Anecdotal, Historical and Critical Commentaries on Genetics: The Szilard Hypothesis on the Nature of Aging Revisited

This year marks the 50th anniversary of a nearly forgotten hypothesis on aging by Leo Szilard, best known for his pioneering work in nuclear physics, his participation in the Manhattan Project during World War II, his opposition to the nuclear arms race in the postwar era, and his pioneering ideas in biology. Given a specific set of assumptions, Szilard hypothesized that the major reason for the phenomenon of aging was aging hits, e.g., by ionizing radiation, to the gene-bearing chromosomes and presented a mathematical target-hit model enabling the calculation of the average and maximum life span of a species, as well as the influence of increased exposure to DNA-damaging factors on life expectancy. While many new findings have cast doubt on the specific features of the model, this was the first serious effort to posit accumulated genetic damage as a cause of senescence. Here, we review Szilard's assumptions in the light of current knowledge on aging and reassess his mathematical model in an attempt to reach a conclusion on the relevance of Szilard's aging hypothesis today.     

Anecdotal,Historical and Critical Commentaries on Genetics: The Utility of Comparative Genetics to Inform Breast Cancer Prevention Strategies

My research seeks to aid in developing approaches to prevent breast cancer. This research evolved from our early empirical studies for discovering natural compounds with anticancer activities, coupled with clinical evaluation to a genetics-driven approach to prevention. This centers on the use of comparative genomics to discover risk-modifying alleles that could help define population and individual risk and also serve as potential prevention drugable targets to mitigate risk. Here, we initially fine map mammary cancer loci in a rat carcinogenesis model and then evaluate their human homologs in breast cancer case-control association studies. This approach has yielded promising results, including the finding that the compound rat QTL Mcs5a''s human homologous region was associated with breast cancer risk. These and related findings have the potential to yield advancements both in translation-prevention research and in basic molecular genetics.WRITING this Perspectives for Genetics allows me to examine how a cancer biologist focused on cancer prevention morphed into a practicing geneticist. In addition, it allows me to review a decade of our investigations into the complexity of the genetic risk to breast cancer development using comparative genomics.Our comparative genomic strategy consists of genetically identifying mammary cancer risk loci using fine mapping studies in a rat mammary carcinogenesis model. Human homologs of these loci are then evaluated in human breast cancer association studies for their potential to modify risk. This genetics approach provides an integrated discovery platform to identify and mechanistically characterize novel breast cancer risk alleles. We predict that this platform will serve as a foundation for a cancer prevention drug development pipeline.My early work focused on the etiology and prevention of breast cancer. It is work on these interrelated areas that led me to investigate breast cancer genetics. While studying the etiology of breast cancer after joining the faculty of the University of Wisconsin, my interest targeted early events in the etiology of cancer. These range from altering the metabolic activation of environmental xenobiotics to metabolites capable of adducting DNA to destroying clones premalignant cells. At the time we began work in this area, cancer chemoprevention was an emerging field that was assumed to be less complex than cancer therapy. This was, in part, based on the fact that normal and premalignant tissues were genetically more stable than cancer cells and thus less likely to develop resistance to anticancer drugs.Our chemoprevention studies focused on a novel class of nontoxic monoterpenes widely found in the essential oils of fruits. These compounds were found to have both preventive and therapeutic anticancer activities in being able to inhibit both premalignant and malignant cells. Our lead compound was limonene, found in orange peel oil, and the first monoterpene we entered into FDA-approved clinical trials was perillyl alcohol (POH), found originally in lavender oil. For expediency, our first trial was a therapeutic one. This therapeutic phase I trial showed limited promising results (Ripple et al. 2000). We later discovered that POH inhibited the antiapoptotic ability of cancers via a calcium channel interaction that led