ± 12	770	826	932	983 ± 21
WSB/EiJ	629	886	1148	n.a.	470	1005	1110	1213 ± 19
C57BL/10J	692	889	1035	1135 ± 9	677	792	852	893 ± 13
C57BL/6J	782	914	1006	1075 ± 13	838	901	971	1061 ± 17

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n.a., not available; SEM, standard error of the mean

^aLifespan traits reported by Yuan and colleagues (2009) and updated in August 2009. Age of 25%, 50%, and 75% at death and mean lifespan of the 20% longest-lived mice were calculated using JMP 6.0.4 software.

^bMean lifespan of the 20% longest-lived mice is not available for strains for which mice are still alive.

^cAge at death of 75% MOLF/EiJ was not available because there were too few mice to evaluate.

Among the 32 inbred strains, circulating insulinlike growth factor (IGF)-1 levels significantly (p < 0.05) correlated with body weight at 6, 12, and 18 months in both females and males (data available in the MPD): lower levels were associated with lighter body weight, which in turn was associated with extended longevity in a heterogeneous mouse population (Miller et al. 2002c). Our analysis found that IGF-1 levels at 6 months negatively correlated with median lifespan (R = −0.33, p = 0.01) (Yuan et al. 2009). After excluding the six short-lived strains (median lifespan less than 600 days), which presumably died of a particular strain-specific disease (e.g., leukemia in strain AKR), the negative correlation of IGF-1 and lifespan among long-lived strains became stronger and more significant (R = 0.53, p < 0.01).

These results underscore the importance of genetic regulation of IGF-1 signaling in regulating body weight and longevity, as has been suggested by studies in other models. For example, in domesticated dogs a single nucleotide polymorphism (SNP) in Igf1 significantly correlated with body weight (Sutter et al. 2007); in human populations, genetic polymorphisms of IGF-1 receptor (IGF-1R) (Suh et al. 2008) and phosphatidylinositol 3-kinase catalytic beta polypeptide (PIK3CB) (Bonafe et al. 2003) associated with human longevity. The variation in circulating IGF-1 levels among inbred strains of mice and the correlation of these levels with longevity suggest that they may be a useful focus in research on the genetic regulation of longevity.

Genes Implicated in Aging

Single-gene mutations that affect lifespan provide valuable tools for exploring the molecular basis for aging mechanisms. A number of mutations, either spontaneous or genetically engineered, that affect lifespan in the mouse are known; these are summarized in Table 2 and their location on the genome shown in Figure 1. The first of these mutants were spontaneous dwarf mice (e.g., the Ames dwarf, the Snell dwarf) and the “little” mouse, which have defects in the growth hormone (GH)/IGF/insulin signaling pathway (Brown-Borg et al. 1996; Flurkey et al. 2001, 2002). The little mouse has a defect in the gene Ghrhr (growth hormone–releasing hormone receptor), and the Ames and Snell dwarf rats in the genes Prop1 (paired-like homeodomain transcription factor 1) and Pou1f1 (POU domain, class 1, transcription factor 1), respectively. These three mutations result in abnormal development of the anterior pituitary gland and corresponding deficiency of pituitary hormones such as growth hormone, thyroid-stimulating hormone, and prolactin. These dwarf mutants all have extended lifespan compared to controls.

Mice as a Mammalian Model for Research on the Genetics of Aging (2)

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Figure 1

Quantitative trait loci (QTLs) for mouse longevity and genomewide association (GWA) peaks for human longevity, both depicted on the mouse genome (mapped in Mb). The length of the colored bars represents the 95% confidence interval if reported or an estimated 40 Mb if not reported; the black bars across the colored bars represent QTL peaks. We determined the Mb position using a recently revised mouse map (Cox et al. 2009) and the Mouse Map Converter from the Center for Genome Dynamics (http://cgd.jax.org/mousemapconverter/). Arrows on the left of chromosomes represent human GWA peaks at the hom*ologous mouse genome locations. Chr, chromosome, Mb, megabase (millions of base pairs).

Table 2

Mutations in mouse genes that increase longevity

Gene information				Type of mutation	Target gene expression	Effect on lifespan (sex)	Reference

Symbol^a	Full name	Chr	Mb
Adcy5	Adenylate cyclase 5	16	35	Knockout	Reduces	Increases (pooled)	Yan et al. 2007
Cebpb	CCAAT/enhancer binding protein (C/EBP), beta	2	168	Knock-in	Increases	Increases (female, male)	Chiu et al. 2004
Coq7	Demethyl-Q 7	5	126	Knockout	Reduces	Increases (female, male)	Liu et al. 2005
Ghr	Growth hormone receptor	15	3	Knockout	Reduces	Increases (female, male)	Coschigano et al. 2003
Ghrhr	Growth hormone–releasing hormone receptor	6	55	Spontaneous	Reduces	Increases (pooled)	Flurkey et al. 2001
Igf1^b	Insulin-like growth factor 1	10	88	Transgene	Increases	Increases (male)	Li and Ren 2007
Igf1r	Insulin-like growth factor I receptor	7	75	Knockout	Reduces	Increases (female)	Holzenberger et al. 2003
Insr	Insulin receptor	8	3	Knockout	Reduces	Increases (female, male)	Bluher et al. 2003
Irs1	Insulin receptor substrate 1	1	82	Knockout	Reduces	Increases (female)	Selman et al. 2008a
Irs2^c	Insulin receptor substrate 2	8	11	Knockout	Reduces	Increases (pooled)	Taguchi et al. 2007
Kl^d	Klotho	5	152	Transgene	Increases	Increases (female, male)	Kurosu et al. 2005
Mcat^b	Malonyl CoA:ACP acyltransferase (mitochondrial)	15	83	Transgene	Increases	Increases (female, male)	Schriner et al. 2005
Mt^b	Metallothionein	8	97	Transgene	Increases	Increases (male)	Yang et al. 2006
Pappa	Pregnancy-associated plasma protein A	4	65	Knockout	Reduces	Increases (female, male)	Conover and Bale 2007
Pck1	Phosphoenolpyruvate carboxykinase 1, cytosolic	2	103	Transgene	Increases	Increases (female, male)	Hakimi et al. 2007
Pou1f1	POU domain, class 1, transcription factor 1	16	66	Spontaneous	Reduces	Increases (female, male)	Flurkey et al. 2002
Pparg	Peroxisome proliferator–activated receptor gamma	6	115	Knock-in	Increases	Increases (male)	Heikkinen et al. 2009
Prop1	Paired-like homeodomain transcription factor 1	11	51	Spontaneous	Reduces	Increases (female, male)	Brown-Borg et al. 1996
Rps6kb1	Ribosomal protein S6 kinase, polypeptide 1	11	86	Knockout	Reduces	Increases (female)	Selman et al. 2009
Shc1	Src hom*ology 2 domain–containing transforming protein C1	3	89	Knockout	Reduces	Increases (not specified)	Migliaccio et al. 1999
Surf1	Surfeit gene 1	2	27	Knockout	Reduces	Increases (female, male)	Dell’agnello et al. 2007
Ucp2^e	Uncoupling protein 2	7	108	Transgene	Increases	Increases (female, male)	Conti et al. 2006
Mif	Macrophage migration inhibitory factor	10	75	Knockout	Reduces	Increases (female)	Harper et al. 2010

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Chr, chromosome; Mb, megabase (millions of base pairs)

^aGene names and symbols are according to the Mouse Genome Informatics database (www.informatic.jax.org).

