4 ILAR Journal

Abstract

Mice are an ideal mammalian model for studying the genet-ics of aging: considerable resources are available, the gen-eration time is short, and the environment can be easily controlled, an important consideration when performing mapping studies to identify genes that infl uence lifespan and age-related diseases. In this review we highlight some sa-lient contributions of the mouse in aging research: lifespan intervention studies in the Interventions Testing Program of the National Institute on Aging; identifi cation 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 homologous locations in the genome. Thus, the vast bioinformatic and genetic resources of the mouse can be used to screen candidate genes identifi ed in both mouse and human mapping studies, followed by functional testing, of-ten not possible in humans, to determine their infl uence on aging.

Key Words: aging; calorie restriction (CR); gene mutation; genetics; lifespan; longevity; mouse genome; quantitative trait locus (QTL)

Introduction

M uch has been learned from the study of aging in worms and fl ies, 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 mamma-lian research model that shares 99% of its genes with hu-mans (Boguski 2002), outstanding genetic resources and

Rong Yuan, Luanne L. Peters, and Beverly Paigen

Rong Yuan, PhD, MD, is a research scientist and Animal Core Leader; Luanne L. Peters, PhD, is a professor and Director; and Beverly Paigen, PhD, is a professor and member of the Leadership Team, all at the Jackson Aging Center of the Jackson Laboratory in Bar Harbor, Maine.

Address correspondence and reprint requests to Dr. Beverly Paigen, The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609 or email bev.paigen@jax.org.

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

sophisticated genetic engineering technology are available for manipulating its genome (Paigen 1995). The many ge-netic resources of the mouse have been reviewed recently (Peters et al. 2007), and new resources, such as the Collab-orative Cross (Churchill et al. 2004; Threadgill et al. 2011), are being developed at a steady pace.

Among the many aging studies that have used mouse models, we discuss testing of interventions (especially com-pounds that may extend lifespan) (Harrison et al. 2009; Miller et al. 2007; Strong et al. 2008), retardation of aging by calorie restriction, spontaneous or genetically engineered mutations that affect lifespan, the determination of lifespan in multiple inbred strains (Yuan et al. 2009), and quantitative trait locus (QTL1) studies to fi nd genomic regions associated with aging (de Haan et al. 1998; Gelman et al. 1988; Jackson et al. 2002; Klebanov et al. 2001; Lang et al. 2010; Miller et al. 1998, 2002a; Rikke et al. 2010; Yunis et al. 1984). Space limitations of this review prevent an in-depth discus-sion of the many aspects of aging; we refer the reader to re-cent outstanding reviews on calorie restriction (Fontana et al. 2010; Kemnitz 2011), the role of mitochondria (Larsson 2010) and telomeres in aging (Sahin and Depinho 2010), pathways known to affect aging (Kenyon 2010), and other mouse models of aging (Chen et al. 2010).

As in any animal research, environmental and animal hus-bandry conditions may affect the outcome of aging studies. Lifespan may be affected by husbandry issues such as compo-sition of food, water, type of housing, density of mice/cage, enrichment, and animal room size and noise level, but very little is known about the impact of these factors on lifespan.

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 mam-malian 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 Labo-ratory, University of Michigan, and University of Texas Health Science Center at San Antonio (Miller et al. 2007).2

1Abbreviations used in this article: Chr, chromosome; CR, calorie restriction; QTL, quantitative trait locus2Information is available at the ITP website (www.nia.nih.gov/research-information/scientifi cresources/interventionstestingprogram.htm); this and other websites cited in this article were accessed on December 22, 2010.

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Table 1 Lifespan characteristics of 32 inbred mouse strainsa

Strain

Female Male

Age (in days) of20% longest-lived (mean SEM)