to the downregulation of NFκB (Berchtold et al. 2005). This mechanism of action could underlie the cytostatic and cytotoxic actions of POH toward both premalignant and malignant cells.The monoterpenes and POH were found through empirical screening. Like the monoterpenes, many chemopreventive and therapeutic agents are found to be of low overall efficacy. Many also have undesirable toxicity, in part due to the lack of target specificity. As such, we felt the need to develop nonempirical methods to develop prevention strategies and drugs.To develop chemopreventative agents for common diseases, we sought an approach that would identify both appropriate drug targets and high-risk populations. For example, we aimed to develop prevention strategies for the large number of individuals at risk for breast cancer but not those who specifically carried the rare but highly penetrant susceptibility alleles of the breast cancer genes such as Brca1 and -2; these and other highly penetrant breast cancer risk alleles collectively account for <25% of inherited breast cancer risk in humans (Pharoah et al. 2008).We thus sought to identify moderately penetrant breast cancer susceptibility alleles that were common (high population frequency). Ten years ago it was difficult to identify such loci directly in human populations. In fact, most association studies at that time were based on a “candidate gene” approach; these studies were rarely successful (Pharoah et al. 2007). We thus adapted a comparative genomics strategy in which such loci are identified in a model organism using a nonbiased linkage approach and then evaluated in humans. We chose what we believe is the in vivo breast cancer model most closely related to the human—the rat.The rat, in contrast to the mouse and like the human, develops a spectrum of hormonally responsive and nonresponsive breast cancers. Importantly, almost all rat and human cancers have a ductal cell origin (Gould 1995). At the time we began this research, however, the rat had far fewer genetic resources and tools than the mouse (Gould 1995). This can be illustrated by our need to use a M13 minisatellite marker to identify our first rat mammary susceptibility QTL (Hsu et al. 1994). Over the course of this research and subsequent studies, rat geneticists have substantially narrowed this technology gap (see Aitman et al. 2008). For example, in pursuing this project we developed a technology that produced the first gene inactivation (“knockout”) rat models (Zan et al. 2003).

Comparative genetics studies:

The first major results of these genomewide comparative studies were published by Shepel et al. (1998) in Genetics. In this study we crossed two rat strains with large differences in their susceptibility to the induction of mammary carcinomas by the chemical carcinogen dimethylbenzanthracene (DMBA). The susceptible strain was the Wistar-Furth (WF) rat, while the resistance strain was the Copenhagen (COP) rat. F₁ hybrid rats were backcrossed (WF × COP) F₁ × WF or intercrossed (F₁ × F₁). Large groups of these rats were orally gavaged with DMBA, and the average number of mammary carcinomas per rat was quantified at necropsy. Rats were also genotyped using microsatellite markers, which had become available for the rat in the 1990s.The QTL genetically identified in this study accounted for most of the genetic variance controlling susceptibility to mammary cancer by identifying the Mammary carcinoma susceptibility (Mcs) loci—Mcs1, -2, -3, and -4. The COP allele of Mcs1, -2, and -3 conferred resistance while Mcs4 conferred an increased susceptibility to mammary cancer development. This study demonstrated the ability to use the rat model to identify the major COP vs. WF polymorphic loci controlling susceptibility. These loci interacted in an additive manner. Interestingly, the almost completely mammary cancer-resistant COP rat strain was shown to carry a polymorphic allele at the Mcs4 locus predicted to increase mammary cancer risk.In extending this study, we asked whether other mammary cancer-resistant strains varied at polymorphic mammary cancer susceptibility loci shared with those genetically identified in the WF × COP cross. A similar analysis was performed by conducting a QTL analysis of a cross between WF and a second resistant strain Wistar-Kyoto (WKy). In this backcross analysis we genetically identified four loci that accounted for most of the genetic variance associated with the susceptibility phenotype. As with the COP cross, the WKy cross identified three loci in which the WKy allele contributed to resistance and one locus at which the WKy allele contributed to increased susceptibility (Lan et al. 2001). Of