^bModels were generated by transferring the human gene.

^cIrs2 knockout heterozygotes showed an extended lifespan in Taguchi’s study (Taguchi and White 2008) but failed to extend lifespan in a study by Selman and colleagues (2008a). The authors of the two studies discuss possible reasons for the different results: differences in the lifespan of controls, number of times the knockout was backcrossed to C56BL/6, diet, and housing conditions.

^dKurosu’s model is a transgenic model that overexpresses Klotho.

^eThe transgenic model overexpresses Ucp2 in hypocertin neurons, which causes elevated temperature in the thermostat center and results in a lowering of core body temperature.

Mutations in several other genes (Ghr, Igf1r, Insr, Irs1, Irs2 and Pappa) reduce GH/IGF/insulin signaling and extend lifespan. Cardiac-specific overexpression of IGF-1 significantly prolongs lifespan, probably due to the protective effects of IGF-1 on cardiac failure. Mutations such as knockouts of Shc1, Surf1, Adcy5, and Coq7, as well as transgenes of Mcat and Mt, which increase resistance to stress, also successfully extend longevity. Knock-in/transgenic models that increase the expression of Pparg, Cebpb, Pck1, and Ucp2 have shown increased lifespan by regulating metabolism and energy expenditure (Table 2).

Mutations that extend lifespan are likely to affect the rate of aging, while those that reduce lifespan either alter aging or increase the risk or severity of a particular disease. According to Mouse Genome Informatics (www.informatics.jax.org), 301 mutations decrease survival (by causing or promoting susceptibility to disease) and 46 promote features of premature aging. In Table 3 we list genes whose mutations decrease longevity and appear to alter aging. The roles of these genes, similar to the mutations that extend longevity, suggest that maintaining DNA stability and antioxidative stress are important molecular mechanisms that regulate aging and longevity. For example, a knockout of Bub1b induces chromosome (Chr¹) instability, reduced expression of PolgA increases mutations in mitochondrial DNA, and knockouts of Msra and Prdx1 increase oxidative stress.

Table 3

Mutations in mouse genes that reduce longevity

Gene information				Type of mutation	Target gene expression	Effect on lifespan (sex)	Reference

Symbol^a	Full name	Chr	Mb
Bub1b	Budding uninhibited by benzimidazoles 1 hom*olog, beta	2	118	Knockout	Reduces	Reduces (female, male)	Baker et al. 2004
Kl	Klotho	5	152	Transgene^b	Reduces	Reduces (female, male)	Kuro-o et al. 1997
Lmna	Lamin A	3	88	Knock-in	n.a.^c	Reduces (not specified)	Mounkes et al. 2003
Msra	Methionine sulfoxide reductase A	14	65	Knockout	Reduces	Reduces (female, male)	Moskovitz et al. 2001
PolgA	Polymerase (DNA directed), gamma	7	87	Knock-in	Reduces	Reduces (pooled)	Trifunovic et al. 2004
Prdx1	Peroxiredoxin 1	4	116	Knockout	Reduces	Reduces (not specified)	Neumann et al. 2003
Top3b	Topoisomerase (DNA) III beta	16	17	Knockout	Reduces	Reduces (not specified)	Kwan and Wang 2001

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Chr, chromosome; Mb, megabase (millions of base pairs); n.a, not available

^aGene names and symbols are according to Mouse Genome Informatics database (www.informatic.jax.org).

^bhe transgene causes an insertional mutation in the Klotho gene that suppresses its expression.

^chis knock-in model introduces a nucleotide polymorphism that results in the substitution of proline for leucine at amino acid 530 in the Lmna gene.

Aging studies in mutant gene models also provide clues for understanding the molecular mechanisms that extend lifespan by CR. For example, mice heterozygous for a Foxo1 (forkhead box O1) knockout did not differ significantly in lifespan compared to wild-type controls under AL or CR conditions. However, Foxo1 may play a role in CR’s antineoplastic effect, which, as indicated by reduced incidence of tumors at death in the diet-restricted wild-type mice, was mostly abrogated in the heterozygous knockout mice (Yamaza et al. 2010). The noticeable increase of MIF (macrophage migration inhibitory factor) in CR mice suggests that it may be important for CR-related lifespan extension, but the significantly extended longevity in Mif knockout mice challenges this hypothesis (Harper et al. 2010). Interestingly, deletion of S6k1 not only extended longevity but also induced gene expression patterns similar to those seen in CR or with pharmacological activation of adenosine monophosphate (AMP)-activated protein kinase (AMPK), a conserved regulator of the metabolic response to CR (Selman et al. 2009). This suggests that therapeutic manipulation of S6K1 and AMPK might mimic CR and could provide broad protection against diseases of aging.

One problem with a lifespan extension study is that altering the risk of a disease may change the mean or median lifespan but not reduce the rate of aging. One method to distinguish between these outcomes is to calculate the age-specific mortality rate (de Magalhaes et al. 2005). For example, CR changes age-specific mortality and delays aging, as do mutations of Cebpb, Msra, Shc1, Ghr, Pou1f1, and Polg, but studies in other mutants were either insufficiently powered for such calculations or changed disease risk without changing the rate of aging.

Lifespan Studies

QTLs in Mice

Examining spontaneous or genetically engineered mutants to determine a gene’s effect on lifespan is one way to unravel the genetic basis of aging. Another approach, which is unbiased and does not start with a defined hypothesis, is to conduct a quantitative trait locus study to determine the genomic locations of genes that affect lifespan. Although all the QTL studies performed so far on aging in the mouse were underpowered—in the number of animals or markers genotyped or both—we think these QTLs are worthy of further investigation, especially if they have been replicated in another mouse cross or if a human genomewide association study has identified a peak at a hom*ologous location. Thus, we list all the suggestive and significant QTLs in Table 4 and depict them on the mouse genome in Figure 1.

