GENETIC MODIFIERS. KENT W. HUNTER Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland

Transcription

1 15 GENETIC MODIFIERS DAVID W. THREADGILL Department of Genetics and Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina KENT W. HUNTER Laboratory of Population Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland FEI ZOU Department of Biostatistics, University of SouthNorth Carolina, Chapel Hill, North Carolina KENNETH F. MANLY Department of Cellular and Molecular Biology, Roswell Park Cancer Institute, Buffalo, New York Cancer is caused by a complex interaction between an individual s intrinsic genetic composition and extrinsic environmental exposures. Cancer modifiers, a special class of cancer genes that accounts for the majority of the intrinsic genetic differences in cancer susceptibility among individuals, contribute to a significant proportion of all human cancers. Modifiers are often normal allelic variants of genes that do not produce abnormal phenotypes alone, but rather modify the action of other cancer genes. Frequently, these genes are also called low-penetrance cancer genes since, individually, they typically have small effects on cancer phenotypes. However, cumulatively, they can have major influences on all stages of cancer development from initiation through response to therapy. Similar to the unique characteristics that distinguish cancer modifiers from high-penetrance cancer-causing genes, the methods used to identify these genes are distinct. Because of the low-penetrance nature of their actions, this class of cancer gene is particularly difficult to detect using human populations. As such, the mouse has become the predominant model to identify cancer modifiers. The unique characteristics, methods for detection and identification of cancer modifiers, and their use in human studies are described in this chapter. INTRODUCTION Most cancers arise because of mutations or epigenetic changes within a limited number of genes but progress through an accumulation and selection for additional mutations. The development and progression of most cancers is also strongly influenced by natural allelic differences between individuals. The action of these alleles, called cancer modifiers, can range from small, difficult-to-detect influences to profound alterations on virtual any characteristic of cancer, from initiation to therapy response. Although cancer modifiers typically have minor individual effects, cumulatively, they have a major influence on cancer susceptibility and progression. As such, these genes are also known as low-penetrance cancer genes, as distinguished from high-penetrance genes described in previous chapters that are associated with familial cancer syndromes like the APC gene with familial adenomatous polyposis (Groden et al., 1991; Kinzler et al., 1991; Nishisho et al., 1991) or BRAC1 with familial breast cancer (Miki et al., 1994). Despite this distinction, many low-penetrance cancer modifiers are also likely to modulate the effects of the highpenetrance cancer genes. For example, knockouts of Trp53 are highly prone to tumors in many cell types. However, Mouse Models of Human Cancer, edited by Eric C. Holland ISBN X Copyright 2004 John Wiley & Sons, Inc. 263

2 264 GENETIC MODIFIERS the spectrum of tumors that develop is highly dependent upon the genetic background, and thus cancer modifiers, carrying the mutation (Backlund et al., 2001; Donehower et al., 1992, 1995; Ochiai et al., 2003). The goals of cancer modifier analysis are to establish the number and location of polymorphic genes affecting cancer development, to investigate the function of alternative modifier alleles, and to determine how alleles of different modifier genes interact with each other, with high-penetrance cancer genes, and with the environment. Collectively, the information about alleles, genes, and environments is referred to as the architecture of the cancer phenotype. A model for the architecture of a cancer phenotype is usually built sequentially by the successive characterization of individual or groups of modifiers. Technically, modifier genes are variants that have little or no effect themselves but influence aberrant phenotypes caused by one or more major effect genes. However, in practice, cancer modifier genes can also be considered interchangeable with quantitative trait loci (QTLs) since cancer modifiers often have quantitative effects on cancer phenotypes and the approach to their detection, localization, and identification is similar to QTL analysis. Because of their broad effects and complex interactions, the identification of cancer modifier genes, like QTLs, has historically been difficult. However, combined with current genome technologies, the mouse offers a powerful tool to identify these important cancerassociated genes. Finding the genetic basis of cancer modifiers is dominated by a Catch-22. Similar to QTL analysis, the power with which a cancer modifier can be detected and the precision with which it can be mapped are profoundly affected by the number, strength of effect, and interactions of the modifiers to be detected. That is, the ability to characterize cancer modifiers is controlled by the unknown properties of the modifiers themselves. If the cancer phenotype of interest is dominated by a small number of strong, unlinked modifiers with few interactions, characterizing and ultimately cloning those modifiers may be comparatively easy. In contrast, if the phenotype is controlled by a large number of weak modifiers, by several closely linked modifiers, or by modifiers with complex interactions, characterizing them may be a significant challenge. Further complicating cancer modifier analysis is the fact that individual modifiers can have positive or negative effects depending on alleles at other genes (Tripodis et al., 2001) and on the cancer type (Czarnomska et al., 2003). Thus, a major challenge of cancer research is to move beyond the high-penetrance cancer genes associated with rare hereditary cancer syndromes to the lowpenetrance cancer modifiers that cumulatively account for the vast majority of cancers in the human population. Although complementary mutagenesis approaches have been proposed to induce new mutations conferring cancer modifier phenotypes with potentially