these four WKy loci, only one broadly overlapped with those identified in the COP × WF cross, i.e., Mcs2 (COP) with Mcs6 (WKy). This study also used a novel statistical approach developed by our statistical collaborator, Christina Kendziorski, to identify alleles with no main effect that modify QTL with main effects. Mcs-modifier 1 (Mcsm1) was the most strongly supported locus of this class. The WKy allele of this locus fully negated the effects of the resistance conferred by the WKy allele of the Mcs8 QTL. Thus it appeared that there could be a large number of polymorphic loci in rats that could contribute to mammary cancer risk.It is important to keep in mind that genetically identified QTL are the product of statistical modeling and analysis of segregating populations from crosses. It is thus critical that their existence be confirmed in more homogeneous genetic material. An established method for QTL validation is to breed and phenotype congenic animals carrying only the region surrounding the QTL allele of interest on an alternative genetic background. So far we have generated and characterized six of the eight candidate WKy and COP QTL by genetically introgressing them onto the WF background. All six have the phenotype predicted by our quantitative models.Most congenic substitutions include tens of megabases encompassing the introgressed allele. The next step is to fine map this congenic interval to first determine whether this interval harbors more than one independent susceptibility locus. In addition, the fine mapping process allows for an increased genomic resolution of the locus and thus a more limited set of candidates. We have fine mapped two Mcs loci–Mcs1 (COP) and Mcs5 (WKy). Each was found to be complex, containing at least three separable subloci termed Mcs1a, -b, -c and Mcs5a, -b, -c. In the case of Mcs1, all three identified loci within it contributed to the cancer resistance phenotype of Mcs1. This led us to speculate that this apparent clustering might be biologically “random”; their strong-combined phenotype allowed us to readily identify Mcs1 over the experimental background. In contrast, the Mcs5 also had at least three subloci, Mcs5a, -b, and -c, but two of these, a and c, contribute to resistance while b confers an increased sensitivity. Each of the three had similar absolute relative risk (RR) contributions. If they interact in a purely additive manner, it might have been difficult to identify Mcs5. However, Mcs5 had the strongest of LOD scores of any identified locus in the WKy cross (Lan et al. 2001). When we explored the interaction of the alleles at the Mcs5 loci, we found complex epistatic interactions. The strongest was the complete neutralization of the effect of the sensitive WKy allele of Mcs5b by the resistant WKy allele of Mcs5a (Samuelson et al. 2005).It is interesting to explore an alternative hypothesis that suggests that the clustering of mammary cancer susceptibility alleles arise from evolutionary selection. Data supporting such a possibility in rodents has been published by Petkov et al. (2005). Their findings suggest that alleles controlling certain phenotypes cluster to assure joint inheritance, in that in concert with one another, they provide for an enhanced survival advantage. This could account for the clustering of risk-related genes at the Mcs1 and Mcs5 QTL.Many of the most comprehensive published mammalian fine-mapping studies achieve mapping resolutions in the order of several megabases. Such intervals, while carrying a limited number of genes, often require choosing one or more candidate genes for intensive study. These are usually chosen on the basis of how they might functionally relate to the specific disease risk under investigation. This negates the potential of positional cloning to identify an unbiased candidate. As mentioned above, experience suggests that functional candidate selection rarely identifies disease-specific modifier genes. For example, in breast cancer, when 120 such published candidates (710 SNPs) were rigorously evaluated, none met minimal statistical significance in a study of a large population of women in a breast cancer case-control study (Pharoah et al. 2007).We explored the ability of ultrafine mapping to annotate the Mcs5a locus. We mapped this locus to >100-kb resolution by phenotyping congenic rats recombinant within this locus. We found it to contain two elements. The WKy allele of each element by itself failed to elicit a mammary cancer phenotype; however, when combined, the resistance phenotype was obvious. These elements, termed Mcs5a1 and Mcs5a2, synthetically interact, making Mcs5a one of the first-identified compound QTL in