Age (in days) of20% 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 18PL/J 373 471 596 736 17 365 469 558 674 19SJL/J 393 515 632 740 30 330 505 555 632 21MRL/MpJ 455 555 626 681 9 549 645 669 711 10NZO/H1LtJ 418 575 700 782 18 286 423 637 762 26CAST/EiJ 219 589 754 n.a. 239 591 754 n.a.KK/H1J 564 608 653 720 13 545 616 700 826 43BTBR T+tf/J 550 611 668 743 19 444 575 728 822 20BUB/BnJ 392 621 755 876 23 354 493 873 906 23SWR/J 499 630 814 n.a. 411 726 904 1020 29CBA/J 476 637 786 855 11 532 679 808 872 10A/J 505 639 739 806 19 541 623 708 785 18P/J 546 660 791 n.a. 439 607 673 n.a.NOD.B10-H2b 599 667 770 827 13 501 696 878 954 11C3H/HeJ 532 683 797 833 7 623 728 834 894 15DBA/2J 443 687 823 872 7 410 701 759 825 17MOLF/EiJ 590 705 n.a.c n.a. 503 686 730 n.a.C57L/J 700 721 749 800 5 658 736 768 806 9NZW/LacJ 600 732 866 950 16 607 792 1013 1126 14SM/J 650 733 817 902 15 730 783 833 873 6FVB/NJ 518 760 952 1023 13 553 591 708 879 56129S1/SvImJ 651 791 920 1012 25 798 882 992 1044 12BALB/cByJ 700 795 877 936 10 512 714 840 927 13NON/ShiLtJ 631 806 861 887 5 793 847 919 958 11RIIIS/J 691 813 883 938 5 779 886 940 970 12LP/J 715 833 966 1047 17 721 822 862 984 28PWD/PhJ 600 839 929 993 12 575 813 905 956 12C57BR/CDJ 757 861 917 973 7 737 849 943 993 21C57BLKS/J 672 867 926 989 12 770 826 932 983 21WSB/EiJ 629 886 1148 n.a. 470 1005 1110 1213 19C57BL/10J 692 889 1035 1135 9 677 792 852 893 13C57BL/6J 782 914 1006 1075 13 838 901 971 1061 17

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

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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-specifi c effects on disease susceptibil-ity while ensuring the replicability of the study.

Although all three sites follow the same standardized pro-tocols, both control and drug-exposed mice at the University of Michigan site were signifi cantly smaller throughout adult life than those at the other two sites, and researchers observed signifi cant 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 wean-lings (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-specifi c factors, such as minor differences in water quality, noise level, ventilation, extraneous odors, or cage-changing frequency contribute to site-specifi c differences.

The ITP website provides the list of compounds in test-ing. So far, one of the major fi ndings of the study is that ra-pamycin, an inhibitor of mTOR (mammalian target of rapamycin) signaling, signifi cantly increased lifespan in both males and females even though treatment did not start until mice were 600 days old (Harrison et al. 2009). How-ever, the rapamycin-treated mice did not differ from control mice in the pattern of diseases as shown by pathology. Two other compoundsNDGA (p = 0.0006) and aspirin (p = 0.01), as assessed using the log rank test, which evalu-ates survivorship of the entire cohortextended 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 specifi c 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, fl ies, worms, fi sh, rodents, and rhesus monkeys (Fontana et al. 2010) and, in mammals, decrease the risk of age-related diseases such as diabetes, cardiovascular diseases, and can-cers (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. G

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Tabl

e 2

(cont

inued

)

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One of the most interesting recent studies was an investiga-tion 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 signifi cantly extended fe-male lifespan in nine strains, it signifi cantly reduced lifespan in four other strains and had no signifi cant 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 fi nding that CR acts only in certain genetic backgrounds contradict the widespread observation that CR usually extends lifespan in species with mixed ge-netic background. The mean lifespan under CR showed no signifi cant 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 effi ciency 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 func-tion (Tsaih et al. 2009; Xing et al. 2009), hematology, hor-mone 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 re-pair, and chromosome fragility. A reproductive study evalu-ated 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. mus-culus, CAST/EiJ for M. castaneus, and MOLF/RkJ for M. molossinus. The remaining 28 strains were chosen for ge-netic diversity and common use. Median lifespan varied dra-matically 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 confi rmed that genetics plays an important role in determining longevity. Median lifespans for females and males were signifi cantly correlated with each other (R = 0.88; p < 0.001). Propor-tional hazard analysis showed that sex did not signifi cantly affect lifespan for most strains (Yuan et al. 2009).

Among the 32 inbred strains, circulating insulinlike growth factor (IGF)-1 levels signifi cantly (p < 0.05) corre-lated with body weight at 6, 12, and 18 months in both fe-males 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 me-dian 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-specifi c disease (e.g., leukemia in strain AKR), the negative correlation of IGF-1 and lifespan among long-lived strains became stronger and more signifi cant (R = 0.53, p < 0.01).