Table 4

Significant and suggestive lifespan quantitative trait loci (QTLs) detected in the mouse

Chr^a	Peak (Mb)	Cross	High allele strain (sex)	Reference	Replicated in mice	Replicated in humans
1	34	B6 × D2 RI strains	D2 (male)	Lang et al. 2010
	120	B6 × D2 RI strains	D2 (female)	Gelman et al. 1988	X
	128	B6 × D2 RI strains	D2 (female)	Lang et al. 2010	X
	163	B6 × D2 RI strains	B6 (female)	Gelman et al. 1988		X
2	65	B6 × D2 RI strains	B6 (female)	Lang et al. 2010
	103	B6 × D2 RI strains	B6 (female)	Gelman et al. 1988	X
	108	(BALB/cJ × B6) × (C3H × D2)	C3H (female)	Jackson et al. 2002; Miller et al. 2002a	X
	121	B6 × D2 RI strains	D2 (female)	Gelman et al. 1988
4	80	(B6 × D2) × D2	B6 (female)	Yunis et al. 1984
5	80	B6 × D2 RI strains	D2 (female)	Lang et al. 2010
6^b	77	B6 × D2 RI strains	D2 (male)	Lang et al. 2010
	98	B6 × D2 RI strains	D2 (male)	Lang et al. 2010
	113	B6 × D2 RI strains	D2 (male)	Lang et al. 2010
	3	B6 × D2 RI strains	B6 (female)	Lang et al. 2010	X
	11	B6 × D2 RI strains	B6 (female)	Gelman et al. 1988	X
	66	(BALB/cJ × B6) × (C3H × D2)	BALB (male)	Miller et al. 1998	X	X
	73	B6 × D2 RI strains	B6 (female)	Lang et al. 2010	X	X
	92	B6 × D2 RI strains	B6 (female and male)	Lang et al. 2010
	111	(BALB/cJ × B6) × (C3H × D2)	BALB (male)	Miller et al. 1998
8	15	B6 × D2 RI strains	B6 (female)	Lang et al. 2010	X	X
	26	(LP × MOLD) × (NZW × BALB)	MOLD (pooled)	Klebanov et al. 2001	X	X
	111	B6 × D2 RI strains	B6 (female)	Lang et al. 2010
9	91	(BALB/cJ × B6) × (C3H × D2)	C3H (male)	Jackson et al. 2002; Miller et al. 2002a		X
10	48	(BALB/cJ × B6) × (C3H × D2)	D2 (male)	Miller et al. 1998		X
	66	(BALB/cJ × B6) × (C3H × D2)	D2 (male)	Jackson et al. 2002; Miller et al. 2002a
	109	B6 × D2 RI strains	D2 (male)	Lang et al. 2010	X
	119	(ST × B6) × (CAST × D2)	CAST (pooled)	Klebanov et al. 2001	X
11^a	15	B6 × D2 RI strains	B6 (female)	de Haan et al. 1998	X
	18	B6 × D2 RI strains	B6 (male)	Lang et al. 2010	X
	35	B6 × D2 RI strains	B6 (female)	Lang et al. 2010
	56	B6 × D2 RI strains	B6 (male)	Lang et al. 2010		X
12	60		D2 (female)	Gelman et al. 1988
	105	(BALB/cJ × B6) × (C3H × D2)	B6/C3H (female and male)	Jackson et al. 2002; Miller et al. 2002a
16	6	(BALB/cJ × B6) × (C3H × D2)	BALB (female)	Jackson et al. 2002; Miller et al. 1998, 2002a
	32	B6 × D2 RI strains	B6 (male)	Lang et al. 2010		X
	64	B6 × D2 RI strains	B6 (female)	Lang et al. 2010
17	34	(B6 × D2) × D2	B6 (male)	Yunis et al. 1984
18	53	(BALB/cJ × B6) × (C3H × D2)	D2 (male)	Miller et al. 1998
19	30	(BALB/cJ × B6) × (C3H × D2)	BALB (female)	Miller et al. 1998	X
	32	ILS × ISS	ILS (female)	Rikke et al. 2010	X
	47	(BALB/cJ × B6) × (C3H × D2)	D2 (male)	Miller et al. 1998
X	49	B6 × D2 RI strains	D2 (female)	Lang et al. 2010
	126	B6 × D2 RI strains	D2 (female)	Lang et al. 2010

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Chr, chromosome; RI, recombinant inbred

Each suggestive and significant QTL is listed with the chromosomal peak in Mb (derived from the corrected mouse map [Cox et al. 2009] and the Mouse Map Converter from the Center for Genome Dynamics [http://cgd.jax.org/mousemapconverter/]), the cross in which the QTL was found, the allele conferring longer lifespan, and the reference. The QTL near the bottom of Chr 7 was originally reported with D12Mit38 as peak marker (Miller et al. 1998), but this particular marker was incorrectly mapped; it properly belongs on Chr 7 at Mb 111 and is now called D7Mit1000.

^aChromosomes 3 and 13–15 are missing because no QTLs affecting lifespan have been reported on them.

^bAlthough Lang and colleagues (2010) reported QTLs for males and females separately, we combined the two examples for which QTLs were found in both sexes at the same spot (Chr 6 at 96 Mb and Chr 11 at 18 Mb).

The earliest study was a (C57BL/6J × DBA/2) × C57BL/6 backcross using only four markers: two coat color genes on Chrs 4 and 9, the H2 antigen on Chr 17, and sex (Yunis et al. 1984). Subsequent studies tested 20 of the BXD (C57BL/6J × DBA/2J) recombinant inbred (RI) lines for lifespan (de Haan et al. 1998; Gelman et al. 1988), using, as markers, 101 genes that are distinguishable between B6 and D2, but these markers were not evenly distributed and only 14 chromosomes were covered. The QTL on Chr 17 identified in these two studies contains the major histocompatibility complex region, and thus may be related to the infection that occurred in the colony before the end of the study. A recent study of longevity using BXD RI strains, a more sophisticated lifespan analysis, and 671 markers failed to replicate the Chr 17 QTL (Lang et al. 2010). No infection occurred in the colony during this second study, which is, to date, the QTL lifespan study with the greatest statistical power (Lang et al. 2010) and will prove to be very useful, as considerable infrastructure resources (e.g., genotyping, sequence, and expression data) are available for these RI lines at GeneNetwork (www.genenetwork.org) and will enable the application of bioinformatics and system genetics approaches to the study of aging.