mammals. Because Mcs5a acts in a semidominant manner, we could use heterozygous congenic recombinants to ask whether both elements of Mcs5a needed to lie in cis on the same chromosome, or could they interact in trans from separate homologs. They interact only in cis (Samuelson et al. 2007). Another interesting observation arising from the fine mapping of Mcs5a is that it localizes to noncoding DNA. All four Mcs loci that we have fine mapped to high resolution are localized to noncoding DNA (in progress).The observations that the rat compound locus Mcs5a consists of two synthetically interacting elements separated by ∼50-60 kb (based on the human sequence), interact only when on the same chromosome, and are noncoding suggest the hypothesis that they may be localized in closer proximity than suggested by the linear genomic distance that separates them. Recent observations in our laboratory using chromosome confirmation capture suggest that most of the sequences between these elements form a CTCF-mediated loop bringing both elements in close physical proximity to each other. The ability of this compound locus to control local and interchromosomal gene expression is being studied (in progress).To determine whether our findings in our rat model could be extended to women, we next asked whether the human ortholog of Mcs5a (-a1 and -a2) could influence breast cancer risk. In contrast to the method of searching for modifier genes using genomewide association studies (GWAS), we restricted our search to an ∼100-kb region of the human genome. Focusing on this orthologous locus defined by comparative genomics vastly reduced the number of SNP-tagged alleles needed for testing for association, greatly reducing the statistical penalty for multiple testing. We tested several SNPs in the orthologous MCS5A1 and -5A2 regions of the human genome in a total of ∼12,000 women in a breast cancer case-control study. We found that a tagged SNP in both MCS5A1 and -5A2 was significantly associated with risk to breast cancer in this population of women. The minor allele of SNP rs56476643 (MCS5A1) acts in a recessive manner to increase risk. Its allele frequency is 25% and it increases risk in homozygous women by 19%. In contrast, the minor allele of MCS5A2 (rs2182317) has an allele frequency of 13% and acts in a dominant manner to reduce by 14% the risk of breast cancer in the 24% of women carrying one or two copies of this allele (Samuelson et al. 2007).Not only does this human study support the use of comparative genomics to identify human cancer risk modifier alleles, it also extends the resolution obtained in the rat in localizing the two genetic elements of the Mcs5a allele. The rat localizes Mcs5a1 and -a2 to 32 and 84 kb, respectively, while the human studies resolved these determinants to 5.7 kb and 16.8 kb (Samuelson et al. 2007). Thus, we have demonstrated a clear advantage in using comparative genomics to localize target regions within QTL.Both MCS5A1 and -5A2 have similar allele frequencies and genetic penetrance (relative risk) as do most breast cancer alleles identified by GWAS studies. However, unlike alleles identified by GWAS studies, those identified by comparative genomics also provide in vivo models to functionally characterize risk alleles. For example, it is often assumed that breast cancer modifiers are likely to act within breast tissue to modulate risk. Using the rat as a model we have been able to show that Mcs5a, a noncoding allele, acts to differentially regulate its neighboring FBXO10 gene in immune but not mammary tissues (Samuelson et al. 2007).It is also intriguing to consider the observation that breast cancer risk-associated alleles such as Mcs5a1 and -5a2 are either conserved over millions of evolutionary years or are highly mutable and functionally neutral, suggesting that these alleles do not significantly reduce fitness. If so, one then speculates that they would make good targets for chemoprevention drugs by possessing low toxicity and as such a good therapeutic index. In particular, converting sensitive to resistant allelic function with drug therapy would mimic the conserved resistance allele that persists in the human population and should therefore show a low side-effects profile.Our current research on these genetically identified Mcs loci focuses on molecular, cellular, and organismal mechanisms by which they modify risk. Not only will these investigations provide insight into the function of each noncoding Mcs locus, but collectively they will provide a mechanistic framework to facilitate integrative genetic studies of the plethora of polymorphic risk loci identified by GWAS in multiple diseases.     