These results underscore the importance of genetic regu-lation of IGF-1 signaling in regulating body weight and lon-gevity, as has been suggested by studies in other models. For example, in domesticated dogs a single nucleotide poly-morphism (SNP) in Igf1 signifi cantly 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 lon-gevity. The variation in circulating IGF-1 levels among in-bred 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 mecha-nisms. A number of mutations, either spontaneous or ge-netically 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 fi rst 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 hor-monereleasing hormone receptor), and the Ames and Snell dwarf mice 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 defi ciency of pituitary hormones such as growth hormone, thyroid-stimulating hormone, and

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Figu

re 1

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Table 3 Mutations in mouse genes that reduce longevity

Gene informationType of mutation

Target geneexpression

Effect on lifespan (sex) ReferenceSymbola Full name Chr Mb

Bub1b Budding uninhibited by benzimidazoles 1 homolog, beta

2 118 Knockout Reduces Reduces (female, male)

Baker et al. 2004

Kl Klotho 5 152 Transgeneb Reduces Reduces (female, male)

Kuro-o et al. 1997

Lmna Lamin A 3 88 Knock-in n.a.c Reduces (not specifi ed)

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 specifi ed)

Neumann et al. 2003

Top3b Topoisomerase (DNA) III beta

16 17 Knockout Reduces Reduces (not specifi ed)

Kwan and Wang 2001

Chr, chromosome; Mb, megabase (millions of base pairs); n.a., not availableaGene names and symbols are according to Mouse Genome Informatics database (www.informatics.jax.org). bThe transgene causes an insertional mutation in the Klotho gene that suppresses its expression.cThis knock-in model introduces a nucleotide polymorphism that results in the substitution of proline for leucine at amino acid 530 in the Lmna gene.

prolactin. These dwarf mutants all have extended lifespan compared to controls.

Mutations in several other genes (Ghr, Igf1r, Insr, Irs1, Irs2 and Pappa) reduce GH/IGF/insulin signaling and ex-tend lifespan. Cardiac-specifi c overexpression of IGF-1 sig-nifi cantly prolongs lifespan, probably due to the protective effects of IGF-1 on cardiac failure. Mutations such as knock-outs of Shc1, Surf1, Adcy5, and Coq7, as well as transgenes of Mcat and Mt, which increase resistance to stress, also suc-cessfully 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. Ac-cording to Mouse Genome Informatics (www.informatics.jax.org), 301 mutations decrease survival by causing or pro-moting 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 ag-ing 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.

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 signifi cantly in lifespan compared to wild-type controls under AL or CR conditions. However, Foxo1 may play a role in CRs antineo-plastic 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 (mac-rophage migration inhibitory factor) in CR mice suggests that it may be important for CR-related lifespan exten-sion, but the signifi cantly extended longevity in Mif knock-out 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 manipu-lation of S6K1 and AMPK might mimic CR and could pro-vide 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 be-tween these outcomes is to calculate the age-specifi c mortality rate (de Magalhaes et al. 2005). For example, CR changes age-specifi c mortality and delays aging, as do mutations of Cebpb,

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Msra, Shc1, Ghr, Pou1f1, and Polg, but studies in other mutants were either insuffi ciently 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 genes effect on lifespan is one way to unravel the genetic basis of aging. Another approach, which is unbi-ased and does not start with a defi ned hypothesis, is to con-duct 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 under-poweredin the number of animals or markers genotyped or bothwe think these QTLs are worthy of further investi-gation, especially if they have been replicated in another mouse cross or if a human genomewide association study has identifi ed a peak at a homologous location. Thus, we list all the suggestive and signifi cant QTLs in Table 4 and depict them on the mouse genome in Figure 1.

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 mark-ers were not evenly distributed and only 14 chromosomes were covered. The QTL on Chr 17 identifi ed in these two studies contains the major histocompatibility complex re-gion, 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 ge-netics approaches to the study of aging.