stronger effects than natural alleles (Schindewolf et al., 2000; Shima et al., 2003), this chapter concentrates on the approaches that have been successfully applied to characterize and clone natural alleles that act as cancer modifiers and on their use in human cancer studies. PARTITIONING CANCER PHENOTYPES Cancer is not a single disease but rather an amalgamation of diseases that all share the common trait of aberrant growth control. Similarly, the phenotypic characteristics contributing to any particular cancer or cancer model are numerous, often with multiple genes contributing to each particular characteristic. As such, studies that precisely define and partition the cancer phenotype under study have a greater chance to successfully identify modifier genes. These characteristics are often distinguishable and can include gross measurements like tumor incidence, cancer growth rate, or frequency of metastasis to fine cellular details of individual cancers such as quantification of vascularization, morphologic differentiation of the cancer cells, and the relative proportion of stroma or inflammation, among others. Ultimately, the decision of how to partition cancer phenotypes will depend upon the type and unique characteristics of the cancer under study. A carcinogen-induced mouse model of lung cancer is an example of the interrelatedness between cancer phenotypes. Using this model, eight cancer modifiers affecting three-dimensional tumor shape have been localized (Tripodis and Demant, 2003). Interestingly, the nuclear morphology of these lung cancers was previously associated with polymorphisms a t the K-Kras2 gene (Tripodis a nd Demant, 2001); the putative -Kras2 K modifier w as originally mapped a s a susceptibility modifier for pulmonary adenomas (Gariboldi et al., 1993). Similar to this example, of the 12 modifiers detected using a carcinogen-induced mouse model of skin cancer, only a subset of the modifiers were responsible for survival time of tumor-bearing mice (Nagase et al., 1999). Intermediate molecular phenotypes leading to cancer development and progression can also be partitioned in a similar manner. Intermediate phenotypes are often referred to as endo-phenotypes and can range from the transcriptional differences across cancers, either for single genes or an entire transcriptional profile, to the expression or absence of specific molecular markers at the protein or other macromolecular level to changes in physiologic measures or serum markers. Furthermore, these characteristics can be autonomous to the cancer cells or nonautonomous through interactions with non cancer

3 cells or macromolecules. The importance of understanding the biologic basis of the action of cancer modifiers is related to the primary goal of cancer modifier analysis to identifing genes modifying particular cancer phenotypes. The success of this endeavor and, in particular, the effort required to fine-map and identify modifier genes are inversely correlated to the level of biologic knowledge known about the particular cancer phenotype under study (Fig. 15.1). Cancer modifiers do not typically display Mendelian patterns of inheritance, and because of their low penetrance, properly partitioning cancer phenotypes increases the probability of successful modifier identification in at least two ways. First, by precisely defining the cancer characteristics under study, more accurate mapping data can be produced providing a narrower genomic interval from which to develop lists of gene candidates, as described in the section on detection and localization. Since particular characteristics of cancers can be masked or otherwise altered through cellular or molecular-level epistasis, it is important to know what is actually being mapped. For example, gross tumor size can be caused by expansion in the number of cancer cells, accumulation of intra-tumor stromal cells, or cystic inclusions. Different genes may, and probably do, control each of these characteristics. Mapping gross tumor size without properly defining what is causing variation in size may give a false impression of genetic heterogeneity when in fact the data are noisy because of inaccurate identification of the Physiology Biological resolution Organ Cell Pathway Course Too little data Too much data Genetic resolution Fine Figure Graph depicting the relationship between biologic knowledge of cancer modifiers and genetic resolution required to select potential candidates from those residing in a defined genomic interval. Ideally, the information content for the two sources of data will be near the diagonal to maximize the balance between data collected and identification of strong candidates. If inadequate biologic or genetic data are collected, placing the total information content above the diagonal, there will be insufficient information to identify appropriate lists of candidate genes. MOUSE STRAINS AND CROSSES Mul cancer phenotype. An example is the mapping of metastasis genes using the FVB/N-TgN(MMTVPyVT) 634 Mul mouse model of breast cancer (Lifsted et al., 1998). Modifiers for a variety of metastatic phenotypes could be analyzed, including presence or absence of macroscopic lesions and the size of individual secondary tumors. However, genes controlling the size of metastatic lesions can only be mapped in mice containing metastasis. In mice that do not have metastasis, it is unknown whether the genome has the metastasis modifiers or not since modifiers controlling whether metastasis occurs will probably be different than the modifiers controlling the size of the metastatic lesions. Proper partitioning of cancer phenotypes contributes to improving the odds of successfully identifying cancer modifier genes for a second reason. Knowing what characteristic is being mapped enhances the informed selection of potential candidates within genomic intervals known to harbor cancer modifier genes. The selected candidates must be further tested through validation as described in the section on fine mapping and localization; the fewer the candidates, the less work involved in the final step of cancer modifier analysis. Illustrative of this effect, the Apmt1 and Apmt2 cancer modifiers in the FVB/N-TgN(MMTVPyVT) model are known to interact epistatically (Le Voyer et al., 2000) and to