Both the backcross and RI QTL designs carry hom*ozygous alleles that may cause deleterious effects on lifespan without affecting aging. To minimize such effects, researchers conducted three different QTL studies using a four-way cross population. The first, using a (BALB/cJ × C57BL/6) × (C3H/HeJ × DBA/2J) cross, showed that different loci were involved in regulating the lifespans of female and male mice (Jackson et al. 2002). In a post hoc study of the same population, Miller and colleagues (1998, 2002a) found that the genotype associated with increased survival in mice dying of cancer also correlated with a similar degree of lifespan extension in mice dying of other causes, suggesting that many forms of late-life disease may be influenced by shared pathophysiologic mechanisms that are under coordinated genetic control.

Miller and colleagues (2002b) suggested that wild mice or inbred strains recently derived from the wild may carry alleles that delay sexual maturation and aging and that are missing in domesticated inbred strains. Thus, two additional four-way cross QTL studies each included one wild-derived inbred strain, MOLD or CAST (LP/J × MOLD/Rk) × (NZW/LacJ × BALB/cJ) and (ST/bJ × C57BL/6J) × (CAST/EiJ × DBA/2J) (Klebanov et al. 2001). These crosses revealed the alleles of wild-derived inbred strains that confer extended longevity on Chr 8 and Chr 10 (Klebanov et al. 2001).

Although we have included all the suggestive and significant QTLs for lifespan in Figure 1 and Table 4, we have more confidence that replicated QTLs are true positives. Eight of these QTLs—Chr 1, Chr 2, Chr 7 (proximal and mid-), Chr 8, Chr 10 distal, Chr 11 proximal, and Chr 19—have been replicated in another mouse cross (Table 4). We have counted as replicated those whose QTL peaks are within 10 Mb of each other, but further investigation may reveal that some of these are independent QTLs.

Concordance of Human and Mouse Lifespan Peaks

A recent genomewide association study of longevity, a meta-analysis of four separate studies by the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, compared 1900 human subjects that lived to age 90 with an equal number of controls that died earlier (Newman et al. 2010). Although none of the peaks reached statistical significance, we have included the 10 highest peaks on the mouse map (arrows in Figure 1). Remarkably, eight of the 10 are located in a mouse QTL; the probability that this is due to chance is very low (p = 0.0025 using Fisher’s exact test, based on lifespan QTLs covering 860 Mb of the 2700 Mb genome and each human peak being 1 Mb in size). Five of these human peaks (Chrs 1, 9, 10, 11, 16) are located within 10 Mb of a mouse QTL peak. Concordance of human and mouse QTLs has been reported previously (Garrett et al. 2010; Sugiyama et al. 2001; Wang and Paigen 2005), but for traits such as plasma lipids, hypertension, and kidney disease. Lifespan as a trait would be highly influenced by chance and by environmental factors, so one might think that concordance would be reduced or perhaps even nonexistent. Yet Figure 1 clearly shows concordance between humans and mice for lifespan, suggesting that the data for both species can be integrated and that the bioinformatic and genetic resources of the mouse can be used to narrow the QTL and test candidate genes.

Future Directions

Mouse models are valuable for studies of the genetics of human aging not only because of the availability of extensive mouse resources but also because of the similarity of the mouse and human genomes. As genes are identified in humans, mouse models will continue to be very useful in efforts to investigate underlying mechanisms of the genes that affect aging. We expect to see growing numbers of translational studies demonstrating the relevance of the mouse to human aging. This rise, combined with increasingly refined bioinformatic tools and mouse models, will accelerate the identification of genes that delay human aging and extend healthful lifespan.

Acknowledgments

The authors thank Drs. Kevin Flurkey and James Nelson for their constructive comments on the manuscript, Jesse Hammer for preparation of the figure, and Joanne Currer for editing of the manuscript. This work was supported by grants from the Glenn Foundation (BP), the Ellison Medical Foundation (BP), and the Nathan Shock Center (grant AG038070; LLP).

Footnotes

²Information is available at the ITP website (www.nia.nih.gov/researchinformation/scientificresources/interventionstestingprogram.htm); this and other websites cited in this article were accessed on December 22, 2010.

¹Abbreviations that appear >3 × throughout this article: Chr, chromosome; CR, calorie restriction; QTL, quantitative trait locus

Contributor Information

Rong Yuan, Research Scientist and Animal Core Leader, The Jackson Laboratory Aging Center, The Jackson Laboratory, Bar Harbor, ME.

Luanne L. Peters, Professor and Director, The Jackson Laboratory Aging Center, The Jackson Laboratory, Bar Harbor, ME.

Beverly Paigen, Professor, member of the Leadership Team, The Jackson Laboratory Aging Center, The Jackson Laboratory, Bar Harbor, ME.