Both the backcross and RI QTL designs carry homozy-gous 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 fi rst, 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 associ-ated 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 infl uenced by shared pathophysiologic mech-anisms 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 sig-nifi cant QTLs for lifespan in Figure 1 and Table 4, we have more confi dence that replicated QTLs are true positives. Eight of these QTLsChr 1, Chr 2, Chr 7 (proximal and mid-), Chr 8, Chr 10 distal, Chr 11 proximal, and Chr 19have 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 signifi cance, 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 Fishers 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 dis-ease. Lifespan as a trait would be highly infl uenced 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 spe-cies 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 hu-man aging not only because of the availability of extensive mouse resources but also because of the similarity of the

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Table 4 Signifi cant and suggestive lifespan quantitative trait loci (QTLs) detected in the mouse

ChraPeak (Mb) Cross

High allele strain (sex) Reference

Replicated in mice

Replicated in humans

1 34 B6 D2 RI strains D2 (male) Lang et al. 2010120 B6 D2 RI strains D2 (female) Gelman et al. 1988 X128 B6 D2 RI strains D2 (female) Lang et al. 2010 X163 B6 D2 RI strains B6 (female) Gelman et al. 1988 X

2 65 B6 D2 RI strains B6 (female) Lang et al. 2010103 B6 D2 RI strains B6 (female) Gelman et al. 1988 X108 (BALB/cJ B6)

(C3H D2)C3H (female) Jackson et al. 2002;

Miller et al. 2002aX

121 B6 D2 RI strains D2 (female) Gelman et al. 19884 80 (B6 D2) D2 B6 (female) Yunis et al. 19845 80 B6 D2 RI strains D2 (female) Lang et al. 20106b 77 B6 D2 RI strains D2 (male) Lang et al. 2010

96 B6 D2 RI strains D2 (male and female) Lang et al. 2010113 B6 D2 RI strains D2 (male) Lang et al. 2010

7 3 B6 D2 RI strains B6 (female) Lang et al. 2010 X11 B6 D2 RI strains B6 (female) Gelman et al. 1988 X66 (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 X92 B6 D2 RI strains B6 (female and male) Lang et al. 2010111 (BALB/cJ B6)

(C3H D2)BALB (male) Miller et al. 1998

8 15 B6 D2 RI strains B6 (female) Lang et al. 2010 X X26 (LP MOLD)

(NZW BALB)MOLD (pooled) Klebanov et al. 2001 X X

111 B6 D2 RI strains B6 (female) Lang et al. 20109 91 (BALB/cJ B6)

(C3H D2)C3H (male) Jackson et al. 2002;

Miller et al. 2002aX

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 X119 (ST B6)

(CAST D2)CAST (pooled) Klebanov et al. 2001 X

11a 15 B6 D2 RI strains B6 (female) de Haan et al. 1998 X18 B6 D2 RI strains B6 (male and female) Lang et al. 2010 X35 B6 D2 RI strains B6 (female) Lang et al. 201056 B6 D2 RI strains B6 (male) Lang et al. 2010 X

12 60 B6 D2 RI strains D2 (female) Gelman et al. 1988105 (BALB/cJ B6)

(C3H D2)B6/C3H (female and male)

Jackson et al. 2002; Miller et al. 2002a

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Table 4 (continued)

ChraPeak (Mb) Cross

High allele strain (sex) Reference

Replicated in mice

Replicated in humans

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 X64 B6 D2 RI strains B6 (female) Lang et al. 2010

17 34 (B6 D2) D2 B6 (male) Yunis et al. 198418 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 RI strains ILS (female) Rikke et al. 2010 X47 (BALB/cJ B6)

(C3H D2)D2 (male) Miller et al. 1998

X 49 B6 D2 RI strains D2 (female) Lang et al. 2010126 B6 D2 RI strains D2 (female) Lang et al. 2010

Chr, chromosome; RI, recombinant inbredEach suggestive and signifi cant 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).

mouse and human genomes. As genes are identifi ed in hu-mans, mouse models will continue to be very useful in ef-forts to investigate underlying mechanisms of the genes that affect aging. We expect to see growing numbers of transla-tional studies demonstrating the relevance of the mouse to human aging. This rise, combined with increasingly refi ned bioinformatic tools and mouse models, will accelerate the identifi cation 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 fi gure, and Joanne Currer for editing of the manuscript. This work was supported by grants from the Glenn Foundation (BP), the Ellison Medical Founda-tion (BP), and the Nathan Shock Center (grant AG038070; LLP).

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