influence latency to tumor detection (Cozma et al., 2002). Therefore, it was hypothesized that the modifiers altered a cell growth phenotype. Using a bioinformatics approach, Cdc25a and Myc, two extensively characterized cell cycle genes, were found to be strong candidates for Apmt1 and Apmt2, respectively, dramatically reducing the effort required to identify candidates for validation testing. In a second example, tumors arise earlier in hybrids between I/LnJ and FVB/N- TgN(MMTVPyVT) 634 Mul than on the parental FVB/N background (Le Voyer et al., 2001). However, the hybrids accumulate less tumor mass. While genetic mapping of modifiers contributing to this difference detected three loci, detailed analysis of the cancer phenotype revealed that the growth reduction correlated to a reduction in the tumors ability to recruit microvessels, providing strong biologic data to assist in the eventual identification of candidate modifiers. MOUSE STRAINS AND CROSSES The first step in beginning a modifier study is to identify the strains of mice that differ in the genetic control of the cancer phenotype under study. Since cancer modifiers can influence any stage of tumor development, from initiation to therapy response, the selection of appropriate strains must be carefully weighted to ensure that they

4 ž Q3 266 GENETIC MODIFIERS differ sufficiently in their genetic control of the cancer phenotype. A commonly used approach for identifying appropriate strains is a strain survey, a phenotypic analysis of a variety of different inbred strains using an assay to measure the cancer phenotype of interest. If the phenotype is dependent upon an engineered alteration, the transgenic or knockout cancer models can be outcrossed to a panel of other inbred strains to produce F1 hybrids or segregating N2 or F2 progeny before measuring the subsequent effects on the cancer phenotype as was done for the FVB/N-TgN(MMTVPyVT) 634 Mul mouse model of breast cancer metastasis (Lifsted et al., 1998). An important fact to keep in mind is that even though significant phenotypic variation across strains is not observed, strains can still have differences in their genetic control of the particular phenotype; the existence of genetic heterogeneity, a situation when individuals have similar phenotypes but different combinations of alleles, can only be determined by analyzing a pilot cross between strains and observing the phenotypic variation of the resulting off spring progeny. Most commonly used strains are available commercially, with The Jackson Laboratory ( having by far the largest collection of inbred strains. Although any set of strains can be chosen, ideally the selection of strains for a survey should encompass strains already selected for genetic diversity as part of the Mouse Phenome Project (MPP; Paigen and Eppig, 2000), a communitywide effort to produce baseline phenotypes for 40 genetically diverse inbred strains, including susceptibilities to various types of cancers. Since a large quantity of phenotypic data is being accumulated in the MPP database, meta-analysis with many other types of traits can be performed with the cancer phenotypes. Although the choice of inbred strains for a cancer modifier project will be primarily driven by those that demonstrate phenotypic variation, other factors should also considered. If there is the option, C57BL/6J, 129S1/SvImJ, DBA/2J, or A/J mice should be considered since several genetic resources have been developed using these strains that greatly simplify fine mapping and candidate gene validation, as described later. Foremost is that these strains have been used as templates for the public and private mouse genome sequencing efforts ( Waterston et al., 2002), creating significant sequence variation data that may enable more rapid narrowing of candidate intervals through haplotype analysis or identification of potential candidate genes. Embryonic stem cells and bacterial artificial chromosome (BAC) libraries are also commercially available for several of these strains ( supporting the construction of knockin or transgenic mouse models as described under Fine Mapping and Validation to directly test the effect of a particular polymorphism on the phenotype of interest. Other genetic resources that have been or are being built using these strains as founders include recombinant inbred and chromosome substitution (CS; also known as consomic) strains described below. Ultimately, the choice of the appropriate strains needs to be made based upon the cancer phenotype of interest. However, given a choice of strains, those with the greatest supporting resources greatly enhance the prospects for quickly identifying and validating cancer modifiers. Additional cancer-specific resources are also available to help guide in strain selection. The organ-site reports of the Mouse Models of Human Cancer Consortium (MMHCC; provide valuable information on current cancer models and strains that have been used to detect the cancer phenotypes. The MMHCC has also compiled a database of cancer models that have been characterized on various genetic backgrounds ( Likewise, the Mouse Tumor Biology Database ( informatics.jax.org/fmpro?-db=tumorinstance&-format =mtdp.html&-view; Bult et al., 2000) provides historical information on strain susceptibilities to specific types of cancers and genetic and histologic information for a wide variety of spontaneous and induced mouse tumors. The design of the mapping cross to genetically detect and localize the cancer modifiers also depends on a variety of factors. When designing the experiments, it is essential to ensure that the experimental design provides enough statistical power to achieve the recommended level of genomewide significance, as described under Detection and Localization. This will depend on the number of modifiers segregating in a cross and the fraction of the total phenotypic variance for which each modifier accounts. As with any genetic mapping experiment, consultation with a statistician is strongly recommended during the design of the experiments. The cross design is often of personal preference, but specific characteristics of the modifiers may limit which cross can be chosen (Table 15.1; Fig. 15.2). Backcrosses and Intercrosses The most commonly used cross designs for cancer modifier analysis are backcrosses and intercrosses. These are robust designs for generating low-resolution localization of loci that modulate specific phenotypes. The choice between backcross and intercross depends on several factors (Darvasi, 1998). Intercrosses, generally requiring fewer animals than backcrosses since each intercross animal has twice the number of recombination events as a backcross animal, are frequently used to approximate the