References

Baker DJ, Jeganathan KB, Cameron JD, Thompson M, Juneja S, Kopecka A, Kumar R, Jenkins RB, de Groen PC, Roche P, van Deursen JM. BubR1 insufficiency causes early onset of aging-associated phenotypes and infertility in mice. Nat Genet. 2004;36:744–749. [PubMed] [Google Scholar]
Bluher M, Kahn BB, Kahn CR. Extended longevity in mice lacking the insulin receptor in adipose tissue. Science. 2003;299:572–574. [PubMed] [Google Scholar]
Boguski MS. Comparative genomics: The mouse that roared. Nature. 2002;420:515–516. [PubMed] [Google Scholar]
Bonafe M, Barbieri M, Marchegiani F, Olivieri F, Ragno E, Giampieri C, Mugianesi E, Centurelli M, Franceschi C, Paolisso G. Polymorphic variants of insulin-like growth factor I (IGF-I) receptor and phosphoinositide 3-kinase genes affect IGF-I plasma levels and human longevity: Cues for an evolutionarily conserved mechanism of life span control. J Clin Endocrinol Metab. 2003;88:3299–3304. [PubMed] [Google Scholar]
Brown-Borg HM, Borg KE, Meliska CJ, Bartke A. Dwarf mice and the ageing process. Nature. 1996;384:33. [PubMed] [Google Scholar]
Chen YF, Wu CY, Kao CH, Tsai TF. Longevity and lifespan control in mammals: Lessons from the mouse. Ageing Res Rev. 2010;9:S28–S35. [PubMed] [Google Scholar]
Chiu CH, Lin WD, Huang SY, Lee YH. Effect of a C/EBP gene replacement on mitochondrial biogenesis in fat cells. Genes Dev. 2004;18:1970–1975. [PMC free article] [PubMed] [Google Scholar]
Churchill GA, Airey DC, Allayee H, Angel JM, Attie AD, Beatty J, Beavis WD, Belknap JK, Bennett B, Berrettini W, Bleich A, Bogue M, Broman KW, Buck KJ, Buckler E, Burmeister M, Chesler EJ, Cheverud JM, Clapcote S, Cook MN, Cox RD, Crabbe JC, Crusio WE, Darvasi A, Deschepper CF, Doerge RW, Farber CR, Forejt J, Gaile D, Garlow SJ, Geiger H, Gershenfeld H, Gordon T, Gu J, Gu W, de Haan G, Hayes NL, Heller C, Himmelbauer H, Hitzemann R, Hunter K, Hsu HC, Iraqi FA, Ivandic B, Jacob HJ, Jansen RC, Jepsen KJ, Johnson DK, Johnson TE, Kempermann G, Kendziorski C, Kotb M, Kooy RF, Llamas B, Lammert F, Lassalle JM, Lowenstein PR, Lu L, Lusis A, Manly KF, Marcucio R, Matthews D, Medrano JF, Miller DR, Mittleman G, Mock BA, Mogil JS, Montagutelli X, Morahan G, Morris DG, Mott R, Nadeau JH, Nagase H, Nowakowski RS, O'Hara BF, Osadchuk AV, Page GP, Paigen B, Paigen K, Palmer AA, Pan HJ, Peltonen-Palotie L, Peirce J, Pomp D, Pravenec M, Prows DR, Qi Z, Reeves RH, Roder J, Rosen GD, Schadt EE, Schalkwyk LC, Seltzer Z, Shimomura K, Shou S, Sillanpaa MJ, Siracusa LD, Snoeck HW, Spearow JL, Svenson K, Tarantino LM, Threadgill D, Toth LA, Valdar W, de Villena FP, Warden C, Whatley S, Williams RW, Wiltshire T, Yi N, Zhang D, Zhang M, Zou F. The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat Genet. 2004;36:1133–1137. [PubMed] [Google Scholar]
Conover CA, Bale LK. Loss of pregnancy-associated plasma protein A extends lifespan in mice. Aging Cell. 2007;6:727–729. [PubMed] [Google Scholar]
Conti B, Sanchez-Alavez M, Winsky-Sommerer R, Morale MC, Lucero J, Brownell S, Fabre V, Huitron-Resendiz S, Henriksen S, Zorrilla EP, de Lecea L, Bartfai T. Transgenic mice with a reduced core body temperature have an increased life span. Science. 2006;314:825–828. [PubMed] [Google Scholar]
Coschigano KT, Holland AN, Riders ME, List EO, Flyvbjerg A, Kopchick J. Deletion, but not antagonism, of the mouse growth hormone receptor results in severely decreased body weights, insulin, and insulin-like growth factor I levels and increased life span. Endocrinology. 2003;144:3799–3810. [PubMed] [Google Scholar]
Cox A, Ackert-Bicknell CL, Dumont BL, Ding Y, Bell JT, Brockmann GA, Wergedal JE, Bult C, Paigen B, Flint J, Tsaih SW, Churchill GA, Broman KW. A new standard genetic map for the laboratory mouse. Genetics. 2009;182:1335–1344. [PMC free article] [PubMed] [Google Scholar]
de Haan G, Gelman R, Watson A, Yunis E, Van Zant G. A putative gene causes variability in lifespan among genotypically identical mice. Nat Genet. 1998;19:114–116. [PubMed] [Google Scholar]
de Magalhaes JP, Cabral JA, Magalhaes D. The influence of genes on the aging process of mice: A statistical assessment of the genetics of aging. Genetics. 2005;169:265–274. [PMC free article] [PubMed] [Google Scholar]
Dell'agnello C, Leo S, Agostino A, Szabadkai G, Tiveron C, Zulian A, Prelle A, Roubertoux P, Rizzuto R, Zeviani M. Increased longevity and refractoriness to Ca(2+)-dependent neurodegeneration in Surf1 knockout mice. Hum Mol Genet. 2007;16:431–444. [PubMed] [Google Scholar]
Flurkey K, Papaconstantinou J, Miller RA, Harrison DE. Lifespan extension and delayed immune and collagen aging in mutant mice with defects in growth hormone production. Proc Natl Acad Sci U S A. 2001;98:6736–6741. [PMC free article] [PubMed] [Google Scholar]
Flurkey K, Papaconstantinou J, Harrison DE. The Snell dwarf mutation Pit1(dw) can increase life span in mice. Mech Ageing Dev. 2002;123:121–130. [PubMed] [Google Scholar]
Fontana L, Klein S. Aging, adiposity, and calorie restriction. JAMA. 2007;297:986–994. [PubMed] [Google Scholar]
Fontana L, Partridge L, Longo VD. Extending healthy life span: From yeast to humans. Science. 2010;328:321–326. [PMC free article] [PubMed] [Google Scholar]
Garrett MR, Pezzolesi MG, Korstanje R. Integrating human and rodent data to identify the genetic factors involved in chronic kidney disease. J Am Soc Nephrol. 2010;21:398–405. [PMC free article] [PubMed] [Google Scholar]
Gelman R, Watson A, Bronson R, Yunis E. Murine chromosomal regions correlated with longevity. Genetics. 1988;118:693–704. [PMC free article] [PubMed] [Google Scholar]
Goodrick CL. Life-span and the inheritance of longevity of inbred mice. J Gerontol. 1975;30:257–263. [PubMed] [Google Scholar]
Grubb SC, Maddatu TP, Bult CJ, Bogue MA. Mouse phenome database. Nucleic Acids Res. 2009;37:D720–D730. [PMC free article] [PubMed] [Google Scholar]
Hakimi P, Yang J, Casadesus G, Massillon D, Tolentino-Silva F, Nye CK, Cabrera ME, Hagen DR, Utter CB, Baghdy Y, Johnson DH, Wilson DL, Kirwan JP, Kalhan SC, Hanson RW. Overexpression of the cytosolic form of phosphoenolpyruvate carboxykinase (GTP) in skeletal muscle repatterns energy metabolism in the mouse. J Biol Chem. 2007;282:32844–32855. [PMC free article] [PubMed] [Google Scholar]
Harper JM, Wilkinson JE, Miller RA. Macrophage migration inhibitory factor-knockout mice are long lived and respond to caloric restriction. FASEB J. 2010;24:2436–2442. [PMC free article] [PubMed] [Google Scholar]