Anecdotal,Historical and Critical Commentaries on Genetics: Wright and Fisher on Inbreeding and Random Drift

Sewall Wright and R. A. Fisher often differed, including on the meaning of inbreeding and random gene frequency drift. Fisher regarded them as quite distinct processes, whereas Wright thought that because his inbreeding coefficient measured both they should be regarded as the same. Since the effective population numbers for inbreeding and random drift are different, this would argue for the Fisher view.SEWALL Wright and R. A. Fisher were central figures in mathematical population genetics; along with J. B. S. Haldane they effectively invented the field and dominated it for many years. On most issues the three were in agreement. In particular, all favored a neo-Darwinian gradualist approach and believed in the importance of a mathematical theory for understanding the evolutionary process. Yet on a few questions Fisher and Wright differed profoundly and argued vehemently. Fisher was contentious and was often involved in controversy, frequently attacking his opponents mercilessly. Wright, in contrast, was very gentle to most people. But there were a few exceptions and Fisher was one. Haldane mostly stayed out of the arguments between them.One question on which the two disagreed was the importance of random gene frequency drift and its role in Wright''s shifting-balance theory of evolution. Wright thought that a structured population with many partially isolated subpopulations, within which there was random drift and among which there was an appropriate amount of migration, offered the greatest chance for evolutionary novelty and could greatly increase the speed of evolution. Fisher thought that a large panmictic population offered the best chance for advantageous genes and gene combinations to spread through the population, unimpeded by random processes. They also disagreed on dominance, Fisher believing that it evolved by selection of dominance modifiers and Wright that it was a consequence of the nature of gene action. These differences were widely argued by population geneticists in the middle third of the twentieth century, and the interested community divided into two camps. Although the issues are not settled, Wright''s shifting-balance theory has less support than it formerly had. As for dominance, there is general quantitative disagreement with Fisher''s explanation of modifiers, but other mechanisms (e.g., selection for more active alleles) have to some extent replaced it. Wright''s theory remains popular and has been generalized and extended (Kacser and Burns 1973).     

Anecdotal,Historical and Critical Commentaries on Genetics: The Impact of Whole Genome Sequencing on Model System Genetics: Get Ready for the Ride

Much of our understanding of how organisms develop and function is derived from the extraordinarily powerful, classic approach of screening for mutant organisms in which a specific biological process is disrupted. Reaping the fruits of such forward genetic screens in metazoan model systems like Drosophila, Caenorhabditis elegans, or zebrafish traditionally involves time-consuming positional cloning strategies that result in the identification of the mutant locus. Whole genome sequencing (WGS) has begun to provide an effective alternative to this approach through direct pinpointing of the molecular lesion in a mutated strain isolated from a genetic screen. Apart from significantly altering the pace and costs of genetic analysis, WGS also provides new perspectives on solving genetic problems that are difficult to tackle with conventional approaches, such as identifying the molecular basis of multigenic and complex traits.GENETIC model systems, from bacteria, yeast, plants, worms, flies, and fish to mice allow the dissection of the genetic basis of virtually any biological process by isolating mutants obtained through random mutagenesis, in which the biological process under investigation is defective. Such forward genetic analysis is unbiased and free of assumptions. The rigor and conceptual simplicity of forward genetic analysis is striking, some may say, beautiful; and the unpredictability of what one finds—be that an unexpected phenotype popping out of a screen or the eventual molecular nature of the gene (take the discovery of miRNAs as an example; Lee et al. 1993)—appeals to the adventurous. Even though mutant phenotypic analysis alone can reveal the logic of underlying biological processes (take Ed Lewis'' analysis of homeotic mutants as an example; Lewis 1978)—it is the identification of the molecular lesions in mutant animals that provides the key mechanistic and molecular details