5 MOUSE STRAINS AND CROSSES 267 Table Comparison of Mapping Methods Frequently Used for Cancer Modifier Analysis Mapping Method Advantages Disadvantages Backcross Any strain of mouse Each genome assayed once Unlimited numbers Only one homozygous class Low marker density needed for initial detection Can test dominance and interactions involving dominance Full genotyping required Lower number of recombinations limit resolution for strong modifiers Intercross Any strain of mouse Each genome assayed once Unlimited numbers Full genotyping required Low marker density needed for initial detection Can test dominance and interactions involving dominance All genotype combinations Twice the number of recombinations per mouse as backcrosses Lower number of recombinations limit resolution for strong modifiers Interspecific backcross High degree of marker polymorphisms Each genome assayed once Greater haplotype diversity Cannot perform intercrosses Low marker density needed for initial detection Only one homozygous class Full genotyping required Can test dominance and interactions involving dominance REMOVE SACE ABOVE THIS Lower number of recombinations limit resolution for strong modifiers Recombinant inbred Unlimited number of identical mice Limited founder combinations Optimal for gene environment interaction studies Limited number of strains available Higher marker density required because of increased number of recombinants No genotyping required REMOVE SPACE ABOVE THIS Cannot detect dominance effects REMOVE SPACE ABOVE Recombinant congenic Unlimited number of identical mice Limited founder combinations Donor genome partitioned, making is easier to identify epistatic interactions Limited number of strains available Only partial genome coverage No genotyping required for initial detection Can test for dominance and interactions involving dominance Need to perform backcross for localization with partial genotyping number of modifier loci present and to obtain estimates of their additive or dominance effects. Conversely, backcrosses, because there are only two genotypic classes at each locus, compared with three for intercrosses, are more efficient in detecting simple epistatic interactions between unlinked modifiers. For modifiers with additive effects, an intercross is somewhat more powerful than a backcross, but for dominant modifiers, a backcross can be twice as powerful as an intercross (Darvasi, 1998). One of the first cancer modifiers to be identified in mice was localized using backcrosses (Dietrich et al., 1993). The Mom1 locus, which has a semidominant suppressor effect on tumor multiplicity in the Apc Min mouse model of intestinal tumorigenesis (Gould et al., 1996), was localized in crosses between C57BL/6J and AKR/J. A major strength of both the backcross and intercross methods, unlike many of the other strategies, is that any two genetically distinct strains of mice can be used to generate a mapping cross. Thus, the genetic components of any cancer phenotype that differs between two of the multitude of inbred mouse strains can be subjected to genetic dissection. This capability includes c ommon ( Mock et al., 1993) and f erally derived ž Q4 inbred strains (Nagase et al., 2001) as well as transgenic (Le Voyer et al., 2000) and knockout cancer models (Bolivar et al., 2001). Interspecific Backcrosses A specialized type of backcross that has been used successfully for cancer modifier studies are crosses between different species or subspecies of mice. The most common are between Mus spretus and any inbred strain of Mus musculus origin. Although these species diverged approximately 3 million years ago, they are still able to interbreed to produce fertile F1 female

6 268 GENETIC MODIFIERS Figure Schematic representation of different genetic mapping crosses and resources available for cancer modifier mapping. For illustration, two homologous pairs of autosomes, the sex chromosomes (X and Y) and mitochondrial genome (M) are shown for two inbred mouse strains, M (maternal) and P (paternal), whose DNA is represented in white and black, respectively. The color in subsequent crosses represents DNA from the respective founder strain. The origin of various types of crosses and derivative strains is shown with their consequences on mixing DNA from the two parental strains. Adapted from Hunter and Williams (2002). progeny. Backcrosses can be carried out to either parent to map modifiers, although intercrosses are not possible because F1 males are sterile. Because of the large degree of genetic divergence between these species, all crosses between M. spretus and common laboratory strains are highly informative with numerous polymorphic differences that facilitate modifier mapping. This divergence also means that the genetic differences being surveyed in interspecific crosses are much greater than in intraspecific crosses between any two common laboratory strains that are much more closely related and therefore share alleles across larger proportions of their genomes. This has become particularly evident with the development of first-generation haplotype maps for several mouse strains (Wade et al., 2002; Wiltshire et al., 2003). Consequently, M. spretus typically shows a large number of major differences in cancer phenotypes when compared to M. musculus derived strains (Guenet and