Harrison DE, Strong R, Sharp ZD, Nelson JF, Astle CM, Flurkey K, Nadon NL, Wilkinson JE, Frenkel K, Carter CS, Pahor M, Javors MA, Fernandez E, Miller RA. Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature. 2009;460:392–395. [PMC free article] [PubMed] [Google Scholar]
Heikkinen S, Argmann C, Feige JN, Koutnikova H, Champy MF, Dali-Youcef N, Schadt EE, Laakso M, Auwerx J. The Pro12Ala PPARgamma2 variant determines metabolism at the gene-environment interface. Cell Metab. 2009;9:88–98. [PubMed] [Google Scholar]
Holzenberger M, Dupont J, Ducos B, Leneuve P, Geloen A, Even PC, Cervera P, Le Bouc Y. IGF-1 receptor regulates lifespan and resistance to oxidative stress in mice. Nature. 2003;421:182–187. [PubMed] [Google Scholar]
Jackson AU, Galecki AT, Burke DT, Miller RA. Mouse loci associated with life span exhibit sex-specific and epistatic effects. J Gerontol A Biol Sci Med Sci. 2002;57:B9–B15. [PubMed] [Google Scholar]
Kemnitz J. Caloric restriction and aging in nonhuman primates. ILAR J. 2011;52:XX–XX. [PMC free article] [PubMed] [Google Scholar]
Kenyon CJ. The genetics of ageing. Nature. 2010;464:504–512. [PubMed] [Google Scholar]
Klebanov S, Astle CM, Roderick TH, Flurkey K, Archer JR, Chen J, Harrison DE. Maximum life spans in mice are extended by wild strain alleles. Exp Biol Med (Maywood) 2001;226:854–859. [PubMed] [Google Scholar]
Kuro-o M, Matsumura Y, Aizawa H, Kawaguchi H, Suga T, Utsugi T, Ohyama Y, Kurabayashi M, Kaname T, Kume E, Iwasaki H, Iida A, Shiraki-Iida T, Nishikawa S, Nagai R, Nabeshima YI. Mutation of the mouse klotho gene leads to a syndrome resembling ageing. Nature. 1997;390:45–51. [PubMed] [Google Scholar]
Kurosu H, Yamamoto M, Clark JD, Pastor JV, Nandi A, Gurnani P, McGuinness OP, Chikuda H, Yamaguchi M, Kawaguchi H, Shimomura I, Takayama Y, Herz J, Kahn CR, Rosenblatt KP, Kuro-o M. Suppression of aging in mice by the hormone Klotho. Science. 2005;309:1829–1833. [PMC free article] [PubMed] [Google Scholar]
Kwan KY, Wang JC. Mice lacking DNA topoisomerase IIIbeta develop to maturity but show a reduced mean lifespan. Proc Natl Acad Sci U S A. 2001;98:5717–5721. [PMC free article] [PubMed] [Google Scholar]
Lang DH, Gerhard GS, Griffith JW, Vogler GP, Vandenbergh DJ, Blizard DA, Stout JT, Lakoski JM, McClearn GE. Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clin Exp Res. 2010;22:8–19. [PubMed] [Google Scholar]
Larsson NG. Somatic mitochondrial DNA mutations in mammalian aging. Annu Rev Biochem. 2010;79:683–706. [PubMed] [Google Scholar]
Li Q, Ren J. Influence of cardiac-specific overexpression of insulin-like growth factor 1 on lifespan and aging-associated changes in cardiac intracellular Ca2+ homeostasis, protein damage and apoptotic protein expression. Aging Cell. 2007;6:799–806. [PubMed] [Google Scholar]
Liao CY, Rikke BA, Johnson TE, Diaz V, Nelson JF. Genetic variation in the murine lifespan response to dietary restriction: From life extension to life shortening. Aging Cell. 2010;9:92–95. [PMC free article] [PubMed] [Google Scholar]
Liu X, Jiang N, Hughes B, Bigras E, Shoubridge E, Hekimi S. Evolutionary conservation of the clk-1-dependent mechanism of longevity: Loss of mclk1 increases cellular fitness and lifespan in mice. Genes Dev. 2005;19:2424–2434. [PMC free article] [PubMed] [Google Scholar]
Migliaccio E, Giorgio M, Mele S, Pelicci G, Reboldi P, Pandolfi PP, Lanfrancone L, Pelicci PG. The p66shc adaptor protein controls oxidative stress response and life span in mammals. Nature. 1999;402:309–313. [PubMed] [Google Scholar]
Miller RA, Chrisp C, Jackson AU, Burke D. Marker loci associated with life span in genetically heterogeneous mice. J Gerontol A Biol Sci Med Sci. 1998;53:M257–M263. [PubMed] [Google Scholar]
Miller RA, Chrisp C, Jackson AU, Galecki AT, Burke DT. Coordinated genetic control of neoplastic and nonneoplastic diseases in mice. J Gerontol A Biol Sci Med Sci. 2002a;57:B3–B8. [PubMed] [Google Scholar]
Miller RA, Harper JM, Dysko RC, Durkee SJ, Austad SN. Longer life spans and delayed maturation in wild-derived mice. Exp Biol Med. 2002b;227:500–508. [PubMed] [Google Scholar]
Miller RA, Harper JM, Galecki A, Burke DT. Big mice die young: Early life body weight predicts longevity in genetically heterogeneous mice. Aging Cell. 2002c;1:22–29. [PubMed] [Google Scholar]
Miller RA, Harrison DE, Astle CM, Floyd RA, Flurkey K, Hensley KL, Javors MA, Leeuwenburgh C, Nelson JF, Ongini E, Warner HR, Strong R. An Aging Interventions Testing Program: Study design and interim report. Aging Cell. 2007;6:565–575. [PubMed] [Google Scholar]
Morley JE, Chahla E, Alkaade S. Antiaging, longevity and calorie restriction. Curr Opin Clin Nutr Metab Care. 2010;13:40–45. [PubMed] [Google Scholar]
Moskovitz J, Bar-Noy S, Williams WM, Requena J, Berlett BS, Stadtman ER. Methionine sulfoxide reductase (MsrA) is a regulator of antioxidant defense and lifespan in mammals. Proc Natl Acad Sci U S A. 2001;98:12920–12925. [PMC free article] [PubMed] [Google Scholar]
Mounkes LC, Kozlov S, Hernandez L, Sullivan T, Stewart CL. A progeroid syndrome in mice is caused by defects in A-type lamins. Nature. 2003;423:298–301. [PubMed] [Google Scholar]
Nadon NL, Strong R, Miller RA, Nelson J, Javors M, Sharp ZD, Peralba JM, Harrison DE. Design of aging intervention studies: The NIA Interventions Testing Program. Age (Dordr) 2008;30:187–199. [PMC free article] [PubMed] [Google Scholar]
Neumann CA, Krause DS, Carman CV, Das S, Dubey DP, Abraham JL, Bronson RT, Fujiwara Y, Orkin SH, Van Etten RA. Essential role for the peroxiredoxin Prdx1 in erythrocyte antioxidant defence and tumour suppression. Nature. 2003;424:561–565. [PubMed] [Google Scholar]
Newman AB, Walter S, Lunetta KL, Garcia ME, Slagboom PE, Christensen K, Arnold AM, Aspelund T, Aulchenko YS, Benjamin EJ, Christiansen L, D’Agostino RB, Sr, Fitzpatrick AL, Franceschini N, Glazer NL, Gudnason V, Hofman A, Kaplan R, Karasik D, Kelly-Hayes M, Kiel DP, Launer LJ, Marciante KD, Massaro JM, Miljkovic I, Nalls MA, Hernandez D, Psaty BM, Rivadeneira F, Rotter J, Seshadri S, Smith AV, Taylor KD, Tiemeier H, Uh HW, Uitterlinden AG, Vaupel JW, Walston J, Westendorp RG, Harris TB, Lumley T, van Duijn CM, Murabito JM. A meta-analysis of four genome-wide association studies of survival to age 90 years or older: The Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. J Gerontol A Biol Sci Med Sci. 2010;65:478–487. [PMC free article] [PubMed] [Google Scholar]
Paigen K. A miracle enough: The power of mice. Nat Med. 1995;1:215–220. [PubMed] [Google Scholar]