that propel our understanding of biological processes.The identification of the molecular lesion in mutant organisms depends on how the mutation was introduced. Classically, two types of mutagens have been used in most model systems: biological agents such as plasmids, viruses, or transposons whose insertions disrupt functional DNA elements (either coding or regulatory elements) or chemical mutagens, such as ethyl methane sulfonate (EMS) or N-ethyl N-nitroso urea (ENU), that introduce point mutations or deletions. Point mutation-inducing chemical mutagens are in many ways a superior mutagenic agent because their mutational frequency is high and because the spectrum of their effects on a given locus—producing hypomorphs, hypermorphs, amorphs, neomorphs, etc.—is hard to match by biological mutagens. Moreover, chemical mutagens do not display the positional bias of many biological agents. In addition, point mutations in a gene are often crucial in dissecting the functionally relevant domains of the gene product. In spite of the advantages of chemical mutagens, model system geneticists often prefer biological mutagens simply because the molecular lesions induced by those agents are characterized by the easily locatable DNA footprint that these agents generate. In contrast, the location of a point mutation (or deletion) has to be identified through conventional mapping strategies, which tend to be tedious and time consuming. Even in model systems in which positional cloning is quite fast and straightforward (e.g., Caenorhabditis elegans, which has a short generation time and a multitude of mapping tools available), it nevertheless is a significant effort that can occasionally present hurdles that are difficult to surmount (e.g., if the gene maps into a region with few genetic markers that allow for mapping). These difficulties explain why RNAi-based “genetic screens” have gained significant popularity in C. elegans; they circumvent mapping and reveal molecular identities of genes involved in a given process straight away (Kamath and Ahringer 2003). However, genes and cells show differential susceptibility to RNAi; off-target effects and lack of reproducibility can be a problem, and the range of effects that RNAi has on gene activity is generally more limited compared to chemically induced gene mutations.The recent application of next generation, deep sequencing technology (see Bentley 2006; Morozova and Marra 2008 for technology reviews) is beginning to significantly alter the landscape of genetic analysis as it allows the use of chemical mutagens without having to deal with its disadvantages. Deep sequencing technology incorporated into platforms such as Illumina''s Genome Analyzer or ABI''s SOLiD, allows one-shot sequencing of the entire model system''s genome, resulting in the detection of mutagen-induced sequence alterations compared to a nonmutagenized reference genome. Proof-of-concept studies have so far been conducted in bacteria, yeast, plant, worms, and flies, all published within the last year (Sarin et al. 2008; Smith et al. 2008; Srivatsan et al. 2008; Blumenstiel et al. 2009; Irvine et al. 2009; Rigola et al. 2009). Many more studies are under way; for example, since our first proof-of-principle study (Sarin et al. 2008), my laboratory has identified the molecular basis of >10 C. elegans strains defective in neuronal development and homeostasis (V. Bertrand, unpublished data; M. Doitsidou, unpublished data; E. Flowers, unpublished data; S. Sarin, unpublished data).The advantages of whole genome sequencing (WGS) are obvious. The process is extraordinarily fast with the sequencing taking only ∼5 days and the subsequent sequence data analysis only a few hours, particularly if the end user employs bioinformatic tools customized for mutant detection (Bigelow et al. 2009). The process is also remarkably cost effective. For example, a C. elegans genome can be sequenced with a required sequence coverage of ∼10 times for <$2,000 in reagent and machine operating costs. The capacity of deep sequencing machines—and hence the costs associated with sequencing a genome—apparently follow Moore''s law of doubling its capacity about every 2 years, like many technological innovations do (Pettersson et al. 2009). That is, the <$1,000 genome for C. elegans (∼100-Mb genome) and Drosophila (∼123-Mb genome) is just around the corner and other models will sooner or later follow suit. The cost effectiveness becomes particularly apparent if one compares the cost of WGS to the personnel and reagent costs associated with multiple-month to multiple-year mapping-based cloning efforts.WGS identifies sequence variants between a mutated