7 Bonhomme, 2003). Using M. musculus M. spretus interspecific backcrosses, 12 cancer susceptibility loci influencing skin tumor development and progression in a carcinogen-induced model have been detected and localized (Nagase et al., 1995, 1999). Although not as optimal as the recombinant congenic strains described later, interspecific backcrosses can also be used to detect epistatic interactions (Nagase et al., 2001). MOUSE STRAINS AND CROSSES 269 of limitations. First, RI sets exist for relatively few inbred strains. As a result, only the subset of polymorphic loci that differ among these strains can be mapped using this strategy. Fortunately, these strains, including C57BL/6J, A/J, BALB/cJ, and DBA/2J, are among the most widely used and characterized inbred mouse strains (Taylor, 1996). Second, despite the increased power associated with the ability to assay identical genotypes repeatedly, the limited number of lines in each RI panel precludes mapping cancer modifiers that do not account for a substantial fraction of the genetic variance (Belknap et al., 1996; Pataer et al., 1997). Mapping modifiers that account for less variance requires a two-tiered approach, identifying putative loci using RI analysis followed by a second experiment to Recombinant Inbred Panels Recombinant inbred (RI) strains offer compelling advantages for mapping cancer modifiers, particularly those that have low heritability or low penetrance or are otherwise associated with highly variable cancer phenotypes. Since a panel of RI lines originates from the inbreeding of independent F2 offspring, progeny, generated from two parental confirm the modifier assignment in an independent cross. Third, inbred like moderately sized backcross and intercross strains, each RI genome is replicated in the form of an isogenic line and has roughly the same utility for mapping strategies, the precision with which modifiers can be mapped is generally poor, with candidate regions often modifiers as 4 10 intercross progeny (Bailey, 1981); the spanning centimorgans (cm). number varies as a function of the recombination load carried by the RI lines. Mapping with RI panels is performed using an association between the differences in phenotypic RIX and RIB Derivatives trait values for each RI line and the strain distribution patterns (SDPs) across the panel for each marker; an SDP A straightforward extension to increase the power of RI is the distribution of the two parental genotypes at each mapping is through the generation of recombinant inbred marker across the entire RI panel. This approach successfully localized three modifiers for a carcinogen-induced relies on a potentially large set of F1 hybrids generated intercrosses (RIXs; Threadgill et al., 2002 ). This method ž Q5 mouse model of lung cancer in the AXB and BXA panels of RI lines (Malkinson et al., 1985); AXB and BXA RI strains within an RI panel; unlike the parental RI lines, by breeding all or a subset of pairwise combinations of strains are reciprocal RI lines derived from the A/J and RIX panels are similar to replicable F2 progeny with all C57BL/6J inbred strains (Marshall et al., 1992). three genotypic classes at each locus. RIX mice share all A major advantage of RI panels is that measurement of the advantages of RI mapping (Bailey, 1981; Belknap of replicate individuals can reduce the variance associated with experimental and uncontrolled error to very large sample of unique but predictable genotypes. Two et al., 1996) and have the added benefit of providing a low levels (Belknap, 1998); this effectively elevates heritability and greatly improves prospects for mapping low- used to assess phenotypes and the ability to make each additional advantages are hybrid vigor of the F1 progeny penetrance cancer modifiers. The RI design also permits F1 by reciprocal crosses, a feature that can be used to testing gene effects under a spectrum of environmental perturbations to expose gene environment interac- used to confirm a modifier and refine its position through investigate parental effects. The RIX method can also be tions. The use of RI strains allows the average effect of virtual congenics by generating a subset of RIX that have a particular environment on the phenotype to be more known genotypes in the relevant interval hypothesized to accurately determined for a specific genotype, requiring fewer animals than conventional crosses. In contrast, Recombinant inbred strains can also be backcrossed contain the modifier. gene environment interactions are more difficult to study to either a parental or a nother inbred strain to generate using other mapping populations since each animal represents a unique recombinant genome. Another advantage of the RIB crosses share segregation ratios similar to those recombinant inbred backcrosses (RIBs). Unlike RI lines, RI strains is that phenotypes generated by different groups of standard backcross, with a 1 : 1 ratio of homozygous using a variety of methods can be compared when collected on the same RI strains, including genotypes for the RIB mice has a predictable genotype defined by the par- to heterozygous genotypes. Similar to RIXs, each litter of strains. As a result, loci that segregate in RI sets can usually be localized without additional genotyping (Williams be used to solve one obvious limitation of RI lines, their ticular combination of parental genomes. RIB progeny can et al., 2001). lack of heterozygous genotypes. The RIB cross is particularly useful in that modifiers of dominant and semidom- Although RI strains can significantly reduce the effort required to obtain linkage information, there are a number inant mutations, engineered alleles, and transgenes can be

8 270 GENETIC MODIFIERS easily mapped in a single generation cross without genotyping. This approach was used to map a modifier for breast cancer metastatic progression efficiency by crossing the FVB/N-TgN(MMTVPyVT) 634 Mul mouse model to the AKXD RI panel (Hunter et al., 2001). Recombinant Congenic and Chromosome Substitution Strains chromosome on a recipient background. The percent of donor genome is determined by the size of the chromosome, ranging from 7.7% for chromosome 1 to 3.2% for chromosome 19. Both RC and CS resources can be used for modifier analysis by identifying recombinant lines that differ phenotypically from the recipient line. Unlike mapping using an entire RI panel, RC and CS strains are used in standard intercrosses or backcrosses with the recipient strain to identify the portion of the donor genome or chromosome, respectively, contributing to the cancer phenotype. Any phenotype in CS strains that differs from the recipient parental strains is obligatorily localized to the donor chromosome. For example, modifiers of testicular teratoma incidence on MOLF/Ei chromosome 