Peters LL, Robledo RF, Bult CJ, Churchill GA, Paigen BJ, Svenson KL. The mouse as a model for human biology: A resource guide for complex trait analysis. Nat Rev Genet. 2007;8:58–69. [PubMed] [Google Scholar]
Petkova SB, Yuan R, Tsaih SW, Schott W, Roopenian DC, Paigen B. Genetic influence on immune phenotype revealed strain-specific variations in peripheral blood lineages. Physiol Genomics. 2008;34:304–314. [PMC free article] [PubMed] [Google Scholar]
Rikke BA, Liao CY, McQueen MB, Nelson JF, Johnson TE. Genetic dissection of dietary restriction in mice supports the metabolic efficiency model of life extension. Exp Gerontol. 2010;45:691–701. [PMC free article] [PubMed] [Google Scholar]
Sahin E, Depinho RA. Linking functional decline of telomeres, mitochondria and stem cells during ageing. Nature. 2010;464:520–528. [PMC free article] [PubMed] [Google Scholar]
Schriner SE, Linford NJ, Martin GM, Treuting P, Ogburn CE, Emond M, Coskun PE, Ladiges W, Wolf N, Van Remmen H, Wallace DC, Rabinovitch PS. Extension of murine life span by overexpression of catalase targeted to mitochondria. Science. 2005;308:1909–1911. [PubMed] [Google Scholar]
Selman C, Lingard S, Choudhury AI, Batterham RL, Claret M, Clements M, Ramadani F, Okkenhaug K, Schuster E, Blanc E, Piper MD, Al-Qassab H, Speakman JR, Carmignac D, Robinson IC, Thornton JM, Gems D, Partridge L, Withers DJ. Evidence for lifespan extension and delayed age-related biomarkers in insulin receptor substrate 1 null mice. FASEB J. 2008a;22:807–818. [PubMed] [Google Scholar]
Selman C, Lingard S, Gems D, Partridge L, Withers DJ. Comment on "Brain IRS2 signaling coordinates life span and nutrient homeostasis". Science. 2008b;320:1012. author reply 1012. [PubMed] [Google Scholar]
Selman C, Tullet JM, Wieser D, Irvine E, Lingard SJ, Choudhury AI, Claret M, Al-Qassab H, Carmignac D, Ramadani F, Woods A, Robinson IC, Schuster E, Batterham RL, Kozma SC, Thomas G, Carling D, Okkenhaug K, Thornton JM, Partridge L, Gems D, Withers DJ. Ribosomal protein S6 kinase 1 signaling regulates mammalian life span. Science. 2009;326:140–144. [PMC free article] [PubMed] [Google Scholar]
Storer JB. Longevity and gross pathology at death in 22 inbred mouse strains. J Gerontol. 1966;21:404–409. [PubMed] [Google Scholar]
Strong R, Miller RA, Astle CM, Floyd RA, Flurkey K, Hensley KL, Javors MA, Leeuwenburgh C, Nelson JF, Ongini E, Nadon NL, Warner HR, Harrison DE. Nordihydroguaiaretic acid and aspirin increase lifespan of genetically heterogeneous male mice. Aging Cell. 2008;7:641–650. [PMC free article] [PubMed] [Google Scholar]
Sugiyama F, Churchill GA, Higgins DC, Johns C, Makaritsis KP, Gavras H, Paigen B. Concordance of murine quantitative trait loci for salt-induced hypertension with rat and human loci. Genomics. 2001;71:70–77. [PubMed] [Google Scholar]
Suh Y, Atzmon G, Cho MO, Hwang D, Liu B, Leahy DJ, Barzilai N, Cohen P. Functionally significant insulin-like growth factor I receptor mutations in centenarians. Proc Natl Acad Sci U S A. 2008;105:3438–3442. [PMC free article] [PubMed] [Google Scholar]
Sundberg JP, Sundberg BA, Schofield P. Integrating mouse anatomy and pathology ontologies into a phenotyping database: Tools for data capture and training. Mamm Genome. 2008;19:413–419. [PMC free article] [PubMed] [Google Scholar]
Sutter NB, Bustamante CD, Chase K, Gray MM, Zhao K, Zhu L, Padhukasahasram B, Karlins E, Davis S, Jones PG, Quignon P, Johnson GS, Parker HG, Fretwell N, Mosher DS, Lawler DF, Satyaraj E, Nordborg M, Lark KG, Wayne RK, Ostrander EA. A single IGF1 allele is a major determinant of small size in dogs. Science. 2007;316:112–115. [PMC free article] [PubMed] [Google Scholar]
Taguchi A, White MF. Response to Comment on "Brain IRS2 signaling coordinates life span and nutrient homeostasis". Science. 2008;320:1012. [PubMed] [Google Scholar]
Taguchi A, Wartschow LM, White MF. Brain IRS2 signaling coordinates life span and nutrient homeostasis. Science. 2007;317:369–372. [PubMed] [Google Scholar]
Threadgill D, Miller D, Churchill G, de Villena FP. The Collaborative Cross: Recombinant inbred mouse population for the system genetics era. ILAR J. 2011;52:XX–XX. [PubMed] [Google Scholar]
Trifunovic A, Wredenberg A, Falkenberg M, Spelbrink JN, Rovio AT, Bruder CE, Bohlooly YM, Gidlof S, Oldfors A, Wibom R, Tornell J, Jacobs HT, Larsson NG. Premature ageing in mice expressing defective mitochondrial DNA polymerase. Nature. 2004;429:417–423. [PubMed] [Google Scholar]
Tsaih SW, Pezzolesi MG, Yuan R, Warram JH, Krolewski AS, Korstanje R. Genetic analysis of albuminuria in aging mice and concordance with loci for human diabetic nephropathy found in a genome-wide association scan. Kidney Int. 2009;77:173–175. [PMC free article] [PubMed] [Google Scholar]
Wang X, Paigen B. Genetics of variation in HDL cholesterol in humans and mice. Circ Res. 2005;96:27–42. [PubMed] [Google Scholar]
Wooley CM, Xing S, Burgess RW, Cox GA, Seburn KL. Age, experience and genetic background influence treadmill walking in mice. Physiol Behav. 2009;96:350–361. [PMC free article] [PubMed] [Google Scholar]
Xing S, Tsaih SW, Yuan R, Svenson KL, Jorgenson LM, So M, Paigen BJ, Korstanje R. Genetic influence on electrocardiogram time intervals and heart rate in aging mice. Am J Physiol Heart Circ Physiol. 2009;296:H1907–H1913. [PMC free article] [PubMed] [Google Scholar]
Yamaza H, Komatsu T, Wakita S, Kijogi C, Park S, Hayashi H, Chiba T, Mori R, Furuyama T, Mori N, Shimokawa I. FoxO1 is involved in the antineoplastic effect of calorie restriction. Aging Cell. 2010;9:372–382. [PubMed] [Google Scholar]
Yan L, Vatner DE, O'Connor JP, Ivessa A, Ge H, Chen W, Hirotani S, Ishikawa Y, Sadoshima J, Vatner SF. Type 5 adenylyl cyclase disruption increases longevity and protects against stress. Cell. 2007;130:247–258. [PubMed] [Google Scholar]
Yang X, Doser TA, Fang CX, Nunn JM, Janardhanan R, Zhu M, Sreejayan N, Quinn MT, Ren J. Metallothionein prolongs survival and antagonizes senescence-associated cardiomyocyte diastolic dysfunction: Role of oxidative stress. FASEB J. 2006;20:1024–1026. [PubMed] [Google Scholar]
Yuan R, Tsaih SW, Petkova SB, Marin de Evsikova C, Xing S, Marion MA, Bogue MA, Mills KD, Peters LL, Bult CJ, Rosen CJ, Sundberg JP, Harrison DE, Churchill GA, Paigen B. Aging in inbred strains of mice: Study design and interim report on median lifespans and circulating IGF1 levels. Aging Cell. 2009;8:277–287. [PMC free article] [PubMed] [Google Scholar]
Yunis EJ, Watson AL, Gelman RS, Sylvia SJ, Bronson R, Dorf ME. Traits that influence longevity in mice. Genetics. 1984;108:999–1011. [PMC free article] [PubMed] [Google Scholar]