genome and a premutagenesis reference genome. Chemical mutagens randomly introduce many mutations in the genome and, therefore, the phenotype-causing sequence variant needs to be identified as such out of a large pool of sequence variants. Sequence variants that have no impact on the phenotype can be outcrossed before sequencing or eliminated through some rough mapping of the mutation, which allows the experimenter to focus only on those variants contained in a specific sequence interval. Ensuing functional tests such as transformation rescue or phenocopy by RNAi and the availability of other alleles of the same locus are critical means to validate a phenotype-causing sequence variant (Sarin et al. 2008). The latter approach—the availability of multiple alleles of the same locus—is in many ways the most powerful one to sift through a number of candidate variants revealed by WGS. In this approach, candidate loci revealed by WGS are resequenced by conventional Sanger sequencing in allelic strains and only those that are indeed phenotype causing will show up mutated in all allelic variants of the locus (Sarin et al. 2008). The availability of multiple alleles of a locus is highly desirable for many aspects of genetic analysis anyway and therefore does not represent an additional and specific burden for undertaking a WGS project.The importance of WGS on model system genetics will be substantial and wide ranging. Speed and cost effectiveness means that the wastelands of genetic mapping can be trespassed fast enough to allow an experimenter to multitask a whole mutant collection in parallel, thereby closing in on the “holy grail” of genetic analysis—the as-complete-as-possible mutational saturation of a biological process and the resulting deciphering of complete genetic pathways and networks. What will become limiting steps are not any more the tediousness of mapping, but rather the effectiveness with which mutant collections can be built. Novel technologies that involve machine-based, semiautomated selection of mutant animals have been developed over the past few years to study a variety of distinct biological processes in several metazoan model systems, e.g., gfp-based morphology or cell fate screens in worms (Crane et al. 2009; Doitsidou et al. 2008) or behavioral screens in flies (Dankert et al. 2009) and are important steps in this direction. Such an “industrial revolution” of genetic screening (i.e., the mutant selection part, followed by WGS) moves us geneticists away from, not into the trenches of factory life and frees us up to do what we should like to enjoy most—thinking of designing interesting screens, seeing how genes interact, and interpreting it all.Another important impact of WGS is that it will allow tackling problems that were previously hard to deal with. For example, the tediousness of following subtle phenotypes, low penetrance phenotypes, or phenotypes that are cumbersome to score often hampers positional cloning approaches that rely on identifying rare recombinants in a large sibling pool. Moreover, many genetic traits such as behavioral genetic traits are very sensitive to genetic background and are therefore also often hard to map in the conventional way. WGS hones in on candidate genes straight away. Taking this notion a step further, WGS will also be able to get at the molecular basis of multigenic traits and quantitative trait loci, which again are hard to molecularly identify through conventional mapping strategies; a proof-of-principle study has made this point already in bacteria (Srivatsan et al. 2008). In principle, such multigenic traits may have been mutationally induced or could be present in natural variants of a species, which provides intriguing perspective for the population geneticist.Model organisms of biological interest that were previously relatively intractable for classic genetic mutant analysis due to the absence of genetic markers or other practical problems such as prohibitive generation times, may also now be movable into the arena of genetic model systems, through the WGS-mediated molecular analysis of mutagen-induced variants or through the study of natural variants.The sequencing of human cancer genomes has already begun to illustrate the impact of WGS on human genetics (Campbell et al. 2008; Ley et al. 2008). However, those human WGS studies illustrate why model systems will continue to be extremely important—their experimental accessibility allows us to address which of the many variants detected by WGS is indeed the phenotype-causing one.The message to model system geneticists is clear: Get access to a deep sequencer, buckle up, and get ready for the ride.     