19 have been detected using a 129.MOLF-Chr 19 CS strain (Matin et al., 1999). If a full panel of 22 CS strains is constructed, chromosomes 1 19, X, Y, and the mitochondrial genome (also known as a conplastic strain), the chromosomal assignment of a modifier can be rapidly determined for any phenotype that differs between the parental strains. Like RI panels, RC strains, and congenic strains, CS strains have defined and fixed genotypes. Likewise, CS strains have increased power to detect modifiers because the variance due to environmental or experimental fluctuation can be averaged out by repeated analysis of each genotype. CS strains are also excellent starting points for high-resolution mapping, as described under Fine Mapping and Validation, since they can be used to develop series of overlapping interval-specific congenic animals. A disadvantage of this approach is that the initial modifier assignment is to a whole chromosome rather than to a chromosomal region. In addition, without additional crosses, CS strains cannot distinguish between single and multiple modifiers on a single donor chromosome. Although the use of reciprocal CS strains can identify the presence of interactions between host and donor loci, no linkage information on the host modifiers would be obtained. Furthermore, CS strains, by isolating individual donor chromosomes, preclude the possibility of detecting epistatic interactions between unlinked donor loci without additional breeding studies. Identifying the complex and often nonlinear interactions among many polymorphic genes and their products is difficult. Standard backcross, intercross, and recombinant inbred analyses often lack the power required to dissect multiple unlinked genes, particularly if they interact epistatically. To overcome this limitation, a variation of recombinant inbred panels, the recombinant congenic (RC) line, was developed (Groot et al., 1996). Like RI panels, RC strains are composites of the genomes of two inbred progenitor parent strains. Unlike the RI strains, which are generated by serial brother sister matings after an initial outcross, two rounds of backcrossing to one of the progenitor strains precede the brother sister matings in RC strains. The resulting RC strains contain, on average, a random 12.5% of the donor strain genome on the background of the recipient strain. Because of the reduced complexity of RC genomes, gene interactions and epistasis can be efficiently investigated (Fijneman et al., 1996, 1998a,b; Tripodis et al., 2001). The added power is due to a reduction in the amount of donor genome present in RC strains relative to other genetic resources, increasing the probability of separating interacting genes into individual strains where their effects can be studied in isolation. The OcB RC strains, developed by transferring O20/A donor genome onto a C57BL10/A recipient strain, were used to detect loci-modulating susceptibility to a carcinogen-induced lung cancer model (Tripodis et al., 2001). Despite assaying only half the genome, these studies detected and localized over 30 lung cancer modifiers. More striking was the fact that only a single locus had independent activity; all others showed epistatic interactions ranging from simple two-locus interactions to one locus having interactions with five other modifiers. Similarly, using the CcS RC strains, produced from the donor STS/A and recipient BALB/cHeA strains, over 10 Genome Scans colon cancer modifiers have been mapped (Moen et al., 1992, 1996; van Wezel et al., 1999), several of which show strong gene interaction effects (van Wezel et al., 1996). The AcB and BcA strains are a third, more extensive RC set containing reciprocal donor and recipient crosses using the widely available A/J and C57BL/6J strains (Fortin et al., 2001); these have not yet been used for cancer modifier studies. Unlike the random distribution of donor genome present in R C strains, Chromosome Substitution (C S) strains have a defined donor All mapping approaches not using isogenic lines like RI panels require the progeny to be genotyped in order to statistically associate the cancer phenotype with particular allelic differences across the genome. The strategy of choice for performing genome scans has historically been microsatellite based. Over 6000 mouse microsatellite markers have been developed ( Dietrich et al., 1996), enabling the identification of polymorphic

9 ž Q6 markers between any strains. All of the Massachusetts Institute of Technology (MIT) developed microsatellites have been tested in 12 inbred strains. A subset of more than 400 markers has also been screened in greater than 50 strains ( Thus, most, if not all, of the markers needed to perform an initial genome scan between any commonly used strains should already be available. Primary genome scans can be performed in several ways. To reduce time and cost within individual laboratories, initial genome scans are frequently performed on the two extremes of the phenotypic distribution curve, often the top and bottom 10% to 15% of the animals. This subset of animals provides the bulk of the statistical power for modifier detection, enabling preliminary localization. Genomic regions that demonstrate the potential presence of modifier genes can then be typed on the entire set of animals, with an increased density of markers to confirm and improve linkage. Although increasing the number of markers typed in a putative modifier region increases the likelihood that existing modifiers will be detected, generally in the preliminary genome scan it is not necessary to exceed a 20-cM resolution; higher marker densities are unlikely to provide any greater statistical power because of the limited number of recombination sites present in single generation crosses (Darvasi et al., 1993; Dupuis and Siegmund, 1999). Publicly accessible genotyping services, supported by the National Institutes of Health (NIH), are also available through the Center for Inherited Disease Research ( and the Marshfield Clinic ( Service/mgsver2.htm). An alternative to microsatellite markers that is gaining in popularity is single-nucleotide polymorphism (SNP) markers. These have the advantage of being scored without electrophoresis. Representative gel-less technologies include MALDI-TOF and