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Because mice live short lives compared to humans — about two years in laboratory care, but much less in the wild — it's possible to learn a great deal about how chronic diseases progress over a lifetime, and about the processes of aging.

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Why are mice used in genetic research? ›

Among these rodents, the majority of genetic studies, especially those involving disease, have employed mice, not only because their genomes are so similar to that of humans, but also because of their availability, ease of handling, high reproductive rates, and relatively low cost of use.

Why do you think the mouse was selected as a model organism for mammals? ›

Mice are the model of choice not just because they are strikingly similar to humans at the genomic level, but also because the pathophysiology of disease in mice is similar to that of humans. Mice are a cost-effective and efficient tool to speed research and drug testing.

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What is the genetic relatedness between humans and mice answer with a number? ›

ODENTS and people may not appear to be closely related, but consider this the next time you look in a mirror: the genes of human beings and mice are 85 percent identical.

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Are mice truly good genetic and scientific models for humans? ›

Laboratory rats and mice provide ideal animal models for biomedical research and comparative medicine studies because they have many similarities to humans in terms of anatomy and physiology.

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Why are mice used as models of choice for experiments? ›

Mice experience many of the same diseases as humans and have the same types of organs and bodily systems, which makes them excellent models for human disease. Around 95% of the genes that code for proteins are identical in humans and mice.

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What are the advantages of using mice in research? ›

They can help researchers gain clinical insight due to: Genetic similarity: Mice share common genetic features with humans. As such, scientists can manipulate the mouse genome, model specific diseases, test new drugs, and investigate the genetics of diseases before trying them on human models.

Why scientists would want mice that are genetically alike? ›

Genetically defined mice are important for basic and biomedical research. They provide reproducible systems that enable investigators to replicate experiments and enable different scientists to use genetically similar or identical research animal models.

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What are the advantages of using mice as a model for studies in neuroscience? ›

The mouse or M. musculus is often used as a preferred model organism due to the similarity with the human genome of 85% and genome size ^~2.5 Gbp. Despite being bigger than the other model organisms discussed in this series, mice are still relatively easily to care for, breed and study.

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Why is the mouse a particularly model for studying human disease _____? ›

Short Answer: Model organisms, such as mice and fruit flies, are used to study human genetic diseases because they share similar genetic characteristics with humans and have shorter lifecycles, making them ideal for research.

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Do we share 90% of our DNA with mice? ›

In the study, the researchers found that about 90 percent of the human genes in chromosome 19 were also located in similar sections of mouse DNA, Stubbs said.

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What is the evolutionary relationship between mice and humans? ›

Humans and mice derive from a common mammalian ancestor but have evolved independently in distinct biospheres over ~90 million years. This evolutionary process is responsible for the similarities between humans and mice that enable biomedical research and for the differences that must be transcended.

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Why do we experiment on rats and mice for human research? ›

There are several reasons why the use of animals is critical for biomedical research: Animals are biologically very similar to humans. In fact, mice share more than 98% DNA with us! Animals are susceptible to many of the same health problems as humans – cancer, diabetes, heart disease, etc.

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What are the advantages of studying mice? ›

Researchers identified early on that mice share many biological and genetic similarities with humans. As such, researchers could study mice to gain insight into various medical conditions, including cancer and rare diseases.

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What are the advantages of rat as experimental animal? ›

Almost all disease-linked human genes have counterparts in the rat. Pinpointing these should help researchers to develop rat genetic models of human disease. Rats are often used to study behaviour in psychology experiments. Their brains are larger than mice, and the animals are less timid and more intelligent.

Why are rats good experimental models? ›

In studies of cognition and memory, the rat is superior to other models because the physiological systems involved in learning and memory have been so extensively studied in this animal.