Anecdotal,Historical and Critical Commentaries on Genetics: Personal Reflections on the Origins and Emergence of Recombinant DNA Technology

The ability to identify genetic markers in nonmodel systems has allowed geneticists to construct linkage maps for a diversity of species, and the sex-determining locus is often among the first to be mapped. Sex determination is an important area of study in developmental and evolutionary biology, as well as ecology. Its importance for organisms might suggest that sex determination is highly conserved. However, genetic studies have shown that sex determination mechanisms, and the genes involved, are surprisingly labile. We review studies using genetic mapping and phylogenetic inferences, which can help reveal evolutionary pattern within this lability and potentially identify the changes that have occurred among different sex determination systems. We define some of the terminology, particularly where confusion arises in writing about such a diverse range of organisms, and highlight some major differences between plants and animals, and some important similarities. We stress the importance of studying taxa suitable for testing hypotheses, and the need for phylogenetic studies directed to taxa where the patterns of changes can be most reliably inferred, if the ultimate goal of testing hypotheses regarding the selective forces that have led to changes in such an essential trait is to become feasible.THE ever-increasing accessibility of genetic markers is allowing sex-determining regions to be genetically mapped in a growing number of nonmodel organisms. There are several reasons for studying sex determination. In animals, gonadal differences are often accompanied by striking somatic secondary sexual dimorphisms, which are interesting in an evolutionary context (Shine 1989; Badyaev 2002). In plants, females and males often differ in flower morphology and abundance (Dawson and Geber 1998), and, although sex differences are often minor outside the flowers (or inflorescences), they do exist (Dawson and Geber 1998; Eppley and Wenk 2001). The genetic control of these phenotypes is a fundamental biological process, and studying sex determination pathways is important in animal developmental biology (Adams and McLaren 2002; Pinyopich et al. 2003), including genetic pathway evolution (Wilkins 1995; Williams and Carroll 2009).Until recently, sex determination was generally studied by testing for genetic control vs. partial or complete environmental influences. Genetic systems were examined cytologically to determine the level of heteromorphism between the sex chromosomes and to identify whether females or males are heterogametic (see the comprehensive review in Bull 1983). Male heterogametic systems, referred to as XY, were also tested to identify whether the Y chromosome carries a male-determining gene, as in almost all therian mammals and most dioecious plants so far studied, or whether sex is determined through X–autosome balance, as in Drosophila and Caenorhabditis elegans (Haag 2005). For female heterogametic (ZW) species, analogous tests were used to identify how femaleness is determined.Until recently, sex-determining genes and regions could be genetically mapped in only a few model species, but now that molecular genetic markers can be developed in nonmodel species, new information is becoming available about how genetic sex determination (GSD) mechanisms have changed during evolutionary history. It has long been known from genetic mapping in model systems, including mammals, and (more recently) birds, that sex chromosomes often have large nonrecombining regions (Bull 1983; Charlesworth 1991; Charlesworth et al. 2005). However, in other organisms, nonrecombining regions are not always large and may sometimes be absent. The evolution of sexual reproduction and recombination have been the focus of many years of discussion in evolutionary biology (Otto 2009), and studies of sex chromosomes are important for understanding why recombination is often lost, and elucidating the evolutionary consequences of recombination suppression (Charlesworth 1996; Otto and Barton 1997; Barton and Charlesworth 1998). The adaptation of genes on the sex chromosomes is also interesting, because this location affects the outcome of sex-specific selection pressures (Rice 1984; Charlesworth et al. 1987; Vicoso and Charlesworth 2006; Mank 2009a). Finally, the mechanism of sex determination can affect sex ratios (West and Sheldon 2002; Dorken and Pannell 2008; West 2009) and is therefore significant in evolutionary ecology.Sex determination is also relevant in applied biology. In many domesticated animals, one sex may be of greatest economic interest to farmers and breeders. Modern meat production is largely based on males, including industrial production of chicken, cattle, and many fish, whereas females are the sex required for milk (cattle) and egg (chicken) production. Similarly, a few crop plants are dioecious, and, in some of these, the crop is produced by females (e.g., grapes, dates, and papaya), while in other species the sexes differ in characteristics such as fiber or chemical content. Because immature birds, fish, and plants have no obvious phenotypic sex differences, maximizing agricultural returns often requires genetically sexing juveniles. Mapping sex determination is an important first step toward identifying the sex-determining genes or finding other sex-specific markers to develop molecular sexing methods.In this review, we first summarize recent developments in genetic mapping of sex determination, concentrating on nonmodel plants and animals with genetic sex determination. We show how this information can be useful for understanding the evolution of sex determination and sex chromosomes and identify some important unanswered questions.