Taqman platforms (Ranade et al., 2001; Ross et al., 1998). Alternatively, fluorescent label techniques can be used to pool and analyze genotypes on DNA sequencers (Inazuka et al., 1997). At present, however, there are a number of disadvantages that limit these approaches. While a large number of SNPs have become available through public and private efforts (Wade et al., 2002; Wiltshire et al., 2003), the number of validated SNPs is low and these have been screened using a limited number of inbred strains. In addition, to screen SNPs in a high-throughput manner requires relatively sophisticated equipment that may not be available to many laboratories. Yet, even for small laboratories, SNPs can provide additional markers for regions in which there may not be a useful microsatellite marker and should therefore be considered either when attempting to fill in regions lacking sufficient DETECTION AND LOCALIZATION 271 polymorphic markers or during candidate gene searches. There are a number of web-accessible databases for mouse SNPs, including the subscription-only Celera database ( the Genetic Annotation Initiative of the Cancer Genome Anatomy Project (CGAP; and the Whitehead Genome Center and Roche SNP collections, accessible through the MPP database ( mpdcgi?rtn=studies/details&id=96&dsonly=1). Many of the putative SNPs in the Celera and CGAP databases have not been validated and should be tested prior to use in large-scale genotyping efforts. DETECTION AND LOCALIZATION Following production of the mapping cross and collection of genotypic and phenotypic data, there are two primary stages of cancer modifier mapping: detection and localization. In practice, the two stages overlap and together are often associated with the genome scan, but they are conceptually different and have distinct statistical requirements. Importantly, however, details of detection and localization need to be considered up front to ensure that the experimental phase of the modifier analysis is adequately powered to allow both detection and localization of existing modifier genes. Modifier Detection The detection of a cancer modifier depends on a statistical test. The power of this statistical test, defined as the probability of detecting a modifier given that one exists, is dependent on many variables, including the number of mice genotyped and phenotyped, the number and strength of the modifiers, potential interactions between modifiers, the type of cross, the magnitude of the environmental and genetic components of the phenotypic trait value variance, the method of analysis, and the false-positive rate tolerated by the analysis. The number of mice required for detecting a modifier is, roughly speaking, proportional to the variance of the phenotypic trait value contributed by nongenetic factors and inversely proportional to the square of the strength of the modifier. Consequently, weak modifiers, which account for a few percent of the trait variance, may require several hundred mice for even a statistical power of 50% (Darvasi, 1998). ; statistical power is the probability that a given modifier will be detected during analysis. If cancer modifiers are detected in small, low-power experiments, the modifiers detected will be those that are fortuitously overestimated in that cross. For example, simulation studies suggest that modifiers detected in experiments with a power of 10% will, on average,

10 272 GENETIC MODIFIERS be overestimated by fourfold (Beavis, 1998). If several modifiers contribute equally to a trait, two low-power experiments may even detect distinct subsets of the actual modifiers, as each experiment will detect a different overestimated minority of the total. Some important factors that influence modifier detection are discussed in more detail below. Heritability The fraction of the total phenotypic trait value variability that can be attributed to genetic control is referred to as heritability, which is a summary measure for all loci contributing to a particular phenotype but does not imply any particular genetic model. Thus, heritability is proportional to the role that genetic differences contribute to phenotypic variation. Even highly heritable cancers like human breast cancer have heritabilities around 25%, meaning that the total genetic differences between individuals accounts for less than 25% of the differences in their breast cancer susceptibility (Czene et al., 2002). Cancer phenotypes with lower heritability have weaker genetic effects and, consequently, require larger populations to distinguish genetic effects in the context of large nongenetic phenotypic variance. Although mice within an inbred strain are genetically identical, they also exhibit variation in phenotypes due to nongenetic factors like developmental variation, often referred to as stochastic variation, or environmental differences. Generally, cancer modifiers are easily detected and characterized if they account for at least 10% of the total phenotypic trait variance in an intercross. If the difference in average trait values between two inbred lines is twice the standard deviation of the trait in each line and the difference was due to two equal, additive modifiers, each would account for 12.5% of the variance in an intercross. Such a trait, therefore, would be a reasonable candidate for an attempt to identify modifiers. However, such a large difference between the parental lines is not absolutely necessary since strong modifiers of opposite effect could also control the trait. Starting with a cancer phenotype with a smaller parental difference makes being lucky more important. Modifier Strength The statistical power for detecting cancer modifiers is determined by modifier strength. The strength of a cancer modifier is measured with one of two quantities, either the allele substitution effect or the fraction (percent) of total phenotypic variance explained. The allele substitution effect is the expected change in trait value if the allele on one chromosome is changed to the alternative allele. This can be divided into additive and dominance components. The additive effect is half the difference in the trait value between individuals homozygous for one modifier allele and those homozygous for the other. The dominance allele substitution effect is the amount by which the trait value of a heterozygote differs from the expected additive effect. In other words, it is the difference between the trait value for a heterozygote and the average of the trait values for the two homozygotes. Although the strength of modifiers affecting a cancer phenotype is unknown when mapping experiments are planned, they are often estimated so that the minimum modifier strength that provides adequate power for modifier detection can be calculated. Number of Mice for Detection Although the number of mice needed to detect a cancer modifier is dependent upon the characteristics of the modifier, this can be easily estimated for a hypothetical modifier with an additive effect (Fig. 15.3). For example, Figure 15.3a shows the number of mice required for detection of a single additive or dominant modifier in a backcross or intercross population at a statistical power of 50%. This figure is derived from two sources, simulation-based relationships (Darvasi and Soller, 1997) and theoretical derivation (Lynch and Walsh, 1998) using genomewide significance levels (Lander and Kruglyak, 1995). The power is almost entirely a function of the strength of the modifier, measured as the fraction of the total phenotypic variance explained by the hypothetical modifier and not of the type of cross. Thus, detecting modifiers that control 10% of the phenotypic variance requires about 150 mice; detecting those that control 5% requires 300 mice. The numbers of mice shown in Figure 15.3a should be considered a bare minimum. The statistical power for this number of mice is 50%, which means that half of the existing modifiers at the threshold strength will not achieve significance. Furthermore, both methods assume a high density of fully informative markers; using a lower marker density or less informative markers would reduce the power. To be more realistic, Figure 15.3b shows the number of mice required to detect a modifier at a statistical power of 80%. This figure represents a more prudent experimental design and is based on the theoretical method used in Figure 15.3a. Detecting modifiers that control 10% of the variance requires about mice; detecting those that control 5% requires mice. Significance of Modifier Detection A common method for determining the significance of any potential modifier localization is the permutation test (Churchill and Doerge, 1994; Doerge and Churchill,

11 DETECTION AND LOCALIZATION 273 Number of individuals Number of individuals Number of individuals Fraction variance explained (a) Fraction variance explained (b) 20% 10% Confidence interval (cm) (c) 5% Figure Theoretically determined number of progeny in experimental crosses needed for different stages of cancer modifier mapping. (a) Number of progeny needed to detect 50% of modifiers with a given strength as a function of the trait variance explained by the modifier. The figure includes both theoretical (Lynch and Walsh, 1998) and simulation-based (Darvasi and Soller, 1997) requirements for backcrosses or intercrosses with dominant or codominant alleles. (b) Number of progeny needed to detect 80% of modifiers of a given strength as a function of the trait variance explained by the modifier. (c) Expected size of a 95% confidence interval for a modifier location as a function of the number of progeny in the mapping cross. Based on a simple function fitted to the results of simulation experiments (Darvasi and Soller, 1997). 1996). The permutation test is an empirical test designed to determine the significance of an association in any given data set. The data are randomly permuted; that is, the trait values are randomly reassigned to other individuals to destroy any relationship between trait values and the genotypes of the marker loci used for the analysis. Using the permuted data sets, the maximum-likelihood score between the original trait value and the randomized genotypes is then determined by repeatedly mapping independently permuted data sets hundreds or thousands of times; this results in a distribution of likelihood values where there were no actual modifiers linked to any of the marker loci. Mapping values from the original, unpermuted data that exceed the 37th, 95th, and 99.9th percentiles of the maximum-likelihood scores determined by the frequency of association between specific markers and the trait values using the permuted data sets would be considered suggestive, significant, and highly significant according to widely adopted genomewide thresholds (Lander and Kruglyak, 1995). Modifier Localization The second stage in modifier mapping is estimating how many modifiers significantly affect a trait and the approximate location of those modifiers, usually by interval mapping methods (Lander and Botstein, 1989). Interval mapping consists of testing the effect of hypothetical modifiers at each marker locus and at regular intervals between the marker loci. The expected effect of a hypothetical modifier at each location is estimated from the genotypes at marker loci flanking each interval and assumes that intermarker recombinations are randomly distributed. Unlike single-marker analysis, interval mapping requires that the relative order of all markers is known. Furthermore, interval mapping can separate modifier location from modifier effect. A likelihood ratio test is performed at each locus across the entire genome. Scores for the likelihood ratio statistic (LRS; Haley and Knott, 1992), related to the logarithm of the odds (LOD) that is commonly used in human genetics, are the measures of the significance of associations. The genetic location with the highest LRS that is above the genomewide threshold is a putative modifier position while the value of the LRS at that position provides a measure of the statistical significance of the association between marker genotype and phenotype. Roughly speaking, the LRS can be considered as an approximation of a chi square 2 (χ ) statistic. ž Q7 The confidence interval for a modifier location determined by interval mapping is inversely proportional to the square of the strength of the modifier and the number of mice (Darvasi, 1998; Darvasi et al., 1993). Said another way, the stronger the modifier effect and the greater the number of mice used, the narrower the confidence interval